Development of an Immersive VR-Based Training Platform Integrating FMECA for Wind Turbine Maintenance: FMECA-VR-0.1 Prototype

Abstract

This paper presents FMECA-VR-0.1 Prototype, a Maintenance 4.0-oriented immersive Virtual Reality (VR)-based training platform that integrates tools in a digital and virtual environment with Failure Modes, Effects, and Criticality Analysis (FMECA) and the Qualitative Risk Criticality Matrix (QRCM) to enhance reliability-oriented maintenance training in the wind energy sector. The methodological framework is aligned with the Maintenance Management Model (MMM) developed by INGEMAN. It is applied to a VESTAS V100–2.0 MW wind turbine operating at the Valle de los Vientos Wind Farm in northern Chile. The study includes the definition of the operational context, subsystem-level criticality assessment, and a detailed FMECA of the blade subsystem, which are integrated as analytical layers within the immersive VR environment. The proposed platform enables users to visualize critical components, analyze physical failure modes, understand associated consequences, and review preventive and corrective maintenance strategies in an interactive 3D scenario. Preliminary qualitative feedback suggests potential improvements in user engagement and conceptual understanding; however, no formal experimental validation has been conducted at this stage. The FMECA-VR-0.1 prototype demonstrates a feasible path for incorporating risk-based engineering logic into immersive training ecosystems. It establishes the foundation for future developments involving digital twins, real-time monitoring data, multi-subsystem modeling, and quantitative assessment of learning performance.

1. Introduction

The accelerated digitalization of industrial systems—driven by the evolution of Industry 4.0—has transformed traditional maintenance processes, asset management strategies, and technical training requirements. Modern production environments now depend on interoperable automation systems, cyber-physical infrastructures, IoT-enabled monitoring, AI-driven diagnostics, and real-time analytics to maintain operational continuity and ensure high reliability, safety, and cost efficiency [1,2,3,4,5,6]. As organizations increasingly adopt these technologies, the competencies required from maintenance professionals become more complex, demanding a deeper understanding of failure mechanisms, risk prioritization, and data-driven decision-making conditions.

In asset-intensive sectors such as renewable energy, these challenges are particularly significant. Wind farms operate under harsh environmental conditions, involve geographically dispersed assets, and require precise reliability-oriented maintenance strategies to avoid costly unplanned failures. Given the high risk associated with physical inspections and corrective interventions, conventional training approaches often fail to provide realistic, safe, or immersive learning scenarios. These limitations have motivated the integration of Virtual Reality (VR), immersive visualization technologies, and simulation platforms to support the acquisition of operational and diagnostic skills without exposing technicians to hazardous conditions [7,8,9,10,11,12].

From a scientific standpoint, the novelty of this work lies in the integration of structured reliability engineering methodologies (FMECA and QRCM) into an immersive VR-based training platform through a data-driven architecture. Unlike conventional VR training systems that focus primarily on procedural visualization, the proposed approach embeds risk-based decision-making logic directly into the interactive environment. This enables users not only to visualize components but also to understand failure mechanisms, criticality levels, and maintenance strategies in a unified and context-aware framework [13,14].

However, a critical limitation remains in the literature: the lack of integration between structured reliability methodologies and immersive virtual environments. FMECA is a cornerstone technique in reliability engineering, enabling systematic identification of failure modes, evaluation of effects, and prioritization of actions to mitigate the consequences of failures. Its structured nature makes it ideal for inclusion in training processes; however, most VR-based maintenance tools do not incorporate formal risk analysis methods, resulting in limited alignment with real-world asset management practices [15,16].

To address this gap, the present work introduces FMECA-VR-0.1, an immersive training tool developed using Unity 3D and Oculus VR technologies. The tool integrates FMECA and the Qualitative Risk Criticality Matrix (QRCM) within an immersive virtual environment and is aligned with the Maintenance Management Model (MMM) proposed by INGEMAN [17,18], which provides a structured framework for maintenance and asset management integration, and further discussed in Industry 4.0 contexts [10,13]. The platform is validated through application to the VESTAS V100–2.0 MW wind turbine at the Valle de los Vientos Wind Farm in Chile, enabling users to explore the operational context, perform criticality analysis of subsystems, understand failure mechanisms, and review maintenance strategies in an interactive VR environment.

The main contributions of this work are as follows:

• Development of a novel immersive platform that integrates engineering-based reliability methods (FMECA, QRCM, MMM) within an immersive VR training environment.
• Application to a real wind turbine system, demonstrating the feasibility of embedding analytical maintenance processes into interactive 3D scenarios.
• Preliminary qualitative assessment indicating potential improvements in user engagement and conceptual understanding, without formal quantitative validation.
• Identification of future research pathways, including digital-twin integration, real-time sensor data, multi-subsystem expansion, and quantitative assessment of learning performance.

The remainder of this article is organized as follows: Section 2 presents background information on Industry 4.0 technologies, VR applications in maintenance, and the engineering concepts supporting the proposed method. Section 3 describes the methodological framework used to develop the FMECA-VR-0.1 tool. Section 4 introduces the wind turbine case study and criticality assessment. Section 5 details the architecture and implementation of the VR prototype. Section 6 and Section 7 discuss cost estimation, practical implications, industrial benefits, and limitations. Section 8 summarizes the conclusions and outlines future research directions. From a Maintenance 4.0 perspective, the proposed platform contributes to the digital transformation of maintenance training by integrating engineering-based risk analysis with immersive, human-centric digital environments.

2. Literature Review and Background

The digital transformation of industrial systems is a direct consequence of the technological breakthroughs introduced by Industry 4.0, which encompasses a wide range of enabling technologies such as industrial IoT, AI-driven analytics, cyber-physical systems (CPS), smart automation, augmented and virtual reality, and digital twins [1,2,3,4,5,6]. These technologies have redefined how assets are monitored, controlled, and maintained, creating pathways for predictive diagnostics, real-time decision-making, and intelligent optimization of operations.

2.1. Industry 4.0 and Its Impact on Maintenance Engineering

Industry 4.0 initiatives emerged as early as 2011 and have since been widely adopted across Europe, the United States, and Asia, leading to an unprecedented integration of digital technologies into manufacturing and infrastructure systems [2,3]. The adoption of smart technologies has enabled industries to increase responsiveness, reduce downtime, enhance flexibility, and manage complex operations with higher reliability.

In the domain of maintenance engineering, Industry 4.0 has accelerated the transition from reactive and preventive strategies to predictive and prescriptive approaches, supported by advanced data analytics, probabilistic modeling, and automated monitoring [4,5]. Digital twins—virtual replicas of physical assets capable of simulating performance under real operating conditions—have become important tools for predictive maintenance, improving early failure detection, and enabling the optimization of maintenance plans [6,11]. These tools leverage high-frequency sensor data and machine-learning models to support operational decision-making in near real time.

Furthermore, governmental and institutional initiatives such as Industry 5.0, promoted by the European Commission, aim to complement Industry 4.0 by placing emphasis on resilient, sustainable, and human-centric industrial systems [13,14,15,16]. This new paradigm reinforces the importance of advanced technologies not only for efficiency but also for workforce development and safer working environments [12].

2.2. Cyber-Physical Systems, IoT, and the Emergence of the Industrial Metaverse

Cyber-Physical Systems (CPS), a central element of Industry 4.0, integrate computational algorithms with physical processes, enabling autonomous coordination, remote monitoring, and intelligent control of industrial assets [9]. These systems provide the foundation for digital twins and the emerging Industrial Metaverse, an ecosystem where virtual representations of assets interact with real-world data, enabling immersive analysis, remote operations, and collaborative maintenance planning.

Recent research highlights the potential of the Industrial Metaverse to enhance predictive maintenance, operational awareness, and training effectiveness by integrating VR, AR, digital twins, robotics, and AI within a unified immersive environment [10]. In this context, technicians can interact with equipment at full scale, analyze system behavior, and explore complex operational scenarios without safety risks.

Companies such as Capgemini have demonstrated the effectiveness of immersive digital twins for real-world training and decision-making, showing that Metaverse tools can replicate equipment behavior with high fidelity and improve the understanding of machine–environment interactions [11]. This new technological ecosystem is reshaping maintenance education, facilitating data-driven insights while reducing operational exposure.

In this work, the term “Metaverse” is used in a restricted engineering sense, referring to an immersive virtual environment that enables real-time interaction between users and digital representations of industrial assets. However, to avoid ambiguity and align with established terminology, the system developed in this study is more precisely defined as an immersive Virtual Reality (VR)-based training platform rather than a fully integrated industrial metaverse ecosystem.

2.3. VR and Immersive Tools for Maintenance Training

Previous studies on VR applications in industrial training can be categorized into three main groups:

-

Safety-oriented VR training: Studies such as [7,8] focus on hazard identification and safety procedures, demonstrating improvements in risk awareness and accident prevention.

-

Process and assembly training: Works such as [9,10] emphasize procedural learning, including assembly, disassembly, and maintenance operations.

-

Cognitive and adaptive learning systems: Research such as [11,12] explores the cognitive impact of immersive environments, including user engagement, cognitive load, and adaptive learning strategies.

Despite these advances, none of these approaches integrate formal reliability engineering methodologies such as FMECA, highlighting a critical research gap addressed in this work. Most current applications focus on procedural tasks or basic equipment familiarization rather than risk-based decision-making.

2.4. FMECA and Risk-Based Maintenance Frameworks

Failure Modes, Effects, and Criticality Analysis (FMECA) is a well-established technique in reliability engineering used to identify failure modes, assess their effects, and prioritize corrective and preventive actions. It provides a structured methodology aligned with international standards and asset management best practices. The technique is frequently integrated with the Qualitative Risk Criticality Matrix (QRCM) to classify failures by their frequency and severity, enabling maintenance teams to align interventions with risk tolerance and business objectives [17].

In parallel, the Maintenance Management Model (MMM) developed by INGEMAN offers a comprehensive, eight-phase framework for designing, optimizing, and controlling maintenance operations within the asset management lifecycle [10,17,18,19]. The MMM integrates strategic alignment, asset prioritization, failure elimination, preventive plan design, optimization techniques, cost analysis, and continuous improvement through digital technologies and Industry 4.0 tools. This framework provides an ideal structure for connecting risk analysis methodologies (such as FMECA) with immersive digital environments.

