A dual channel ConvLSTM wind turbine bearing life prediction model is proposed to address the issues of insufficient feature extraction and large prediction errors in the process of feature extraction in or-dinary rolling bearing life prediction models.First,the original bearing vibration signal is subjected to wave-let threshold denoising to remove the noise interference in the vibration signal;secondly,to fully extract fea-tures,this paper uses two channels to extract vibration signal features,one of which is the bearing vibration signal information,and the other is the frequency domain amplitude signal;then use the ConvLSTM model for feature extraction,which can take into account the dependence of spatial local features and time series at the same time,and has good feature extraction capabilities;finally,the two-way feature fusion is deep into the fully connected layer,and the model prediction results are output;in addition,In order to improve the prediction accuracy of the model,this paper also makes corresponding improvements to the loss function.The experimental results show that the percentage of error in predicting the remaining life of bearings in the proposed model is below 20%,which is smaller than other deep learning based models.
life predictiondeep learningconvolutional long short term memory networkvibration signalfeature extraction
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