嵌入式轴承故障诊断算法的研究
|
摘要
滚动轴承是旋转机械中使用最多、最为关键,同时也是最易被损坏的机械零件之一。轴承的好坏对机器工作状况的影响极大,许多旋转机械的故障都与滚动轴承的状态有关,所以对轴承进行状态检测与故障诊断是十分必要的。
本文在对轴承振动检测以及故障诊断技术的发展、现状和趋势有较全面了解的基础上,提出了共振解调法和小波多尺度分析的方法,从滚动轴承振动信号中获取故障特征信息,有效地实现了滚动轴承的故障诊断。
本论文主要完成的工作如下:
论文总结分析了滚动轴承典型故障的机理及其振动类型,详细介绍了滚动轴承振动诊断方法;简要介绍了滚动轴承的简易诊断方法——时域特征参数法,对该方法中的信号预处理部分和时域特征指标作了简要的分析,并用该方法对故障轴承进行了简易的检测。
论文还介绍了目前轴承诊断中较为成熟的共振解调法,详细介绍了该方法中的三个最重要的环节:滤波器的设计、包络解调和频谱细化,给出了该方法基于matlab的仿真和基于DSP的实现。经过对大量实测数据的处理和分析,证明该诊断方法的诊断结果是令人满意的。
最后,本文简要地介绍了小波的基本理论,针对共振解调法中因不同类型轴承故障特征频率的不同,需要相应地设计不同中心频率的带通滤波器这一问题,为了简化程序,提出了基于小波多分辨分析和信号重构技术的包络分析法。该方法通过采用不同的小波尺度因子,实现了具有不同中心频率和带宽的带通滤波器,可对振动信号在不同的频率范围内进行分析,对轴承这类非平稳信号是一种很好的处理方法。本文给出了该方法在Matlab上的仿真,详细地介绍了Mallat算法在DSP上的实现,并将该方法应用于实际的嵌入式轴承检测系统上,取得了较共振解调法更好的诊断结果。
Roller bearings are one of most used and important mechanical parts in rotating machines, and vnlnerable to damage. Many faults of rotating mechanism are related to the state of roller bearings. The performance of roller bearings directly affects the performance of the whole machine. Thus, developing state testing and fault diagnosis of roller bearings is necessary.Based on the understangding of bearing vibration test technology and bearing fault analysis theory, this thesis puts forword the the rolling bearing fault diagnosis method of the resonance demodulation and wavelet analysis. In these methods, characteristic information for faults is gained from rolling bearing vibration signals. By using these methods, valid results are obtained.This thesis analyzes the fault principle and the fault vibration types. The means of the fault diagnosis of the rolling bearing are also introduced in detail. This thesis introduces the method of the fault diagnosis of the rolling bearing baesd on some character parameters. In this method, the part of pretreatment and character parameters in domain are introduced briefly. We give a simple bearing test by this method.Then, this thesis presents the method of resonance demodulation, which includes the design of filter, envelop demodulation and spectrum zooming. This thesis gives the simulation of the method based on matlab and the realization on DSP. Throuth processing and analysis of many data, the diagnosis result is satisfactory.Lastly, we know that different types of bearings have different character frequencies, so in the method of resonance demodulation, it is needed to design different band-pass filters. In order to predigest the program, an envelope analysis approach based on wavelet multiresolution analysis and reconstruction is proposed. This method adopts different wavelet genes, so it can carry out different band-pass filters which have different center frequency and bandwidth. This method is very applicable in nonstationary vibration signals, such as the bearing signals. This thesis introduces the basic theory of wavelet, and gives the simulation of this method based on matlab and presents the realization of the method on DSP. This method is applied into the embedded bearing fault diagnosis system, and gets better diagnosis result than the method of resonance demodulation.
