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 November 2025 | Viewed by 2
Special Issue Editors
Interests: optimization and modeling 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
Interests: combinatorial optimization: heuristics and meta-heuristics design for discrete problems; scheduling and applications: mixed blocking, health systems, and non-identical parallel machines; mathematical modeling of coupled problems and optimization; supply chain; facility location; allocation problems; financial investment 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
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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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