特征矢量优化的滚动轴承故障诊断

张锐戈; 谭永红

doi:10.13433/j.cnki.1003-8728.2014.0617

特征矢量优化的滚动轴承故障诊断

doi: 10.13433/j.cnki.1003-8728.2014.0617

张锐戈¹,
谭永红²

1.
西安电子科技大学,电子工程学院西安 710071;
2.
上海师范大学上海 200235

基金项目:

国家自然科学基金项目(61171088)

福建省教育厅A类科技计划项目(JA12303)

福建省科技厅重点科技计划项目(2013N0032)资助

详细信息

作者简介:
张锐戈(1977-)，副教授，博士研究生，研究方向为轴承故障诊断和智能信号处理，ruig_zhang@126.com

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- 文章访问数: 126
- HTML全文浏览量: 25
- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2013-01-13
- 刊出日期: 2015-06-10

Application of the Optimized Feature Vectors for Fault Diagnosis of rolling Element Bearings

Zhang ruige¹,
Tan Yonghong²

1.
School of Electronic Engineering,Xidian University,Xi'an 710071;
2.
Shanghai Normal University,Shanghai 200235

摘要

摘要: 为提取小波包频带中的有效故障信息,基于Fisher线性测度提出一种新的特征矢量优化方法。轴承振动信号经小波包分解后,各子频带数据片段的能量值作为参数构建特征矢量。使用差异性和相似性优化相结合方法,分别选出不同轴承状态下Fisher距离较大的小波包频带,以及同种轴承状态下Fisher距离最小的频带,提取出易于区分不同轴承状态的故障信息。故障辨识使用连续型隐马尔可夫模型,在3种故障程度下实现了轴承正常状态、滚动体故障、内圈和外圈故障的有效判别,辨识精度大于94%。比较实验表明文中方法的辨识精度优于文献方法。
- 特征矢量优化 /
- 小波包分解 /
- 隐马尔可夫模型 /
- 故障诊断
Abstract: A new approach of feature vector optimization for extracting the effective fault information was presentedusing Fisher linear distance. Firstly, the vibration signals were decomposed into the sub-bands with the waveletpacket transform, and the energy of which was used to construct the feature vectors. Then, the methods ofdifference and similarity optimization were applied to select the sub-bands which have greater Fisher distancebetween the different bearing statuses, and meantime has the minimal Fisher distance within the same bearingstatus. The fault identification applied the continuous hidden Markov models, which successfully identified normalstatus, ball fault, inner race fault and outer race fault in three kinds of fault severities, and the identificationaccuracy was greater than 94%. The result of compared experiments showed the identification accuracy of thepresented method was better than the reference method.
- eigenvalues and eigenfunctions /
- entropy /
- failure analysis /
- fault detection

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参考文献(16)

