From Machine Health to Supply Chain Resilience: Intelligent Diagnostics and Prognostics in Manufacturing Systems
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 1280
Editors
Interests: artificial intelligence; computer vision; machine learning; reinforcement learning; high-performance computing (hpc); fault diagnostics & prognostics; autonomous systems; manufacturing; additive manufacturing; deep learning; structural health monitoring
Special Issue Information
Dear Colleagues,
Modern machines, manufacturing systems, and autonomous platforms are becoming increasingly complex due to advances in sensors, additive manufacturing, mechatronics, and electromechanical systems. Ensuring reliable, safe, and efficient operation requires continuous condition monitoring, early fault detection, and accurate prognostics. Recent progress in deep learning, structural health monitoring, and data-driven diagnostics is enabling new capabilities for predictive maintenance and intelligent control. Machine reliability, fault propagation, and maintenance decisions play a critical role in shaping production continuity, capacity utilisation, and ultimately the resilience and performance of supply chains. This Special Issue aims to provide a cross-disciplinary forum for the latest research on intelligent fault diagnostics and prognostics. It covers areas such as machine design and theory, condition monitoring, automation, and advanced manufacturing, ultimately addressing the impact of these issues in the supply chain. By integrating machine intelligence with supply chain perspectives, this Special Issue provides a platform for advancing next-generation, data-driven manufacturing ecosystems.
Original research articles and reviews are welcome. Topics of interest include, but are not limited to, the following:
- AI‐enabled condition monitoring and predictive maintenance for electromechanical systems;
- Deep learning, probabilistic modelling, and sensor fusion for fault diagnostics and prognostics;
- Structural health monitoring and non-destructive evaluation for mechanical and additive manufacturing components;
- Digital twins and data-driven models for intelligent manufacturing and autonomous systems;
- Role of additive manufacturing in enabling decentralised, responsive, and resilient supply chains;
- Edge computing, IoT, and smart sensors for real-time machine health management;
- Quality assurance and process control in additive manufacturing using intelligent algorithms;
- Quality control and defect propagation in manufacturing and their impact on downstream supply chain stages;
- Data-driven approaches for linking condition monitoring with inventory management and demand fulfilment;
- Case studies on vehicle engineering, robotics, turbomachinery, and micro/nano electromechanical systems.
This Special Issue seeks contributions that demonstrate how intelligent diagnostics and prognostics can advance machine design, improve reliability, and enable smart manufacturing ecosystems.
Dr. Shubhendu Kumar Singh
Dr. Sube Singh
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly 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
- fault diagnosis and prognostics
- intelligent automation & AI-enabled systems
- advanced manufacturing & digital twin
- condition monitoring & diagnostics
- robotics, mechatronics & intelligent machines
- supply chain
- supply chain resilience
- smart manufacturing/intelligent manufacturing
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