Fault diagnosis of scroll compressor based on wavelet transform and CNN SU Yingying，MAO Haixu ( School of Mechanical Engineering, Shenyang University, Shenyang Liaoning, China 110000)
Abstract: In order to solve the problem that traditional single-scale signal analysis is difficult to effectively solve the problem of multi-scale coupling of fault feature information in the fault diagnosis of scroll compressors, a fault diagnosis method based on wavelet transform and convolutional neural network(CNN) is proposed. Firstly, the collected vibration signal is analyzed by Continuous wavelet transform to generate time-frequency diagram. And the generated time-frequency diagram is gridded and normalized. Then, it has to be inputtd to Alexnet convolutional neural network, and the network parameters are adjusted to obtain
