Prediction method of bearing remaining useful life based on CNN and attention BiLSTM

GUO Yurong; MAO Jian; ZHAO Man

doi:10.12299/jsues.21-0244

Volume 37 Issue 1

Mar. 2023

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Article Navigation > Journal of Shanghai University of Engineering Science > 2023 > 37(1): 96-104

GUO Yurong, MAO Jian, ZHAO Man. Prediction method of bearing remaining useful life based on CNN and attention BiLSTM[J]. Journal of Shanghai University of Engineering Science, 2023, 37(1): 96-104. doi: 10.12299/jsues.21-0244

Citation:

GUO Yurong, MAO Jian, ZHAO Man. Prediction method of bearing remaining useful life based on CNN and attention BiLSTM[J]. Journal of Shanghai University of Engineering Science, 2023, 37(1): 96-104. doi: 10.12299/jsues.21-0244

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Prediction method of bearing remaining useful life based on CNN and attention BiLSTM

doi: 10.12299/jsues.21-0244

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2021-11-07
Available Online: 2023-05-13
Publish Date: 2023-03-31

Abstract

Abstract

The remaining useful life (RUL) prediction of rolling bearings is of great significance for the operation and maintenance of rotating machinery. Although the deep learning method represented by convolutional neural network (CNN) can automatically extract features from bearing vibration signals, it can not adaptively select features to improve the attention of the model to important features. In response to the above problems, a bearing RUL prediction method based on CNN and attention bidirectional long short-term memory (BiLSTM) was proposed. Firstly, the spatial features of vibration signals were extracted by CNN, the temporal features were extracted by BiLSTM. Then, the attention mechanism was used to enhance the attention of the model to important features, and the full connection layer was used as the decoder to predict the health indicator (HI). Finally, the weighted average method was used to modify the HI prediction value, and the polynomial fitting curve was used for RUL prediction. The results show that the absolute percentage error of the proposed method is 14.36% lower than that of convolutional long short-term memory (CNN-LSTM) and 21.28% lower than that of traditional self-organizing map network (SOM), which can be effectively used for RUL prediction in multiple fault modes.
- rolling bearing,
- remaining useful life (RUL),
- convolutional neural network (CNN),
- bidirectional long short-term memory (BiLSTM),
- attention mechanism

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References(22)

References

[1]	张小丽, 王保建, 马猛, 等. 滚动轴承寿命预测综述[J] . 机械设计与制造,2015(10):221 − 224. doi: 10.3969/j.issn.1001-3997.2015.08.059
[2]	范强飞, 廖爱华, 丁亚琦. 基于RVM和WPHM的滚动轴承剩余寿命预测[J] . 上海工程技术大学学报,2019,33(4):334 − 338. doi: 10.3969/j.issn.1009-444X.2019.04.007
[3]	WANG Y, PENG Y Z, ZI Y Y, et al. A two-stage data-driven-based prognostic approach for bearing degradation problem[J] . IEEE Transactions on Industrial Informatics,2016,12(3):924 − 932. doi: 10.1109/TII.2016.2535368
[4]	ZHANG Z, WANG Y, WANG K. Fault diagnosis and prognosis using wavelet packet decomposition, fourier transform and artificial neural network[J] . Journal of Intelligent Manufacturing,2013,24(6):1213 − 1227. doi: 10.1007/s10845-012-0657-2
[5]	CAO L X, QIAN Z, PEI Y . Remaining useful life prediction of wind turbine generator bearing based on EMD with an indicator[C]//Proceedings of 2018 Prognostics and System Health Management Conference. Chongqing: IEEE, 2018: 375 − 379.
[6]	HUANG H Z, WANG H K, LI Y F, et al. Support vector machine based estimation of remaining useful life: Current research status and future trends[J] . Journal of Mechanical Science and Technology,2015,29(1):151 − 163. doi: 10.1007/s12206-014-1222-z
[7]	BASTAMI A R, AASI A, ARGHAND H A. Estimation of remaining useful life of rolling element bearings using wavelet packet decomposition and artificial neural network[J] . Iranian Journal of Science and Technology, Transactions of Electrical Engineering,2019,43(S1):233 − 245.
[8]	WU T, GAO C X, FU Z Y. Prediction of remaining life of rolling bearing based on optimized EEMD[C]//Proceedings of 2018 Chinese Intelligent Systems Conference. Singapore: Springer, 2018: 223 − 230.
[9]	张焱, 汤宝平, 韩延, 等. 融合失效样本与截尾样本的滚动轴承寿命预测[J] . 振动与冲击,2017,36(23):17 − 23.
[10]	CHAUHAN S, YADAV P, TIWARI P, et al. Performance prediction of rolling element bearing with utilization of support vector regression[C]// Proceedings of Reliability, Safety and Hazard Assessment for Risk-Based Technologies. Singapore: Springer, 2020: 535 − 543.
[11]	WU Q H, FENG Y, HUANG B Q. RUL Prediction of bearings based on mixture of gaussians bayesian belief network and support vector data description[C]//Proceedings of AsiaSim 2016, SCS AutumnSim 2016: Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. Singapore: Springer, 2016: 118 − 130.
[12]	HONG S, ZHOU Z, ZIO E, et al. Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method[J] . Digital Signal Processing,2014,27:159 − 166. doi: 10.1016/j.dsp.2013.12.010
[13]	REN L, SUN Y Q, WANG H, et al. Prediction of bearing remaining useful life with deep convolution neural network[J] . IEEE Access,2018,6:13041 − 13049. doi: 10.1109/ACCESS.2018.2804930
[14]	WANG F, LIU X, DENG G, et al. Remaining life prediction method for rolling bearing based on the long short-term memory network[J] . Neural Processing Letters,2019,50(3):2437 − 2454. doi: 10.1007/s11063-019-10016-w
[15]	孙鑫, 孙维堂. 基于多尺度卷积神经网络的轴承剩余寿命预测[J] . 组合机床与自动化加工技术,2020(10):168 − 171.
[16]	张继冬, 邹益胜, 邓佳林, 等. 基于全卷积层神经网络的轴承剩余寿命预测[J] . 中国机械工程,2019,30(18):2231 − 2235.
[17]	HINCHI A Z, TKIOUAT M. Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network[J] . Procedia Computer Science,2018,127:123 − 132. doi: 10.1016/j.procs.2018.01.106
[18]	CHEN Y S, JIANG H L, LI C Y, et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J] . IEEE Transactions on Geoscience and Remote Sensing,2016,54(10):6232 − 6251. doi: 10.1109/TGRS.2016.2584107
[19]	GREFF K, SRIVASTAVA R K, KOUTNIK J, et al. LSTM: A search space odyssey[J] . IEEE Transactions on Neural Networks and Learning Systems,2016,28(10):2222 − 2232.
[20]	ZOU F Q, ZHANG H F, SANG S T, et al. Bearing fault diagnosis based on combined multi-scale weighted entropy morphological filtering and Bi-LSTM[J] . Applied Intelligence,2021,51(10):6647 − 6664. doi: 10.1007/s10489-021-02229-1
[21]	FRANCESCHI D, JANG J. Demystifying batch normalization: analysis of normalizing layer inputs in neural networks[C]//Proceedings of International Conference on Optimization and Learning. Cham: Springer, 2020, 1173: 49 − 57.
[22]	WANG Q B, ZHAO B, MA H B, et al. A method for rapidly evaluating reliability and predicting remaining useful life using two-dimensional convolutional neural network with signal conversion[J] . Journal of Mechanical Science and Technology,2019,33(6):2561 − 2571. doi: 10.1007/s12206-019-0504-x