Prediction method for bearing residual life based on a FCVAE network

PDF(1055 KB)

Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (19) : 13-18.

ZHANG Jidong, ZOU Yisheng, JIANG Yuliang, ZENG Dayi

Author information +

History +

Abstract

Due to influences of manufacturing technology and working condition, service lives of the same type bearings often have great individual difference. If the generalization ability of features extracted in signals is insufficient, the stability of bearing residual life prediction results is poorer. Here, a feature extraction method based on a fully convolutional variational auto-encoder (FCVAE) network was proposed for bearing residual life prediction. In this method, the fully convolutional neural network (FCNN) was used to improve the variational auto-encoder (VAE), reduce the network complexity and strengthen the generalization ability of features extracted. Frequency domain signals were taken as model input to further reduce difficulty of feature learning. At the same time, a weighted average method was designed to smooth the predicted results. Multi-condition test data were used to verify the effectiveness of the proposed method. Results showed that compared with traditional support vector regression (SVR), the average error of prediction results with the proposed method reduces 64%; compared to the convolutional neural network (CNN) and VAE, it reduces 45.5% and 47.5%, respectively.

Key words

fully convolutional variational auto-encoder (FCVAE) / bearings / feature extraction / residual life prediction

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

ZHANG Jidong, ZOU Yisheng, JIANG Yuliang, ZENG Dayi. Prediction method for bearing residual life based on a FCVAE network[J]. Journal of Vibration and Shock, 2020, 39(19): 13-18

References

[1] Lei Y , Li N , Guo L , et al. Machinery health prognostics: A systematic review from data acquisition to RUL prediction[J]. Mechanical Systems and Signal Processing, 2018, 104:799-834.
[2] Li N , Lei Y , Lin J , et al. An improved exponential model for predicting remaining useful life of rolling element bearings[J]. IEEE Transactions on Industrial Electronics, 2015, 62(12):1-1.
[3] 李洪儒,于贺,田再克, 等. 基于二元多尺度熵的滚动轴承退化趋势预测[J]. 中国机械工程, 2017, 28(20):2420-2425+2433.
LI Hongru, YU He, TIAN Zaike, et al. Degradation trend prediction of rolling bearing based on two-element multiscale entropy[J]. China Mechanical Engineering, 2017,28(20):2420-2425+2433.
[4] 佘道明,贾民平,张菀.一种新型深度自编码网络的滚动轴承健康评估方法[J].东南大学学报(自然科学版),2018,48(5):801-806.
SHE Daoming, JIA Minping, ZHANG Wan. Deep auto-encoder network method for health assessment of rolling bearings[J]. Journal of Southeast University ( Natural Science Edition ), 2018,48(5): 801-806.
[5] 赵光权,刘小勇,姜泽东,等.基于深度学习的轴承健康因子无监督构建方法[J].仪器仪表学报,2018,39(6):82-88.
ZHAO Guangquan, LIU Xiaoyong, JIANG Zedong, et al. Unsupervised health indicator of bearing based on deep learning. Chinese Journal of Scientific Instrument, 2018, 39(6): 28-88.
[6] Cheng F , Qu L , Qiao W , et al. Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes[J]. IEEE Transactions on Industrial Electronics, 2018, 66(6):4738-4748.
[7] 文娟, 高宏力. 一种基于UPF的轴承剩余寿命预测方法[J]. 振动与冲击, 2018, 37(24): 225-230+260.
WEN Juan, GAO Hongli. Remaining useful life prediction of bearings with the unscented particle filter approach[J]. Journal of Vibration and Shock, 2018, 37(24):225-230+260.
[8] 陈法法, 杨勇, 陈保家, 等. 基于模糊信息粒化与小波支持向量机的滚动轴承性能退化趋势预测[J]. 中国机械工程, 2016, 27(12): 1655-1661.
CHEN Fafa, YANG Yong, CHEN Baojia, et al. Degradation trend prediction of rolling bearing based on fuzzy information granulation and wavelet support vector machine[J]. China Mechanical Engineering, 2016, 27(12):1655-1661.
[9] 张星辉, 康建设, 赵劲松, 等. 基于混合高斯输出贝叶斯信念网络模型的设备退化状态识别与剩余使用寿命预测方法研究[J]. 振动与冲击, 2014, 33(8):171-179.
ZHANG Xinghui, KANG Jianshe, ZHAO Jinsong, et al. Equipment degradation state identification and residual life prediction based on MoG-BBN[J]. Journal of Vibration and Shock, 2014, 33(8):171-179.
[10] Jun Z, Nan C, Weiwen P. Estimation of bearing remaining useful life based on multiscale convolutional neural network[J]. IEEE Transactions on Industrial Electronics, 2018, 18(2):466-485.
[11] Li X, Jiang H, Xiong X, et al. Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network[J]. Mechanism and Machine Theory, 2018, 133: 229-249.
[12] 伊恩•古德费洛等著. 深度学习[M]. 北京，人民邮电出版社, 2017: 425-427.
Ian Goodfellow,etc. Deep Learning[M]. Beijing, Posts & Telecom Press, 2017: 425-427.
[13] Kingma D P, Welling M. Auto-Encoding Variational Bayes[C]// International Conference on Learning Representations (ICLR), Banff. 2014
[14] Nectoux P, Gouriveau R, Medjaher K, et al. PRONOSTIA: An experimental platform for bearings accelerated degradation tests[C]// IEEE International Conference on Prognostics and Health Management. IEEE, Denver, 2012: 1-8.
[15] 雷亚国. 旋转机械智能故障诊断与剩余寿命预测[M]. 西安: 西安交通大学出版社, 2017: 294-299.
LEI Yaguo. Intelligent fault diagnosis and remaining useful life prediction of rotating machinery[M]. Xi'an: Xi'an Jiaotong University Press, 2017: 294-299.
[16] Guo L, Lei Y, Li N, et al. Deep convolution feature learning for health indicator construction of bearings[C]// Prognostics and System Health Management Conference. IEEE, Harbin, 2017: 1-6.
[17] 任浩,屈剑锋,柴毅,等. 深度学习在故障诊断领域中的研究现状与挑战[J].控制与决策,2017,32(08):1345-1358.
REN Hao, QU Jianfeng, CHAI Yi, et al. Deep learning for fault diagnosis: The state of the art an challenge[J]. Control and Decision,2017,32(08):1345-1358.