Segmental prediction method of rolling bearing life based on few data samples
Aiming at the problem that it is difficult to accurately predict the remaining useful life(RUL)of rolling bearings with few data samples,a life segment prediction method combining convolutional long short-term memory(ConvLSTM)and adversarial discriminative domain adaptation(ADDA)for lifetime segmentation prediction was proposed.Firstly,the feature set was screened by ProbSpare self-attention,and the feature set with time-varying characteristics was extracted to obtain the optimal global features,which were used to determine the segmentation point and used as inputs to the ADDA model.Secondly,the corresponding health assessment indexes were established for the degradation characteristics at different stages.Then,ConvLSTM network was used to predict the life of the bearing in the healthy stage,the predicted data of the healthy stage was input as local features into the ADDA network with the optimal set of features(global features)for adversarial training to realize the life prediction in the failure stage,and output the predicted remaining life of the rolling bearing through the fully connected layer.Finally,the model with the PHM2012 dataset and engineering test data was validated.The research results show that comparing to the ConvLSTM model,the RNN-HI model,and the CNN-LSTM model,the proposed ConvLSTM-ADDA life prediction method reduces the mean absolute error by 78.16％,53.14％,and 67.13％respectively,improves the mean score by 66.42％,66.81％,and32.37％respectively.Comparing to the LSTM model,the CNN-LSTM model,and the Transformer model,the proposed ConvLSTM-ADDA life prediction method reduces the mean square error by 80.11％,54.95％,and 55.94％respectively.Therefore,the algorithmic model can realize the RUL prediction purpose of bearing life for fewer data samples with high accuracy.
adversarial discriminative domain adaptation(ADDA)convolutional long short-term memory(ConvLSTM)ProbSpare self-attentionfewer data samplesphased life projectionremaining useful life(RUL)
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