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

Author

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  • Huixin Tian

    (Tiangong University
    Tiangong University
    State Key Laboratory of Process Automation in Mining and Metallurgy/Beijing Key Laboratory of Process Automation in Mining and Metallurgy Research)

  • Daixu Ren

    (Tiangong University
    Tiangong University)

  • Kun Li

    (Tiangong University)

  • Zhen Zhao

    (Civil Aviation University of China)

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.

Suggested Citation

  • Huixin Tian & Daixu Ren & Kun Li & Zhen Zhao, 2021. "An adaptive update model based on improved Long Short Term Memory for online prediction of vibration signal," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 37-49, January.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:1:d:10.1007_s10845-020-01556-3
    DOI: 10.1007/s10845-020-01556-3
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    References listed on IDEAS

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    1. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    2. Pedro Malaca & Luis F. Rocha & D. Gomes & João Silva & Germano Veiga, 2019. "Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 351-361, January.
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    Cited by:

    1. Chi Ma & Hongquan Gui & Jialan Liu, 2023. "Self learning-empowered thermal error control method of precision machine tools based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 695-717, February.
    2. Sun, YongTeng & Ma, HongZhong, 2024. "Research progress on oil-immersed transformer mechanical condition identification based on vibration signals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
    3. Hasan Tercan & Philipp Deibert & Tobias Meisen, 2022. "Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 283-292, January.
    4. Hanting Zhou & Wenhe Chen & Jing Liu & Longsheng Cheng & Min Xia, 2024. "Trustworthy and intelligent fault diagnosis with effective denoising and evidential stacked GRU neural network," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3523-3542, October.

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