关键词: 多通道, 并行多通道卷积神经网络, 长短时记忆网络, 轴承, 剩余使用寿命预测

Abstract: Timing characteristics between data was an important hidden information that might be utilized while predicting the remaining life of the bearings. In order to better extract features with timing informations for prediction, a remaining useful life prediction model was proposed based on PMCCNN-LSTM. The model was consist of two parts. The front end was PMCCNN, which extracted the signal features, mined for the timing characteristics of the data, and used layer-by-layer training and fine-tuning to improve the convergence of the parameters. The back end was an LSTM network with remaining useful life prediction based on features, and the weighted average method was used to smooth the prediction results. The accuracy of the model was verified by using leave-one-out method on a public dataset of bearing accelerated life tests. The experimental results show that the mean errors and maximum errors of the proposed model are 23.38% and 15.84% lower than that of CNN, 24.14% and 19.01% lower than LSTM, 30.32% and 23.09% lower than that of CNN-LSTM respectively.

Key words: multi-channel, parallel multi-channel convolution neural network(PMCCNN), long short term memory(LSTM) network, bearing, remaining useful life(RUL) prediction