传动系统状态监测与故障诊断研究
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摘要
振动分析是进行传动系统的状态监测与故障诊断的重要手段。传动系统振动信号中常常含有大量噪声,这使得信号和噪声的频谱在频域内发生了重叠。本文旨在研究适于传动系统的振动信号处理方法。盲源分离(BSS)是目前信号处理中最热门的新兴技术之一,而将盲源分离技术应用于实际机械振动信号处理方面,还不多见。本文以传动系统故障诊断实验为基础,展开这方面的研究。
本文介绍了小波分析和盲源分离(BSS)的原理,研究了小波阈值去噪算法和基于负熵的快速固定点算法及其改进的牛顿迭代形式。在实际情况下,传动系统的振动信号受到噪声影响,本文提出了综合采用时延自相关和小波去噪的方法对信号进行预处理,再用改进的快速固定点算法进行信号分离,即“小波去噪-盲源分离-小波去噪”方法。之后,搭建了传动系统故障试验平台,获得了关于齿轮振动信号的丰富的数据。同时,对轴承故障数据进行了研究,得到了若干分析结果。通过分析验证了所研究的算法的有效性和适用性,为盲源分离技术在传动系统上的应用打下了基础,同时也为传动系统故障诊断提供了一个新的手段。
Vibration analysis has been widely used in the condition monitoring and fault diagnosis of the transmission system. Usually, there is much noise in the vibration signals of transmission system, which makes the spectra of signal and noise overlap in the frequency domain. So, this paper mainly aims at the research of vibration signals processing techniques that suitable for the transmission system. Now Blind Source Separation (BSS) is one of the hottest and most exciting topics in the fields of signals processing, but its real application on the mechanical vibration is few reported. This paper will do such research on the basis of transmission vibration experiment.
This paper deals with the research on wavelet analysis and BSS, and then obtains some related algorithms of wavelet de-noising, FastICA and its improved Newton iterations. As the real conditions of vibration signals are often corrupted by noise, a new method is proposed to select the delayed autocorrelation and wavelet de-noising as a signal preprocessing, and then using the improved FastICA to separate the preprocessed signals, which called“wavelet de-noising-BSS-wavelet de-noising”in this paper. After that, a faults experimental platform for the transmission system is established, and a lot of gear vibration signals are acquired with it. At the same time, a research on the bearing fault data is completed, and some useful analysis results are also obtained. By the experiments, this paper checks and proves the practicability and applicability of the new method. These experiments not only build a foundation for the real application of BSS but also provide a new method for fault diagnosis on the transmission system.
本文介绍了小波分析和盲源分离(BSS)的原理,研究了小波阈值去噪算法和基于负熵的快速固定点算法及其改进的牛顿迭代形式。在实际情况下,传动系统的振动信号受到噪声影响,本文提出了综合采用时延自相关和小波去噪的方法对信号进行预处理,再用改进的快速固定点算法进行信号分离,即“小波去噪-盲源分离-小波去噪”方法。之后,搭建了传动系统故障试验平台,获得了关于齿轮振动信号的丰富的数据。同时,对轴承故障数据进行了研究,得到了若干分析结果。通过分析验证了所研究的算法的有效性和适用性,为盲源分离技术在传动系统上的应用打下了基础,同时也为传动系统故障诊断提供了一个新的手段。
Vibration analysis has been widely used in the condition monitoring and fault diagnosis of the transmission system. Usually, there is much noise in the vibration signals of transmission system, which makes the spectra of signal and noise overlap in the frequency domain. So, this paper mainly aims at the research of vibration signals processing techniques that suitable for the transmission system. Now Blind Source Separation (BSS) is one of the hottest and most exciting topics in the fields of signals processing, but its real application on the mechanical vibration is few reported. This paper will do such research on the basis of transmission vibration experiment.
This paper deals with the research on wavelet analysis and BSS, and then obtains some related algorithms of wavelet de-noising, FastICA and its improved Newton iterations. As the real conditions of vibration signals are often corrupted by noise, a new method is proposed to select the delayed autocorrelation and wavelet de-noising as a signal preprocessing, and then using the improved FastICA to separate the preprocessed signals, which called“wavelet de-noising-BSS-wavelet de-noising”in this paper. After that, a faults experimental platform for the transmission system is established, and a lot of gear vibration signals are acquired with it. At the same time, a research on the bearing fault data is completed, and some useful analysis results are also obtained. By the experiments, this paper checks and proves the practicability and applicability of the new method. These experiments not only build a foundation for the real application of BSS but also provide a new method for fault diagnosis on the transmission system.
