Research on residual life prediction of gear based on long term and short term memory network

In recent years, deep learning has been widely concerned. Deep learning constructs a neural network model with many hidden layers through massive training data, transforms the feature representation of samples in the original space into a new feature space, and extracts the potential mapping relationship in the input and output samples layer by layer. Recurrent neural networks (RNN) can memorize the previous sequence input and apply it to the later calculation, but it can not solve the long-term dependence problem. Long short term memory (LSTM) model can avoid the problem of gradient vanishing through model parameters and gating unit system controlling information flow, which makes time series information prediction more accurate. Haitao Zhao and others applied long-term and short-term memory network in fault diagnosis to classify faults, but empirical mode decomposition is needed for data. If the monitoring signal is interrupted, it will cause mode aliasing, which is not conducive to fault diagnosis. Zhao Jianpeng et al. Applied LSTM to single step state prediction of rotating machinery, and achieved better results than support vector regression. Wang Xin applied LSTM to the prediction of aircraft fault time series. Compared with many time series prediction models, LSTM has strong applicability and higher accuracy.

Aiming at the large-scale sequence prediction, a new real-time residual life prediction model based on memory mechanism recurrent neural network is established, and the accuracy of network model prediction is improved from the construction of network structure, learning rate and window setting. The random gradient descent algorithm with nesterov momentum is used to prevent the model from falling into local optimum, and the rmsprops algorithm is used to modify the adaptive rate of the model. Finally, the gear bending fatigue test data are used to verify, and compared with the time series prediction model, the test results show that the new memory mechanism recurrent neural network real-time residual life prediction model has good advantages in residual life prediction.

By studying the characteristics of gear vibration data, an improved real-time residual life prediction model based on memory mechanism recurrent neural network is established, and the parameters of the prediction model are optimized. The prediction model is applied to the research of gear residual life prediction. The results show that the memory mechanism recurrent neural network model has higher accuracy in predicting real-time residual life. The next step is to fuse the data of multiple monitoring points and study the residual life prediction model.

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