[1]	何庆飞,姚春江,陈桂明,等．基于改进小波包奇异值法的齿轮泵振动信号去噪[J]. 机械科学与技术，2012,31 (9): 1445-1448He Q F,Yao C J,Chen G M,et al．De-noising of gear pump vibration signal based on improved wavelet packet singular value decompositon[J]. Mechanical Science and Technology for Aerospace Engineering,2012,31(9): 1445-1448 (in Chinese)
[2]	李辉,郑海起,唐力伟．基于双树复小波包峭度图的轴承故障诊断研究[J]. 振动与冲击,2012,31 (10):13-18Li H,Zheng H Q,Tang L W．Bearing fault diagnosis based on kurtogram of duan-tree complex wavelet packet transform[J]. Vibration and Shock,2012,31 (10): 13-18 (in Chinese)
[3]	Pan Y,Chen J,Li X．Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means[J]. Mechanical Systems and Signal Processing,2010,24(2): 559-566
[4]	赵志宏,杨绍普．基于小波包变换与样本熵的滚动轴承故障诊断[J]. 振动·测试与诊断,2012,32 (4):640-644Zhao Z H,Yang S P．roller bearing fault diagnosis based on wavelet packet transform and sample entropy[J]. Journal of Vibration,Measurement & Diagnosis,2012，32(4): 640-644 (in Chinese)
[5]	李宏坤,赵长生,周帅,等．基于小波包-坐标变换的滚动轴承故障特征增强方法[J]. 机械工程学报,2011，47(19): 74-80Li H K,Zhao C S,Zhou S,et al．Fault feature enhancement method for rolling bearing based on wavelet packet-coordinate transformation [J]. Journal of Mechanical Engineering,2011,47 (19): 74-80 (in Chinese)
[6]	苏文胜,王奉涛,朱泓,等．基于小波包样本熵的滚动轴承故障特征提取[J]. 振动．测试与诊断,2011,32(2): 162-166Su W S,Wang F T,Zhu H,et al．Feature extraction of rolling element bearing fault using wavelet packet sample entropy [J]. Journal of Vibration,Measurement &Diagnosis,2011,31 (2): 162-166 (in Chinese)
[7]	肖文斌,陈进,周宇,等．小波包变换和隐马尔可夫模型在轴承性能退化评估中的应用[J]. 振动与冲击，2011,30(8): 32-35Xiao W B,Chen J,Zhou Y,et al．Wavelet packet transform and hidden Markov model based bearing performance degradation assessment[J]. Vibration and Shock,2011,30(8): 32-35 (in Chinese)
[8]	Yan r,Gao r X．An efficient approach to machine health diagnosis based on harmonic wavelet packet transform [J]. robotics and Computer-Integrated Manufacturing,2005,21 (4-5): 291-301
[9]	Huang Y,Liu C,Zha X F,et al．A lean model for performance assessment of machinery using second generation wavelet packet transform and Fisher criterion[J].Expert Systems with Applications,2010,37 (5):3815-3822
[10]	Altmann J,Mathew J．Multiple band-pass autoregressive demonulation for rolling-element bearing fault diagnosis[J].Mechanical Systems and Signal Processing,2001,15(5): 963-977
[11]	徐增丙,轩建平,史铁林,等．基于小波灰度矩向量与连续马尔可夫模型的轴承故障诊断[J]. 中国机械工程,2008,19(15): 1858-1862 Xu Z B,Xuan J P,Shi T L,et al．Fault diagnosis of bearings based on wavelet gray moment vector and CHMM[J]. China Mechanical Engineering,2008,19(15): 1858-1862 (in Chinese)
[12]	曾庆虎,刘冠军,邱静．基于小波相关特征尺度熵的预测特征信息提取方法研究[J]. 中国机械工程，2008,19(10): 1193-1196Zeng Q H,Liu G J,Qiu J．research on approach of prognostics feature informantion extraction based on wavelet correlation feature scale entropy [J]. China Mechanical Engineering,2008,19 (10): 1193-1196 (in Chinese)
[13]	胡海峰,安茂春,秦国军,等．基于隐半 Markov 模型的故障诊断和故障预测方法研究[J]. 兵工学报，2009,30(1): 69-75Hu H F,An M C,Qin G J,et al．Study on fault diangosis and prognosis methods based on hidden semi Markov model[J]. Acta Armamentarii,2009,30 (1):69-75 (in Chinese)
[14]	陶新民,徐晶,杜宝祥,等．基于小波域隐马尔可夫模型故障诊断方法[J]. 振动与冲击,2009,28(4): 33-37Tao X M,Xu J,Du B X,et al．Bearing fault diagnosis using wavelet-domain hidden Markov model [J].Vibration and Shock,2009,28(4): 33-37 (in Chinese)
[15]	张锐戈,谭永红．基于最优 Morlet 小波和隐马尔可夫模型的轴承故障诊断[J]. 振动与冲击,2012,31(12): 5-8 Zhang r G,Tan Y H．Fault diagnosis of rolling element bearings based on optimal Morlet wavelet and hidden Markov model[J]. Vibration and Shock,2012,31 (12): 5-8 (in Chinese)
[16]	rabiner L r．A tutorial on hidden Markov models and selected applications in speech recognition [J].readings in Speech recognition,1990,53(3): 267-296