2.5. Identified Research Gaps and Motivation for the Present Study

Despite the maturity of these methodologies, their integration into VR-based training tools has not been widely explored. Most VR systems lack structured processes for modeling risks, evaluating failure consequences, or linking immersive learning with engineering decision-making. This gap presents a significant opportunity for innovation.

The literature review reveals four unresolved gaps:

-

VR systems rarely integrate engineering methodologies such as FMECA.

-

No existing tools combine FMECA, QRCM, MMM, and VR into a unified immersive VR-based platform.

-

There is limited research on immersive training for wind turbine maintenance using formal risk methodologies.

-

Few studies apply VR in combination with asset management frameworks (ISO 55000, MMM) [20].

These gaps provide the motivation for the present study, which proposes and evaluates FMECA-VR-0.1, an immersive VR-based training platform that integrates reliability analysis, digital modeling, and interactive visualization for wind turbine maintenance training. A summary of the main references related to VR-based industrial training, Digital Twin integration, reliability engineering, and Industry 4.0/5.0 transformation is presented below (see

Table 1. Summary of Recent References Related to VR, AR, XR, Digital Twins, and Maintenance 4.0 (2023–2025).

Reference	Main Contribution	Application Area
Sun et al. (2025) [21]	Proposes a multi-scenario immersive VR system for maintenance inspection training in steel rolling mills.	Industrial VR training; maintenance inspection.
Di Pasquale et al. (2024) [22]	Systematic review showing the effectiveness of VR for assembly and disassembly training.	VR for technical training; systematic literature review.
Qawqzeh et al. (2025) [23]	Demonstrates the effectiveness of VR-based safety training for industrial workers through controlled experimentation.	Occupational safety; VR human-performance evaluation.
Fry et al. (2025) [24]	Develops a VR training platform for foundry operations, improving understanding and task execution training—framework developed for the Tennessee Tech University (TTU) Foundry.	Advanced industrial VR education.
Costa et al. (2025) [25]	Integrates Digital Twins with VR/AR and IoT using FIWARE; demonstrates real-time industrial monitoring.	Digital Twin applications; Industry 4.0.
Coupry et al. (2023) [26]	Evaluates XR for remote collaborative maintenance tasks, showing improvements in communication and efficiency.	XR for remote collaboration; maintenance operations.
Matam et al. (2025) [27]	Introduces CLAd-Vr system’s architecture, including the EEG sensing hardware, real-time inference model, and adaptive VR interface.	Adaptive VR learning; cognitive engineering.
Strazzeri et al. (2024) [28]	Presents VR integration for engineering education and industrial maintenance training (case study: thermography for a carbon fiber plate in the aerospace domain).	Engineering VR education; maintenance instruction.
Akpan and Offodile (2024) [29]	This study undertakes a science mapping of research on the role of Virtual Reality Simulation (VRSIM) in manufacturing in the 4IR.	Predictive maintenance; Digital Twins; manufacturing sector.
Spadoni et al. (2023) [30]	Reviews current trends of XR in advanced manufacturing systems.	XR in manufacturing; Industry 4.0.
Yanytska (2025) [31]	Defines the Industry 5.0 paradigm with emphasis on human-centered manufacturing and resilience.	Industry 5.0; human–machine interaction.
Breitkreuz et al. (2022) [32]	Systematic review of AR applications in industrial maintenance and proposes a research agenda.	AR in maintenance, Maintenance 4.0

).

3. Methodological Framework

The methodological framework adopted in this study integrates three complementary engineering components: (1) the Maintenance Management Model (MMM) proposed by INGEMAN, which provides a structured and comprehensive process for designing and optimizing maintenance systems; (2) the Failure Modes, Effects, and Criticality Analysis (FMECA), used as the foundation for identifying and evaluating failure mechanisms; and (3) its implementation within an immersive VR-based training platform designed to enhance technical training.

3.1. Overview of the Maintenance Management Model (MMM)

For the development of the digital tool FMECA-VR-01, the eight-phase Maintenance Management Model (MMM) developed by INGEMAN will serve as a reference framework (Figure 1—Maintenance Management Model) [17]. The goal is to enhance the profitability of production systems by prioritizing asset components in order to carry out a series of analyses aimed at improving the efficiency and effectiveness of maintenance management processes [18].

Below is a brief introduction to each phase of the MMM:

-

Phase 1. Techniques for defining a maintenance management strategy. To ensure that maintenance operational objectives and strategies are not misaligned with overall business goals [13], techniques such as the Balanced Scorecard (BSC) can be introduced and implemented in the maintenance area. The BSC is customized for each organization and enables the creation of key performance indicators (KPIs) to measure maintenance performance, aligned with strategic objectives [13]. Unlike conventional control-oriented measures, the BSC centers its analysis on corporate strategy and vision, emphasizing the achievement of performance goals. These goals are defined through a participatory process involving both internal and external stakeholders, including company leadership, operational staff, and end users. As a result, maintenance performance measures are directly linked to the overall success of the organization.

-

Phase 2. Techniques for ranking production assets. Once maintenance objectives and strategies are defined, various qualitative and quantitative techniques are available to systematically classify assets based on their criticality for achieving business objectives. Many quantitative approaches employ variations in probabilistic risk assessment (PRA) or generate probabilistic risk indices (PRI) for each asset. Assets with higher indices are prioritized. In cases where historical data is unavailable, qualitative techniques may be applied to ensure effective initial decision-making. After asset prioritization, a clear maintenance strategy must be defined for each category, to be refined over time.

-

Phase 3. Tools for eliminating weak points in high-impact equipment or systems. In critical assets, it is advisable to identify and eliminate chronic, recurring failures before developing maintenance plans. Conducting such can yield an early return on investment. One of the most widely used techniques for this purpose is Root Cause Analysis (RCA) [17], which identifies the physical, human, and latent causes of failures. Physical causes refer to the technical explanation for the failure, human causes involve errors or omissions that lead to failure, and latent causes relate to organizational or management deficiencies that enable those errors to persist. Addressing latent causes is typically the main focus of this phase.

-

Phase 4. Support for defining an effective preventive maintenance plan. Designing a preventive maintenance plan for a system requires identifying its functions and potential failure modes, and establishing tasks that ensure safety and cost-effectiveness. A structured methodology such as Reliability Centered Maintenance (RCM) [9] is commonly used to achieve this objective.

-

Phase 5. Optimization techniques for improving maintenance programs. Maintenance plans and programs can be optimized to improve the effectiveness and efficiency of the initial design. Optimization models differ based on the time horizon. Long-term models address capacity, spare inventory, and task intervals. Medium-term ones focus on scheduled shutdowns, while short-term models aim to enhance resource allocation and control. Both analytical and empirical approaches are employed, often requiring simplifications to manage complexity [18].

-

Phase 6. Monitoring and control of maintenance operations. Once maintenance activities are designed, planned, and scheduled, their execution must be monitored, and deviations controlled to meet business objectives and KPI targets. Many high-level KPIs are built from technical and economic sub-indicators, making it essential to capture accurate and aggregated maintenance data.

-

Phase 7. Lifecycle cost analysis and control. This phase evaluates the total cost of an asset throughout its lifecycle, including planning, R&D, production, operation, maintenance, and disposal. Lifecycle cost analysis depends heavily on reliability data such as failure rates, repair times, and spare part costs. It supports decisions about new equipment acquisition or replacement and offers three major benefits [20]:

○

All costs associated with an asset become visible.

○

It enables cross-functional analysis, e.g., how low R&D investment may increase future maintenance costs.

○

It allows management to make accurate forecasts.

-

Phase 8. Techniques for continuous improvement in maintenance. Continuous improvement (CI) in maintenance is possible through emerging technologies in high-impact areas identified in earlier phases. Concepts such as Maintenance 4.0, e-maintenance, and e-manufacturing are key elements of Industry 4.0, which leverages ICT to create collaborative, multi-user corporate environments [4,10]. Maintenance 4.0 involves the integration of resources, services, and management tools to enable proactive, data-driven decisions. This support includes ICT, web-based, wireless, and infotronic technologies, and “e-maintenance” functions such as e-monitoring, e-diagnosis, and e-prognosis. Additionally, the active participation of maintenance personnel is critical to success. While high levels of knowledge, training, and expertise are required, the inclusion of simple, operator-driven tasks is essential to achieving high-quality maintenance and overall equipment effectiveness.

As illustrated in the reference figure, the first three phases influence the effectiveness of maintenance management; the next phases (4 and 5) focus on planning and scheduling, while phases 6 and 7 address monitoring and cost control throughout the asset lifecycle. Phase 8 is devoted to designing actions that ensure continuous improvement through the integration of emerging Industry 4.0 technologies.

The continuous improvement process outlined in Phase 8 relies on the application of these emerging technologies, particularly under the Industry 4.0 paradigm. Tools such as IoT, machine learning (ML), neural networks (NN), and digital twins (DT) can exponentially enhance industrial management processes. The key challenge for a comprehensive maintenance model is to provide a roadmap for optimizing the use of Industry 4.0 tools to improve technical performance and maximize asset profitability throughout their life cycle (see Figure 2—MMM and Digital Transformation). This represents a functional perspective of the new digital framework for intelligent asset management [8].

3.2. FMECA Structure and Criticality Assessment

The Failure Modes, Effects, and Criticality Analysis (FMECA) is central to the methodological framework. The technique provides a systematic, traceable, and engineering-based assessment of risks associated with equipment failures [17]. Its structure includes:

-

Failure Mode: how a component or subsystem fails;

-

Cause: the underlying mechanism behind the failure;

-

Effect: the functional consequence at local, subsystem, and system levels;

-

Detection Method: existing means for identifying the onset of the failure;

-

Severity Level: qualitative estimation of impact on safety, production, quality, or cost;

-

Frequency or Occurrence: expected rate or probability of the failure;

-

Criticality: combined assessment of severity and frequency using the QRCM.

