Weak Signal Detection and Fault Diagnosis
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 258
Editors
Interests: intelligent machine learning and artificial neural network; intelligent fault diagnosis technology; weak signal identification and intelligent health diagnosis; intelligent vehicle design and lightweight technology
Special Issues, Collections and Topics in MDPI journals
Interests: mechanical condition monitoring; health assessment; life prediction; intelligent diagnosis; and vibration control
Special Issue Information
Dear Colleagues,
When the working status of various equipment is abnormal, this is often first discovered by detecting weak signals. It is of the utmost importance to accurately detect the characteristics of weak signals, identify them, and confirm whether these weak abnormal signals are faults through intelligent diagnostic methods. Although research in this field began many years ago, research using artificial intelligence technology has only been ongoing for a decade or so. The intelligent diagnosis of these faint abnormal signals, which are often hard to see, time-varying, nonlinear, extremely weak, and have low recognition accuracy due to environmental factors, is quite significant. Of course, finding dynamic cross domain diagnostic patterns is an important research method. There are also some weak fault diagnosis studies that integrate model-driven and data-driven approaches, which are important research directions in fault diagnosis. We look forward to receiving papers on the research achievements of our peers in this field for different operating equipment.
Prof. Dr. Shunming Li
Prof. Dr. Guangfu Bin
Guest Editors
Manuscript Submission Information
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Keywords
- weak signal
- fault diagnosis
- artificial intelligence
- recognition accuracy
- cross domain diagnostic
- integration
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