Prognostics and Health Management for Machinery Safety and Maintenance
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 55
Special Issue Editor
Interests: prognostics and health management (PHM); smart manufacturing; application of machine learning in fault diagnosis of rotating machinery; application of deep learning in fault diagnosis of rotating machinery; mine winder drives; mine electrical safety; fault detection in electrical drives; e-mobility PHM
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Prognostics and health management have gained significant attention across different domains. This Special Issue aims to focus on the intersection of PHM with machinery and asset safety. The objective here is to present innovative and real-world applications of PHM methodologies across multiple domains. This Issue aims to save operating expenses, guarantee safety standards, and increase machinery uptime by implementing PHM approaches. It will cover strategies for fault prediction, maintenance schedule optimization, and improving the general dependability of machinery systems in a range of industrial sectors. Keywords: Prognostics, health management, machinery safety, predictive maintenance, fault detection, reliability engineering, structural health monitoring, damage detection, Artificial intelligence applications, anomaly detection.
This special issue’s theme is entirely in line with the Machines journal’s purview. Machines encompasses research on automation, reliability engineering, and the development and application of mechanical systems. One important topic that aligns with the journal’s emphasis on enhancing the effectiveness, security, and lifespan of mechanical systems is the integration of PHM for machinery safety and maintenance. This Special Issue advances the field of machinery design and operational excellence by examining the most recent developments in predictive maintenance, fault detection algorithms, and condition monitoring systems.
Dr. Prashant Kumar
Guest Editor
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 100 words) can be sent to the Editorial Office for announcement on this website.
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. 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
- prognostics
- health management
- machinery safety
- predictive maintenance
- fault detection
- reliability engineering
- structural health monitoring
- damage detection
- artificial intelligence applications
- anomaly detection
- condition monitoring
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