The Qualitative Risk Criticality Matrix (QRCM) is used to map failure events into categories such as low, medium, high, and very high criticality depending on their risk ranking. These classifications allow maintenance planners and learners to understand which failures demand immediate attention, priority resource allocation, or improvement actions.

This structured approach provides a rigorous and academically validated foundation for embedding engineering reasoning into the VR training system.

3.3. Integration of MMM and FMECA into the VR-Based Training Tool (FMECA-VR-0.1)

The four stages that make up the implementation process of the FMECA-VR-0.1 tool are described below (see Figure 3—implementation procedure). These four stages are directly related to specific phases of the Maintenance Management Model (MMM) (see Figure 1):

-

Stages 1 and 2 correspond to the development of the operational context and the criticality matrix (linked to Phase 2 of the MMM).

-

Stage 3 corresponds to the definition of failure modes, effects, and criticality (linked to Phase 4 of the MMM).

-

Stage 4 corresponds to the development of maintenance strategies (linked to Phases 4 and 5 of the MMM).

3.3.1. Stage 1. Operational Context

To define the operational context of the system to be evaluated, it is important to consider the following aspects:

-

Operational Summary: Purpose of the system, equipment involved, processes, safety devices, and environmental objectives.

-

Personnel: Definition of shifts, operations, and quality parameters.

-

Process Breakdown: Structuring of the process into systems, definition of boundaries, and listing of components.

The initial information that must be collected to develop the operational context includes:

-

Operational profile.

-

Operating environment.

-

Quality/availability of required inputs (fuel, air, etc.).

-

Alarms and monitoring.

-

Spare parts policies, resources, and logistics.

-

P&IDs (Piping and Instrumentation Diagrams) of the system.

-

System schematics and/or block diagrams, typically developed from the P&IDs.

A graphical tool that facilitates visualization of the operational context is the Input–Process–Output (IPO) diagram (see Figure 4). These diagrams must clearly identify the inputs, processes, and main outputs of the system.

The key elements of the IPO diagram are detailed below:

• Inputs can be categorized into three types:

-

Raw materials: Resources directly processed or transformed by the system or equipment (e.g., gas, crude oil, wood).

-

Services: Resources used by the process to transform raw materials (e.g., electricity, water, steam).

-

Controls: Inputs related to control systems and their effects on the equipment or processes. These are usually not recorded as separate functions, as their failure is generally associated with a loss of output signal at some point in the process.

• Outputs are associated with the inherent functions of the system and may be classified as:

-

Primary products: Represent the main purpose of the system, generally defined by production rate and quality standards.

-

Secondary products: Derived from primary functions performed by the system. Loss of secondary products can often lead to the failure of primary functions and may have catastrophic consequences.

-

Controls and alarms: Related to the system’s protection and control functions.

Processes should be recorded as a description of the function to be performed by the system in a specific location, allowing maintenance efforts to focus on the function under analysis and determine which maintenance activities must be performed for the asset to fulfill its role within the operational context. A recommended guideline for developing the operational context is ISO 14224 [33], which provides a framework for defining system boundaries and identifying maintainable items within equipment subsystems. The standard includes examples of equipment types, general descriptions of operational context elements, classification of equipment types, boundary definitions, hierarchical breakdowns, and key reference data for each equipment class.

3.3.2. Stage 2. Equipment-Level Criticality Analysis

Criticality analysis techniques are used to prioritize systems, facilities, and equipment based on their overall impact, allowing for optimized resource allocation. A common qualitative method is ranking through the use of criticality matrices, which assess risk by considering the failure frequency and the severity of consequences [21]. To conduct a criticality analysis, the following elements must be considered:

-

Define the scope and purpose of the analysis.

-

Establish importance criteria, such as safety, environment, production, cost, failure frequency, and repair time.

-

Select or develop a method to rank systems/equipment.

Criticality analysis is essential for optimizing maintenance, as it prioritizes assets based on their operational, safety, and environmental impact [34]. One of the most widely used approaches is qualitative risk assessment, performed using criticality matrices, which are extensively applied in industries such as oil and gas, mining, and aviation. This study proposes a risk factor-based criticality model, specifically designed for application in the renewable energy sector. The case study focuses on the VESTAS V100–2.0 MW wind turbine at the Valle de los Vientos Wind Farm. Additionally, the equations used in the model are presented, providing an analytical framework for ranking critical assets and optimizing maintenance strategies.

Risk = FF × C

(1)

C = SHE + IP + CDF

(2)

where the following applies:

-

FF = Failure frequency per equipment (number of failures in a given time period)

-

C = Consequences of failures related to safety, environment, quality, production, etc.

-

SHE = Safety, Health, and Environmental Impact

-

IP = Impact of Production

-

CDF = Costs Direct of Failure

Next, the evaluation factors and scales associated with failure frequency and consequences for each piece of equipment must be selected. These factors are adapted to the subsystems of the VESTAS V100–2.0 MW wind turbine.

Adjusted Factors for the VESTAS V100–2.0 MW Wind Turbine Components:

-

Failure Frequency (FF)

1. Excellent: One occurrence after 8760 h.

2. Good: At least one occurrence between 4380 and 8760 h.

3. Average: At least one occurrence between 2190 and 4380 h.

4. Poor: At least one occurrence between 730 and 2190 h.

5. Very Poor: At least one occurrence within 730 h.

-

Failure Consequences (C)

Safety, Health, and Environmental Impact (SHE)

5. High impact: Fatalities or catastrophic environmental damage.

4. Medium–high impact: Serious injuries and significant environmental damage.

3. Low–medium impact: Minor injuries and no lasting environmental damage.

2. Low impact: Slight environmental damage.

1. No impact: No environmental effect.

-

Impact of Production (IP)

5. 60–100% production loss [MW], (>40 h downtime).

4. 40–60% production loss [MW], (20 < X < 40 h downtime).

3. 35–40% production loss [MW], (5 < X < 20 h downtime).

2. 25–35% production loss [MW], (1 < X < 5 h downtime).

1. 0–25% production loss [MW], (<1 h downtime).

-

Costs Direct of Failure (CDF)

5. Costs over USD 300,000.

4. Costs between USD 100,000 and 299,999.

3. Costs between USD 50,001 and 99,999.

2. Costs between USD 5001 and 50,000.

1. Costs below USD 5000.

Consequence factors must be adapted to each industrial context, as values may vary across operational settings. The final result for the horizontal axis (Consequences) is the sum of the individual consequence factors (SHE + IP + CDF, maximum score: 15 points). The results are then presented in a Qualitative Risk Criticality Matrix (QRCM) of 5 × 5 (Figure 5—Criticality Matrix), where the vertical axis represents five failure frequency categories (ranging from 1 to 5), and the horizontal axis represents five consequence categories (ranging from 1 to 15) [21].

Afterward, failure frequency and consequence factors are assessed for each piece of equipment, assigning a position on the risk matrix and calculating a criticality value (Risk = FF × C). The resulting criticality values are located within the matrix and classified into four criticality levels, as shown below:

-

Criticality Levels by Equipment (Based on QRCM—Figure 5):

B	=	Low Criticality: Gray zone	3	≤	Cr	≤	18
M	=	Medium Criticality: Yellow zone	9	≤	Cr	≤	27
A	=	High Criticality: Green zone	13	≤	Cr	≤	48
MA	=	Very High Criticality: Red zone	35	≤	Cr	≤	75

3.3.3. Stage 3. Failure Modes, Effects, and Criticality Analysis

FMECA is a systematic methodology that identifies physical failure modes and their effects and consequences within a given operational context. By defining failure modes, basic information is obtained to help prevent failure effects through the selection of maintenance activities. These activities are specifically designed to target each physical failure mode and mitigate its consequences.

Stage 3.1. Failure Modes

Identifying physical failure modes that reduce asset functionality is essential for optimizing maintenance plans. The level of detail at which maintenance is managed for an asset should be directly related to the level at which failure modes are identified—this is known as the maintainable item level (i.e., the minimum disaggregation level at which a maintenance plan can be executed, as referenced in ISO 14224).

In many cases, the level at which failure modes are identified will not match the level of detail selected for analyzing the asset and its functions. Therefore, in order to develop an effective maintenance management system for a specific group of assets within an operational context, it is essential to determine the level at which various failure modes will occur, as they relate to the functional performance of the asset in that specific operational environment. The working group gathers information from the following sources during the failure mode analysis:

-

Generic lists of failure modes.

-

Operations and/or maintenance personnel with extensive experience with the asset.

-

Existing technical records and maintenance histories.

-

Asset manufacturers and suppliers.

-

Other users operating the same type of asset.

Examples of physical failure modes associated with maintainable items:

-

Burned-out electric motor (detail level: equipment)

-

Fractured impeller shaft (detail level: component)

-

Impeller jammed due to foreign objects (detail level: component)

-

Completely blocked suction line (detail level: component)

-

Worn mechanical seal (detail level: component)

-

Damaged piston ring in hydraulic cylinder (detail level: component)

Stage 3.2. Effects and Criticality of Failure Modes

In this part of the process, the main objective of the working group is to identify the effects (i.e., consequences) of each previously identified failure mode. Specifically, the team must describe the impact of the failure on safety, the environment, operations, maintenance, etc., within the defined operational context. This step should include all necessary information to support the assessment of failure consequences [17]. To accurately identify and describe the effects produced by each failure mode, the working group should generally answer the following questions:

-

What evidence confirms that the failure has occurred?

-

How does it affect safety and the environment?

-

How does it impact production or operations?

-

What are the operational consequences?

-

Is it necessary to shut down the process?

-

Is there an impact on quality? If so, how much?

-

Is there an impact on customer service?

-

Does it cause damage to other systems?

-

How much time is required to repair the failure (corrective actions)?

-

What is the economic loss caused by the failure (direct costs, production impact, environmental and safety costs, etc.)?

Example of a failure mode and its effects:

-

Failure Mode:

Damaged piston rings in the hydraulic cylinder

-

Effects of Failure Mode:

○

Visible/Not visible: Yes. No impact on safety or environment.

