Entropy-Based Fault Diagnosis: From Theory to Applications

Dear Colleagues,

When a fault occurs in large-scale machinery (e.g., wind turbines, gas turbines, aero-engines, compressors, railway vehicles, and industrial robots), it will result in economic losses for the enterprise, and even cause serious accidents and endanger the safety of technicians. Therefore, it is of great research value to explore promising machinery condition monitoring and fault diagnosis techniques. As a tool for quantifying uncertainty and complexity, signal entropy can be applied to detect changes in system behavior and thus be used for equipment fault diagnosis and prediction. This also makes entropy an important theory for improving the efficiency of system monitoring and maintenance decision. Due to its prominent role in measuring the uncertainty and complexity of time series, entropy theory has been shown to be an effective and state-of-the-art technique in machinery condition monitoring and fault diagnosis. Research into advanced entropy-based methods for the real-time monitoring and diagnosis of machinery equipment conditions is an important trend in line with the current development of large-scale intelligent machinery equipment, as such methods comprehensively guarantee the operational safety and stability of machinery equipment, improve production efficiency, and reduce maintenance costs.

The aim of this Special Issue is to collect recent results on entropy theory-related condition monitoring and fault diagnosis methods in machinery equipment. We also accept contributions on novel perspectives, ongoing research, and discussions regarding existing methods. Thus, recent developments, ideas, and applications of entropy theory in the field of machinery condition monitoring and fault diagnosis all fall under the requirements of our Special Issue.

Dr. Xiaoan Yan
Prof. Dr. Ling Xiang
Prof. Dr. Jinde Zheng
Dr. Zhixin Yang
Guest Editors

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