Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications (2nd Edition)
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: 20 April 2026
Special Issue Editor
Interests: rotor dynamics; fault diagnostics; rolling element bearings; oil-film bearings
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The current operation of both transportation and industry must be efficient and safe—in particular, safety is the prerequisite of high-efficiency operation. The development of sensor technology and signal processing makes it possible to detect the real-time health status of mechanical equipment in the above two fields. Most existing methods for fault diagnosis work well only under light noise and in stationary conditions. However, strong noise and non-stationary conditions (including load variation, speed variation and temperature variation) are very common in these two fields, making it challenging to detect and monitor the severity of machine defects.
Bandpass filtering, wavelet transform, singular value decomposition, and empirical mode decomposition, among others, are often applied to extract fault components from original signals with strong noise. Envelope demodulation, moving average, and other methods have been applied to solve the problem of load variation. In addition, many order tracking methods have been proposed to address the velocity variation problem. However, considering application potential, the diagnosis of machine failure under strong noise and in non-stationary conditions can still be improved.
The Special Issue “Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications (2nd Edition)” welcomes original and review articles on fault diagnosis in transportation and industry, particularly with high noise and in non-stationary conditions, with a strong emphasis on real-world applications.
Prof. Steven Chatterton
Guest Editor
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Keywords
- machine fault diagnosis
- gear fault diagnosis
- rolling element bearing diagnosis
- heavy noise
- nonstationary condition
- experimental implementations
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