滚动轴承故障诊断系统开发
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摘要
滚动轴承是各种机械设备中的重要组成部件,同时也是易损坏的元件之一,据统计,现有机械设备的故障中,有30%是由滚动轴承故障造成的,可见其工作状态直接影响着机械设备的工作性能。由于滚动轴承寿命的随机性较大,并不适合采用传统的定时维修制度。对滚动轴承进行状态监测与诊断,变定时维修于视情维修或预知维修,可以防止机械设备功能性能下降,减小或避免事故发生,具有重要的现实意义。
本文研究了滚动轴承的振动机理,给出了故障特征频率和固有频率的计算方法,分析了各时域、频域参数指标；研究了基于小波变换及小波包分解提取故障特征的方法,构建了初始特征集,并针对Mallat算法的频率混叠现象,给出了单子带重构改进算法,通过仿真实验对比分析了两种算法的性能；阐述了主成分分析与RBF网络的基本原理及计算步骤,结合实例分析了基于主成分分析特征提取的必要性和有效性；最后搭建了滚动轴承故障诊断系统的框架,采用VC++与Matlab语言混合编程,开发了滚动轴承故障诊断系统软件,采用滚动轴承故障模拟实验台实测数据对系统进行了功能检测,结果表明该系统对于滚动轴承状态识别率可达到90%以上,较好的完成了滚动轴承故障诊断任务。
Rolling bearing is an important part of mechanical and electrical equipments. According to statistics, the existing mechanical equipment failures,30% are caused by the rolling bearing failure, we can see their work directly affects the performance of machinery and equipment. Because the randomness of life of large rolling bearings, the timing is not suitable for traditional maintenance system. Rolling element bearing condition monitoring and diagnosis, changes in maintaining the condition maintenance or predictive maintenance to prevent performance degradation machinery and equipment capabilities, reduce or avoid the accident, has important practical significance.
In this paper,we first introduced the vibration mechanism of rolling bearing and the function of characteristic and natural frequency to fault bearing, the indicators of time-domain parameters and the frequency domain parameter index; Study the method of based on wavelet transform and wavelet packet decomposition to extract fault features and constructed the initial feature set,because the mallat algorithm of discrete wavelet transform exist frequency alias, we put forward the improved single sideband reconstruction algorithm, provid the arithmetic software, Compare the performance of two algorithms.Explain the basic principles and calculation steps of principal component analysis and RBF Neural Networks, With examples of PCA-based feature extraction the necessity and effectiveness; Finally we built a framework for rolling bearing fault diagnosis system, using VC++and Matlab mixed programming language,developed rolling bearing fault diagnosis system software, using real data that gathered from Bearing Fault simulation sets the system functional testing, The results show that the system state recognition rate for the rolling bearing 90% or more, the better to complete the rolling bearing fault diagnosis tasks.
本文研究了滚动轴承的振动机理,给出了故障特征频率和固有频率的计算方法,分析了各时域、频域参数指标；研究了基于小波变换及小波包分解提取故障特征的方法,构建了初始特征集,并针对Mallat算法的频率混叠现象,给出了单子带重构改进算法,通过仿真实验对比分析了两种算法的性能；阐述了主成分分析与RBF网络的基本原理及计算步骤,结合实例分析了基于主成分分析特征提取的必要性和有效性；最后搭建了滚动轴承故障诊断系统的框架,采用VC++与Matlab语言混合编程,开发了滚动轴承故障诊断系统软件,采用滚动轴承故障模拟实验台实测数据对系统进行了功能检测,结果表明该系统对于滚动轴承状态识别率可达到90%以上,较好的完成了滚动轴承故障诊断任务。
Rolling bearing is an important part of mechanical and electrical equipments. According to statistics, the existing mechanical equipment failures,30% are caused by the rolling bearing failure, we can see their work directly affects the performance of machinery and equipment. Because the randomness of life of large rolling bearings, the timing is not suitable for traditional maintenance system. Rolling element bearing condition monitoring and diagnosis, changes in maintaining the condition maintenance or predictive maintenance to prevent performance degradation machinery and equipment capabilities, reduce or avoid the accident, has important practical significance.