引文
[1]唐定国,陈立民.齿轮传动技术的现状和展望.机械工程学报,1993,20(3):35~41
[2]杨成云.齿轮传动系统耦合振动响应及抗冲击性能研究.[博士学位论文],重庆大学,2006
[3]马建仓,牛奕龙,陈海洋.盲信号处理.国防工业出版社,2006
[4]焦卫东,杨世锡,吴昭同.机械声源信号的带通滤波盲分离.振动工程学报,2003,16(3):344~348
[5]焦卫东,杨世锡,吴昭同.基于独立分量分析的噪声消除技术研究.浙江大学学报,2004,38(7):872~876
[6]吴军彪,陈进,伍星.基于盲源分离技术的故障特征信号分离方法.机械强度,2002,24(4):485~488
[7]张贤达,保铮.非平稳信号分析与处理.国防工业出版社,1998
[8] A.V.奥本海姆,R.W.谢弗.离散时间信号处理.科学出版社,1998
[9] Leon Cohen. Time-Varying Spectral Analysis—New Concepts and Unsolved Problems. Applied Signal Processing, 1996(3):2~12
[10] Stephane Mallat著.信号处理的小波导引,杨力华等译.机械工业出版社,2002
[11] W.J.Wang, P.D.McFadden. Application of Wavelets to Gearbox Vibration Signals for Fault Detection. Journal of Sound and Vibration, 1996(5):927~939
[12] Sun minggui, Sclabassi,Robert J. Enhancing Weak Signal Components in Time-Frequency Distributions by Wavelet Pre-Processings. IEEE International Conference on Acoustics, Speech and Signal Processing-Procedings 2 Jun 2000(5):673~676
[13]张善文,甄蜀春,赵兴录.基于Morlet小波的反辐射导弹告警技术探讨.航天电子对抗,2000(4):4~6
[14] L.科恩.时-频分布:理论与应用.西安交通大学出版社,2002
[15] J.Jeong, W.J.Williams. Alias-Free Generalized Discrete Time Time-Frequency Distributions. IEEE Transactions on Signal Processing, 1992(11):2757~2765
[16] G.T.Zheng, P.D.McFadden. A Time-Frequency Distribution for Analysis of Signals with Transient Components and Its Application to Vibration Analysis. Journal of Vibration andAcoustics, ASME Transactions, 1999(3):328~333
[17] Jutten C,Herault J.Blind separation of source Part:An adaptive algorithm based on neuromimetic architecture.Signal Processing, 1991,24:1~10
[18] Tong L, Liu R, Soon V C. Indeterminacy and identifiability of blind identification.IEEE Trans. On Circuits and systems, 1991,38(5):499~506
[19] Cardoso J-F, Souloumiac A. Blind beamforming for non-Gaussian signals.IEEE Proc.-F, 1993,140(6):362~370
[20] Comon P . Independent component analysis, a new concept?. Signal Processing, 1994,36:287~314
[21] Bell A J, Sejnowski T J. An information-maximization approach to blind separation and blind deconvolution. Neural computation, 1995,7:1129~1159
[22] Cardoso J F, B.-H.Laheld. Equivariant adaptive source separation. IEEE Trans.On Signal Processing, 1996,44(12):3017~3030
[23] Amari S, Cichocki A, Yang H H. A new learning algorithm for blind signal separation [A]. Advances in Neural Information Processing Systems [C]. Cambridge, MA:MIT Press, 1996,8:657~663
[24] Aapo Hyv?rinen, Oja E. A fixed-point algorithm for independent component analysis. Neural Computation, 1997,9(7):1483~1492
[25] Aapo Hyv?rinen. A Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. On Neural Networks, 1999,10(3):626~634
[26] Stone JV. Blind source separation using temporal predictability. Neural Computation, 2001,13(7):1559~1574
