Transfer fault diagnosis for rolling bearings based on manifold embedded distribution alignment

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (8) : 110-116.

WANG Xiaoyu，TONG Jinyu，ZHENG Jinde，PAN Haiyang，PAN Ziwei

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Abstract

Vibration signals of rolling bearings with labels are difficult to obtain under variable working conditions, which leads to low accuracy of fault diagnosis. Aiming at this problem, a new fault diagnosis method for rolling bearings was proposed based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and manifold embedding distribution alignment. Firstly, CEEMDAN was used to decompose the vibration signals of rolling bearings under different working conditions, and some intrinsic mode components (IMF) were obtained. Secondly, the time-domain and frequency-domain features of IMF components with larger kurtosis were extracted to construct a multi-features sample set. The extracted features were embedded into the manifold space for manifold feature transformation and the transformed manifold features were aligned dynamically. Finally, the classification model was trained with source data and target data to obtain the fault diagnosis results of rolling bearings with unknown labels. The experimental results show that the proposed method can minimize the difference of feature distribution between domains, and improve the accuracy of rolling bearings state recognition effectively.

Key words

fault diagnosis / transfer learning / domain adaption / rolling bearings / variable working conditions

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WANG Xiaoyu，TONG Jinyu，ZHENG Jinde，PAN Haiyang，PAN Ziwei. Transfer fault diagnosis for rolling bearings based on manifold embedded distribution alignment[J]. Journal of Vibration and Shock, 2021, 40(8): 110-116

References

［1］胥永刚, 孟志鹏, 赵国亮. 基于双树复小波变换的轴承复合故障诊断研究［J］. 仪器仪表学报, 2014(2): 447-452.
XU Yonggang, MENG Zhipeng, ZHAO Guoliang. Study on compound fault diagnosis of rolling bearing based on dual-tree complex wavelet transform［J］. Chinese Journal of Scientific Instrument, 2014(2): 447-452.

［2］雷亚国, 贾峰, 孔德同,等. 大数据下机械智能故障诊断的机遇与挑战［J］. 机械工程学报, 2018,54(5): 94-104.
LEI Yaguo, JIA Feng, KONG Detong, et al. Opportunities and challenges of machinery intelligent fault diagnosis in big data era［J］. Journal of Mechanical Engineering, 2018,54(5): 94-104.

［3］PANDYA D H, UPADHYAY S H, HARSHA S P, et al. Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-kNN［J］. Expert Systems with Applications, 2013,40(10): 4137-4145.

［4］CUI L H, LIU Y, ZHOU D H, et al. Fault diagnosis of the planetary gearbox based on ssDAG-SVM［J］. IFAC-Papers on Line, 2018,51(24): 263-267.

［5］李从志, 郑近德, 潘海洋,等. 基于精细复合多尺度散布熵与支持向量机的滚动轴承故障诊断方法［J］. 中国机械工程, 2019,30(14): 1713-1719.
LI Congzhi, ZHENG Jinde, PAN Haiyang. et al. Fault diagnosis method of rolling bearings based on refined composite multiscale dispersion entropy and support vector machine ［J］. China Mechanical Engineering, 2019,30(14): 1713-1719.

［6］张钰, 陈珺, 王晓峰, 等. 随机森林在滚动轴承故障诊断中的应用［J］. 计算机工程与应用, 2018,54(6): 100-104.
ZHANG Yu, CHEN Jun, WANG Xiaofeng, et al. Application of random forest on rolling element bearings fault diagnosis［J］. Computer Engineering and Applications, 2018,54(6): 100-104.

［7］陈超, 沈飞, 严如强. 改进LSSVM迁移学习方法的轴承故障诊断［J］. 仪器仪表学报, 2017,38(1): 33-40.
CHEN Chao, SHEN Fei, YAN Ruqiang. Enhanced least squares support vector machine-based transfer learning strategy for bearing fault diagnosis［J］. Chinese Journal of Scientific Instrument, 2017,38(1): 33-40.

