Advances in Machinery Fault Diagnosis and Condition Monitoring
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: 30 May 2025 | Viewed by 1045
Special Issue Editors
Interests: fault diagnostics; rolling element bearings; signal analysis
Special Issues, Collections and Topics in MDPI journals
Interests: rotordynamics; fault diagnostics; rolling element bearings; oil-film bearings
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the ever-evolving landscape of modern industry, machinery plays a pivotal role. The scientific background of machinery fault diagnosis and condition monitoring is grounded in the need to ensure the reliable and efficient operation of complex industrial systems. As industries become more automated and reliant on advanced machinery, the importance of this research area cannot be overstated.
Effective machinery fault diagnosis and condition monitoring are crucial for preventing unexpected breakdowns, reducing maintenance costs, and maximizing productivity. By detecting faults early and accurately, businesses can avoid costly downtime and ensure the smooth running of their operations. This research area is not only essential for individual companies but also has a significant impact on the overall economy and sustainability of the industrial sectors.
We are pleased to invite you to contribute to our Special Issue on the Advances in Machinery Fault Diagnosis and Condition Monitoring. This Special Issue aims to showcase the latest research and innovations in this field, providing a platform for researchers and practitioners to share their findings and insights.
Suggested themes and article types for submissions:
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Advanced signal processing techniques for fault detection: This could involve the application of innovative signal processing algorithms to extract meaningful information from machine signals and identify early signs of faults.
- Data-driven approaches to fault diagnosis: Researchers might explore the use of machine learning and artificial intelligence techniques to analyze large amounts of data and detect faults in real time.
- Prognostics and remaining useful life prediction: Considerations could include developing models to predict the remaining useful life of machinery components and plan maintenance activities accordingly.
- Case studies on successful fault diagnosis and condition monitoring implementations: Opportunities for research might lie in sharing real-world examples of effective strategies and solutions.
We look forward to receiving your contributions.
Dr. Lang Xu
Prof. Steven Chatterton
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 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. 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
- machinery fault diagnosis
- condition monitoring
- signal processing
- data-driven approaches
- remaining useful life prognostics
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