基于Transformer-LSTM网络的轴承寿命预测

Abstract
Figure/Table
References (19)
Related Citation (15)

Download: PDF (1759 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Bearing is an important component in rotating machinery. Due to the working conditions, materials, and processing methods, the lifetimes of bearing have large fluctuations. Traditional parallel or serial neural network prediction methods have seriously dependent on the data sets. Therefore, there is a need for bearings RUL prediction network that can be applied to different data lengths. To overcome this challenge, a Transformer-LSTM serial-parallel neural network prediction model is proposed, which can predict the RUL for bearings with different lifetimes. By reconfiguring the Transformer decoding layer and fusing it with the LSTM network structure, the serial-parallel prediction processing of bearing life data is achieved. The experimental results show that the Transformer-LSTM neural network can accurately predict the bearing failure time for different lifetimes, including: long, medium, and short. Moreover, the model has a stronger generalization ability which also indicates that the proposed method can improve the prediction accuracy of bearing life.

Key words： rolling bearings bearing remaining useful life prediction Transformer neural network LSTM neural network Non-linear time series forecasting

Received: 26 April 2023 Published: 28 March 2024

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	ZHANG Fan1
	2
	YAO Dechen1
	2
	YAO Shengzhuo1
	YANG Jianwei1
	2
	WANG Yanliang1
	2
	WEI Minghui1
	2
	HU Zhongshuo3

Cite this article:

ZHANG Fan1,2,YAO Dechen1, et al. Lifetime prediction of bearings based on the Transformer-LSTM network[J]. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(6): 320-328.

URL:

http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2024/V43/I6/320

[1] 王立炜,栾孝驰,沙云东,袁琳.基于快速ICA的复杂路径下滚棒轴承外圈划伤故障特征提取[J].机械设计与制造, 2021(12):77-81. Wang Liwei, Luan Xiaochi, Sha Yundong, Yuan, Lin. Fault Extraction of Roller Bearing Outer Ring Scratch in Complex Path Based on Fast ICA[J]. Journal of machinery design and manufacture, 2021(12):77-81. [2] Arinez J F, Chang Q, Gao R X, et al. Artificial intelligence in advanced manufacturing: Current status and future outlook[J]. Journal of Manufacturing Science and Engineering, 2020, 142(11). [3] 刘恒畅, 姚德臣, 杨建伟, 张骄.基于多分支深度可分离卷积神经网络的滚动轴承故障诊断研究[J].振动与冲击, 2021,40(10):95-102. Liu Hengchang, Yao Dechen, Yang Jianwei, Zhang Jiao. Fault Diagnosis of Rolling Bearing Based on Multi Branch Depth Separable Convolutional Neural Network[J]. Journal of Vibration and Shock, 2021,40(10):95-102. [4] Liu K, Shang Y, Ouyang Q, et al. A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery[J]. IEEE Transactions on Industrial Electronics, 2020, 68(4): 3170-3180. [5] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436. [6] Chen Y, Peng G, Zhu Z, et al. A novel deep learning method based on attention mechanism for bearing remaining useful life prediction[J]. Applied Soft Computing, 2020, 86: 105919. [7] Lei Ren, Yaqiang Sun, Jin Cui, et al. Bearing remaining useful life prediction based on deep autoencoder and deep neural networks[J]. Journal of Manufacturing Systems, 2018, 48:71-77. [8] Ahmed Zakariae Hinchi, Mohamed Tkiouat. Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network[J]. Procedia Comput Sci, 2018,127:123-132. [9] Ahmed Elsheikh, Soumaya Yacout, Mohamed-Salah Ouali. Bidirectional handshaking lstm for remaining useful life prediction[J]. Neurocomputing, 2018,323:148- 156. [10] 姚德臣, 李博阳, 刘恒畅, 姚娟, 皮雁南. 基于注意力GRU算法的滚动轴承剩余寿命预测[J]. 振动与冲击, 2021,40(17):116-123. YAO Dechen, LI Boyang, LIU Hengchang, YAO Juan, PI Yannan. Residual life prediction of rolling bearings based on attention GRU algorithm[J]. Journal of Vibration and Shock, 2021, 40(17):116-123. [11] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//31st International Conference on Neural Information Processing Systems. Long Beach: ACM, 2017. [12] Yu Mo, Qianhui Wu, Xiu Li, et al.Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit[J]. Journal of Intelligent Manufacturing, 2021(prepublish):1-10. [13] Hao W, Li Z, Qin G, et al. A Novel Prediction Method Based on Bi-Channel Hierarchical Vision Transformer for Rolling Bearings’ Remaining Useful Life[J]. Processes, 2023, 11(4): 1153. [14] Ding Y, Jia M. Convolutional Transformer: An Enhanced Attention Mechanism Architecture for Remaining Useful Life Estimation of Bearings[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-10. [15] LI shiyang, JIN xiaoyong, XUAN yao, et al. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. [J]. CoRR,2019. [16] Huixin Tian, Daixu Ren, Kun Li, et al. An adaptive update model based on improved long short term memory for online prediction of vibration signal[J]. Journal of Intelligent Manufacturing, 2020, 32(prepublish):1-13. [17] YAO Dechen, LI Boyang, LIU Hengchang, et al. Remaining useful life prediction of roller bearings based on improved 1d-cnn and simple recurrent unit[J]. Measurement, 2021, 175. [18] QIU H, LEE J, LIN J, et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration, 2006, 289(4/5):1066-1090. [19] WANG biao, LEI Yaguo, LI Naipeng, et al. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings[J]. IEEE Transactions on Reliability, 2020, 69(1):401-412.