[27] Pando Georgiev, Fabian Theis, Andrzej Cichocki. Sparse component analysis and blind source separation of underdetermined mixtures. IEEE Trans On Neural Networks, 2005.16(4):992~996
[28]凌燮亭.近场宽带信号源的盲分离.电子学报,1996,24(7):87~92
[29]汪军,何振亚.瞬时混叠信号盲分离.电子学报,1997,25(4):1~5
[30]何振亚,刘琚,杨绿溪等.盲均衡和信道参数估计的一种ICA和进化计算方法.中国科学(E辑),2000,30(1):1~7
[31]刘琚,聂开宝,何振亚.线性混迭信号中独立源的盲提取.应用科学学报,2001,19(3):24~29
[32]林秋华,殷福亮.盲源分离自适应算法的统一形式.大连理工大学学报,2002(4):34~39
[33]张智林,皮亦鸣.基于独立分量分析的降噪技术.电子科技大学学报,2005(3):20~25
[34] http://www.cs.helsinki.fi/u/ahyvarin/
[35] http://www.salk.edu/faculty/faculty/details.php?id=48
[36] http://www.cis.hut.fi/aapo/papers/IJCNN99_tutorialweb/
[37] http://www.brain.riken.jp/labs/mns/amari/home-E.html
[38] Aapo Hyv?rinen. Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood Neurocomputing, 1998(22):49~67
[39] A. Cichocki, S.C. Douglas, S. Amari. Robust techniques for independent component analysis with noisy data. Neurocomputing, 1998(22):113~129
[40]邹原,万明习,牛海军,等.嗓音源谐波噪声独立分量分离估计方法.声学学报,2001.26(6):521~526
[41]焦卫东,杨世锡,钱苏翔,等.乘性噪声消除的同态变换盲源分离算法.浙江大学学报(工学版),2006.40(4):581~584
[42]廖伯瑜.机械故障诊断基础.冶金工业出版社,1995
[43]盛兆顺,尹琦玲.设备状态监测与故障诊断技术及应用.化学工业出版社,2003
[44] W.J.Wang, P.D.McFadden. Decomposition of Gear Motion Signals and Its Application to Gearbox Diagnostics. Journal of Sound and Vibration, 1995,Vol.117:363~369
[45]丁康,李巍华,朱小勇.齿轮及齿轮箱故障诊断实用技术.机械工业出版社,2005
[46] A. YPMA. Learning Methods for Machine Vibration Analysis and Health Monitoring.ter verkrijging van de graad can doctor aan de techinische Universiteit Delft, 2001
[47]梅宏斌.滚动轴承振动监测与诊断理论、方法、系统.机械工业出版社,1995
[48] http://www.eecs.case.edu/laboratory/bearing/
[49] Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans, Pattern Anal. 1989(11):674~693
[50]崔锦泰.小波分析导论.程正兴译.西安:西安交通大学出版社,1995
[51]孙延奎.小波分析及其应用.北京:机械工业出版社,2005
[52] Donoho. D. L. De-noising by soft-thresholding.IEEE Trans. Inform Theory.1995,41(3): 613~627
[53]潘泉,张磊,孟晋丽.小波滤波方法及应用.北京:清华大学出版社,2005
[54]飞思科技产品研发中心.小波分析理论与MATLAB7实现.北京:电子工业出版社,2005
[55] Aapo Hyv?rinen, Karhunen J, Oja E. Independent component analysis. New York:John Wiley & Sons, Inc, 2001:293~304
[56]刘琚,何振亚.盲源分离和盲反卷积.电子学报,2002,30(4):570~576
[57]杨行峻、郑君里.人工神经网络与盲信号处理.北京:清华大学出版社,2003
[58]邱天爽,张旭秀,李小兵,等.统计信号处理—非高斯信号处理及其应用.北京:电子工业出版社,2004
[59]杨福生,洪波.独立分量分析的原理与应用.北京:清华大学出版社,2006
[60] T.M.Cover, J.A.Thomas. Elements of information theory. New York:John Wiley&Sons, LTD,1991
[61] Aapo Hyv?rinen, E.Oja.Independent component analysis:algorithms and applications.Neural Networks,2000(13):411~430
[62] Aapo Hyv?rinen, Karhunen J, Oja E著.独立成分分析,周宗潭,董国华,徐昕等译.北京:电子工业出版社,2007
[63]张荣,薛国民.修正的三次收敛的牛顿迭代法.大学数学,2005,21(1):80~82
[64]孟涛.齿轮与滚动轴承故障的振动分析与诊断.[博士学位论文],西北工业大学,2006
[65] J.-F. Cardoso. Higher order contrast for independent component analysis. Neural Computation, 1999,11(1):157~193
