Fault Diagnosis of Main Bearing of CNC Machine Tool Based on Parameter Optimization BP Neural Network
In order to further improve the fault diagnosis accuracy of the main bearing of CNC machine tools,a fault diagnosis method of the main bearing of CNC machine tools based on parameter optimization BP neural network is proposed.The sparrow search algorithm is used to optimize all the weights and thresholds in the network,so as to improve the conver-gence difficulties and local extreme value problems that are easy to occur in the diagnosis process of the network.Firstly,wavelet packet decomposition method is used to process the collected vibration acceleration signal,extract the bearing fault energy eigenvalue,and then the optimized BP neural network is used for fault diagnosis.The improved algorithm is test-ed with the rolling bearing data of Case Western Reserve University.The experimental re-sults show that the diagnosis accuracy of BP neural network after parameter optimization can reach 0.997,which is 0.384 higher than that before optimization,and has a good diagnosis effect.
main bearingfault diagnosisparameter optimizationsparrow search algo-rithmBP neural network
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