○

Operational effects: The engine crankcase depressurizes, cylinder compression drops, oil wets the spark plug, smoke is observed in the exhaust, compression capacity is lost, and engine RPMs decrease.

○

Corrective actions: Shut down the engine, depressurize the system, rotate the engine, position the connecting rods, secure the flywheel, loosen connecting rod bolts, remove the piston, inspect the rings, and replace them if necessary. Required personnel: 4 mechanics. Repair time: 16 h/failure.

○

Production impact: USD 120,000/h. Total impact per failure: USD 1,920,000.

Once the effects of the failure mode have been defined, the criticality of each failure mode is determined using the risk model previously explained in Stage 2. In this case, the Qualitative Risk Criticality Matrix (QRCM) is applied specifically at the level of failure modes.

Risk per failure mode = FF × (SHE + IP + CDF)

(3)

where the following applies:

-

FF = Frequency of the failure mode (number of failures in a given time period)

-

C = Consequences of the failure mode in terms of safety, environment, quality, production, etc.

-

SHE = Impact on Safety and the Environment

-

IP = Impact on Production

-

CDF = Direct Failure Costs

Each failure mode is assessed using the criticality model introduced in Stage 2. Values are plotted on the Qualitative Risk Criticality Matrix (QRCM) (Figure 5), where the vertical axis represents five frequency categories (ranging from 1 to 5), and the horizontal axis represents five consequence categories (ranging from 1 to 15) [34]. The risk value (Risk = FF × C) is calculated and used to determine the criticality level of each failure mode.

-

Criticality Levels by Failure Mode (based on QRCM—Figure 5):

B	=	Low Criticality: Gray zone	3	≤	Cr	≤	18
M	=	Medium Criticality: Yellow zone	9	≤	Cr	≤	27
A	=	High Criticality: Green zone	13	≤	Cr	≤	48
MA	=	Very High Criticality: Red zone	35	≤	Cr	≤	75

Stage 3.3. Probable Causes

In the process of developing maintenance plans, it is recommended to define the probable cause of each failure mode at a basic level. Specifically, as part of the proposed FMECA-VR-0.1 procedure, the probable causes refer to the factors or conditions that may trigger the physical failure mode leading to a loss of equipment function.

It is important to note that the cause defined for each failure mode within the FMECA-VR-0.1 tool is not the result of a detailed root cause analysis. Rather, it is a proposed probable cause, identified by the expert team conducting the FMECA based on their technical experience and field knowledge. ISO 55000 [35], focused on asset management, promotes the identification of failure causes as a means to minimize risk and optimize asset performance.

Example of a probable cause:

-

Failure Mode: Damaged piston rings in the hydraulic cylinder

-

Probable Cause: Accelerated wear due to increased temperature

3.3.4. Stage 4. Maintenance Plans

Once the FMECA has been completed, the working team must select the type of maintenance activity that can help prevent the occurrence of each previously identified failure mode. In general, the most appropriate maintenance activity should be chosen to avoid the potential consequences of each failure mode. After selecting the type of maintenance activity, the specific maintenance action must be defined, along with its execution frequency. One of the main objectives is to prevent or at least reduce the possible consequences related to human safety, environmental impact, and operational disruptions caused by the failure modes.

As part of the development of the FMECA-VR-0.1 tool, maintenance activities are classified into two main groups:

-

Preventive (proactive) maintenance

-

Corrective (reactive) maintenance, applied only when no effective preventive alternative exists.

Preventive (Proactive) Maintenance Activities

Preventive maintenance activities can be grouped into four categories [17]:

-

Condition-Based Maintenance (CBM)

CBM is scheduled based on the condition of the asset, acknowledging that most failure modes do not occur suddenly but develop progressively over time. If the onset of a failure can be detected during normal operation, actions based on the asset’s condition can be taken to prevent failure and its effects. The point at which a failure can be detected is called the potential failure and is defined as a physical condition that indicates an impending or already occurring functional failure. Examples of potential failures include: abnormal vibration readings indicating imminent bearing failure, cracks in metals indicating fatigue failure, metal particles in gear oil indicating wear, hot spots in boiler linings indicating refractory degradation, etc.

-

Overhaul (Reconditioning)

These are scheduled activities aimed at restoring the asset to its original condition. They are performed at intervals shorter than the asset’s operational life limit, based on functional analysis over time. The asset is taken out of service, thoroughly inspected, and defective parts are replaced if necessary. Overhauls are typically applied to major equipment such as compressors, turbines, and boilers.

-

Scheduled Replacement

This activity targets the planned replacement of components or parts before the end of their useful life (before failure occurs). Unlike overhauls, replacements affect individual components, restoring their condition by installing new parts. Overhauls, by contrast, may involve cleaning, repair, or inspection without replacing parts.

-

Hidden Failure Detection

Hidden failure modes are not evident under normal operating conditions and may not have direct consequences—but they can trigger multiple failures within a system. To minimize their occurrence, it is essential to periodically inspect whether these hidden functions are operating correctly. These periodic inspections help reduce failures and enhance the operational reliability of assets.

Corrective (Reactive) Maintenance Activities

When effective preventive maintenance is not feasible, corrective (reactive) actions may be evaluated. These are classified into two categories:

-

Redesign

If no preventive activity can effectively reduce the likelihood of failure modes that impact safety or the environment to an acceptable level, a redesign or modification (of the maintenance strategy or the failure mode itself) must be developed. When the consequences are operational or non-operational, and no preventive action is effective, a redesign becomes an economically justified decision-making process.

-

Run-to-Failure (Unscheduled Maintenance)

When no preventive strategy is more cost-effective than the potential consequences of failure (operational or non-operational), the decision may be made to allow the failure to occur and act reactively. This strategy is applicable only when the failure does not affect the following: safety, environment, or operations.

4. Application of the FMECA-VR-0.1 Tool in the Renewable Energy Sector

The following presents a preliminary case study on the application of the FMECA-VR-0.1 tool to the VESTAS V100–2.0 MW wind turbine system, located at the Valle de los Vientos Wind Farm, in the Calama region of Chile. For the development of the FMECA-VR-0.1 tool, each of the stages outlined in the flowchart shown in Figure 3 will be implemented.

4.1. Stage 1: Definition of the Operational Context

The wind farm is responsible for supplying electrical energy to the Northern Interconnected System (SING) in Chile. It has an installed capacity of 90 MW, using wind turbines rated at 2 MW each, enabling the generation of more than 200 GWh of renewable energy annually [36]. The facility comprises 45 wind turbines, providing enough power to supply approximately 80,000 households.

• General system: Valle de los Vientos Wind Farm

Each VESTAS V100–2.0 MW wind turbine contributes to an annual generation capacity exceeding 200 GWh of renewable energy [24].

-

Reference Data:

Tower height: 80 m

Rotor diameter: 100 m

Environmental conditions: Desert climate with high dust exposure and temperatures ranging from −5 °C to 40 °C

-

Controllers and Alarms:

Generator overload alarms

Vibration alarms in the gearbox

Automatic shutdown in extreme wind conditions (wind speeds > 25 m/s)

Temperature monitoring in bearings and the generator

• Specific system description

Each wind turbine includes eight subsystems that form part of the wind energy generation process [24]:

-

Blades: Capture the wind’s kinetic energy and convert it into mechanical rotational energy.

-

Main Shaft Assembly: Transmits the rotational mechanical energy from the blades to the generator.

-

Gearbox (Multiplier): Increases the rotational speed of the main shaft.

-

Doubly-Fed Induction Generator (DFIG): Converts the rotational mechanical energy—transmitted through the shaft system—into electrical energy.

-

Frequency Converter: Manages and adapts the electrical energy generated by the DFIG to match grid standards in frequency and voltage.

-

Transformer: Adjusts or steps up the voltage of the generated electricity for safe and efficient transmission to the grid.

-

Electrical Panel: Also known as the electrical control cabinet, it manages, distributes, and protects the turbine’s electrical systems.

-

Yaw System: Orients the nacelle and blades toward the wind direction.

• Key process variables (power curve)

-

Minimum Operating Wind Speed:

The turbine does not generate power until the wind speed reaches 3 m/s.

-

Power Growth:

Between 3 m/s and 10 m/s, power increases linearly from 0 to 2000 kW.

Between 10 m/s and 12 m/s, power stabilizes at 2000 kW.

-

Operational Stability:

From 12 m/s onward, the turbine maintains a constant output of 2000 kW.

-

Safety Shutdown:

If wind speeds exceed 25 m/s, the turbine shuts down to prevent damage.

• Input—Process—Output (IPO) diagram

-

INPUT: The necessary resources for wind turbine operation include:

• Wind (Kinetic Energy): The primary input driving the rotor blades. Wind speed and direction determine how much energy can be captured.
• Auxiliary Electrical Power: Powers control systems, sensors, and blade/pitch adjustment when the turbine is not generating power.
• Control System: Receives monitoring and automated adjustment signals (via SCADA) to optimize performance and protect the equipment.

-

PROCESS: The wind turbine converts kinetic wind energy into electrical energy through the following steps:

• Wind capture: Rotor blades spin as they are impacted by wind.
• Mechanical conversion: The rotation is transmitted through the main shaft and accelerated via the gearbox.
• Power generation: The generator transforms mechanical energy into electrical energy through electromagnetic induction.
• Regulation: Control systems adjust blade pitch and yaw direction to maximize wind capture.
• Voltage transformation: An integrated transformer adjusts voltage for transmission to the substation.

-

OUTPUT: Products and by-products generated by the wind turbine include:

• Generated electrical power: The main output, ready to be delivered to the electrical grid via transmission lines.
• Monitoring and operational data: Real-time performance data transmitted to the SCADA system for analysis and control.
• Residual heat: A by-product of the energy conversion process, dissipated by the turbine’s ventilation systems.

The IPO Diagram is presented below (Figure 6):

4.2. Stage 2: Selection of the Critical System

The criticality model proposed in this document is based on the estimation of the risk factor and is applied to the Valle de los Vientos Wind Farm in Calama, focusing on the VESTAS V100–2.0 MW wind turbine system. Below are the results of the criticality analysis process (

Table 2. Criticality Assessment of Wind Turbine Subsystems.

