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Applied Sciences
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System Reliability and Predictive Maintenance in Industrial Engineering—2nd Edition
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System Reliability and Predictive Maintenance in Industrial Engineering—2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 6077

Special Issue Editors

Dr. Natalia Trapani

E-Mail Website
Guest Editor
Department of Electrical, Electronics and Computer Engineering (DIEEI), University of Catania, 95123 Catania, Italy
Interests: industrial plant engineering; industrial reliability; safety and maintenance of industrial plants; job safety; multicriteria decision making methodologies applied to industrial plant engineering and operations
Special Issues, Collections and Topics in MDPI journals
Dr. Filippo De Carlo

E-Mail Website
Guest Editor
Department of Industrial Engineering (DIEF), University of Florence, 50134 Florence, Italy
Interests: industrial plant engineering; maintenance; industrial safety and risk; reliability; manufacturing systems; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the second edition of a Special Issue that will bring together cutting-edge research, innovative methodologies, and practical applications in the field of system reliability and predictive maintenance.

Today, companies focus on achieving operational excellence by optimizing the performances of physical assets. A critical element in achieving this goal is ensuring high levels of asset reliability and availability, leveraging technological evolution in ICTs and system automation. The widespread integration of sensors and monitoring systems in industrial plants, coupled with artificial intelligence (AI) and data-driven tools and techniques, allow decision-makers to access real-time data on operating conditions, performance, and safety. Furthermore, these technologies provide advanced forecasting capabilities for more efficient and effective maintenance decisions.

The Special Issue is open to contributions from both academics and industry engineers who want to share their experiences, insights, and research findings. It will discuss state-of-the-art approaches, methods, tools, and techniques in systems reliability and predictive maintenance.

Topics

We welcome original and high-quality contributions that explore innovative solutions, methodologies, and industrial applications in predictive maintenance and system reliability. Topics of interest include, but are not limited to, the following:

  • Predictive maintenance and Analytics: Data-driven and AI-driven predictive maintenance techniques for industrial systems; artificial intelligence (AI) for reliability analysis; machine learning (ML) for maintenance decisions; big data and asset performance management; operational benefits of predictive maintenance strategies.
  • Condition monitoring and Diagnostics: Real-time monitoring; anomaly detection; fault diagnosis of assets/equipment.
  • Risk based reliability: Reliability allocation and optimization; reliability for business continuity; innovative computing technologies in reliability; reliability for safety.
  • Digital twins and Advanced Simulations: Research and development of digital twins for predictive maintenance and decision support.
  • IoT and Industry 4.0 and 5.0 in Maintenance: IoT-enabled smart maintenance systems and their implementation in industrial plants; reliability of monitoring systems and sensor networks.
  • Case Studies in Industrial Contexts.

Application Areas:

  • Manufacturing industry.
  • Chemical and process industry.
  • Oil and gas industry.
  • Energy production and distribution.

Dr. Natalia Trapani
Dr. Filippo De Carlo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • system reliability
  • predictive maintenance
  • condition monitoring and diagnostics

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Related Special Issue

Published Papers (3 papers)

