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

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.

Automated summary

This paper introduces an online learning algorithm based on Error-LSTM model, which improves the accuracy and efficiency of the model based on test error. Experimental results show that the proposed model outperforms LSTM-based models in terms of model accuracy and efficiency.