In this paper,we first introduced the vibration mechanism of rolling bearing and the function of characteristic and natural frequency to fault bearing, the indicators of time-domain parameters and the frequency domain parameter index; Study the method of based on wavelet transform and wavelet packet decomposition to extract fault features and constructed the initial feature set,because the mallat algorithm of discrete wavelet transform exist frequency alias, we put forward the improved single sideband reconstruction algorithm, provid the arithmetic software, Compare the performance of two algorithms.Explain the basic principles and calculation steps of principal component analysis and RBF Neural Networks, With examples of PCA-based feature extraction the necessity and effectiveness; Finally we built a framework for rolling bearing fault diagnosis system, using VC++and Matlab mixed programming language,developed rolling bearing fault diagnosis system software, using real data that gathered from Bearing Fault simulation sets the system functional testing, The results show that the system state recognition rate for the rolling bearing 90% or more, the better to complete the rolling bearing fault diagnosis tasks.
引文
[1]虞和济,韩庆大,李沈等.设备故障诊断工程[M],北京：冶金工业出版社,2001
[2]钟秉林,黄仁.机械故障诊断学(第2版)[M].北京：机械工业出版社,2002
[3]李国华.机械故障诊断[M].化学工业出版社,2002.
[4]梅宏斌.滚动轴承振动检测与诊断-理论方法系统[M].北京：机械工业出版社,1996.
[5]盛兆顺,尹琦岭.设备状态监测与故障诊断技术及应用[M]化学工业出版社,2003
[6]GustafssonO. G. TaUianT Detection of damage in assemble drolling bearings. Trans. of ASLE.1962.5: 197-209.
[7]Martin R. Detection of ball bearing malfunctions. Instruments and Control Systems.1976, (11).
[8]徐尹格,颜玉玲。相关技术在滚动轴承故障诊断中的应用,应用数学和力学,1992,13(7)。
[9]Y Kim Paul and CoweI. R. G. A review of rolling element bearing health monitoring. Proceeding of Machinery Vibration Monitoring and Analysis Meeting[C]. April 19—21.1983.
[10]毛怀东,陈家骥.滚动轴承故障诊断参数的研究,第三届全国机械设备故障诊断学术会议论文集,1991年5月28日—31日,592～597.
[11]Hansen J. Steven and Roger G. Harken A new method for rolling element bearing monitoring in the petrochemical industry, Proceeding of Machinery Vibration Monitoring Meeting[C]. June26—28, 1984.
[12]Su Y.T and LinS. T On initial fault detection of a tapered roller bearing [J]. Sound and Vibration, Jan.1992.
[13]RubibiR. And Meneghetti U. Application of the envelope and wavelet transforin analyses for the diagnosis of incipient faults in ball bearings. Mechanical Systems and Signal Processing,2001,15(2), 287—302.
[14]张贤达,保铮。非平稳信号分析与处理,北京：国防工业出版社,1998。
[15]Kim Yang—Hann.Fault detection in a ball bearing system using a moving window.Mechanical system sand signal processing,1991,5(6).
[16]Meng Qingfeng, Qu Liangsheng. Rotating machinery fault diagnosis using wigner distribution. Mechanical system sand signal processing,1991,5(3).
[17]梅宏斌.滚动轴承故障诊断及其在高速铁路轴承实验中的应用,华中理工大学博士论文,1993.
[18]夏利民,谷士文等。基于小波包分析的货车滚动轴承故障诊断,中国铁道科学,2002,23(6)。
[19]李世玲,李治等。基于小波包能量特征的滚动轴承故障监测方法,系统仿真学报,2003,15(1)。
[20]Bo Li and Mo—Yuen Chow, etc. Neural—network—based motor rolling bearing fault diagnosis, IEEE transactionson industrial electronics, October,2000,47(5).
[21]侯祥林,李永强等。非线性机械设备系统的分形神经网络诊断方法,振动工程学报,2001,14(2)。
[22]夏利民。基于Kohonen神经网络的滚动轴承故障诊断,计算机工程,2003,29(3)。
[23]N.E.Huang,etc.The empirical mode decom—osition and the Hilbert spectrum for nonlinear and non —tationary time series analysis. Proc.R.Soc.Lond.A,Vol.454,PP.903—995,1998.
[24]杨宇,于德介等。经验模态分解(EMD)在滚动轴承故障诊断中德应用,湖南大学学报(自然科学版),2003,30(5).
