Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 30 April 2025 | Viewed by 36
Special Issue Editor
Interests: structural dynamics; vibration and control of linear and nonlinear dynamical systems and mechanisms; optimal design and finite element analysis of structures; parametric modal identification, fault detection and finite element model updating techniques in structures and machines; integrated reverse engineering of structures; dynamic analysis, vibration monitoring and fault detection of geared rotor-bearing systems; structural health monitoring and fatigue analysis
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Special Issue Information
Dear Colleagues,
This Special Issue aims to explore innovative methodologies and innovations in the field of fault detection, diagnosis, and prognostics for machinery. With the increasing complexity of industrial systems, ensuring reliable operation and minimizing downtime has become paramount. This issue highlights novel approaches, including advanced sensing technologies, data-driven analytics, machine learning algorithms, and predictive maintenance strategies. Contributions will cover various applications across various industries, addressing challenges such as early fault detection, accurate diagnosis, and reliable prognostics. By fostering interdisciplinary research and collaboration, this Special Issue endeavors to advance state-of-the-art machinery health monitoring and enhance industrial operations' overall reliability and efficiency.
By fostering interdisciplinary research and collaboration among researchers, engineers, and industry practitioners, this Special Issue aims to advance state-of-the-art machinery health monitoring. It seeks to contribute significantly to improving operational efficiency, reducing maintenance costs, and extending the lifespan of critical machinery components. Ultimately, the insights and innovations presented will pave the way for more resilient and sustainable industrial operations in the face of increasing complexity and technological advancement.
Dr. Dimitrios Giagopoulos
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
- fault detection
- fault diagnosis
- prognostics
- predictive maintenance
- condition monitoring
- machinery health monitoring
- machine learning
- machinery
- machines
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