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Sustainable Reliability, Maintenance, and Fault Diagnosis Strategies for Mechanical and...
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Sustainable Reliability, Maintenance, and Fault Diagnosis Strategies for Mechanical and Manufacturing Systems

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2467

Special Issue Editors

Dr. Jérémie Schutz

E-Mail Website
Guest Editor
LGIPM, Université de Lorraine, F-57000 Metz, France
Interests: optimization and modelling of industrial systems; repair and maintenance area; development of innovative maintenance strategies; systems performance and efficiency enhancing; systems reliability; maintenance planning; industrial optimization; industrial engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Christophe Sauvey

E-Mail Website
Guest Editor
LGIPM, Université de Lorraine, F-57000 Metz, France
Interests: combinatorial optimization: heuristics and meta-heuristics design for discrete problems; scheduling and applications: mixed blocking, health systems, non-identical parallel machines; mathematical modelling of coupled problems and optimization; supply chain; facility location; allocation problems; financial investments; decision support
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As industries strive toward more sustainable and resilient operations, the reliability and maintenance of mechanical and manufacturing systems have become critical pillars in the pursuit of efficiency, longevity, and reduced environmental impacts. Ensuring the continuous operation of complex systems while minimizing downtime, energy consumption, and material waste requires innovative strategies that combine engineering insight with data-driven intelligence.

In this evolving context, sustainable approaches to system reliability, predictive maintenance, and fault diagnosis have emerged as key enablers for Industry 4.0 and the transition toward Industry 5.0. These strategies not only support the optimization of industrial assets, but also align with broader objectives such as life cycle management, resource efficiency, and reduced carbon footprints.

This Special Issue of Applied Sciences aims to gather high-quality research contributions that address recent advances in sustainable reliability engineering and smart maintenance strategies. We welcome theoretical developments, methodological innovations, and case studies related to the following:

  • Sustainable maintenance and asset management practices;
  • Fault detection and diagnosis using AI, IoT, and data analytics;
  • Condition-based and predictive maintenance under sustainability constraints;
  • Reliability modeling of complex mechanical and manufacturing systems;
  • Optimization of maintenance schedules to reduce environmental impact;
  • Life cycle assessment and eco-design integration in reliability strategies;
  • Digital twins and simulation for predictive maintenance and fault prevention;
  • Human–machine collaboration in sustainable maintenance systems;
  • Decision-making under uncertainty for maintenance planning and risk mitigation.

We particularly encourage interdisciplinary submissions that bridge operations research, mechanical engineering, and sustainability science to propose robust, scalable, and energy-efficient solutions for real-world systems.

Dr. Jérémie Schutz
Prof. Dr. Christophe Sauvey
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

  • sustainable maintenance
  • reliability engineering
  • fault diagnosis
  • predictive maintenance
  • condition monitoring
  • life cycle assessment
  • digital twin
  • industry 5.0
  • smart manufacturing
  • mechanical systems

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Published Papers (3 papers)