Subsystems	SHA	IP	CDF	TC: Total	F:	Risk	Criticality Level
				Consequences	Frequency	(TC × F)
Blade System	5	4	4	13	5	65	Very High Criticality
Electric Generator	4	5	4	13	5	65	Very High Criticality
Main Shaft Assembly	5	5	5	15	4	60	Very High Criticality
Tower	5	4	3	12	4	48	High Criticality
Gearbox (Transmission)	2	5	4	11	4	44	High Criticality
Transformer	3	4	3	10	4	40	High Criticality
Electrical Panel	3	4	3	10	2	20	Medium Criticality
Hydraulic System	2	4	3	9	2	18	Low Criticality
Anemometer and Wind Vane	3	3	2	8	2	16	Low Criticality
Frequency Converter	3	3	3	9	1	9	Low Criticality
Yaw System	2	3	3	8	1	8	Low Criticality
Control and Communication	1	3	2	6	1	6	Low Criticality

), based on the Qualitative Risk Criticality Matrix (QRCM) explained in Section 3.2 (Figure 5). The vertical axis of the matrix represents the failure frequency (5 categories, ranging from 1 to 5), while the horizontal axis represents the failure consequences at the subsystem/equipment level (5 categories, ranging from 1 to 15).

The results of the subsystem criticality analysis indicate the following distribution:

-

Subsystems with Very High Criticality: 25% of total

-

Subsystems with High Criticality:    25% of total

-

Subsystems with Medium Criticality:    42% of total

-

Subsystems with Low Criticality:      8% of total

According to the percentage-based analysis, 25% of the evaluated subsystems fall within the Very High Criticality category. The most critical subsystems are the Blades and the Electric Generator, each with a risk value of 65, followed by the Main Shaft Assembly with a value of 60. Based on these results, one of the three most critical subsystems will be selected for detailed analysis. For this study, the Blades subsystem has been chosen. In the following section, its components will be broken down to carry out a Failure Modes and Effects Analysis (FMEA) and assess the criticality of its physical failure modes.

4.3. Stage 3: Development of FMECA and Criticality Analysis

For this example, a partial FMECA will be developed for one critical subsystem: the blades of the wind turbine (Figure 7).

4.3.1. Definition of Failure Modes and Criticality Analysis

Physical failure modes of the blade subsystem:

-

Blade Damage: Modern wind turbines typically use three blades to ensure lower oscillations and better balance of gyroscopic forces. These blades are mainly made of lightweight and highly durable materials such as carbon fiber and fiberglass combined with epoxy resin. They can reach lengths of up to 50 m and rotate at speeds ranging from 10 to 60 RPM. For large-scale turbines, the most common operating speeds are between 10 and 20 RPM. The blades are mounted on the turbine hub [37].

-

Hub Damage: The hub connects the blades and transmits the captured wind energy from the rotor to the gearbox. It is generally a hollow metal structure that acts as a rigid central piece [37].

-

Nose Cone Issues: The nose cone is a conical structure that faces the wind and directs it toward the drivetrain. Its aerodynamic shape helps prevent turbulence and protects the wind turbine from potential damage [37].

Using the QRCM (explained in Section 3.2) as a reference, the criticality levels of the physical failure modes in the blade subsystem are determined (see

Table 3. Criticality Assessment of Physical Failure Modes—Blade Subsystem.

Physical Failure	SHA	IP	CDF	TC: Total	F:	Risk	Criticality Level
Modes				Consequences	Frequency	(TC × F)
Blades	5	5	5	15	4	60	Very High Criticality
Nose Cone	3	5	5	13	3	39	Medium Criticality
Hub	2	5	5	12	3	36	Medium Criticality

).

The results show that blade damage is the failure mode with the highest level of criticality in the blade subsystem, with a risk value of 60, classified as Very High Criticality. The next step will be to identify the probable causes for each physical failure mode.

4.3.2. Definition of Probable Causes

In this stage, the probable causes that may trigger the physical failure modes responsible for functional losses in the equipment are identified. It is important to note that the probable causes defined for each failure mode within the FMECA-VR-0.1 tool are based on expert experience and do not constitute a detailed root cause analysis. Below is the FMEA table corresponding to the blade subsystem of the Vestas V100–2.0 MW wind turbine, which includes the criticality level of each physical failure mode along with its respective probable causes (

Table 4. FMEA and Criticality Assessment of Physical Failure Modes—Blade Subsystem (Wind Turbine: Vestas V100–2.0 MW).

Subsystem	Function	Physical Failure Modes (Components)	Failure Effect	Criticality Level	Value	Probable Causes
Blades	Capture wind energy and convert it into mechanical energy to drive the wind turbine rotor.	Blade	Loss of aerodynamic capability.	Very High Criticality	60	Blades showing buckling
						Blade structure with cracks
						Blade structure with delamination
						Blades showing bending
		Nose	Loss of aerodynamic capability.	Medium Criticality	39	Nose structure with cracks
						Impact damage from foreign objects on nose
						Nose looseness due to poor installation
						Presence of corrosion on nose
		Hub	Loss of aerodynamic capability.	Medium Criticality	36	Hub structure with cracks
						Hub with misalignment
						Malfunction in hub orientation mechanism
						Presence of corrosion on hub

).

Following the completion of the FMEA, it becomes beneficial to develop a maintenance plan for each physical failure mode. This enables the implementation of preventive and corrective actions that contribute to improving the efficiency of asset management and operational processes. Ultimately, this methodology serves as a data-driven foundation for the case study under development.

4.4. Stage 4: Maintenance Plans

At this stage, maintenance plans are defined for each physical failure mode of the blade subsystem. However, for this case study, only the maintenance plans related to the blade subsystem’s failure modes will be addressed. The steps involved in building the maintenance plan are as follows:

-

Select the blade subsystem for the development of action plans related to its physical failure modes.

-

Identify the three physical failure modes and their previously assigned criticality levels.

-

Define the probable causes for each failure mode.

-

Specify the maintenance tasks associated with each failure mode.

-

Estimate the execution frequency for each task.

-

Assign a responsible specialist for each task.

-

Estimate the annual cost of each maintenance plan.

-

Estimate the annual maintenance hours for each task.

Here is the final FMEA table for the blade subsystem (

Table 5. Maintenance Plans for Physical Failure Modes of Blade Subsystems.

Subsystem	Physical Failure Mode (Components)/Ranking	Probable Causes	Type of Maintenance	Maintenance Task	Application Frequency	Responsible Specialist	Maintenance Plan Costs (USD/year)	Annual Maintenance Hours
Blades	Blades/Very High Criticality	Blades showing buckling	Condition-Based Maintenance	Thermographic winding inspection	Monthly	Electromechanical Technician	$5000	12
			Condition-Based Maintenance	Ultrasound Inspection	Monthly	Electromechanical Technician		12
			Condition-Based Maintenance	X-ray Inspection	Quarterly	Electromechanical Technician		6
		Blade structure with cracks	Condition-Based Maintenance	Thermographic winding inspection	Monthly	Electromechanical Technician	$5000	12
			Condition-Based Maintenance	Ultrasound Inspection	Monthly	Electromechanical Technician		12
			Condition-Based Maintenance	X-ray Inspection	Quarterly	Electromechanical Technician		6
		Blade structure with delamination	Condition-Based Maintenance	Thermographic winding inspection	Monthly	Electromechanical Technician	$5000	12
			Condition-Based Maintenance	Ultrasound Inspection	Monthly	Electromechanical Technician		12
			Condition-Based Maintenance	X-ray Inspection	Quarterly	Electromechanical Technician		6
		Blades showing bending	Condition-Based Maintenance	Thermographic winding inspection	Monthly	Electromechanical Technician	$5000	12
			Condition-Based Maintenance	Ultrasound Inspection	Monthly	Electromechanical Technician		12
			Condition-Based Maintenance	X-ray Inspection	Quarterly	Electromechanical Technician		6
Nose	Nose/Medium Criticality	Nose structure with cracks	Condition-Based Maintenance	Thermographic winding inspection	Monthly	Electromechanical Technician	$5000	12
			Condition-Based Maintenance	Ultrasound Inspection	Monthly	Electromechanical Technician		12
		Impact damage from foreign objects on nose	Condition-Based Maintenance	X-ray Inspection	Quarterly	Electromechanical Technician		6
			Condition-Based Maintenance	Thermographic winding inspection	Monthly	Electromechanical Technician	$5000	12
			Condition-Based Maintenance	Ultrasound Inspection	Monthly	Electromechanical Technician		12
			Condition-Based Maintenance	X-ray Inspection	Quarterly	Electromechanical Technician		6
		Nose looseness due to poor installation	Condition-Based Maintenance	Thermographic winding inspection	Monthly	Electromechanical Technician	$5000	12
			Condition-Based Maintenance	Ultrasound Inspection	Monthly	Electromechanical Technician		12
			Condition-Based Maintenance	X-ray Inspection	Quarterly	Electromechanical Technician		6
		Presence of corrosion on the nose	Condition-Based Maintenance	Thermographic winding inspection	Monthly	Electromechanical Technician	$5000	12
			Condition-Based Maintenance	Ultrasound Inspection	Monthly	Electromechanical Technician		12
			Condition-Based Maintenance	X-ray Inspection	Quarterly	Electromechanical Technician		6
Hub	Hub/Medium Criticality	Hub structure with cracks	Condition-Based Maintenance	Thermographic winding inspection	Monthly	Electromechanical Technician	$5000	12
			Condition-Based Maintenance	Ultrasound Inspection	Monthly	Electromechanical Technician		12
			Condition-Based Maintenance	X-ray Inspection	Quarterly	Electromechanical Technician		6
		Hub with misalignment	Condition-Based Maintenance	Thermographic winding inspection	Monthly	Electromechanical Technician	$5000	12
			Condition-Based Maintenance	Ultrasound Inspection	Monthly	Electromechanical Technician		12
			Condition-Based Maintenance	X-ray Inspection	Quarterly	Electromechanical Technician		6
		Malfunction in hub orientation mechanism	Condition-Based Maintenance	Thermographic winding inspection	Monthly	Electromechanical Technician	$5000	12
			Condition-Based Maintenance	Ultrasound Inspection	Monthly	Electromechanical Technician		12
			Condition-Based Maintenance	X-ray Inspection	Quarterly	Electromechanical Technician		6
		Presence of corrosion on the hub	Condition-Based Maintenance	Thermographic winding inspection	Monthly	Electromechanical Technician	$5000	12
			Condition-Based Maintenance	Ultrasound Inspection	Monthly	Electromechanical Technician		12
			Condition-Based Maintenance	X-ray Inspection	Quarterly	Electromechanical Technician		6

Note: Although similar inspection techniques (thermography, ultrasound, and X-ray) are repeated across multiple failure modes, in real industrial applications, these activities would be optimized and grouped to reduce operational costs, inspection time, and system downtime. Therefore, the table should be interpreted as a conceptual framework rather than an optimized maintenance strategy.

