Deep Learning Based Intelligent Fault Diagnosis—2nd Edition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: 31 March 2027 | Viewed by 42
Editors
Interests: signal processing and intelligent fault diagnosis of complex mechanical systems
Special Issues, Collections and Topics in MDPI journals
Interests: fault diagnosis; deep learning; transfer learning; anomaly detection
Special Issues, Collections and Topics in MDPI journals
Interests: health monitoring; intelligent identification; transfer learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: diagnostics, prognostics and health management (PHM) for electromechanical and hydraulic equipment; artificial intelligence and signal processing; digital twins
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid development of deep learning has significantly transformed various fields, including fault diagnosis in complex systems. Intelligent fault diagnosis, leveraging deep learning techniques, offers unprecedented opportunities to improve the reliability, safety, and efficiency of machinery and equipment. By harnessing deep learning, researchers can uncover intricate patterns, enhance fault identification accuracy, and adapt to diverse operational conditions, addressing challenges such as non-stationary signals, data scarcity, and cross-domain variability. The first edition of the Special Issue “Deep Learning Based Intelligent Fault Diagnosis” achieved remarkable success, attracting extensive submissions, broad academic attention, and positive feedback from reviewers and contributing authors worldwide. To further continue this valuable academic exchange, expand cutting-edge discussions, and gather more frontier research outcomes within this fast-growing domain, we are delighted to launch a second edition of the Special Issue, and sincerely invite you to contribute your innovative research and insightful reviews. This Special Issue aims to highlight recent advances in combining deep learning with sensing technologies, multi-sensor information fusion, and diagnostic techniques while emphasizing innovative solutions for real-world engineering problems and fostering multidisciplinary approaches to enhance system diagnostics. Beyond the research directions covered in the first volume, this second edition also welcomes emerging trends, practical industrial deployments, and forward-looking explorations that advance the whole field of intelligent fault diagnosis. The scope of this Special Issue encompasses theoretical advances, algorithm development, and practical applications, including (but not limited to) the following topics of interest:
- Novel deep learning architectures for fault diagnosis;
- Explainable AI techniques in fault diagnosis;
- Cross-domain fault diagnosis; • Real-time fault detection and prediction;
- Data augmentation and imbalance handling in deep learning for fault diagnosis;
- Case studies of deep learning-based fault diagnosis in industrial applications (e.g., railway vehicles, wind turbines, aerospace).
Dr. Long Zhang
Dr. Jiayang Liu
Dr. Zhenghong Wu
Dr. Xiaoli Zhao
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors 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 2600 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 diagnosis
- deep learning
- transfer learning
- domain generalization
- feature extraction
- condition monitoring
- fault prognostics
- anomaly detection
- condition assessment
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
Related Special Issue
- Deep Learning Based Intelligent Fault Diagnosis in Sensors (18 articles)