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16 pages, 833 KB  
Article
Systemic Predictive and Prescriptive Maintenance
by Magnus Löfstrand and Patrik Eklund
Appl. Sci. 2026, 16(2), 1088; https://doi.org/10.3390/app16021088 - 21 Jan 2026
Viewed by 352
Abstract
In this paper we introduce a systemic approach for predictive and prescriptive maintenance framed within the larger system of systems, as exemplified by a use case in mining. Developments are presented as systematic, while there is a focus on improved availability modeling using [...] Read more.
In this paper we introduce a systemic approach for predictive and prescriptive maintenance framed within the larger system of systems, as exemplified by a use case in mining. Developments are presented as systematic, while there is a focus on improved availability modeling using the time usage model with a corresponding UML/SysML StateMachine representation. Data becomes connected with a more elaborated definition of time, with failure modes and analytics in reliability engineering being supported by improved underlying information structures. Full article
(This article belongs to the Special Issue Sustainable Reliability, Maintenance, and Fault Diagnosis Strategies for Mechanical and Manufacturing Systems)
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20 pages, 1680 KB  
Article
Reliability Modeling of Complex Ball Mill Systems with Stress–Strength Interference Theory
by Ruijie Gu, Haotian Ye, Hao Xing, Shuaifeng Zhao, Yang Liu and Yan Wang
Appl. Sci. 2026, 16(2), 815; https://doi.org/10.3390/app16020815 - 13 Jan 2026
Viewed by 323
Abstract
The ball mill is a critical size reduction equipment in industries such as mining and metallurgy. However, the sustainable reliability modeling of the entire system is challenging due to its complex service conditions. This paper presents a systematic framework for the reliability analysis [...] Read more.
The ball mill is a critical size reduction equipment in industries such as mining and metallurgy. However, the sustainable reliability modeling of the entire system is challenging due to its complex service conditions. This paper presents a systematic framework for the reliability analysis of ball mills based on Stress–Strength Interference Theory (SSIT). Based on a reliability block diagram (RBD), this study establishes a system-level reliability model for the ball mill. Within this framework, the cylinder model is developed using the energy conservation principle between impact energy and strain energy; the gear model comprehensively considers both contact and bending fatigue failure modes; and the bolt model is constructed through mechanical analysis in conjunction with Hooke’s law. In the case study, a laboratory-scale mill (Φ5.5 × 2.6 m shell, effective grinding chamber: 5.3 m inner diameter × 2.376 m length) operating at 14 RPM under dry grinding conditions is analyzed. The reliability of individual components and the entire system is computed using Monte Carlo simulation. The results indicate that the overall system reliability increases when one of the following three conditions is met: the surface hardness of the gear is higher and the tangential force is lower; the impact velocity on the cylinder is lower and the impacted area is larger; or the tensile force on the bolt is reduced. Full article
(This article belongs to the Special Issue Sustainable Reliability, Maintenance, and Fault Diagnosis Strategies for Mechanical and Manufacturing Systems)
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19 pages, 4048 KB  
Article
Transformer Attention-Guided Dual-Path Framework for Bearing Fault Diagnosis
by Saif Ullah, Wasim Zaman and Jong-Myon Kim
Appl. Sci. 2025, 15(23), 12431; https://doi.org/10.3390/app152312431 - 23 Nov 2025
Cited by 1 | Viewed by 1180
Abstract
Reliable bearing fault diagnosis plays an important role in maintaining the safety and performance of rotating machinery in industrial systems. Although deep learning models have achieved remarkable success in this field, their dependence on a single feature-extraction approach often restricts the diversity of [...] Read more.
Reliable bearing fault diagnosis plays an important role in maintaining the safety and performance of rotating machinery in industrial systems. Although deep learning models have achieved remarkable success in this field, their dependence on a single feature-extraction approach often restricts the diversity of learned representations and limits diagnostic accuracy. To overcome this limitation, this study proposes an attention-guided dual-path framework that integrates spatial and time–frequency feature learning with transformer-based classification for precise fault identification. In the proposed framework, vibration signals collected from an experimental bearing test rig are simultaneously processed through two complementary pipelines: one converts the signals into two-dimensional matrix images to extract spatial features, while the other transforms them into continuous wavelet transform (CWT) scalograms to capture fine-grained temporal and spectral information. The extracted features are fused through a lightweight transformer encoder with an attention mechanism that dynamically emphasizes the most informative representations. This fusion enables the model to effectively capture cross-domain dependencies and enhance discriminative capability. Experimental validation on an industrial vibration dataset demonstrates that the proposed model achieves 99.87% classification accuracy, outperforming conventional CNN and transformer-based approaches. The results confirm that integrating multi-domain features with attention-driven fusion significantly improves the robustness and generalization of deep learning models for intelligent bearing fault diagnosis. Full article
(This article belongs to the Special Issue Sustainable Reliability, Maintenance, and Fault Diagnosis Strategies for Mechanical and Manufacturing Systems)
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