Deep Learning Based Intelligent Fault Diagnosis
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: 20 December 2025 | Viewed by 392
Special Issue 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
Interests: intelligent diagnostics; prognostics and health management (PHM) for electromechanical and hydraulic systems; artificial intelligence and signal processing; digital twins and intelligent robotics
Special Issues, Collections and Topics in MDPI journals
Interests: health monitoring; intelligent identification; transfer learning; deep learning
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.
We are pleased to invite you to contribute to this Special Issue titled “Deep Learning Based Intelligent Fault Diagnosis” to share your innovative research and insights into this vital and evolving field.
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. 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. Xiaoli Zhao
Dr. Zhenghong Wu
Guest Editors
Manuscript Submission Information
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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
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