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Rotating machinery is widely used in industrial systems, and its operation state is directly related to the working performance of the system. Research on equipment condition monitoring and fault diagnosis based on vibration signal analysis is of great significance to ensure the safe and stable operation of equipment. In recent years, the field of deep learning has developed rapidly, and many researchers have applied it to the vibration signal analysis of rotating machinery equipment. Firstly, the development history of deep learning is reviewed. Then, the principles of deep learning models such as convolutional neural network, deep belief network, stacked auto-encoder and their applications in vibration signal analysis of rotating machinery are introduced. Finally, the future development trend of deep learning is discussed.

Review on Vibration Signal Analysis of Rotating Machinery Based on Deep Learning