Search Results (22)

In modern aerospace systems, effective sensor optimisation is essential for ensuring reliable diagnostics, efficient resource allocation, and proactive maintenance. This paper presents Normalised Diagnostic Contribution Index (NDCI) integration into the Multi-Objective Sensor Optimisation Framework (MOSOF) to address application-specific performance nuances. Building on previous work, the proposed approach leverages a multi-objective genetic algorithm to optimise key criteria, including performance, cost, reliability management, and compatibility. NDCI is derived from simulation data obtained via the Boeing 737-800 Environmental Control System (ECS) using the SESAC platform, where degradation level readings across four fault modes are analysed. The framework evaluates sensor performance from the perspectives of Original Equipment Manufacturers (OEM), Airlines, and Maintenance Repair Overhaul (MRO) organisations. Validation against the Minimum Redundancy Maximum Relevance (mRMR) method highlights the distinct advantage of NDCI by identifying an optimal set of three sensors compared to mRMR’s six-sensor solution, and MOSOF’s multi-objective insertion enhances sensor deployment for different stakeholders. This integration not only expands the feasible solution space for sensor-pair configurations but also emphasises diagnostic value over redundancy. Overall, the enhanced NDCI-MOSOF offers a scalable, multi-stakeholder approach for next-generation sensor optimisation and predictive maintenance in complex aerospace systems. The results demonstrate significant improvements in diagnostics efficiency for stakeholders. Full article

Aircraft skin surface defect detection is critical for aviation safety but is currently mostly reliant on manual or visual inspections. Recent advancements in computer vision offer opportunities for automation. This paper reviews the current state of computer vision algorithms and their application in aircraft defect detection, synthesizing insights from academic research (21 publications) and industry projects (18 initiatives). Beyond a detailed review, we experimentally evaluate the accuracy and feasibility of existing low-cost, easily deployable hardware (drone) and software solutions (computer vision algorithms). Specifically, real-world data were collected from an abandoned aircraft with visible defects using a drone to capture video footage, which was then processed with state-of-the-art computer vision models—YOLOv9 and RT-DETR. Both models achieved mAP50 scores of 0.70–0.75, with YOLOv9 demonstrating slightly better accuracy and inference speed, while RT-DETR exhibited faster training convergence. Additionally, a comparison between YOLOv5 and YOLOv9 revealed a 10% improvement in mAP50, highlighting the rapid advancements in computer vision in recent years. Lastly, we identify and discuss various alternative hardware solutions for data collection—in addition to drones, these include robotic platforms, climbing robots, and smart hangars—and discuss key challenges for their deployment, such as regulatory constraints, human–robot integration, and weather resilience. The fundamental contribution of this paper is to underscore the potential of computer vision for aircraft skin defect detection while emphasizing that further research is still required to address existing limitations. Full article

This paper presents an efficient schedule method for maintenance, repair, and overhaul (MRO) tasks for aircraft engines using a constraint programming algorithm. Using data obtained from Korean Air’s MRO maintenance logs, we analyze and predict the optimal scheduling of regular inspections and fault repairs for various engine types. By proposing a proper modeling of the problem and preparing data for the constraint programming algorithm, we demonstrate superior performance in scheduling efficiency and resource utilization. The experimental results show an average utilization of 99.35%, and the method can even achieve 100% utilization in some cases. Full article

This study focuses on the dynamics of the profit margin within the aviation MRO industry, using operational data from a small and medium-sized enterprise (SME) MRO company between 2013 and 2021. Especially in SME MROs, profit margin analysis provides an advantage in competing with the large companies that dominate the industry. Therefore, the operational data were prepared for analysis to identify the variables related to the profit margin. This study’s data cleaning and transformation processes can serve as a guideline for similarly sized companies. The research aims to address the complex relationships among the factors influencing profit margins in this industry. The objective is to utilise these factors in making strategic decisions to increase the profit margin of an SME MRO company. Applying gradient boosting algorithms as the analytical framework should allow identifying the correct relationships between the profit margin and input variables according to time for the SME MRO company. Another important aspect of this study is to increase the accuracy of the gradient boosting model by utilising the interactive grouping methodology. The variable selection was performed by using the Gini indexes of the variables using interactive grouping as a criterion in selecting the variables to be included in the model. After the data cleaning, transformation, and selection, the input variables for the gradient boosting model were Part Description, Parts Billed Current (part cost), Labour Billed Current (labour cost), Diff Shipping Entry (turnaround time (TAT)), Diff Quote Entry (time to quotation (TTQ)), Manager, Department, and Status. In this study, the profitability model indicates that the SME MRO company should initially focus on part numbers and the departments, secondly on standardisation of and expertise in preferred workshop units, and lastly, on highly qualified and effective technical department leaders and increasing labour. The aviation industry emerges as a sector that requires such analytical studies. It is hoped that the study will serve as a foundational work for SME MRO companies in the aviation industry. Full article

