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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. A. (2018). Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies, 11(7), 1–20.
Carino, J. A., Delgado-Prieto, M., Iglesias, J. A., Sanchis, A., Zurita, D., Millan, M., et al. (2018). Fault detection and identification methodology under an incremental learning framework applied to industrial machinery. IEEE Access, 6, 49755–49766.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. P., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194–211.
Chen, Z. S., Yang, Y. A., Hu, Z., & Shen, G. J. (2006). Detecting and predicting early faults of complex rotating machinery based on cyclostationary time series model. Journal of Vibration and Acoustics-Transactions of the Asme, 128(5), 666–671.
Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2015). Training and testing a self-adaptive multi-operator evolutionary algorithm for constrained optimization. Applied Soft Computing, 26, 515–522.
Fei, S. W. (2016). Kurtosis prediction of bearing vibration signal based on wavelet packet transform and Cauchy kernel relevance vector regression algorithm. Advances in Mechanical Engineering, 8(9), 1–7.
Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural Computation, 12(10), 2451–2471.
He, H. B., Chen, S., Li, K., & Xu, X. (2011). Incremental learning from stream data. IEEE Transactions on Neural Networks, 22(12), 1901–1914.
Henriquez, P. A., & Ruz, G. A. (2018). A non-iterative method for pruning hidden neurons in neural networks with random weights. Applied Soft Computing, 70, 1109–1121.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
Liu, J. T., & Yang, X. X. (2018). Learning to see the vibration: A neural network for vibration frequency prediction. Sensors, 18(8), 1–14.
Liu, Y., Duan, L. X., Yuan, Z., Wang, N., & Zhao, J. P. (2019). An intelligent fault diagnosis method for reciprocating compressors based on LMD and SDAE. Sensors, 19(5), 1–19.
Malaca, P., Rocha, L. F., Gomes, D., Silva, J., & Veiga, G. (2019). Online inspection system based on machine learning techniques: Real case study of fabric textures classification for the automotive industry. Journal of Intelligent Manufacturing, 30(1), 351–361.
Mao, W. T., He, L., Yan, Y. J., & Wang, J. W. (2017). Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine. Mechanical Systems and Signal Processing, 83, 450–473.
Mohamed, M. (2018). Parsimonious memory unit for recurrent neural networks with application to natural language processing. Neurocomputing, 314, 48–64.
Ozay, M., Esnaola, I., Vural, F. T. Y., Kulkarni, S. R., & Poor, H. V. (2016). Machine learning methods for attack detection in the smart grid. IEEE Transactions on Neural Networks and Learning Systems, 27(8), 1773–1786.
Prabhavalkar, R., Alsharif, O., Bruguier, A., & McGraw, I. (2016). On the compression of recurrent neural networks with an application to LVCSR acoustic modeling for embedded speech recognition. In IEEE international conference on acoustics, speech and signal processing (pp. 5970–5974).
Razavi-Far, R., Hallaji, E., Saif, M., & Ditzler, G. (2019). A novelty detector and extreme verification latency model for nonstationary environments. IEEE Transactions on Industrial Electronics, 66(1), 561–570.
Rizk, Y., & Awad, M. (2019). On extreme learning machines in sequential and time series prediction: A non-iterative and approximate training algorithm for recurrent neural networks. Neurocomputing, 325, 1–19.
Song, L. Q., Tekin, C., & van der Schaar, M. (2016). Online learning in large-scale contextual recommender systems. IEEE Transactions on Services Computing, 9(3), 433–445.
Tang, X. Y. (2019). Large-scale computing systems workload prediction using parallel improved LSTM neural network. IEEE Access, 7, 40525–40533.
Tian, H. X., Ren, D. X., & Li, K. (2019). A hybrid vibration signal prediction model using autocorrelation local characteristic-scale decomposition and improved long short term memory. IEEE Access, 7, 60995–61007.
Wu, T. Y., & Lei, K. W. (2019). Prediction of surface roughness in milling process using vibration signal analysis and artificial neural network. The International Journal of Advanced Manufacturing Technology, 102(1), 305–314.
Ye, R., & Dai, Q. (2018). A novel transfer learning framework for time series forecasting. Knowledge-Based Systems, 156, 74–99.
Youn, J., Shim, J., & Lee, S. G. (2018). Efficient data stream clustering with sliding windows based on locality-sensitive hashing. IEEE Access, 6, 63757–63776.
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).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
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
- Vibration signal predicting
- LSTM network
- Test error
- Model update
- Online learning