[25]张海军,温广瑞等。一种提高诊断信息质量的方法,西安交通大学学报,2002,36(3).
[26]刘冬霞。滚动轴承故障诊断系统开发研究。五邑大学硕士学位论文.2008.
[27]刘建文.基于共振解调与人工神经网络的轴承智能诊断技术研究.西南交通大学硕士学论文,2004.
[28]孙延奎。小波分析及其应用。[M]北京.机械工业出版社。2005.3.
[29]杨福生.小波变换的工程分析与应用[M].北京：科学出版社,1999-02-01.
[30]杨建国。小波分析及其工程应用。[M]北京。机械工业出版社。2005.6.
[31]封常生。小波分析及其应用。上海交通大学硕士论文.2007.
[32]许飞鸿,苏深坚,李平。消除小波分析中频率混叠现象的改进算法的Matlab实现。第十三届全国设备监测与诊断学术会议论文集2008
[33]程正兴,杨守志,冯晓霞.小波分析的理论算法进展和应用：[M]北京：国防工业出版社.2007
[34]祝晓燕,王继选等。基于小波包特征向量弹性BP算法的故障诊断。汽轮机技术.2008.12.
[35]曾芸,武和雷.基于小波包的频带能量特征提取及智能诊断[A].计算技术与自动化.2008.12
[36]杨淑莹。模式识别与智能计算—atlab技术实现。北京.电子工业出版社.2008.1.
[37]高隽编.人工神经网络原理及仿真实例[M].北京：机械工业出版社,2003.7.
[38]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京：清华大学出版社,2005.7.
[39]陆爽.基于现代信号分析和神经网络的滚动轴承智能诊断技术研究.吉林大学博士学位论文,2004
[40]张德丰等。Matlab神经网络应用设计。北京：机械工业出版社,2009.
[41]魏朗,陈涛。Visual C++程序设计攻略教程。西安.西安电子科技大学出版社.2004.
[42]林勇,宋征。Visual C++6.0应用指南。北京：人民邮电出版社,1999.
[43]葛哲学,陈仲生。Matlab时频分析技术及其应用。人民邮电出版社,2006.1.
[44]熊富强。基于小波分析和神经网络的汽车轮机故障诊断研究,中南大学硕士学位论文,2008.5
[45]李萌。旋转机械轴承故障的特征提取与模式识别方法研究。吉林大学博士学位论文。2008.
[46]盛兆顺,尹琦玲。设备状态监测与故障诊断技术及应用。北京。化学工业出版社。2003.
[47]樊计昌,刘明军等。浅析小波最大分解层。科技导报。2008.26(10).
[2]钟秉林,黄仁.机械故障诊断学(第2版)[M].北京：机械工业出版社,2002
[3]李国华.机械故障诊断[M].化学工业出版社,2002.
[4]梅宏斌.滚动轴承振动检测与诊断-理论方法系统[M].北京：机械工业出版社,1996.
[5]盛兆顺,尹琦岭.设备状态监测与故障诊断技术及应用[M]化学工业出版社,2003
[6]GustafssonO. G. TaUianT Detection of damage in assemble drolling bearings. Trans. of ASLE.1962.5: 197-209.
[7]Martin R. Detection of ball bearing malfunctions. Instruments and Control Systems.1976, (11).
[8]徐尹格,颜玉玲。相关技术在滚动轴承故障诊断中的应用,应用数学和力学,1992,13(7)。
[9]Y Kim Paul and CoweI. R. G. A review of rolling element bearing health monitoring. Proceeding of Machinery Vibration Monitoring and Analysis Meeting[C]. April 19—21.1983.
[10]毛怀东,陈家骥.滚动轴承故障诊断参数的研究,第三届全国机械设备故障诊断学术会议论文集,1991年5月28日—31日,592～597.
[11]Hansen J. Steven and Roger G. Harken A new method for rolling element bearing monitoring in the petrochemical industry, Proceeding of Machinery Vibration Monitoring Meeting[C]. June26—28, 1984.
[12]Su Y.T and LinS. T On initial fault detection of a tapered roller bearing [J]. Sound and Vibration, Jan.1992.
[13]RubibiR. And Meneghetti U. Application of the envelope and wavelet transforin analyses for the diagnosis of incipient faults in ball bearings. Mechanical Systems and Signal Processing,2001,15(2), 287—302.