[2]杨成云.齿轮传动系统耦合振动响应及抗冲击性能研究.[博士学位论文],重庆大学,2006
[3]马建仓,牛奕龙,陈海洋.盲信号处理.国防工业出版社,2006
[4]焦卫东,杨世锡,吴昭同.机械声源信号的带通滤波盲分离.振动工程学报,2003,16(3):344~348
[5]焦卫东,杨世锡,吴昭同.基于独立分量分析的噪声消除技术研究.浙江大学学报,2004,38(7):872~876
[6]吴军彪,陈进,伍星.基于盲源分离技术的故障特征信号分离方法.机械强度,2002,24(4):485~488
[7]张贤达,保铮.非平稳信号分析与处理.国防工业出版社,1998
[8] A.V.奥本海姆,R.W.谢弗.离散时间信号处理.科学出版社,1998
[9] Leon Cohen. Time-Varying Spectral Analysis—New Concepts and Unsolved Problems. Applied Signal Processing, 1996(3):2~12
[10] Stephane Mallat著.信号处理的小波导引,杨力华等译.机械工业出版社,2002
[11] W.J.Wang, P.D.McFadden. Application of Wavelets to Gearbox Vibration Signals for Fault Detection. Journal of Sound and Vibration, 1996(5):927~939
[12] Sun minggui, Sclabassi,Robert J. Enhancing Weak Signal Components in Time-Frequency Distributions by Wavelet Pre-Processings. IEEE International Conference on Acoustics, Speech and Signal Processing-Procedings 2 Jun 2000(5):673~676
[13]张善文,甄蜀春,赵兴录.基于Morlet小波的反辐射导弹告警技术探讨.航天电子对抗,2000(4):4~6
[14] L.科恩.时-频分布:理论与应用.西安交通大学出版社,2002
[15] J.Jeong, W.J.Williams. Alias-Free Generalized Discrete Time Time-Frequency Distributions. IEEE Transactions on Signal Processing, 1992(11):2757~2765
[16] G.T.Zheng, P.D.McFadden. A Time-Frequency Distribution for Analysis of Signals with Transient Components and Its Application to Vibration Analysis. Journal of Vibration andAcoustics, ASME Transactions, 1999(3):328~333
[17] Jutten C,Herault J.Blind separation of source Part:An adaptive algorithm based on neuromimetic architecture.Signal Processing, 1991,24:1~10
[18] Tong L, Liu R, Soon V C. Indeterminacy and identifiability of blind identification.IEEE Trans. On Circuits and systems, 1991,38(5):499~506
[19] Cardoso J-F, Souloumiac A. Blind beamforming for non-Gaussian signals.IEEE Proc.-F, 1993,140(6):362~370
[20] Comon P . Independent component analysis, a new concept?. Signal Processing, 1994,36:287~314
[21] Bell A J, Sejnowski T J. An information-maximization approach to blind separation and blind deconvolution. Neural computation, 1995,7:1129~1159
[22] Cardoso J F, B.-H.Laheld. Equivariant adaptive source separation. IEEE Trans.On Signal Processing, 1996,44(12):3017~3030
[23] Amari S, Cichocki A, Yang H H. A new learning algorithm for blind signal separation [A]. Advances in Neural Information Processing Systems [C]. Cambridge, MA:MIT Press, 1996,8:657~663
[24] Aapo Hyv?rinen, Oja E. A fixed-point algorithm for independent component analysis. Neural Computation, 1997,9(7):1483~1492
[25] Aapo Hyv?rinen. A Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. On Neural Networks, 1999,10(3):626~634
[26] Stone JV. Blind source separation using temporal predictability. Neural Computation, 2001,13(7):1559~1574
