Fault quantitative evaluation of rolling bearings based on shock value of selected frequency band

PDF(1960 KB)

Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (19) : 30-38.

ZHANG Long1,2,3, CHENG Junliang2,LI Xinglin3,YANG Shixi1

Author information +

History +

Abstract

Fault quantitative evaluation of rolling bearings is a basis to realize their effective residual life prediction and condition-based maintenance.The present fault quantitative evaluation method based on distance and probabilistic similarity measure has disadvantages of complicated calculation and easy premature saturation, and is unfavorable to on-line monitoring application.In traditional time domain, the statistical index of root mean square (RMS) has advantages of simple to calculate and good consistency with fault development trend, and it is widely used in engineering practice.Unfortunately, RMS is insensitive to incipient faults.Here, a new fault quantitative evaluation index named shock value of selected frequency band (SVSB) was proposed.Firstly, the crest of envelope spectrum (EC) was used to optimally select the central frequency and band width parameters of Morlet wavelet filter to obtain the adaptive optimal filter and do filtering for the analyzed signals.SVSB was defined as the product of RMS and EC of the filtered signals, RMS and EC represented the signal’s total intensity within the filtered frequency band and the ratio of fault induced impact components to the former, respectively.Thus, SVSB revealed effectively the strength of cyclic impact fault in rolling bearings to quantitatively evaluate their impact fault level.The test data for artificially introduced faults and rolling bearings’ fatigue test data showed that SVSB can trace rolling bearings’ fault development trend; it is sensitive to incipient faults.

Key words

Fault severity assessment / rolling bearings / wavelet filter / resonance demodulation

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

ZHANG Long1,2,3, CHENG Junliang2,LI Xinglin3,YANG Shixi1. Fault quantitative evaluation of rolling bearings based on shock value of selected frequency band[J]. Journal of Vibration and Shock, 2018, 37(19): 30-38

References

[1] 刘尚坤，唐贵基，何玉灵. Teager能量算子结合MCKD的滚动轴承早期故障识别[J]. 振动与冲击. 2016, 35(15): 98-102.
LIU Shang-kun, TANG Gui-ji, HE Yu-ling. Incipient fault diagnosis of rolling bearings based on Teager energy operator and MCKD[J]. Journal of Vibration and Shock. 2016, 35(15): 98-102.
[2] 马新娜，杨绍普. 典型快速谱峭图算法的研究及应用[J]. 振动与冲击. 2016, 35(15): 109-114.
MA Xin-na, YANG Shao-pu. Typical fast kurtogram algorithm and its application[J]. Journal of Vibration and Shock. 2016, 35(15): 109-114.
[3] Ben Ali J, Chebel-Morello B, Saidi L, et al. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network[J]. Mechanical Systems and Signal Processing. 2015, 56-57: 150-172.
[4] 雷亚国，陈吴，李乃鹏，等. 自适应多核组合相关向量机预测方法及其在机械设备剩余寿命预测中的应用[J]. 机械工程学报. 2016, 52(1): 97-93.
LEI Ya-guo, CHEN Wu, LI Nai-peng, et al. A relevance vector machine prediction method based on adaptive multi-kernel combination and Its application to remaining useful life prediction of machinery[J]. Journal of Mechanical Engineering. 2016, 52(1): 97-93.
[5] 张小丽，陈雪峰，李兵，等. 机械重大装备寿命预测综述[J]. 机械工程学报. 2011, 47(11): 100-116.
ZHANG Xiao-li, CHEN Xue-feng, LI Bing, et al. Review of life rrediction for mechanical major equipments[J]. Journal of Mechanical Engineering. 2011, 47(11): 100-116.
[6] Caesarendra W, Widodo A, Yang B. Application of relevance vector machine and logistic regression for machine degradation assessment[J]. Mechanical Systems and Signal Processing. 2010, 24(4): 1161-1171.
[7] Honarvar F, Martin H R. New statistical moments for diagnostics of rolling element bearings[J]. Journal of Manufacturing Science and Engineering. 1997, 119: 425-432.
[8] Martin H R, Honarvar F. Application of statistical moments to bearing failure detection[J]. Applied Acoustics. 1995, 44: 67-77.
[9] Wang W. A model to predict the residual life of rolling element bearings given monitored condition information to date[J]. IMA Journal of Management Mathematics. 2002, 13(1): 3-16.
[10] Li Y, Zhang C, Kurfess T, et al. Diagnostics and prognostics of a single surface defect on roller bearings[J]. Proc. IMechE, Part C: Journal of Mechanical Engineering Science. 2000, 214(9): 1173-1185.
[11] 李力，唐茗. 滚动轴承故障程度诊断方法研究[J]. 轴承. 2009(04): 42-46.
LI Li, TANG Ming. Research on fault severity evaluation method for rolling bearings[J]. Bearing. 2009(04): 42-46.
[12] 王红军，张建民，徐小力. 基于支持向量机的机械系统状态组合预测模型研究[J]. 振动工程学报. 2006, 19(02): 242-245.
WANG Hong-jun, ZHANG Jian-min, XU Xiao-li. Study on combination trend prediction technology for mechaninery system based on SVM[J]. Journal of Vibration Engineering. 2006, 19(02): 242-245.
[13] Pan Y, Chen J, Guo L. Robust bearing performance degradation assessment method based on improved wavelet packet-support vector data description[J]. Mechanical Systems and Signal Processing. 2009, 23(3): 669-681.
[14] Yu J. Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models[J]. Mechanical Systems and Signal Processing. 2011, 25(7): 2573-2588.
[15] Ocak H, Loparo K A, Discenzo F M. Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics[J]. Journal of Sound and Vibration. 2007, 302(4–5): 951-961.
[16] 徐小力，殷健. 旋转机械趋势预测方法的研究[J]. 北京机械工业学院学报. 1999, 14(01): 1-5.
XU Xiao-li, YIN Jian. The method of the prediction of the rotating machine[J]. Journal of Beijing Institute of Machinery. 1999, 14(01): 1-5.
[17] Li Y, Billington S, Zhang C, et al. Adaptive prognostics for rolling element bearing condition[J]. Mechanical Systems and Signal Processing. 1999, 14(5): 747-762.
[18] Rubini R, Meneghetti U. Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings[J]. Mechanical Systems and Signal Processing. 2001, 15(2): 287-302.
[19] 张龙，熊国良，黄文艺. 复小波共振解调频带优化方法和新指标[J]. 机械工程学报. 2015, 51(3): 129-138.
ZHANG Long, XIONG Guo-liang, HUANG Wen-yi. New procedure and index for the parameter optimization of complex wavelet based resonance demodulation[J]. Journal of Mechanical Engineering. 2015, 51(3): 129-138.
[20] 张利群，朱利民，钟秉林. 几个机械状态监测特征量的特性研究[J]. 振动与冲击. 2001,20(01):20-21.
ZHANG Li-qun, ZHU Li-min, ZHONG Bing-lin. Study on the features of some specific parameters utilized for machine condition monitoring[J]. Journal of Vibration and Shock. 2001,20(01):20-21.
[21] Mcfadden P D, Smith J D. Vibration monitoring of rolling element bearings by the high-frequency resonance technique-a review[J]. Tribology International. 1984, 17(1): 3-10.
[22] Lee J, Qiu H, Yu G, et al. 'Bearing Data Set', IMS, University of Cincinnati. NASA Ames Prognostics Data Repository[Z]. 2007.
[23] 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(1): 1066-1090.