Diagnosis of Rolling Bearings Based on Total Vector Convolutional Recurrent Neural Networks

In order to improve the efficiency of rolling bearing fault diagnosis, this paper analyzes the application of full vector convolutional recurrent neural network in rolling bearing diagnosis. Firstly, the operation principle of convolutional recurrent neural network is introduced, and its value for fault diagnosis of rolling bearing is understood. Secondly, the main points of fault diagnosis of full vector convolutional recurrent neural network are introduced, and the faults and characteristic frequencies of rolling bearing faults are analyzed, so as to point out the direction for making a diagnosis scheme. Finally, a full vector convolutional recurrent neural network model is analyzed and constructed for diagnosing rolling bearing faults. In this paper, the advantages of full vector spectrum are applied to improve the diagnostic efficiency and automation level of rolling bearings, and to accumulate experience for the innovation of rolling bearing diagnostic technologies and methods in the future.