[27] Pando Georgiev, Fabian Theis, Andrzej Cichocki. Sparse component analysis and blind source separation of underdetermined mixtures. IEEE Trans On Neural Networks, 2005.16(4):992~996
[28]凌燮亭.近场宽带信号源的盲分离.电子学报,1996,24(7):87~92
[29]汪军,何振亚.瞬时混叠信号盲分离.电子学报,1997,25(4):1~5
[30]何振亚,刘琚,杨绿溪等.盲均衡和信道参数估计的一种ICA和进化计算方法.中国科学(E辑),2000,30(1):1~7
[31]刘琚,聂开宝,何振亚.线性混迭信号中独立源的盲提取.应用科学学报,2001,19(3):24~29
[32]林秋华,殷福亮.盲源分离自适应算法的统一形式.大连理工大学学报,2002(4):34~39
[33]张智林,皮亦鸣.基于独立分量分析的降噪技术.电子科技大学学报,2005(3):20~25
[34] http://www.cs.helsinki.fi/u/ahyvarin/
[35] http://www.salk.edu/faculty/faculty/details.php?id=48
[36] http://www.cis.hut.fi/aapo/papers/IJCNN99_tutorialweb/
[37] http://www.brain.riken.jp/labs/mns/amari/home-E.html
[38] Aapo Hyv?rinen. Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood Neurocomputing, 1998(22):49~67
[39] A. Cichocki, S.C. Douglas, S. Amari. Robust techniques for independent component analysis with noisy data. Neurocomputing, 1998(22):113~129
[40]邹原,万明习,牛海军,等.嗓音源谐波噪声独立分量分离估计方法.声学学报,2001.26(6):521~526
[41]焦卫东,杨世锡,钱苏翔,等.乘性噪声消除的同态变换盲源分离算法.浙江大学学报(工学版),2006.40(4):581~584
[42]廖伯瑜.机械故障诊断基础.冶金工业出版社,1995
[43]盛兆顺,尹琦玲.设备状态监测与故障诊断技术及应用.化学工业出版社,2003
[44] W.J.Wang, P.D.McFadden. Decomposition of Gear Motion Signals and Its Application to Gearbox Diagnostics. Journal of Sound and Vibration, 1995,Vol.117:363~369
[45]丁康,李巍华,朱小勇.齿轮及齿轮箱故障诊断实用技术.机械工业出版社,2005
[46] A. YPMA. Learning Methods for Machine Vibration Analysis and Health Monitoring.ter verkrijging van de graad can doctor aan de techinische Universiteit Delft, 2001
[47]梅宏斌.滚动轴承振动监测与诊断理论、方法、系统.机械工业出版社,1995
[48] http://www.eecs.case.edu/laboratory/bearing/
[49] Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans, Pattern Anal. 1989(11):674~693
[50]崔锦泰.小波分析导论.程正兴译.西安:西安交通大学出版社,1995
[51]孙延奎.小波分析及其应用.北京:机械工业出版社,2005
[52] Donoho. D. L. De-noising by soft-thresholding.IEEE Trans. Inform Theory.1995,41(3): 613~627
[53]潘泉,张磊,孟晋丽.小波滤波方法及应用.北京:清华大学出版社,2005
[54]飞思科技产品研发中心.小波分析理论与MATLAB7实现.北京:电子工业出版社,2005
[55] Aapo Hyv?rinen, Karhunen J, Oja E. Independent component analysis. New York:John Wiley & Sons, Inc, 2001:293~304
[56]刘琚,何振亚.盲源分离和盲反卷积.电子学报,2002,30(4):570~576
[57]杨行峻、郑君里.人工神经网络与盲信号处理.北京:清华大学出版社,2003
[58]邱天爽,张旭秀,李小兵,等.统计信号处理—非高斯信号处理及其应用.北京:电子工业出版社,2004
[59]杨福生,洪波.独立分量分析的原理与应用.北京:清华大学出版社,2006
[60] T.M.Cover, J.A.Thomas. Elements of information theory. New York:John Wiley&Sons, LTD,1991
[61] Aapo Hyv?rinen, E.Oja.Independent component analysis:algorithms and applications.Neural Networks,2000(13):411~430
[62] Aapo Hyv?rinen, Karhunen J, Oja E著.独立成分分析,周宗潭,董国华,徐昕等译.北京:电子工业出版社,2007
[63]张荣,薛国民.修正的三次收敛的牛顿迭代法.大学数学,2005,21(1):80~82
[64]孟涛.齿轮与滚动轴承故障的振动分析与诊断.[博士学位论文],西北工业大学,2006
[65] J.-F. Cardoso. Higher order contrast for independent component analysis. Neural Computation, 1999,11(1):157~193