Smart Fault Diagnosis and Predictive Maintenance in the Era of Industrial AI: Theories, Methods, and Applications
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 142
Editors
Interests: blade fault diagnosis; crack detection; mechanical equipment; fault diagnosis; intelligent maintenance; structural health monitoring
Interests: condition monitoring and fault diagnosis; structural damage identification; health monitoring; intelligent measurement and control technology; signal processing; dynamic system modeling and identification
Interests: solid mechanics; mechanical vibration; smart materials and structures; signal processing; meta-materials design; load path analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Fault identification, detection, and diagnosis (FIDD) are crucial to ensuring the reliability, safety, and performance of complex engineering systems, such as power grids, machinery, aerospace, chemical processes, and medical equipment. With the development of industrial intelligence, FIDD has become a key research focus in systems engineering, control engineering, and industrial informatization.
The advancement of artificial intelligence, big data, and multi-source signal fusion has brought new opportunities to traditional FIDD methods, while practical engineering scenarios still present challenges such as weak fault feature extraction and real-time diagnosis. These issues require innovative solutions from global researchers and practitioners.
This Special Issue aims to provide a platform for researchers and practitioners to share their recent advances, innovations, and challenges in advanced identification, fault detection and diagnosis. We welcome high-quality original research papers, review articles, and case studies that address relevant topics including, but not limited to, the following:
- Advanced FIDD theories and methodologies
- Data-driven fault diagnosis (machine learning, deep learning, etc.)
- Model-based and hybrid fault diagnosis strategies
- Feature extraction and fusion for weak fault detection
- Multi-source signal-based FIDD (vibration, acoustic emission, etc.)
- Fault prediction and health management (PHM)
- FIDD for specific complex engineering systems
- Performance evaluation of fault diagnosis systems
- Industrial application case studies of FIDD technologies
Dr. Di Song
Prof. Dr. Feiyun Xu
Prof. Dr. Nan Wu
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes 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
- fault identification
- fault detection
- fault diagnosis
- complex engineering systems
- feature extraction
- multi-source information fusion
- machine learning
- fault prognosis
- system reliability
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