The field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance capabilities to ensure a reliable navigation experience. The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment. The challenging environment limits the practical application of conventional methods and real-time adaptability to changes. In response to these challenges, recent years research efforts have witnessed advancement with machine learning integration aimed at enhancing navigational capability in both static and dynamic scenarios. However, most of these studies have not been specific to the MRO hangar environment, but related challenges have been addressed, and applicable solutions have been developed. This paper provides a comprehensive review of learning-based strategies with an emphasis on advancements in deep learning, object detection, and the integration of multiple approaches to create hybrid systems. The review delineates the application of learning-based methodologies to real-time navigational tasks, encompassing environment perception, obstacle detection, avoidance, and path planning through the use of vision-based sensors. The concluding section addresses the prevailing challenges and prospective development directions in this domain. Full article

Accurate estimation of spare part demand is challenging in the case of intermittent or lumpy demand, characterised by infrequent demand occurrence and variability in demand size. While prior research has considered the effect of exogenous variables on spare part demand, there is a lack of research considering the effects of repair quality and aggregated spare part demand behaviour across fleets of assets under the influence of multiple simultaneously acting drivers of failure. This research provides new insights towards the problem of estimating variable spare part demand through modelling and simulation of the effects of multiple, simultaneously considered spare part demand drivers. In particular, a contribution to the state of the art is introduced by the use of a Branching Poisson Process (BPP) to model repair quality effects for spare part demand generation in conjunction with several demand drivers. The approach is applied in a numerical study which involves component failure characteristics based on real-life data from an aircraft maintenance, repair and overhaul (MRO) provider. It is shown that repair quality improvements drive down the variance in the demand and the total number of failures over time, and outperform the effect of environmental drivers of failure in terms of demand generation. Full article

Unique identification of machine parts is critical to production and maintenance, repair and overhaul (MRO) processes in the aerospace industry. Despite recent advances in automating these identification processes, many are still performed manually. This is time-consuming, labour-intensive and prone to error, particularly when dealing with visually similar objects that lack distinctive features or markings or when dealing with parts that lack readable identifiers due to factors such as dirt, wear and discolouration. Automation of these processes has the potential to alleviate these problems. However, due to the high visual similarity of components in the aerospace industry, commonly used object identifiers are not directly transferable to this domain. This work focuses on the challenging component spectrum engine tubes and aims to understand which identification method using only object-inherent properties can be applied to such problems. Therefore, this work investigates and proposes a comprehensive set of methods using 2D image or 3D point cloud data, incorporating digital image processing and deep learning approaches. Each of these methods is implemented to address the identification problem. A comprehensive benchmark problem is presented, consisting of a set of visually similar demonstrator tubes, which lack distinctive visual features or markers and pose a challenge to the different methods. We evaluate the performance of each algorithm to determine its potential applicability to the target domain and problem statement. Our results indicate a clear superiority of 3D approaches over 2D image analysis approaches, with PointNet and point cloud alignment achieving the best results in the benchmark. Full article

