In the present review article, the research findings in the field of investigating the vibrational
nature of the rotating machineries under the influence of multiplicative faults and their …

Deep learning (DL) has been widely developed and applied in gearbox fault diagnosis of
smart manufacturing systems due to its powerful feature representation ability. However …

Research on transfer learning in rolling bearing fault diagnosis can help overcome
challenges such as different data distributions and limited fault samples. However, most …

The occurrence of compound faults in real-time conditions leads to the early failure of
components. However, identifying compound faults in a rotor system is more complex …

Compound fault diagnosis of rotating machinery is of great significance for the operational
reliability and security of manufacturing equipment. Since the possible compound fault types …

In the intelligent fault diagnosis of rolling bearings, transfer learning methods extend the
applicability of models to diverse working scenarios. However, real-world scenarios often …

Purpose In rotating machinery, compound faults occur when multiple faults happen
simultaneously, making them challenging to detect. This study targets the diagnosis of …

Due to the varying manufacturing demands placed upon mechanical equipment, it is often
operated under fluctuating loads and diverse working conditions over extended periods …

Wind power is of strategic importance for reducing carbon dioxide emissions, minimizing
environmental pollution, and enhancing the sustainability of energy supply. Health …

For the challenges of diverse compound faults and low identification accuracy of railway
bearings, a new zero-shot diagnosis model based on adaptive resonance demodulation …