［8］LI X, ZHANG W, DING Q. A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning［J］. Neurocomputing, 2018,310: 77-95.

［9］WANG X G, REN J, LIU S. Distribution adaptation and manifold alignment for complex processes fault diagnosis［J］. Knowledge-Based Systems, 2018,156: 100-112.

［10］PAN S J, YANG Q. A survey on transfer learning［J］. IEEE Transactions on Knowledge and Data Engineering, 2010,22(10): 1345-1359.

［11］邵海东, 张笑阳, 程军圣,等. 基于提升深度迁移自动编码器的轴承智能故障诊断［J］. 机械工程学报, 2020,56(9): 98-104.
SHAO Haidong, ZHANG Xiaoyang, CHENG Junsheng, et al. Intelligent fault diagnosis of bearing using enhanced deep transfer auto-encoder ［J］. Journal of Mechanical Engineering, 2020,56(9): 98-104.

［12］ZHANG L, GUO L, GAO H L, et al. Instance-based ensemble deep transfer learning network: a new intelligent degradation recognition method and its application on ball screw［J］. Mechanical Systems and Signal Processing, 2020,140: 106681.

［13］WU Z H, JIANG H K, ZHAO K, et al. An adaptive deep transfer learning method for bearing fault diagnosis［J］. Measurement, 2020,151: 107227.

［14］HE Z, SHAO H, WANG P, et al. Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples［J］. Knowledge-Based Systems, 2020,191: 105313.

［15］刘海宁, 宋方臻, 窦仁杰.小数据条件下基于测地流核函数的域自适应故障诊断方法研究［J］.振动与冲击, 2018,37(18): 36-42.
LIU Haining, SONG Fangzhen, DOU Renjie, et al. Domain adaptive fault diagnosis based on the geodesic flow kernel under small data condition［J］. Journal of Vibration and Shock, 2018,37(18): 36-42.

［16］ZHANG Z W, CHEN H H, LI S M, et al. A novel geodesic flow kernel based domain adaptation approach for intelligent fault diagnosis under varying working condition［J］. Neurocomputing, 2020,376: 54-64.

［17］HAN T, LIU C, YANG W G, et al. Deep transfer network with joint distribution adaptation: a new intelligent fault diagnosis framework for industry application［J］. ISA Transactions, 2020,97: 269-281.

［18］GONG B Q, SHI Y, SHA F, et al. Geodesic flow kernel for unsupervised domain adaptation［C］// 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence: IEEE, 2012.

［19］LONG M S, WANG J D, DING G, et al. Transfer feature learning with joint distribution adaptation［C］//2013 IEEE International Conference on Computer Vision. Sydney: IEEE, 2013.

［20］ZHANG J, LI W, OGUNBONA P, et al. Joint geometrical and statistical alignment for visual domain adaptation［C］// 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017.

［21］BENDAVID S, BLITZER J, CRAMMER K, et al. Analysis of representations for domain adaptation［C］// International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2006.

［22］TORRES M E, COLOMINAS M A, SCHLOTTHAUER G, et al. A complete ensemble empirical mode decomposition with adaptive noise［C］// 2011 IEEE International Conference on Acoustics, Speech and Signal Processing. Prague: IEEE, 2011.

［23］罗金, 童靳于, 郑近德,等. 基于EEMD和堆叠稀疏自编码的滚动轴承故障诊断方法［J］. 噪声与振动控制, 2020,40(2): 115-120.
LUO Jin, TONG Jinyu, ZHENG Jinde, et al. An EEMD and stacked sparse auto-encoder based bearing fault diagnosis method［J］. Noise and Vibration Control, 2020,40(2): 115-120.

［24］BORGWARDT K M, GRETTON A, RASCH M J, et al. Integrating structured biological data by kernel maximum mean discrepancy［J］. Bioinformatics, 2006,22(14): 49-57.