基于形态差值滤波和差分熵的滚动轴承故障诊断

中国机械工程 ›› 2015, Vol. 26 ›› Issue (1): 63-70.

基于形态差值滤波和差分熵的滚动轴承故障诊断

姜万录1,2;郑直1,2;朱勇1,2;李扬1,2

1.燕山大学河北省重型机械流体动力传输与控制重点实验室，秦皇岛，066004
2.先进锻压成形技术与科学教育部重点实验室（燕山大学），秦皇岛，066004

出版日期:2015-01-10 发布日期:2015-01-15
基金资助:
国家自然科学基金资助项目(51475405)；国家重点基础研究发展计划(973计划)资助项目(2014CB046405)；河北省自然科学基金资助项目(E2013203161)

Fault Diagnosis of Rolling Bearings Based on Morphological Difference Filter and Difference Entropy

Jiang Wanlu1,2;Zheng Zhi1,2;Zhu Yong1,2;Li Yang1,2

1.Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control,Yanshan University, Qinhuangdao, Hebei, 066004
2.Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University), Ministry of Education of China, Qinhuangdao, Hebei,066004

Online:2015-01-10 Published:2015-01-15
Supported by:
National Natural Science Foundation of China（No. 51475405）;The National Basic Research Program (973 Program) （No. 2014CB046405） ;Hebei Provincial Natural Science Foundation of China（No. E2013203161）

摘要/Abstract

摘要：

针对滚动轴承内外圈的早期故障，提出了一种新的诊断方法，该方法融合了数学形态学对非线性信号的滤波和信息熵理论在信号表征方面的优越性。首先，利用数学形态差值滤波器对实测的轴承内外圈轻重损伤的故障信号进行消噪处理，充分突出了有用的故障特征信息;然后，利用差分熵提取该信号中的突变特征信息，对其进行不确定性和复杂性度量;最后，根据突变点的冲击时间间隔和内外圈故障周期性冲击的时间间隔一致的思想来完成对滚动轴承的故障诊断。通过对仿真信号和滚动轴承实测内外圈两种故障程度的振动信号的诊断分析，证明该方法能够很好地识别轴承内外圈早期故障的类型，且具有很高的准确率。

关键词: 差值滤波器, 差分熵, 滚动轴承, 故障诊断

Abstract:

Concerning the early fault diagnosis of inner and outer race, a novel method was proposed. The method put the advantages of mathematical morphology filtering nonlinear signals and the advantages of information entropy theory characterizing signals together. Firstly, the measured slight and severe fault signals of inner and outer race faults were filtered by morphological difference filter to get more useful fault characteristic informations. Secondly, the abrupt informations of signals were extracted by difference entropy, and the uncertainty and complexity of abrupt information were measured by difference entropy. Lastly, the faults were diagnosed successfully based on the mind of the periodic time interval of abrupt point coincided with the periodic impulsive time interval of inner and outer race faults. The simulation signals and the two fault signals of the rolling bearing were tested and verified, the result is that the new method can diagnosis the early faults of inner and outer race with high accuracy.

Key words: difference filter, difference entropy; , rolling bearing;fault diagnosis

中图分类号:

TH137
TP277

姜万录, 郑直, 朱勇, 李扬, . 基于形态差值滤波和差分熵的滚动轴承故障诊断[J]. 中国机械工程, 2015, 26(1): 63-70.

Jiang Wanlu, Zheng Zhi, Zhu Yong, Li Yang, . Fault Diagnosis of Rolling Bearings Based on Morphological Difference Filter and Difference Entropy[J]. China Mechanical Engineering, 2015, 26(1): 63-70.

参考文献

[1]Wang Huaqing，Chen Peng. Intelligent Diagnosis Method for Rolling Element Bearing Faults Using Possibility Theory and Neural Network[J]. Computer & Industrial Engineering, 2011, 60(4): 511-518.
[2]Widodo A, Kimb E Y, Sonc Jong-Duk,et al. Fault Diagnosis of Low Speed Bearinging Based on Relevance Vector Machine and Support Vector Machine[J]. Expert Systems with Applications,2009, 36(3): 7252-7261.
[3]Wang Chun-Chieh, Kang Yuan, Shen Ping-Chen,et al. Application of Fault Diagnosis in Rotating Machinery by Using Time Series Analysis with Neural Network[J]. Expert Systems with Applications, 2010, 37(2): 1696-1702.
[4]Kankar P K，Sharma S C, Harsha S P. Fault Diagnosis of Ball Bearinging Using Machine Learning Methods[J]. Expert Systems with Applications, 2011, 38(3): 1876-1886.
[5]Li Hui， Zhang Yuping, Zheng Haiqi. Hilbert-Huang Transform and Marginal Spectrum for Detection and Diagnosis of Localized Defects in Roller Bearings[J]. Journal of Mechanical Science and Technology, 2009, 23(2): 291-301.
[6]Serra J. Image Analysis and Mathematical Morphology. Vol.1[M]. New York: Academic Press, 1982.
[7]Serra J.Image Analysis and Mathematical Morphology.Vol.2: Theoretical Advances[M].New York: Academic Press, 1988.
[8]Matagos P，Schafer R W. Morphological Filters-Part I: Their Set-theoretic Analysis and Relations to Liner Shift-invariant Filters[J].IEEE Transactions on Acoustics, Speech and Signal Processing, 2008, 22(3): 597-610.
[9]Matagos P，Schafer R W. Morphological Filters-Part II: Their Relations to Median, Order-statistic and Stack Filters[J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 2008, 22(3): 1170-1184.
[10]Li Bing,Zhang Peilin.Gear Fault Detection Using Multi-scale Morphological Filters[J].Measurement,2011,44(10):2078-2089.
[11]Shannon C E. A Mathematical Theory of Communication[J]. The Bell System Technical Journal,1984,27:597-610, 623-656.
[12]Dong Yabin,Liao Mingfu,Zhang Xiaolong, et al.Fault Diagnosis of Rolling Element Bearinging Based on Modified Morphological Method[J]. Mechanical Systems and Signal Processing, 2011, 25(4): 1276-1286.
[13]Zhang Lijun， Xu Jinwu, Yang Jianhong, et al. Multiscale Morphology Analysis and Its Application to Fault Diagnosis[J]. Mechanical Systems and Signal Processing, 2008, 22(3): 597-610.
[14]赵静，何正友，钱清泉. 利用广义形态滤波与差分熵的电能质量扰动检测[J].中国电机工程学报,2009, 29(7): 121-127.
Zhao Jing, He Zhengyou, Qian Qingquan. Detection of Power Quality Distribution Utilizing Generalized Morphological Filter and Difference-entropy[J].Proceeding of the CSEE, 2009, 29(7): 121-127.
[15]沈路，周晓军，张文斌，等. 形态解调在齿轮故障特征提取中的应用[J].浙江大学学报(工学版), 2010,44(8): 1514-1519.
Shen Lu, Zhou Xiaojun, Zhang Wenbin,et al. Application of Morphological Demodulation in Gear Fault Feature Extraction[J]. Journal of Zhejiang University(Engineering Science), 2010, 44(8): 1514-1519.
[16]Loparo K A.Bearing Vibration Data Set, Case Western Reserve University.http://csegroups.case.Edu/bearinging data center/home.