本文在对轴承振动检测以及故障诊断技术的发展、现状和趋势有较全面了解的基础上,提出了共振解调法和小波多尺度分析的方法,从滚动轴承振动信号中获取故障特征信息,有效地实现了滚动轴承的故障诊断。
本论文主要完成的工作如下:
论文总结分析了滚动轴承典型故障的机理及其振动类型,详细介绍了滚动轴承振动诊断方法;简要介绍了滚动轴承的简易诊断方法——时域特征参数法,对该方法中的信号预处理部分和时域特征指标作了简要的分析,并用该方法对故障轴承进行了简易的检测。
论文还介绍了目前轴承诊断中较为成熟的共振解调法,详细介绍了该方法中的三个最重要的环节:滤波器的设计、包络解调和频谱细化,给出了该方法基于matlab的仿真和基于DSP的实现。经过对大量实测数据的处理和分析,证明该诊断方法的诊断结果是令人满意的。
最后,本文简要地介绍了小波的基本理论,针对共振解调法中因不同类型轴承故障特征频率的不同,需要相应地设计不同中心频率的带通滤波器这一问题,为了简化程序,提出了基于小波多分辨分析和信号重构技术的包络分析法。该方法通过采用不同的小波尺度因子,实现了具有不同中心频率和带宽的带通滤波器,可对振动信号在不同的频率范围内进行分析,对轴承这类非平稳信号是一种很好的处理方法。本文给出了该方法在Matlab上的仿真,详细地介绍了Mallat算法在DSP上的实现,并将该方法应用于实际的嵌入式轴承检测系统上,取得了较共振解调法更好的诊断结果。
Roller bearings are one of most used and important mechanical parts in rotating machines, and vnlnerable to damage. Many faults of rotating mechanism are related to the state of roller bearings. The performance of roller bearings directly affects the performance of the whole machine. Thus, developing state testing and fault diagnosis of roller bearings is necessary.Based on the understangding of bearing vibration test technology and bearing fault analysis theory, this thesis puts forword the the rolling bearing fault diagnosis method of the resonance demodulation and wavelet analysis. In these methods, characteristic information for faults is gained from rolling bearing vibration signals. By using these methods, valid results are obtained.This thesis analyzes the fault principle and the fault vibration types. The means of the fault diagnosis of the rolling bearing are also introduced in detail. This thesis introduces the method of the fault diagnosis of the rolling bearing baesd on some character parameters. In this method, the part of pretreatment and character parameters in domain are introduced briefly. We give a simple bearing test by this method.Then, this thesis presents the method of resonance demodulation, which includes the design of filter, envelop demodulation and spectrum zooming. This thesis gives the simulation of the method based on matlab and the realization on DSP. Throuth processing and analysis of many data, the diagnosis result is satisfactory.Lastly, we know that different types of bearings have different character frequencies, so in the method of resonance demodulation, it is needed to design different band-pass filters. In order to predigest the program, an envelope analysis approach based on wavelet multiresolution analysis and reconstruction is proposed. This method adopts different wavelet genes, so it can carry out different band-pass filters which have different center frequency and bandwidth. This method is very applicable in nonstationary vibration signals, such as the bearing signals. This thesis introduces the basic theory of wavelet, and gives the simulation of this method based on matlab and presents the realization of the method on DSP. This method is applied into the embedded bearing fault diagnosis system, and gets better diagnosis result than the method of resonance demodulation.
