Recent Advances in Machinery Condition Monitoring and Fault Diagnosis: From Typical Algorithms to the Era of AI Blooming
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 1
Special Issue Editors
Interests: system health condition monitoring; fault detection and diagnosis; prognostics and health management; robotics (robots in manufacturing and robots in domicile); signal processing and filtration; control system and automation; mechatronics systems including applications to railway
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
High-performance, advanced condition monitoring, and fault diagnosis algorithm/solution development is a vital factor the for efficient and safe operation of any advanced innovative machinery. In particular, at present, the 4th Industrial Revolution has led to associated rapid changes in technology, industries, and societal patterns and processes (increased interconnectivity, i.e., Internet of Things (IoT) and smart automation). These advances enable deeper insights into asset failure processes and innovative methods to connect these insights with condition monitoring and maintenance management for industrial assets.
This Special Issue aims to bring researchers together to present recent advances and technologies, welcoming original research and review articles. Topics include, but are not limited to, the following:
- Model-based condition monitoring and fault diagnosis;
- Data-driven condition monitoring and fault diagnosis;
- Handling class imbalance for early fault detection and diagnosis;
- Typical algorithms vs. AI-based solutions for machinery condition monitoring and fault diagnosis;
- Shallow and deep learning algorithms for condition monitoring and fault diagnosis;
- Robptics and industrial applications.
Dr. Moussa Hamadache
Dr. Emiliano Mucchi
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. 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
- condition monitoring
- fault diagnosis
- class imbalance
- maintenance
- AI-based solutions
- shallow and deep learning algorithms
- early fault detection
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