Intelligent Machine Fault Diagnosis

Dear Colleagues,

Machinery has been widely applied in various applications, such as wind turbines, vehicles, and aircrafts; however, these complex and harsh working environments make this machinery prone to failure. Thus, it is vital to conduct an assessment of this machinery to guarantee its safe operation and working efficiency, as well as enabling optimal maintenance for decision making. As a critical part of machine health management, intelligent fault diagnostics and the prognostics of the machinery aim to identify the mode, severity, location, and degradation trend of faults. With this fault information, reliable and predictive maintenance-based decisions can be made to help avoid the sudden shutdown of machinery and some unexpected economic loss. Therefore, intelligent machine fault diagnostics and prognostics can significantly benefit industrial production.

This Special Issue focuses on cutting-edge algorithms/techniques for intelligent machine fault diagnostics and prognostics.

Potential topics include but are not limited to:

• Intelligent machine fault diagnostics and prognostics based on various sensor data;
• Dynamic analysis for machine condition monitoring;
• Digital-twin-based fault diagnostics and prognostics;
• Remaining useful life prediction of the machinery;
• Machine fault diagnostics under non-stationary operating conditions;
• Fatigue analysis of machinery;
• Machine-learning-based fault diagnostics and prognostics.

Dr. Ke Feng
Dr. Qing Ni
Dr. Yongbo Li
Dr. Yuejian Chen
Dr. Xiaoli Zhao
Guest Editors

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