Abstract
To extract the time-series characteristics of the original bearing signals and predict the remaining useful life (RUL) more effectively, a parallel multichannel recurrent convolutional neural network (PMCRCNN) is proposed for the prediction of RUL. Firstly, the time domain, frequency domain, and time-frequency domain features are extracted from the original signal. Then, the PMCRCNN model is constructed. The front of the model is the parallel multichannel convolution unit to learn and integrate the global and local features from the time-series data. The back of the model is the recurrent convolution layer to model the temporal dependence relationship under different degradation features. Normalized life values are used as labels to train the prediction model. Finally, the RUL was predicted by the trained neural network. The proposed method is verified by full life tests of bearing. The comparison with the existing prognostics approaches of convolutional neural network (CNN) and the recurrent convolutional neural network (RCNN) models proves that the proposed method (PMCRCNN) is effective and superior in improving the accuracy of RUL prediction.
Highlights
Prediction and health management is an effective method to improve the safety, integrity, and task success of the system under the actual operating conditions
In modern production machinery, rolling bearings are the key parts to determine the health of machinery. us, it is significant to carry out real-time monitoring of the health status of rolling bearings in operation and prediction of Remaining Useful Life (RUL) [2, 3]
Architecture of the parallel multichannel recurrent convolutional neural network (PMCRCNN) Model e PMCRCNN prediction model proposed in this paper is structured by parallel multichannel convolution building blocks, pooling layers, recurrent convolution layers (RCL), and a fully connected layer
Summary
Prediction and health management is an effective method to improve the safety, integrity, and task success of the system under the actual operating conditions. Us, it is significant to carry out real-time monitoring of the health status of rolling bearings in operation and prediction of Remaining Useful Life (RUL) [2, 3]. Many scholars have done a lot of research on the method of predicting the RUL of rolling bearings and put forward a variety of network models. Mao et al [7], first, used Hilbert–Huang to extract the edge spectrum and health status label of the original vibration signal of the bearing, using CNN to obtain the deep fault characteristics of bearings. Ese features were inputted to the long-short time memory (LSTM) network for training and obtained an effective prediction model. Zhu et al [11] used a new CNN method to predict the bearing RUL through time-series features and MSCNN. Zhu et al [11] used a new CNN method to predict the bearing RUL through time-series features and MSCNN. e method could keep global and local degradation features
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