引文
[01] 万良红,基于小波分析的滚动轴承故障诊断方法研究,轴承,2004,1:23～26
[02] 王军,滚动轴承故障诊断,中国高教论坛,2002,2:27～28
[03] 程军圣,基于小波分析的滚动轴承故障诊断研究,湖南大学硕士学位论文,2000,3:1
[04] 姜小荧等,基于小波变换的水下运动物体回波信号处理,2004年大连国际海事技术交流会论文集,2004,1:342～345
[05] 陆春月等,机械故障诊断的现状与发展趋势,2004,12:85～86
[06] 崔宝珍,基于小波分析的滚动轴承故障诊断方法研究与应用,中北大学硕士学位论文,2002,2:2～3
[07] 何晓霞等,连续小波变换在滚动轴承故障诊断中的应用,机械科学与技术,2000,4:571～575
[08] 林京,机械动态信号的小波处理技术,西安交通大学博士学位论文,1995,5:5～7
[09] 徐金梧等,小波变换在滚动轴承故障诊断中的应用,机械工程学报,1997,33:50～55
[10] 任国全等,基于小波分析的轴承故障诊断研究,河北省科学院学报,2002,2:112～116
[11] 张伟等,西北工业大学硕士学位论文,基于DSP的视频压缩算法的实现,2004,3:25
[12] 雷继尧等,轴承故障诊断,西安交通大学出版社,1991
[13] 任锐,滚动轴承的振动信号分析与故障诊断的研究,燕山大学硕士学位论文,2002,7～8
[14] 周晓凯等,用小波分析铁路车辆滚动轴承诊断方法,1996,8:29～33
[15] 梅宏斌,滚动轴承振动监测与诊断理论·方法·系统,北京:机械工业出版社,1996
[16] 寇惠等,故障诊断中的振动信号处理,北京:冶金工业出版社,1989
[17] 张阿舟,振动数字信号处理程序库,科学技术出版社,1988
[18] 张中民等,滚动轴承故障振动模型及其应用研究,华中理工大学学报,1997,3:2～4
[19] 张国远等,滚动轴承的自动监测与故障诊断系统,轴承,2005,1:32～34
[20] 杨建奎等,基于软件共振解调分析的滚动轴承故障诊断,煤矿机械,2004,10:139～131
[21] 刘亚娟等,基于软件共振解调分析的滚动轴承故障诊断系统开发,仪器仪表与检测技术,2003,11:64～66
[22] 程佩青,数字信号处理教程,北京:清华大学出版社,2001
[23] 朱晓光等,信号包络在DSP系统中的实现,华北电力大学学报,2004,2:91～94
[24] 王敏梁等,关于振动加速度信号幅值解调的五种方法,振动与冲击,2000,1:32～33
[25] 王志刚等,FFT—FS频谱细化技术及其在机械故障诊断中的应用,2000,23(1):44～46
[26] 周霖,算法设计与系统方案,北京:国防工业出版社 2004
[27] 张中民等,基于小波系数包络谱的滚动轴承故障诊断,振动工程学报,1998,1:66～68
[28] 方涛等,小波分析在铁路货车滚动轴承故障诊断中的应用,2005,2:13～14
[29] 董长虹等,小波分析工具箱原理与应用,北京:国防工业出版社,2004
[30] 刘珺等,基于神经网络对轴承故障的智能诊断,2004,8:68～71
[31] 庄哲民等,基于神经网络的滚动轴承检测,2000,2:72～74
[32] 王太勇等,一种改进BP算法及其在滚动轴承故障诊断中的应用,2001,12:1179～1181
[33] 林凤涛等,小波分析在滚动轴承故障诊断中的应用,2004,10:120～122
[34] 孙立宁等,小波变换在数字滤波中的实现,2004,3:33～35
[35] 赵红怡等,基于扩频技术的频谱细化分析方法研究,2002,9:53～55
[36] Peng Tao, Gui Weihua, Wu Min, Xie Yong, Tang Zhaohui;"Fault diagnosis based on intelligent information processing technology". TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Con~nunications, Control and Power Engineering, Volume: 3, 28-31 Oct. 2002 Pages:1708-1712 vol. 3
[37] Xiaoqin Wen, Zhiyi Sun;"Fault diagnosis approach based on fuzzy neural networks and its application". Control and Automation, 2002. ICCA. Final Program and Book of Abstracts. The 2002 International Conference on, June 16-19, 2002 Pages:217～218
[38] J. Shiroishi, Y. Li, S. Liang, S. Danyluk, T. Kurfess;"Vibration Analysis for Bearing Outer Race Condition Diagnostics". Journal of the Brazilian Society of Mechanical Sciences, vol.21 no.3 Rio de Janeiro Sept. 1999
[39] Volder E, Martin H R. Application of Kutosis to Damage Mapping. In: Proceedings of the Fourth International Model Analysis Conference, Los Angeles, Society for Experiment Mechanics, 1986, 629～633
[40] Naoto SASAOKA, Yoshio ITOH, Kensaku, FUJlI;"ANoise Reduetion Methodfor Non-sta Tionary Noise Based on Noise Reconstruction System with ALE". IEICETRANS. FUNDAMENTALS, VOL. E88-A, NO.2 FEBRUARY, 2005 Pages:593～596
[41] 刘益成,TMS320C54X DSP应用程序设计与开发,2002,北京:北京航天航空大学出版社
[42] 张正松等,旋转机械振动监测及故障诊断,1991,北京:机械工业出版社
[43] 侯朝焕等,实用FFT信号处理技术1990,北京:海洋出版社
[44] 吴湘淇,信号、系统与信号处理,1999,北京:电子工业出版社
