An adaptive update model based on improved Long Short Term Memory for online prediction of vibration signal

Abstract

In industrial production, the characteristics of compressor vibration signal change with the production environment and other external factors. Therefore, to ensure the effectiveness of the model, the vibration signal prediction model needs to be updated constantly. Due to the complex structure of Long Short Term Memory (LSTM) network, the LSTM model is difficult to adapt to the scene of online update. Therefore, the update model based on LSTM is difficult to respond quickly to data changes, which affects the accuracy of the model. To solve this problem, the online learning algorithm is introduced into prediction model, Error-LSTM (E-LSTM) model is proposed in this paper. The main idea of E-LSTM model is to improve the accuracy and efficiency of the model according to test error of the model. First, the hidden layer neurons of LSTM network are divided into blocks, and only part of the modules are activated at each time step. The number of modules activated is determined by test error. Thus, the training speed of the model is accelerated and the efficiency of the model is improved. Second, the E-LSTM model can adaptively adjust the training method according to the data distribution characteristics, so as to improve the accuracy of updated model. In experimental part, two types of datasets are used to verify the performance of the proposed model. LSTM model is used for comparative experiments, and the results showed that the updating model based on E-LSTM is better than that based on LSTM in terms of model accuracy and efficiency.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant (61703406 and 71602143), Tianjin Natural Science Foundation (18JCYBJC22000), Tianjin Science and Technology Correspondent Project (18JCTPJC62600 and 19JCTPJC47600), Tianjin high school innovation team training Program (TD13-5038), State Key Laboratory of Process Automation in Mining and Metallurgy/Beijing Key Laboratory of Process Automation in Mining and Metallurgy Research Fund Project (BGRIMM-KZSKL-2019-08).

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Correspondence to Kun Li.

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Appendix

Appendix

See Tables 9, 10, 11 and 12.

Table 9 The effect of number of hidden layers on the model performance
Table 10 The effect of number of neurons on the model performance
Table 11 The effect of window size on the model performance
Table 12 The effect of error blocks size on the model performance

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Tian, H., Ren, D., Li, K. et al. An adaptive update model based on improved Long Short Term Memory for online prediction of vibration signal. J Intell Manuf 32, 37–49 (2021). https://doi.org/10.1007/s10845-020-01556-3

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Keywords

  • Vibration signal predicting
  • LSTM network
  • Test error
  • Model update
  • Online learning