Urban sprawl has resulted in great losses of vegetation areas, an increase in impervious surfaces, and consequently the direct flow of stormwater into stream channels (i.e., the immediate flow of stormwater into stream channels, in comparison to the indirect flow that is represented by practices aiming to retain stormwater for a certain period of time and treat the polluted stormwater prior to flowing into the stream channels such as detention/retention basins, among others). Stormwater management systems such as catch basins (CBs) are needed to reduce the effect of stormwater runoff. Preventative maintenance, repair, and replacement of CBs are critical to achieve stormwater management best practices. Those practices prevent the blockage of the stormwater system, limit the pollutants in storm sewers, and reduce the risk of flooding. However, no preceding research studies have been conducted to model and simulate the serviceability of CBs and to determine optimal strategies for operating CBs. To that extent, this study establishes a framework to develop and validate an optimal and adaptive maintenance, repair, and overhaul (MRO) strategy for CBs. In relation to that, an agent-based model (ABM) integrated with Monte Carlo simulation was developed for all 560 CBs in New York City’s District 5 and was statistically validated using 99% confidence intervals. The MRO parameters were optimized to minimize the total cost of the system and attain the desired level of serviceability of CBs. Sensitivity analysis was conducted to guide the maintenance planning process of CBs and reveal the effect of the input parameters on the model’s behavior. In addition, ten thousand Monte Carlo iterations were simulated to derive the distributions of the defined parameters. The results proved that in order to minimize the overall cost of repair, maintenance, and replacement of CBs and attain a minimum serviceability threshold of 80%, the following optimal MRO policy needs to be implemented: having seven service crews (where service crews are human resources (i.e., MRO teams) needed to perform the required maintenance, repair, and replacement work), implementing a replacing policy, and replacing CBs after five maintenance periods. The findings revealed that the service crews represent the most critical parameter in affecting the total cost and serviceability of CBs. This research contributes to the existing literature by offering a better knowledge of the management process of CBs and devising optimal MRO strategies for properly operating them. Ultimately, this research helps decision-makers and engineers increase the lifespan of CBs and limit their risks of breakdown, increase their efficiency, and avoid unnecessary costs. The proposed model is flexible and can be implemented to any geographical area and with other model/system parameters, which makes it adaptive for any scenario and area presented by the user. Finally, maintaining stormwater management practices helps in protecting the environment by decreasing the demand on stormwater systems, reducing flooding, protecting people and properties, promoting healthier rivers, and consequently creating more sustainable communities. Full article

Aviation maintenance, repair, and overhaul (MRO) has become more important to the air transport industry during the pandemic since it plays a crucial role in improving safety, ensuring profitability, and achieving sustainability in the industry. The growth of the Northeast Asian MRO market is forecasted to be remarkable, making the region the next MRO powerhouse. This study investigates the MRO industry in Northeast Asian countries (China, Japan, and Korea) to gain insights for strategical development of the industry. SWOT analysis was used to understand external macro-environment and internal conditions comprehensively, with comparative analysis then performed to find each country’s competitiveness. SWOT analysis of the external environment of the aviation MRO industry in Northeast Asia finds opportunities from increased competition in the air transport industry and technological development and threats caused by aircraft advancements (less scheduled maintenance checks) and a limited workforce. Internal conditions are analyzed using six factors: cost, workforce, geographic presence, quality with shorter turnaround time, technological advancement, and certification. The results indicate that Korea’s MRO industry has strong human resources but weak technological capabilities. The competitive advantage of the Chinese MRO industry stems from both a large number of aircraft and lower costs. While Japan possesses superior MRO technology, high labor costs reduce their industrial competitiveness. Based on a comparative analysis, this study provides strategic insights into the improvements that can be made in the Korean MRO industry. Since composite MRO of newer aircraft presents a small technological gap which can be overcome with high-quality human resources, Korea should focus its resources and policies on promoting the composite MRO industry due to its high growth potential. Full article

Hydrogen is one of the most promising power sources for meeting the aviation sector’s long-term decarbonization goals. Although on-board hydrogen systems, namely, fuel cells, are extensively researched, the maintenance, repair and overhaul (MRO) perspective remains mostly unaddressed. This paper analyzes fuel cells from an MRO standpoint, based on a literature review and comparison with the automotive sector. It also examines how well the business models and key resources of MRO providers are currently suited to provide future MRO services. It is shown that fuel cells require extensive MRO activities and that these are needed to meet the aviation sector’s requirements for price, safety and, especially, durability. To some extent, experience from the automotive sector can be built upon, particularly with respect to facility requirements and qualification of personnel. Yet, MRO providers’ existing resources only partially allow them to provide these services. MRO providers’ underlying business models must adapt to the implementation of fuel cells in the aviation sector. MRO providers and services should, therefore, be considered and act as enablers for the introduction of fuel cells in the aviation industry. Full article

Under the framework of Industry 4.0, machines and machine tools have evolved to smart and connected things, comprising the Internet of Things (IoT) and leading to the Mass Personalization (MP) paradigm, which enables the production of uniquely made products at scale. MP, fueled by individualization trends and enabled by increasing digitalization, has the potential to go beyond current mass customization. Although this evolution has enabled engineers to gain useful insight for the production, the machine status, the quality of products, etc., machines have become more complex. Thus, Maintenance Repair and Overhaul (MRO) operations should be undertaken by specialized personnel. Additionally, Augmented Reality (AR) can support remote maintenance assistance to handle unexpected malfunctions. Moreover, due to advances regarding Product Service Systems (PSS), manufacturing companies are offering many services to improve user experience. Consequently, in this manuscript the design and development of a method based on the principles of servitization for the provision of an intelligent and adaptable maintenance service assisted by AR are presented. The contribution of the manuscript extends to the provision of an optimization algorithm for adapting the schedules of the stakeholders based on the energy supplier predictions. The developed method was tested and validated on an industrial case study of injection mold maintenance, achieving 11% energy reduction, 50% less time for mold inspection, and a 20% rise in on-time mold deliveries. Full article

