Remaining mechanical useful life prediction for circuit breaker based on convolutional variational autoencoder and multi-head self-attention
Arming at the uncertainty of the degradation of conventional circuit breakers and the perfect mechanical degradation characterization by vibration signals,an opening mechanical mechanism life prediction method based on CVAE and MSA mechanism is proposed.Firstly,the parametric features are extracted based on the different event intervals of the circuit breaker.Then,the depth features in the signal components are mined by CVAE,and the parametric features are fused with the depth features to obtain the complete degradation features.Finally,the quantitative life prediction model of the GRU-MSA is formulated,which introduces MSA to capture the different dependencies of signals in several different representation subspaces and assign greater weights to the important time steps.Finally,the proposed method is tested by using the vibration signal measurement data of three test samples.The results show that the proposed method has life prediction RMSE of 141.46,128.75,and 134.16,and MAE of 112.17,101.52,and 106.22,respectively.The prediction accuracy is high and the stability is good,which has more advantages compared with other hybrid prediction models.
conventional circuit breakerconvolutional variational autoencodermulti-head self-attentionremaining useful life prediction
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