[14]张贤达,保铮。非平稳信号分析与处理,北京：国防工业出版社,1998。
[15]Kim Yang—Hann.Fault detection in a ball bearing system using a moving window.Mechanical system sand signal processing,1991,5(6).
[16]Meng Qingfeng, Qu Liangsheng. Rotating machinery fault diagnosis using wigner distribution. Mechanical system sand signal processing,1991,5(3).
[17]梅宏斌.滚动轴承故障诊断及其在高速铁路轴承实验中的应用,华中理工大学博士论文,1993.
[18]夏利民,谷士文等。基于小波包分析的货车滚动轴承故障诊断,中国铁道科学,2002,23(6)。
[19]李世玲,李治等。基于小波包能量特征的滚动轴承故障监测方法,系统仿真学报,2003,15(1)。
[20]Bo Li and Mo—Yuen Chow, etc. Neural—network—based motor rolling bearing fault diagnosis, IEEE transactionson industrial electronics, October,2000,47(5).
[21]侯祥林,李永强等。非线性机械设备系统的分形神经网络诊断方法,振动工程学报,2001,14(2)。
[22]夏利民。基于Kohonen神经网络的滚动轴承故障诊断,计算机工程,2003,29(3)。
[23]N.E.Huang,etc.The empirical mode decom—osition and the Hilbert spectrum for nonlinear and non —tationary time series analysis. Proc.R.Soc.Lond.A,Vol.454,PP.903—995,1998.
[24]杨宇,于德介等。经验模态分解(EMD)在滚动轴承故障诊断中德应用,湖南大学学报(自然科学版),2003,30(5).
[25]张海军,温广瑞等。一种提高诊断信息质量的方法,西安交通大学学报,2002,36(3).
[26]刘冬霞。滚动轴承故障诊断系统开发研究。五邑大学硕士学位论文.2008.
[27]刘建文.基于共振解调与人工神经网络的轴承智能诊断技术研究.西南交通大学硕士学论文,2004.
[28]孙延奎。小波分析及其应用。[M]北京.机械工业出版社。2005.3.
[29]杨福生.小波变换的工程分析与应用[M].北京：科学出版社,1999-02-01.
[30]杨建国。小波分析及其工程应用。[M]北京。机械工业出版社。2005.6.
[31]封常生。小波分析及其应用。上海交通大学硕士论文.2007.
[32]许飞鸿,苏深坚,李平。消除小波分析中频率混叠现象的改进算法的Matlab实现。第十三届全国设备监测与诊断学术会议论文集2008
[33]程正兴,杨守志,冯晓霞.小波分析的理论算法进展和应用：[M]北京：国防工业出版社.2007
[34]祝晓燕,王继选等。基于小波包特征向量弹性BP算法的故障诊断。汽轮机技术.2008.12.
[35]曾芸,武和雷.基于小波包的频带能量特征提取及智能诊断[A].计算技术与自动化.2008.12
[36]杨淑莹。模式识别与智能计算—atlab技术实现。北京.电子工业出版社.2008.1.
[37]高隽编.人工神经网络原理及仿真实例[M].北京：机械工业出版社,2003.7.
[38]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京：清华大学出版社,2005.7.
[39]陆爽.基于现代信号分析和神经网络的滚动轴承智能诊断技术研究.吉林大学博士学位论文,2004
[40]张德丰等。Matlab神经网络应用设计。北京：机械工业出版社,2009.
[41]魏朗,陈涛。Visual C++程序设计攻略教程。西安.西安电子科技大学出版社.2004.
[42]林勇,宋征。Visual C++6.0应用指南。北京：人民邮电出版社,1999.
[43]葛哲学,陈仲生。Matlab时频分析技术及其应用。人民邮电出版社,2006.1.
[44]熊富强。基于小波分析和神经网络的汽车轮机故障诊断研究,中南大学硕士学位论文,2008.5
[45]李萌。旋转机械轴承故障的特征提取与模式识别方法研究。吉林大学博士学位论文。2008.
[46]盛兆顺,尹琦玲。设备状态监测与故障诊断技术及应用。北京。化学工业出版社。2003.
[47]樊计昌,刘明军等。浅析小波最大分解层。科技导报。2008.26(10).
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