Dear Colleagues,

Nowadays, both the fields of transportation and industry seek to operate efficiently and safely. 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 of the existing methods of fault diagnosis work well only on the basis of light noise and stationary condition. However, strong noise and non-stationary conditions (including load variation, speed variation and temperature variation) are very common in these two fields, and in such conditions, it is really a difficult task to detect and monitor the severity of the machine defect.

Bandpass filtering, wavelet transform, singular value decomposition, empirical mode decomposition and so on are often applied to extract the fault component from the original signal 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, from the application point of view, the diagnosis of machine failure under strong noise and non-stationary condition can still be improved.

The Special Issue "Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications" welcomes original or review articles on fault diagnosis in transportation and industry, particularly with high noise and non-stationary conditions, with a strong emphasis on real-world applications.

You may choose our Joint Special Issue in Sensors.

Prof. Dr. Steven Chatterton
Dr. Lang Xu
Guest Editors

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• Machine fault diagnosis
• Gears fault diagnosis
• Rolling element bearings diagnosis
• Heavy noise
• Nonstationary condition
• Experimental implementations

• Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications in Sensors (13 articles)