), which identifies the physical failure modes, their probable causes, and the corresponding maintenance plans:

5. Development of the Immersive VR-Based Training Platform Prototype: FMECA-VR-0.1

The FMECA-VR-0.1 prototype consists of an immersive VR-based training platform [38,39], which illustrates each of the stages described in the flowchart shown in Figure 2. The case study presented in the previous section is used as a reference: the VESTAS V100–2.0 MW wind turbine system, located in the Valle de los Vientos Wind Farm in Calama, Chile.

The main objective of the FMECA-VR-0.1 tool is to provide a training environment for future maintenance technicians, thereby enhancing the reliability, maintainability, and availability of these installations while also contributing to accident prevention and the reduction in human error. In the field of renewable energy, the integration of Industry 4.0 technologies has been key to improving asset monitoring and maintenance processes—leading to increased equipment reliability and extended service life. Data acquisition plays a fundamental role in enabling a wide range of applications [40,41]. For example, virtual and augmented reality simulators can be used to recreate work scenarios and train operators in maintenance procedures. This helps them identify potential issues and become familiar with the steps and tools required for specific maintenance tasks.

To develop the digital tool FMECA-VR-0.1, it is essential to define the system requirements and features, as this stage is critical in any software development project. It involves specifying the functions the tool must perform and the type of information it will manage, in order to effectively address user needs. Unlike conventional FMECA applications, the proposed VR-based approach enables contextual visualization of failure modes directly on 3D assets, facilitating cognitive association between failure mechanisms, consequences, and maintenance actions.

The following section presents the different types of requirements necessary for the development of the digital platform [42].

5.1. FMECA-VR-0.1 Data Integration and Interaction Model

A key contribution of the FMECA-VR-0.1 system is the integration of structured engineering data (FMECA) with immersive visualization. This integration is achieved through a data-driven architecture that connects failure analysis tables with 3D components in the VR environment.

The process follows four main stages:

• Data Structuring:

FMECA data are structured in tabular format (Excel) and exported into JSON files containing failure modes, causes, effects, criticality levels, and maintenance actions.

2.

Data Import into Unity:

The JSON files are imported into the Unity environment using C# scripts, enabling runtime parsing and dynamic data loading.

3.

Component Binding:

Each failure mode is associated with a specific 3D component using a unique component identifier (Component ID). This mapping allows linking engineering data to the corresponding physical representation of the asset.

4.

Interactive Visualization:

When the user selects a component in the VR environment, the system dynamically retrieves the associated FMECA data and displays it through an interactive UI panel, including:

-

Failure mode description

-

Criticality level

-

Probable causes

-

Recommended maintenance actions

This interaction transforms static FMECA analysis into an immersive and exploratory learning experience, enabling users to understand failure mechanisms directly in the context of the physical asset.

The overall data flow and interaction logic of the system are illustrated in Figure 8, which represents the core architectural contribution of this work. The figure shows how structured FMECA data are transformed into interactive VR elements through a data-driven pipeline: engineering data are exported from Excel, converted into JSON format, imported into Unity, linked to asset geometry through unique component identifiers, and dynamically rendered in the VR user interface during user interaction. This architecture enables the integration of reliability analysis with immersive visualization, bridging the gap between engineering methodologies and interactive learning environments.

5.2. Functional Requirements

An immersive VR-based training platform will be developed that meets the following requirements:

-

The system will allow users to visualize the Valle de los Vientos Wind Farm, located in the Calama region.

-

The system will support the manipulation and visualization of a 3D model of the VESTAS V100–2.0 MW wind turbine.

-

The system will provide access to documents related to the FMEA table and criticality assessment for the blade subsystem.

-

The system will provide access to documents related to the action plan for physical failure modes of the turbine blades.

-

The system will enable visualization of elements related to the wind turbine’s subsystems.

The accessible documentation includes the full analysis conducted using the FMEA methodology for the VESTAS V100–2.0 MW wind turbine.

5.3. Technical Requirements

To develop an immersive 3D programming environment, it is essential to use a game engine that meets certain conditions: comprehensive documentation, multi-platform support, compatibility with various programming languages, a manageable learning curve, and available resources. For this reason, the most appropriate choice for this project is the Unity 3D game engine. The immersive environment will be developed using Unity 3D Version 2019.2.13f1, in combination with the Oculus Quest 2 setup [43]. For this purpose, Oculus virtual reality equipment will be used, as shown in Figure 9.

This device is manufactured by Oculus, a division of Facebook. Oculus specializes in the development of virtual reality technology and has released multiple generations of virtual reality headsets, including the model used in this project. The official technical requirements for using the Oculus Quest 2 headset can be found on the [official Oculus website] [43]. Software and Hardware implementation details:

Software Stack:

-

Unity 3D 2021+ for environment design and VR scripting

-

C# for interaction and scenario logic

-

Blender version 4.3 2024 for 3D model preparation

-

XML/JSON for engineering data import

-

Meta SDK for Oculus Quest integration

Hardware:

-

Oculus Quest 2 VR headset

-

Oculus controllers

-

PC with NVIDIA GPU for development and testing

These specifications ensure accessibility and low deployment cost, making the platform scalable for training centers, universities, and industrial operators.

5.4. System Architecture

The following is the proposed system architecture for the blade subsystem fault simulator using Virtual Reality, designed to meet all previously established requirements (Figure 10). This architecture illustrates the components and modules that interact during the simulation experience. These include:

-

Inputs: The Oculus Quest 2 system provides the following inputs to the immersive application:

• Virtual Reality Headset: Allows the user to visualize virtual reality environments, which is fundamental to achieving an immersive experience.
• Joysticks: Composed of two handheld controllers (one in each hand), these allow the user to interact with objects and elements in the virtual environment. These controls provide an intuitive way to manipulate and navigate the platform.

-

Outputs: The system outputs correspond to the visual elements generated and displayed through the Virtual Reality headset. During the immersive session, the user will be able to view components, diagrams, and relevant information panels within the virtual space.

• Start Menu Scene: The simulator includes a start menu screen, which displays the operational context of the V100–2.0 MW wind turbine. It also features a screen showing the most critical subsystems, where the blade subsystem is identified as the most critical. Additionally, a button is provided to access the FMECA scene of the wind turbine.
• FMECA Scene: In this section, users can visualize the FMECA tool applied to the blade subsystem, where the criticality levels, probable causes, and corrective action plans are presented in an interactive and educational format, overlaid on the 3D model of the blades.

5.5. Practical Implementation of the Digital Tool: FMECA-VR-0.1

This case study focuses on the development of an immersive virtual environment for maintenance training of wind turbines at the Valle de los Vientos Wind Farm, using the VESTAS V100–2.0 MW model as a reference. By integrating Virtual Reality (VR) technologies, the project aims to create an immersive VR-based training platform where technicians and professionals can experience realistic and educational scenarios that support the learning of inspection, diagnostics, and maintenance procedures for the various subsystems of the wind turbine.

5.5.1. Start Menu Scene

In this initial scene, the user is introduced to the Valle de los Vientos Wind Farm, immersing themselves in a virtual environment designed to deliver a realistic training experience (Figure 11). Using Oculus Virtual Reality headsets, users access an interactive, immersive VR-based training platform that allows them to explore the wind farm in a 360° view, facilitating their understanding of its infrastructure and operational dynamics.

The start menu presents several navigation options to guide the user through the various training modules. These include:

-

Panoramic view of the wind farm, where turbines are in operation, and their spatial layout within the environment can be observed.

-

Technical information about the VESTAS V100–2.0 MW wind turbine, including its operational context, main components, operator safety elements, and certifications.

-

Access to fault analysis modules, where critical subsystems such as the blades are presented, along with a link to the FMECA scene.

5.5.2. Description of the Main Tabs

-

Operational Context: The image shows the visual representation of the Operational Context within the FMECA-VR-0.1 platform, used for maintenance training of wind turbines in a virtual environment

-

Criticality System: The image displays the subsystem criticality analysis structure within the FMECA-VR-0.1 tool. Through the immersive VR-based training platform, users can identify and rank key wind turbine components, focusing on those with the greatest impact on system reliability and maintainability (Figure 12).

5.5.3. FMECA Scene

In Scene 2 of the fault simulator, the user enters an interactive environment where the FMECA applied to the blade subsystem is presented. Within this scene, a template interface displays technical documents related to failure analysis, probable causes, and the corresponding maintenance plan for mitigation. Through this interface, users can examine detailed information about the physical failure modes of the blades and the criticality associated with each event.

In this scene, the user appears to be floating in mid-air, providing an immersive panoramic view of the wind turbine and its components. From this elevated perspective, the user can interact with different system elements, observe the graphical distribution of failures, and gain a deeper understanding of both preventive and corrective maintenance strategies.

This Virtual Reality-based approach provides a dynamic and educational experience, facilitating learning in the identification and resolution of critical failures within the broader context of asset management in wind farms.