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Research

24 pages, 2158 KB  
Article
Adaptive OEE: A FUCOM-TOPSIS Framework for Context-Driven Equipment Effectiveness
by Vitor Anes, Pedro Marques and António Abreu
Appl. Sci. 2026, 16(10), 4835; https://doi.org/10.3390/app16104835 - 13 May 2026
Viewed by 157
Abstract
Overall Equipment Effectiveness (OEE) measures manufacturing productivity as the product of Availability (A), Performance (P), and Quality (Q). Despite its widespread adoption, the classical OEE formula embeds a structural limitation, i.e., the three components are treated as equally important regardless of operational context. [...] Read more.
Overall Equipment Effectiveness (OEE) measures manufacturing productivity as the product of Availability (A), Performance (P), and Quality (Q). Despite its widespread adoption, the classical OEE formula embeds a structural limitation, i.e., the three components are treated as equally important regardless of operational context. This fixed-weight assumption distorts maintenance prioritisation in environments where one component dominates operational losses. To the best of the authors’ knowledge, no published framework has formally addressed this limitation through a structured, auditable multi-criteria weighting model. This paper proposes Adaptive OEE, a FUCOM–TOPSIS framework that replaces the fixed A × P × Q product with a context-driven weighting model. FUCOM derives context-specific weights for A, P, and Q from expert judgement with minimum elicitation effort and mathematically guaranteed consistency. TOPSIS is adapted from its classical formulation by replacing data-derived ideal solutions with fixed reference poles defined independently of the observed data, ensuring that the effectiveness score of each asset is not influenced by the performance of other assets in the dataset. Three illustrative case studies covering availability-dominant, performance-dominant, and quality-dominant industrial scenarios suggest that classical OEE rankings are not preserved under asymmetric weight configurations, with ranking divergence being most severe when one component carries strongly asymmetric weight, precisely the condition that equal weighting cannot accommodate. The principal contributions are the formalisation of the equal-weight assumption as a formal methodological limitation, the replacement of multiplicative aggregation with a weighted distance measure, and the adaptation of TOPSIS with fixed reference poles for context-independent asset scoring. The framework is directly applicable by maintenance managers and industrial engineers seeking operationally justified equipment rankings without specialised analytical expertise. Full article
(This article belongs to the Special Issue System Reliability and Predictive Maintenance in Industrial Engineering—2nd Edition)
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30 pages, 5498 KB  
Article
Toward Predictive Maintenance of Biomedical Equipment in Moroccan Public Hospitals: A Data-Driven Structuring Approach
by Jihanne Moufid, Rim Koulali, Khalid Moussaid and Noreddine Abghour
Appl. Sci. 2025, 15(20), 10983; https://doi.org/10.3390/app152010983 - 13 Oct 2025
Cited by 3 | Viewed by 4133
Abstract
Predictive maintenance (PdM) of biomedical equipment is increasingly recognized as a strategic lever to enhance reliability and ensure continuity of care. Yet, in resource-limited hospitals, implementation is hindered by fragmented data sources, non-standardized codification, and weak interoperability. Few studies have demonstrated the feasibility [...] Read more.
Predictive maintenance (PdM) of biomedical equipment is increasingly recognized as a strategic lever to enhance reliability and ensure continuity of care. Yet, in resource-limited hospitals, implementation is hindered by fragmented data sources, non-standardized codification, and weak interoperability. Few studies have demonstrated the feasibility of structuring PdM data from real hospital interventions in middle-income countries. This work presents a prototype data structuring pipeline applied to six public hospitals in the Casablanca–Settat region of Morocco. The pipeline consolidates 6816 validated maintenance interventions from 780 devices across 30 departments and integrates normalized reliability indicators (Failure Rate, MTBF, MTTR corrected with IQR, and Downtime Hours). It ensures semantic harmonization, auditability, and reproducibility, resulting in a structured and interoperable dataset that constitutes a regional first in the Moroccan hospital context. To illustrate predictive potential, a proof-of-concept Random Forest model was evaluated. It achieved AUROC = 0.65 on the full imbalanced dataset and AUROC = 0.82 on a balanced 2000-intervention subset, confirming the dataset’s discriminative value while reflecting real-world challenges. This work bridges the gap between conceptual PdM frameworks and operational hospital realities, and establishes a replicable foundation for AI-driven predictive maintenance in low-resource healthcare environments. Full article
(This article belongs to the Special Issue System Reliability and Predictive Maintenance in Industrial Engineering—2nd Edition)
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19 pages, 3919 KB  
Article
The Estimation of the Remaining Useful Life of Ceramic Plates Used in Iron Ore Filtration Through a Reliability Model and Machine Learning Methods Applied to Industrial Process Variables of a Pims
by Robert Bento Florentino and Luiz Gustavo Lourenço Moura
Appl. Sci. 2025, 15(14), 8081; https://doi.org/10.3390/app15148081 - 21 Jul 2025
Viewed by 828
Abstract
The intensive use of various sensors in industrial machines has the potential to indicate the real-time health status of critical equipment. This is achieved through the connectivity of their automation systems (PIMS and MES), enabling the optimization of the preventive maintenance interval, a [...] Read more.
The intensive use of various sensors in industrial machines has the potential to indicate the real-time health status of critical equipment. This is achieved through the connectivity of their automation systems (PIMS and MES), enabling the optimization of the preventive maintenance interval, a reduction in corrective maintenance and safety-related failures, an increase in productivity and reliability and a reduction in maintenance costs. Through the use of the CRISP-DM data analysis methodology, the fault logs of ceramic plates applied in an iron ore filtration process are coupled with sensor readings of the process variables over the time of operation to create exponential survival models via two techniques: a multiple linear regression model with averaged data and a random forest regression machine learning model with individual instant data. The instantaneous reliability of ceramic plates is then used in the online prediction of the remaining useful life of the components. The model obtained from the instantaneous reading of 12 sensors led to the estimation of the remaining useful life for ceramic plates with up to 5600 h of use, allowing the adoption of a strategy of replacing these components by condition instead of replacing them by a fixed time, leading to increased process reliability and improved stock planning. The linear regression model for reliability prediction had an R2 of 78.32%, whereas the random forest regression model had an R2 of 63.7%. The final model for predicting the remaining useful life had an R2 of 99.6%. Full article
(This article belongs to the Special Issue System Reliability and Predictive Maintenance in Industrial Engineering—2nd Edition)
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