Incipient fault detection and condition monitoring of rolling bearings by using Mahalanobis-Taguchi System

PDF(1885 KB)

Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (12) : 155-162.

YAN Chang-feng1 ZHU Tao1 WU Li-xiao1 Ahmed Y.Y1 GUO Jianfeng2

Author information +

History +

Abstract

Because the sensitivity of feature parameters of vibration signal are different with the four stage of bearing life, the sensitivity to incipient fault and the correlation of degradation condition are analyzed. A new method of incipient fault detection and performance degradation is presented by Mahalanobis-Taguchi System(MTS). The feature parameters which are sensitive to incipient fault and related performance degradation condition are regarded as a reference space for Mahalanobis-Taguchi System(MTS). Two groups of feature parameters are fused with single feature parameters in MTS of Mahalanobis distance. Since MD1 is more sensitive to incipient fault of bearings, it is used to detect the time of incipient fault during the first and second stages of bearing life. According to MD2 increasing with performance degradation, the degradation state of bearings is estimated by variation tendency. By using this method, the uncertainty and instability of single feature parameters can be avoided in different running condition. The incipient fault is also detected accurately and degradation condition of bearings life is distinguished. The validity and accuracy of this method are verified by the service life of rolling bearing in accelerated life test.

Key words

Mahalanobis-Taguchi System (MTS) / rolling bearing / incipient fault / condition monitoring / correlation

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

YAN Chang-feng1 ZHU Tao1 WU Li-xiao1 Ahmed Y.Y1 GUO Jianfeng2. Incipient fault detection and condition monitoring of rolling bearings by using Mahalanobis-Taguchi System[J]. Journal of Vibration and Shock, 2017, 36(12): 155-162

References

[1] Jay Lee, Wu Fangji, Zhao Wenyu, et al. Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications [J]. Mechanical Systems and Signal Processing, 2014, 42(1-2): 314–334.
[2] 佟佩声. 滚动轴承变工况条件下静电监测特征提取及故障程度识别方法研究[D]. 南京：南京航空航天大学，2014.
Tong Peisheng. Electrostatic monitoring feature extraction and fault severity assessment methods for rolling bearing based on variable operating condition[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014.
[3] M. S. Patil, Jose Mathew, P. K. RajendraKumar. Bearing signature analysis as a medium for fault detection a review[J]. Journal of Tribology, 2007，130(1), 14001–14007.
[4] Zhao Xiaomin, Zuo Ming J, Tejas Patel. EMD, ranking mutual information and PCA based condition monitoring [J]. Proceedings of the ASME 2010 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, 2010 .
[5] Piyush Shakya, Makarand S. Kulkarni, Ashish K. Darpe. A novel methodology for online detection of bearing health status for naturally progressing defect [J]. Journal of Sound and Vibration, 2014, 333(21): 5614–5629.
[6] Piyush Shakya, Makarand S. Kulkarni, Ashish K. Darpe. Bearing diagnosis based on Mahalanobis–Taguchi–Gram–Schmidt method. Journal of Sound and Vibration, 2015, 337: 342–362.
[7] Yu Jianbo. Health condition monitoring of machines based on hidden markov model and contribution analysis [J]. IEEE Transactions on Instrumentation and Measurement, 2012, 61(8): 2200-2211.
[8] Wang Zhipeng, Lu Chen, Wang Zili, et al. Fault diagnosis and health assessment for bearings using the Mahalanobis-Taguchi system based on EMD-SVD [J]. Transactions of the Institute of Measurement and Control, 2013, 35(6): 798–807.
[9] 余发军，周凤星，严保康. 基于字典学习的轴承早期故障稀疏特征提取[J]. 振动与冲击，2016，35(6): 181-186.
YU Fa-jun, ZHOU Feng-xing, YAN Bao-kang, Bearing initial feature extraction via sparse representation based on dictionary learning. Journal of Vibration and Shock, 2016, 35(6): 181-186.
[10] Reuben Lim, Chi Keong, David Mba. Bearing time-to-failure estimation using spectral analysis features [J]. Structural Health Monitoring, 2014, 13(2): 219-230.
[11] Hu Jinqiu, Zhang Laibin, Liang Wei. Dynamic degradation observer for bearing fault by MTS–SOM system. Mechanical Systems and Signal Processing, 2013, 36 (2): 385–400.
[12] A Soylemezoglu，S Jagannathan，C Saygin. Mahalnobis Taguchi System, (MTS) as a prognostic tool for rolling element bearing failures. Journal of Manufacturing Science & Engineering, 2010, 132(5):635-645
[13] Shen Zhongjie, He Zhengjia, Chen Xuefeng, et al. A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time [J]. Sensors, 2012, 12(8): 10109-10135.
[14] Zhu Xiaoran, Zhang Youyun, Zhu Yongsheng. Bearing performance degradation assessment based on the rough support vector data description [J]. Mechanical Systems and Signal Processing, 2013, 34 (1-2): 203–217.
[15] Yan Changfeng, You Li, Kong Yuanyuan, et al. A hybrid method to estimate initial faulty time of rolling bearing [J]. International Conference on Control System and Automation, 2013, 08(09): 474-480.
[16] Myeongsu Kang, Jaeyoung Kim, Jong-Myon Kim. Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm [J]. Information Sciences, 2015, 294(2015): 423–438.
[17] Amit R. Bhende, Gajanan K. Awari, Sachin P. Untawale. Comprehensive bearing condition monitoring algorithm for incipient fault detection using acoustic emission [J]. Jurnal Tribologi, 2014, 2 (2014): 1-30.
[18] Wang Zhipeng, Wang Zili, Tao Laifa, et al. Fault diagnosis for bearing based on Mahalanobis-Taguchi System [J]. 2012 Prognostics and System Health Management Conference, 2012.
[19] Elham, Abdollah. Mahalanobis Taguchi system: a review [J]. Emerald Insight, 2015, 32(3): 291-307.
[20] 任江涛，蔡远文，史建伟，等. 基于马田系统的设备健康监测技术研究[J]. 计算机测量与控制，2012，20(3): 634-641.
Ren Jiangtao, Cai Yuanwen, Shi Jianwei, et al. Research on equipment health monitoring using Mahalanobis-Taguchi System [J]. Computer Measurement and Control, 2012, 20(3): 634-641.
[21] 何正嘉，陈进，王太勇，等. 机械故障诊断理论及其应用[M]. 北京：高等教育出版社，2010.
He Zhengjia, Chen Jin, Wang Taiyong, et al. Theories and applications of machinery fault diagnostics [M]. Beijin: Higher education press, 2010.
[22] Brian P. Graney, Ken Starry. Rolling element bearing analysis[J]. Materials Evaluation, 2011, 70(1): 78-85.
[23] P. Nectoux, R. Gouriveau, K. Medjaher, et al. PRONOSTIA: An experimental platform for bearings accelerated life test [J]. IEEE International Conference on Prognostics and Health Management, 2012.
[24] J. Lee, H. Qiu, G. Yu, et al. Rexnord Technical Services: Bearing Data Set [EB/OL].
http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/, 2007.