[45] 黄大卫,数字滤波器1991,北京:中国铁道出版社
[02] 王军,滚动轴承故障诊断,中国高教论坛,2002,2:27～28
[03] 程军圣,基于小波分析的滚动轴承故障诊断研究,湖南大学硕士学位论文,2000,3:1
[04] 姜小荧等,基于小波变换的水下运动物体回波信号处理,2004年大连国际海事技术交流会论文集,2004,1:342～345
[05] 陆春月等,机械故障诊断的现状与发展趋势,2004,12:85～86
[06] 崔宝珍,基于小波分析的滚动轴承故障诊断方法研究与应用,中北大学硕士学位论文,2002,2:2～3
[07] 何晓霞等,连续小波变换在滚动轴承故障诊断中的应用,机械科学与技术,2000,4:571～575
[08] 林京,机械动态信号的小波处理技术,西安交通大学博士学位论文,1995,5:5～7
[09] 徐金梧等,小波变换在滚动轴承故障诊断中的应用,机械工程学报,1997,33:50～55
[10] 任国全等,基于小波分析的轴承故障诊断研究,河北省科学院学报,2002,2:112～116
[11] 张伟等,西北工业大学硕士学位论文,基于DSP的视频压缩算法的实现,2004,3:25
[12] 雷继尧等,轴承故障诊断,西安交通大学出版社,1991
[13] 任锐,滚动轴承的振动信号分析与故障诊断的研究,燕山大学硕士学位论文,2002,7～8
[14] 周晓凯等,用小波分析铁路车辆滚动轴承诊断方法,1996,8:29～33
[15] 梅宏斌,滚动轴承振动监测与诊断理论·方法·系统,北京:机械工业出版社,1996
[16] 寇惠等,故障诊断中的振动信号处理,北京:冶金工业出版社,1989
[17] 张阿舟,振动数字信号处理程序库,科学技术出版社,1988
[18] 张中民等,滚动轴承故障振动模型及其应用研究,华中理工大学学报,1997,3:2～4
[19] 张国远等,滚动轴承的自动监测与故障诊断系统,轴承,2005,1:32～34
[20] 杨建奎等,基于软件共振解调分析的滚动轴承故障诊断,煤矿机械,2004,10:139～131
[21] 刘亚娟等,基于软件共振解调分析的滚动轴承故障诊断系统开发,仪器仪表与检测技术,2003,11:64～66
[22] 程佩青,数字信号处理教程,北京:清华大学出版社,2001
[23] 朱晓光等,信号包络在DSP系统中的实现,华北电力大学学报,2004,2:91～94
[24] 王敏梁等,关于振动加速度信号幅值解调的五种方法,振动与冲击,2000,1:32～33
[25] 王志刚等,FFT—FS频谱细化技术及其在机械故障诊断中的应用,2000,23(1):44～46
[26] 周霖,算法设计与系统方案,北京:国防工业出版社 2004
[27] 张中民等,基于小波系数包络谱的滚动轴承故障诊断,振动工程学报,1998,1:66～68
[28] 方涛等,小波分析在铁路货车滚动轴承故障诊断中的应用,2005,2:13～14
[29] 董长虹等,小波分析工具箱原理与应用,北京:国防工业出版社,2004
[30] 刘珺等,基于神经网络对轴承故障的智能诊断,2004,8:68～71
[31] 庄哲民等,基于神经网络的滚动轴承检测,2000,2:72～74
[32] 王太勇等,一种改进BP算法及其在滚动轴承故障诊断中的应用,2001,12:1179～1181
[33] 林凤涛等,小波分析在滚动轴承故障诊断中的应用,2004,10:120～122
[34] 孙立宁等,小波变换在数字滤波中的实现,2004,3:33～35
[35] 赵红怡等,基于扩频技术的频谱细化分析方法研究,2002,9:53～55
[36] Peng Tao, Gui Weihua, Wu Min, Xie Yong, Tang Zhaohui;"Fault diagnosis based on intelligent information processing technology". TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Con~nunications, Control and Power Engineering, Volume: 3, 28-31 Oct. 2002 Pages:1708-1712 vol. 3
[37] Xiaoqin Wen, Zhiyi Sun;"Fault diagnosis approach based on fuzzy neural networks and its application". Control and Automation, 2002. ICCA. Final Program and Book of Abstracts. The 2002 International Conference on, June 16-19, 2002 Pages:217～218
[38] J. Shiroishi, Y. Li, S. Liang, S. Danyluk, T. Kurfess;"Vibration Analysis for Bearing Outer Race Condition Diagnostics". Journal of the Brazilian Society of Mechanical Sciences, vol.21 no.3 Rio de Janeiro Sept. 1999
[39] Volder E, Martin H R. Application of Kutosis to Damage Mapping. In: Proceedings of the Fourth International Model Analysis Conference, Los Angeles, Society for Experiment Mechanics, 1986, 629～633
[40] Naoto SASAOKA, Yoshio ITOH, Kensaku, FUJlI;"ANoise Reduetion Methodfor Non-sta Tionary Noise Based on Noise Reconstruction System with ALE". IEICETRANS. FUNDAMENTALS, VOL. E88-A, NO.2 FEBRUARY, 2005 Pages:593～596
[41] 刘益成,TMS320C54X DSP应用程序设计与开发,2002,北京:北京航天航空大学出版社
[42] 张正松等,旋转机械振动监测及故障诊断,1991,北京:机械工业出版社
[43] 侯朝焕等,实用FFT信号处理技术1990,北京:海洋出版社
[44] 吴湘淇,信号、系统与信号处理,1999,北京:电子工业出版社
[45] 黄大卫,数字滤波器1991,北京:中国铁道出版社