Thanks to this approach, Virtual Reality becomes an innovative learning tool, offering practical, engaging training that enhances skills in wind turbine maintenance. To strengthen the development of the FMECA-VR-0.1 tool, ongoing efforts are being directed towards a formal user validation phase. Preliminary feedback has been collected through internal testing sessions with engineering students and maintenance professionals, highlighting the intuitive interaction and enhanced understanding of FMECA concepts in the immersive VR-based training platform. Future iterations will incorporate structured user studies with control groups to compare VR-based training against conventional methods, assessing metrics such as learning time, error reduction, and retention. Additionally, further details on interface navigation, document interaction, and user feedback loops are planned for inclusion in the next version of the platform, enabling a more comprehensive technical description and validation of its pedagogical effectiveness.

-

Function and Physical Failure Modes (Figure 13):

○

Circular diagram offering a 90° field of view for the user within a 360° Virtual Reality environment, with numbered tabs marking various sections of information.

○

Analysis categories include:

• Function (e.g., capturing and converting wind energy)
• Physical failure modes (blades, nose cone, and hub).

○

3D visualization of a wind turbine in the Virtual Reality space with floating labels identifying components and related failures.

-

Effects, Failure Criticality, and Probable Causes: The user maintains a 90° view field in a 360° immersive environment, navigating through tabs 1 to 8.

○

Active tabs:

• Tab 3—Effect: Loss of aerodynamic performance
• Tab 4—Criticality: Indicates criticality levels (Very High for blades, Medium for nose and hub)
• Tab 5—Probable Causes: Includes faults such as cracks, delamination, and bending in the blades

-

Action Plan and Assigned Specialist:

○

Active tabs:

• Tab 6—Action Plan: Outlines maintenance actions including thermographic and ultrasound inspections (monthly), X-ray inspections (quarterly), and symptomatic maintenance
• Tab 7—Responsible Specialist: Assigns maintenance responsibility to the electromechanical technician

-

Demonstration Video: A visual demonstration of the tool in use is presented in the following video (duration: 3 min), available on the LinkedIn platform [44]:

🔗 https://www.linkedin.com/feed/update/urn:li:activity:7200352283866349568/, accessed on 20 March 2026

-

Additional interface visualizations are provided in the Supplementary Material (Figures S1–S6) to avoid redundancy and improve readability of the main manuscript.

6. Economic Analysis of the Immersive VR Prototype: FMECA-VR-0.1

The implementation of immersive training technologies in maintenance engineering requires not only technical feasibility but also an assessment of economic viability, operational impact, and scalability. This section presents a preliminary cost analysis of the FMECA-VR-0.1 prototype, discusses its practical implications for industrial training, and outlines limitations and future improvements. The following section presents a series of cost estimation tables associated with the partial development of the Immersive VR prototype, considering a single system: the VESTAS V100–2.0 MW wind turbine.

-

Total Estimated Cost of Pilot Version—FMECA-VR-0.1. By adding the totals from

Table 6. General Execution Costs (Programming and Equipment)—FMECA-VR-0.1 Prototype.

General Budget
Labor Costs
Code	Subsystem	Unit	Hours	Unit Price (CLP)	Subtotal (CLP)
P1	Blades	h	27	$17,046	$460,250
P2	Control and Communication	h	63	$17,046	$1,073,916
P3	Electrical Panel	h	63	$17,046	$1,073,916
P4	Transformer	h	63	$17,046	$1,073,916
P5	Frequency Converter	h	63	$17,046	$1,073,916
P6	Electric Generator	h	63	$17,046	$1,073,916
P7	Tower	h	63	$17,046	$1,073,916
P8	Yaw System	h	63	$17,046	$1,073,916
P9	Anemometer and Wind Vane	h	63	$17,046	$1,073,916
P10	Gearbox	h	63	$17,046	$1,073,916
P11	Main Shaft Assembly	h	63	$17,046	$1,073,916
P12	Hub	h	63	$17,046	$1,073,916
P13	Pitch Control System	h	63	$17,046	$1,073,916
P14	Hydraulic System	h	63	$17,046	$1,073,916
Total (labor) (CLP) $14,421,156
Materials Costs
Item	Component	Unit	Quantity	Unit Price (CLP)	Subtotal (CLP)
1	Oculus Quest 2 VR Headsets	Und	3	$409,052	$1,227,156
2	Laptop Cooler Base	Und	3	$20,000	$60,000
3	Oculus Link Cable	Und	1	$16,990	$16,990
4	HP Victus Notebook (RTX, 8 GB RAM)	Und	3	$749,990	$2,249,970
5	3D Model of Wind Turbine	Und	1	$224,315	$224,315
Total (material): CLP $18,199,587
General Expenses (20%): CLP $3,639,918
Profit Margin (15%): CLP $2,729,938
Net Total: CLP $24,569,443
VAT (19%): CLP $4,669,194
Final Total: CLP $29,237,637

and

Table 7. Costs Associated with Project Management.

Project Management Costs
Professional	Quantity	Monthly Salary (CLP)	5-Month Cost (CLP)
Maintenance Engineer	1	$1,300,000	$6,500,000
Technician	1	$850,000	$4,250,000
Programmer	1	$1,500,000	$7,500,000
Subtotal (Net): CLP $18,250,000
VAT (19%): CLP $3,467,500
Total Project Management Cost: CLP $21,717,500

, the overall development cost of the pilot version of the FMECA-VR-0.1 prototype is:

• CLP $50,955,137 (Chilean Pesos, approximately USD $50,000)

To provide a more meaningful interpretation of the economic value of the proposed system, a comparison with traditional training approaches is necessary. Conventional training for wind turbine maintenance typically involves:

-

Travel and accommodation costs for trainees

-

Equipment downtime during on-site training

-

Safety risks during real inspections

In contrast, the FMECA-VR-0.1 platform allows:

-

Remote training without travel costs

-

Zero operational downtime

-

Safe simulation of critical failure scenarios

Although the present study focuses on estimating development costs, a meaningful economic evaluation requires comparison with traditional training approaches. Conventional training in wind turbine maintenance involves travel costs, equipment downtime, and safety risks during on-site activities. In contrast, the proposed immersive VR-based training platform enables remote training, eliminates operational downtime, and reduces safety exposure. Future work will include a detailed cost–benefit analysis comparing both approaches in terms of lifecycle cost and return on investment (see Table 6 and Table 7).

7. Practical Implications, Industrial Benefits, and Limitations

The long-term objective of this project is to evolve into a full training software in the immersive VR-based training platform, specifically focused on the maintenance field (FMECA-VR-0.1). In this first stage, the work is centered on developing a single subsystem to complete the pilot version (see Table 6 and Table 7). These tables present a cost estimate related to materials and labor, considering the programming and design of all subsystems and their respective failure modes.

This project fosters innovation in industrial maintenance and supports the goals of the Fourth Industrial Revolution by promoting renewable energy over fossil fuels. In this context, technology is positioned as a key enabler for optimizing processes and improving the training of professionals in the sector. The adoption of the FMECA-VR-0.1 platform brings several benefits to industry and training institutions:

-

Enhanced Learning Efficiency. An immersive VR-based training platform that has the potential to improve retention and comprehension by allowing technicians to experience failure mechanisms rather than reading about them.

-

Safer Training Environment. Users are not exposed to the real hazards of turbine maintenance, such as:

-

Working at height,

-

Rotating machinery,

-

High-voltage systems,

-

Harsh environmental conditions.

-

Reduction in Training Time. VR enables:

-

Faster familiarization with components,

-

Repetitive practice without downtime,

-

Reduced dependency on on-site turbine access.

-

Integration of Engineering Logic. Unlike conventional VR simulators, the FMECA-VR-0.1 platform embeds:

-

Failure modes,

-

Risk matrices,

-

Engineering consequences,

-

Maintenance strategies.

This reinforces analytical thinking and risk-informed decision-making.

-

Scalability Across Industries. The architecture can be extended to:

-

Mechanical systems,

-

Power generation assets,

-

Mining equipment,

-

Manufacturing lines,

-

Chemical and process plants.

7.1. Limitations of the Current Prototype (FMECA-VR-0.1)

Despite its strong educational potential, the current prototype presents limitations:

-

Limited Scope to a Single Subsystem. The platform currently focuses on the blade subsystem only. A full turbine training tool would require modeling:

• Gearbox,
• Generator,
• Yaw and pitch systems,
• Hydraulic and control systems.

-

No Real-Time Data Integration. The prototype does not yet incorporate:

• SCADA data
• Condition monitoring systems
• Digital twin streaming
• Vibration or thermal data

-

Simplified 3D Models. Models are optimized for VR performance, not full engineering detail.

-

No Quantitative User Performance Metrics. Assessment is qualitative. Advanced tracking (reaction time, error rates, task completion) is not yet implemented.

-

Limited Multi-User or Collaborative Features. The current version is single-user. Immersive VR-based training platform multi-user collaboration is planned for future releases.

Although the FMECA-VR-0.1 prototype demonstrates significant potential for enhancing maintenance training through immersive technologies, several limitations must be acknowledged. The following section summarizes the general limitations identified during the development process of the FMECA-VR-01 prototype.

-

First, the current version models only one subsystem of the wind turbine—the blade subsystem. While this choice reflects its high criticality, it restricts the ability to validate the methodology across a broader range of mechanical, electrical, and hydraulic components. Expanding the platform to include additional subsystems is required to fully assess its generalizability.

-

Second, the VR environment is based on static engineering data derived from the FMECA and criticality analysis. The system does not yet incorporate real-time operational inputs, such as SCADA variables, vibration signatures, temperature trends, or historical maintenance records. Consequently, the platform cannot simulate dynamic failure progression or predictive maintenance scenarios.

-

Third, the 3D models were optimized for performance on the Oculus Quest 2, which limits their geometric fidelity and prevents precise engineering simulations. The focus of the prototype is pedagogical rather than operational, meaning that visualization accuracy may not be sufficient for high-detail inspection or engineering design purposes.

-

Fourth, although preliminary validation with students and technicians indicates positive educational outcomes, the study does not include a controlled experimental evaluation with quantitative performance metrics. Further research is needed to measure learning gains, error reduction, and long-term retention compared to traditional training methods.