Protective and Thermal Barrier Coatings (TBC) applied on gas-turbine blades gradually degrade due to oxidation, aluminum depletion and impacts of environmental particles. Among various non-destructive coating testing methods (NDT), visual inspection can be undertaken regularly in service, but it provides little quantitative information, and only surface defects can be detected. This work aims at in-service monitoring of turbine blades with multilayer coatings applied by atmospheric plasma spraying (APS) in a few variants. They were validated during a series of accelerated mission tests of a retired military turbofan engine in a test cell together with five other technologies. The fifty-hour rainbow test focused on assessing coating durability. Between engine runs, 12 borescope inspections were conducted to monitor the health of the blades. Finally, the blades were disassembled and examined using computed tomography (CT) and metallographic methods. Throughout the testing, 31 newly-coated blades (66%) withstood the tests, producing results comparable to the reference blades. However, 16 blades suffered intolerable failures observed as increased roughness, gradual loss of the topcoat, spallation and minor foreign object damage. Visual inspection results were generally in agreement with subsequent laboratory tests. Full article

The aircraft maintenance sector has high complexity with many intermediaries, multiple actors sharing data and needs to ensure high data security. The implementation of Blockchain technology can significantly contribute to the aforementioned characteristics. This research explores the implementation of Blockchain technology in Maintenance, Repair and Overhaul (MRO). For this research, a mixed-method approach was followed to obtain a holistic view of the adoption of Blockchain technology in an aircraft maintenance facility. Semi-structured interviews and a case study were used. The interview findings related to the current status of maintenance records management. The findings also highlighted the value of data storage within MRO’s and the benefits of Blockchain. The paper also discusses the readiness/willingness of aircraft maintenance facilities to implement Blockchain and the barriers to implementation. Recommendations and areas for further research are identified. Full article

The architecture design of industrial data analytics system addresses industrial process challenges and the design phase of the industrial Big Data management drivers that consider the novel paradigm in integrating Big Data technologies into industrial cyber-physical systems (iCPS). The goal of this paper is to support the design of analytics Big Data solutions for iCPS for the modeling of data elements, predictive analysis, inference of the key performance indicators, and real-time analytics, through the proposal of an architecture that will support the integration from IIoT environment, communications, and the cloud in the iCPS. An attribute driven design (ADD) approach has been adopted for architectural design gathering requirements from smart production planning, manufacturing process monitoring, and active preventive maintenance, repair, and overhaul (MRO) scenarios. Data management drivers presented consider new Big Data modeling analytics techniques that show data is an invaluable asset in iCPS. An architectural design reference for a Big Data analytics architecture is proposed. The before-mentioned architecture supports the Industrial Internet of Things (IIoT) environment, communications, and the cloud in the iCPS context. A fault diagnosis case study illustrates how the reference architecture is applied to meet the functional and quality requirements for Big Data analytics in iCPS. Full article

Sandwich panels consisting of two Carbon Fibre Reinforced Polymer (CFRP) outer skins and an aluminium honeycomb core are a common structure of surfaces on commercial aircraft due to the beneficial strength–weight ratio. Mechanical defects such as a crushed honeycomb core, dis-bonds and delaminations in the outer skins and in the core occur routinely under normal use and are repaired during aerospace Maintenance, Repair and Overhaul (MRO) processes. Current practices rely heavily on manual inspection where it is possible minor defects are not identified prior to primary repair and are only addressed after initial repairs intensify the defects due to thermal expansion during high temperature curing. This paper reports on the development and characterisation of a technique based on conductive thermography implemented using an array of single point temperature sensors mounted on one surface of the panel and the concomitant induced thermal profile generated by a thermal stimulus on the opposing surface to identify such defects. Defects are classified by analysing the differential conduction of thermal energy profiles across the surface of the panel. Results indicate that crushed core and impact damage are detectable using a stepped temperature profile of 80 ${}^{\circ }$C The method is amenable to integration within the existing drying cycle stage and reduces the costs of executing the overall process in terms of time-to-repair and manual effort. Full article