Deep GRU Bearing Fault Prediction Based on Complex Wavelet Packet Energy Moment Entropy
In order to accurately and timely characterize the performance degradation of bearing and achieve effective early fault prediction,a depth gated regression unit bearing early fault prediction method based on complex wavelet packet energy moment entropy was put forward.Firstly,the complex wavelet packet energy moment entropy was defined as a new monitoring index to characterize the degradation of its bearing capacity.Secondly,a deep gated regression cell network was constructed to capture the hidden linear mapping relationship in the defined monitoring index.Finally,an improved training algorithm based on learning rate decay strategy was proposed to improve the prediction ability of the depth model.The analysis of two experimental results shows that the proposed method has higher sensitivity and accuracy.
Complex WaveletEnergy Moment EntropyDepth Gated Regression UnitFault Prediction
万方数据
维普