-

Finally, the economic analysis presents an estimate of development costs based on a prototype implementation and may not fully reflect the complexities of a commercial-scale deployment, such as licensing, support, scalability, and multi-user capabilities.

These limitations provide clear directions for future work while reinforcing the value of the current prototype as a foundational step toward immersive, reliability-based maintenance training solutions.

7.2. Improvements to Be Included in the FMECA-VR-0.1 Tool

For the upcoming development phases of the FMECA-VR-0.1 tool, it is recommended to implement an indicator module that enables the evaluation of maintenance plan effectiveness. The proposed minimum indicators are divided into two stages:

-

First Stage: Definition of Basic Technical Maintenance Indicators (see Figure 14):

• Failure Frequency, FF (Reliability)
• Mean Time to Repair, MTTR (Maintainability)
• Mean Time to Failure, MTTF (Reliability)
• Availability (A)

-

Second Stage—Development of Probabilistic and Cost-Based Indicators:

• Reliability (Rt): Probability of failure, probability of no failure, time to failure, and failure frequency
• Maintainability (Mt): Probability of successful repair
• Failure Downtime Cost (FDC): Estimated cost due to system unavailability caused by failures

FMECA-VR-0.1 was initially conceived as a training tool. Its long-term vision is to evolve into a comprehensive platform capable of integrating real-time data from maintenance management systems and automatically calculating and analyzing key indicators such as reliability, maintainability, availability, and economic risk using online parameters collected directly from physical assets. These parameters constitute the foundation for the development of a digital twin, providing an innovative technological framework that further enhances the capabilities and future potential of the Metaverse [45,46].

The FMECA-VR-0.1 tool has already demonstrated its potential as an innovative and effective solution for technical training in industrial environments, especially in the renewable energy sector. Nevertheless, its development opens up multiple workstreams and improvement opportunities to be addressed in subsequent phases [47]:

-

Expansion of Functional Scope: Extend the analysis to additional wind turbine subsystems and other critical industrial assets to broaden the technical coverage of the platform and validate its cross-sector applicability [48].

-

Integration with Digital Twins and IoT Systems: Incorporate real-time data through connected sensors to simulate dynamic operational conditions, strengthening the tool’s predictive capabilities and aligning it with the Maintenance 4.0 paradigm [46,49,50].

-

Assessment of Learning Impact: Establish specific metrics to measure improvements in training time, reduction in operational errors, and increased retention of technical knowledge through the use of the VR platform [49,51].

-

Development of Multi-Platform Versions: Adapt the tool to various devices (PCs, tablets, mobile devices, and VIRTUAL REALITY headsets) to facilitate broader adoption and enable flexible implementation in both academic and industrial contexts [49,50].

-

Validation in Real Industrial Environments: Conduct pilot tests in collaboration with energy and industrial companies to gather practical feedback that supports further refinement and alignment of the tool with the needs of the productive sector [52].

Recent advances in immersive technologies have demonstrated that Virtual Reality (VR) constitutes a transformative tool for industrial training, particularly in complex and high-risk environments. Unlike conventional training approaches, VR enables the recreation of realistic operational scenarios where users can interact with equipment, procedures, and failure conditions in a safe and controlled environment. Studies such as Gavish et al. [53] have shown that VR-based training significantly improves task performance, procedural understanding, and error reduction in maintenance and assembly activities. Similarly, Nayak et al. [54] highlight that VR facilitates experiential learning by allowing trainees to develop practical skills through repeated interaction, reducing the dependency on physical assets and minimizing operational risks. These capabilities are especially relevant in asset-intensive industries, where training opportunities under real conditions are often limited due to safety, cost, or availability constraints.

From an educational perspective, VR has been increasingly recognized as a powerful tool for enhancing engineering training and knowledge transfer. Soliman et al. [55] emphasize that immersive environments improve spatial understanding, cognitive engagement, and knowledge retention by allowing users to visualize complex systems in three dimensions and interact with them dynamically. Furthermore, VR-based learning environments support active learning paradigms, where users are not passive recipients of information but active participants in problem-solving processes. This aligns with the emerging Industry 5.0 vision, which promotes human-centric and skill-oriented training approaches. In addition, safety training studies indicate that immersive simulations lead to higher awareness of hazards and better decision-making under critical conditions compared to traditional methods [56].

More recently, systematic reviews and meta-analyses have provided robust empirical evidence supporting the effectiveness of VR in industrial training contexts. Scorgie et al. [57] demonstrate that VR-based safety training significantly outperforms traditional instructional methods in terms of knowledge acquisition, behavioral performance, and risk perception. However, despite these advances, most VR applications remain focused on procedural training or hazard awareness, lacking integration with structured engineering methodologies. This limitation reduces their potential impact on decision-making processes related to reliability, risk assessment, and maintenance optimization. In this context, the present work contributes to the literature by extending the role of VR beyond visualization and procedural training, embedding FMECA and risk-based analysis directly into the immersive environment, thereby enabling a deeper understanding of failure mechanisms and their criticality within real industrial systems.

These proposed enhancements will position FMECA-VR-0.1 as a next-generation immersive training tool, enabling high-fidelity simulation, real-time integration with industrial systems, and deeper analytics—aligning it with the principles of smart maintenance, Industry 4.0, and lifelong technical education.

At the current stage of development, the validation of the FMECA-VR-0.1 prototype is limited to preliminary qualitative feedback obtained from internal testing sessions with engineering students and maintenance professionals. These sessions focused on usability, navigation, and conceptual clarity of the FMECA framework within the immersive environment. No controlled experimental study or quantitative performance evaluation has yet been conducted. Therefore, no claims regarding learning effectiveness, time reduction, or error minimization can be statistically supported at this stage.

Future work will include structured validation through controlled experiments comparing VR-based training with traditional methods. Key performance indicators will include task completion time, accuracy rate, retention, and System Usability Scale (SUS) scores.

8. Conclusions and Future Work

This study presented the development of FMECA-VR-0.1, an immersive Virtual Reality–based training tool that integrates reliability engineering methodologies—including FMECA and QRCM—with the goal that it can be integrated into a Metaverse environment in the future to enhance technical training for wind turbine maintenance. Using the VESTAS V100–2.0 MW turbine as a case study, the platform demonstrated how engineering analysis, risk prioritization, and subsystem-level failure understanding can be effectively translated into interactive VR experiences. The integration of the Maintenance Management Model (MMM) with the FMECA workflow provides a structured, traceable, and pedagogically sound foundation for immersive training. Four principal contributions emerge from this work:

• A novel integration of reliability engineering and immersive technologies. This study demonstrates how FMECA and criticality analysis can be operationalized within a VR environment, enabling risk-based reasoning to be visualized and understood through interactive 3D scenarios.
• A replicable methodological workflow. The proposed framework provides a structured process for transforming analytical reliability outputs—operational context, failure modes, effects, criticality categories, and maintenance strategies—into immersive learning modules.
• Enhanced comprehension of complex failure mechanisms. Preliminary validation suggests that virtual reality-based visualization could help improve user understanding of interactions between subsystems, fault propagation, and the logic behind maintenance prioritization.
• A foundation for scalable Maintenance 4.0 solutions. The immersive VR-based training platform demonstrates feasibility at a development cost consistent with industrial training budgets and offers significant potential advantages in safety, learning efficiency, and standardization of training.

The results indicate that VR-based environments offer significant advantages for maintenance education, including improved spatial awareness of components, enhanced understanding of failure mechanisms, and reduced exposure to real operational hazards. The immersive platform enables learners to visualize the location, causes, effects, and criticality of failure modes in a realistic and engaging manner, supporting experiential learning and improving decision-making skills aligned with reliability principles. The cost analysis suggests that the solution is economically feasible for universities, training centers, and industrial organizations, requiring only commercially available VR hardware and accessible software tools.

Despite its strengths, the current version presents limitations, including a restricted scope focused on the blade subsystem, simplified model geometry, absence of real-time operational data, and limited assessment functionalities. Addressing these limitations represents an opportunity for future development. To advance toward a fully industrial-grade training system, future work will focus on:

• Expanding the scope of analysis to include additional turbine subsystems such as gearbox, generator, pitch and yaw mechanisms, and hydraulic systems.
• Integrating real-time monitoring data (SCADA, IoT sensors, digital twins) to enhance dynamic failure visualization and support predictive maintenance scenarios.
• Developing multi-user collaborative features aligned with the Industrial Metaverse paradigm, enabling instructors and learners to interact within shared virtual environments.
• Enhancing user performance evaluation through quantitative analytics, including response times, mistake tracking, and scenario-based assessment.
• Incorporating full maintenance procedures such as inspection tasks, torque sequences, equipment replacement, and troubleshooting activities in interactive 3D workflows.
• Aligning the immersive platform with asset management standards, particularly ISO 55000 and the extended MMM framework, to provide a complete training solution across the asset lifecycle.
• Establish collaborations with academic institutions and industrial companies to implement technical training pilots using FMECA-VR-0.1 and gather feedback from both academic and production environments.

The results of this study demonstrate the technical feasibility of integrating FMECA-based engineering methodologies into an immersive VR-based training environment. It is important to note that this study focuses on conceptual and architectural validation rather than experimental validation. While preliminary qualitative feedback indicates positive user perception in terms of usability and understanding, no controlled experimental study has yet been conducted. Therefore, the results should be interpreted as a proof-of-concept, with future work aimed at quantitative validation through controlled user studies. With continued development, the proposed framework can evolve into a comprehensive, scalable, and industry-ready solution for competency development within modern maintenance and asset management systems.

Finally, the development of the FMECA-VR-0.1 tool marks a milestone in the convergence of maintenance engineering and immersive technologies, offering an innovative solution for technical training grounded in reliable methodologies and virtual reality environments. The experience applied to the Valle de los Vientos Wind Farm not only demonstrates the technical value of integrating Failure Modes and Effects Analysis with asset criticality in a digital environment, but also highlights the transformative potential of the Metaverse in industrial training processes. This research lays the groundwork for future implementations, positioning virtual environments as strategic tools for smart asset management within Industry 4.0.