Advanced Techniques for Mechanical System Fault Diagnosis and Prognosis
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 31 March 2027 | Viewed by 842
Editor
Interests: system reliability modeling; dynamic fault analysis; BN-based modeling and probability inference; theoretical and applied research on data-driven intelligent fault diagnosis; prediction; health management (PHM)
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Mechanical systems are critical components in a wide range of industrial applications, including wind turbines, automotive systems, aerospace, and manufacturing machinery. Unexpected failures in mechanical systems can lead to significant economic losses, safety hazards, and operational downtime. Therefore, reliable fault diagnosis and prognosis methods are essential for condition monitoring, predictive maintenance, and system reliability enhancement. This Special Issue aims to gather high-quality research contributions focusing on advanced techniques for mechanical system fault detection, diagnosis, and prognosis. Topics of interest include, but are not limited to, the following:
- Signal processing and feature extraction methods;
- Machine learning and deep learning approaches for fault classification;
- Physics-informed networks for fault diagnosis;
- Few-shot fault diagnosis;
- Cross-condition fault diagnosis;
- Multi-sensor fusion;
- Prognostics and health management (PHM) for mechanical systems.
Prof. Dr. Yan-Feng Li
Guest Editor
Manuscript Submission Information
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Keywords
- condition monitoring
- machine learning
- predictive maintenance
- signal processing
- deep learning
- digital twin
- physics-informed networks
- few-shot learning
- cross-condition diagnosis
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