Dear Colleagues,

Machinery condition monitoring and intelligent fault diagnosis have recently come to play a crucial role in automatic and intelligent industrial production processes. Based on machine learning, deep learning, and artificial intelligence, intelligent fault diagnosis has been proposed and achieved remarkable improvements, especially in the face of unknown nonlinear machine behavior and non-stationary data. However, there are still some machinery condition monitoring and intelligent fault diagnosis problems that require further research, such as early fault detection features, a small sample machine learning algorithm, multi-condition transfer learning algorithm, multi-modal data fusion method, and interpretable deep learning algorithm.

To comprehensively report the research progress in this field, disseminate excellent research results, and promote the development and application of machinery condition monitoring and intelligent fault diagnosis, this Special Issue focuses on presenting intelligent fault diagnosis algorithm development, fault feature extraction, and intelligent machine monitoring.

This Special Issue includes, but is not limited to, the following topics:

• failure mechanisms modeling for mechanical equipment; 
• monitoring signal processing for mechanical equipment; 
• intelligent feature extraction for condition monitoring;
• intelligent early fault detection and diagnosis;
• few-shot sample learning for fault detection;
• transfer-learning-based methods for fault diagnosis;
• interpretable deep learning for fault diagnosis;
• hybrid models of data-driven and model-based approaches
• sensor data fusion for fault diagnosis;
• measurement methods, technologies, and systems for fault diagnosis.

Prof. Dr. Hongli Gao
Dr. Zhichao You
Guest Editors

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