基于LMD和SVDD的滚动轴承健康状态评估
|
摘要
为了提高滚动轴承健康状态评估的分类精度,提出了基于局部均值分解(Local mean decomposition,简称LMD)和具有故障样本的支持向量数据描述(Support Vector Data Description,简称SVDD)相结合的滚动轴承故障状态识别方法。该方法首先将利用LMD方法进行滚动轴承振动信号的分解,得到一系列PF(乘积函数,product function)分量之和并具有物理意义,接下来对含有主要故障信息的PF分量进行能量计算并构造特征向量,最后将其输入SVDD分类器,进行滚动轴承的健康状态评估。实验结果证明该方法的可行性和有效性。
In order to improve the classification accuracy of rolling bearing health assessment,a fault state recognition method based on local mean decomposition(LMD)and support vector data description(SVDD)with fault samples is proposed. The method first decomposed rolling bearing vibration signals into several sum of PF(product function,PF) component with physical significance by the LMD method,then calculated the energy of PF component containing the main fault information and structured characteristics,finally input them into SVDD classifier and assessed the health condition of rolling bearing.The experimental results prove that the proposed method is feasible and effective.
In order to improve the classification accuracy of rolling bearing health assessment,a fault state recognition method based on local mean decomposition(LMD)and support vector data description(SVDD)with fault samples is proposed. The method first decomposed rolling bearing vibration signals into several sum of PF(product function,PF) component with physical significance by the LMD method,then calculated the energy of PF component containing the main fault information and structured characteristics,finally input them into SVDD classifier and assessed the health condition of rolling bearing.The experimental results prove that the proposed method is feasible and effective.
引文
[1]钟秉林,黄仁.机械故障诊断学[M].北京:机械工业出版社,2007(1).(Zhong Bing-lin,Huang Ren.Mechanical Fault Diagnosis[M].Beijing:Mechanical Industry Press,2007(1).)
[2]张华,杨贺丽,王恒迪.滚动轴承在线检测与故障诊断系统设计[J].机械设计与制造,2013(5):43-45.(Zhang Hua,Yang He-li,Wang Heng-di.Design of on-line detection and fault diagnosis system for rolling bearing[J].Mechanical Design and Manufacture,2013(5):43-45.)
[3]魏永合,王明华,林梦菊.基于改进EEMD的滚动轴承故障特征提取技术[J].组合机床与自动化加工技术,2015(1):87-90.(Wei Yong-he,Wang Ming-hua,Lin Meng-ju.Fast feature extraction of rolling bearing based on improved EEMD[J].Combined Machine Tool and Automation Processing Technology,2015(1):87-90.)
[4]崔立林,朱海潮,章林柯.基于SVDD的机械设备状态监测方法研究[J].噪声与振动控制,2010(6):157-160.(Cui Li-lin,Zhu Hai-chao,Zhang Lin-ke.Research on condition monitoring method of mechanical equipment based on SVDD[J].Noise and Vibration Control,2010(6):157-160.)
[5]刘震坤.基于LMD和SVDD的滚动轴承故障诊断方法[J].测控自动化,2012,28(10):81-82.(Liu Zhen-kun.Monveling fault diagnosis method of rolling bearing based on LMD and SVDD[J].Measurement and Control Automation,2012,28(10):81-82.)
[6]王涛,李艾华,王旭平.基于SVDD与距离测度的齿轮泵故障诊断方法研究[J].振动与冲击,2013,32(11):62-65.(Wang Tao,Li Ai-hua,Wang Xu-ping.Study on gear pump fault diagnosis method based on SVDD and distance measure[J].Journal of Vibration and Shock,2013,32(11):62-65.)
[7]Jun-sheng Cheng,Mei-li Shi,Yu Yang.A roller bearing fault diagnosis method based on improved LMD and SVM[J].Journal of Measurement Science and Instrumentation,2011,2(1):1-5.
[8]石瑞敏,杨兆建.基于LMD能量特征的滚动轴承故障诊断方法[J].振动、测试与诊断,2015,35(5):832-836.(Shi Rui-min,Yang Zhao-jian.Monveling fault diagnosis method of rolling bearing based on LMD energy characteristics[J].Journal of Vibration,Measurement and Diagnosis,2015,35(5):832-836.)
[9]崔春英,段礼祥,张来斌.基于LMD和FCM的滚动轴承故障诊断方法[J].科学技术与工程,2013,13(7):1764-1767.(Cui Chun-ying,Duan Li-xiang,Zhang Lai-bin.Methods for rolling bearing fault diagnosis based on LMD and FCM[J].Science Technology and Engineering,2013,13(7):1764-1767.)
[10]刘震坤.基于支持向量数据描述的滚动轴承故障诊断方法研究[D].长沙:湖南大学,2012.(Liu Zhen-kun.Study on fault diagnosis method of rolling bearing based on support vector data[J].Changsha:Journal of Hunan University of Science and Technology,2012.)
[11]王昆.支持向量数据描述在故障诊断中的应[D].郑州:郑州大学,2009.(Wang Kun.Effects of support vector data in fault diagnosis[D].Zhengzhou:Zhengzhou University,2009.)
[12]杨宇,王欢欢,程军圣.基于LMD的包络谱特征值在滚动轴承故障诊断中的应用[J].航空动力学报,2012,27(5):1153-1157.(Yang Yu,Wang Huan-huan,Cheng Jun-sheng.Application of characteristic spectral eigenvalues based on LMD in rolling bearing fault diagnosis[J].Journal of Aeronautics and Astronautics,2012,27(5):1153-1157.)
[13]王力敏.基于小波分析和SVDD的工程机械故障诊断研究[D].长沙:湖南大学,2012.(Wang Li-min.Study on fault diagnosis of construction machinery based on wavelet analysis and SVDD[D].Changsha:Hunan University,2012.)
[14]钟鑫,刘文彬,杨剑锋.基于逻辑回归的滚动轴承性能退化评估[J].科技信息,2010(16):504-505.(Zheng Xin,Liu Wen-bin,Yang Jian-feng.Research on performance degradation of rolling bearing based on logistic regression[J].Science and Technology Information,2010(16):504-505.)
[2]张华,杨贺丽,王恒迪.滚动轴承在线检测与故障诊断系统设计[J].机械设计与制造,2013(5):43-45.(Zhang Hua,Yang He-li,Wang Heng-di.Design of on-line detection and fault diagnosis system for rolling bearing[J].Mechanical Design and Manufacture,2013(5):43-45.)
[3]魏永合,王明华,林梦菊.基于改进EEMD的滚动轴承故障特征提取技术[J].组合机床与自动化加工技术,2015(1):87-90.(Wei Yong-he,Wang Ming-hua,Lin Meng-ju.Fast feature extraction of rolling bearing based on improved EEMD[J].Combined Machine Tool and Automation Processing Technology,2015(1):87-90.)
[4]崔立林,朱海潮,章林柯.基于SVDD的机械设备状态监测方法研究[J].噪声与振动控制,2010(6):157-160.(Cui Li-lin,Zhu Hai-chao,Zhang Lin-ke.Research on condition monitoring method of mechanical equipment based on SVDD[J].Noise and Vibration Control,2010(6):157-160.)
[5]刘震坤.基于LMD和SVDD的滚动轴承故障诊断方法[J].测控自动化,2012,28(10):81-82.(Liu Zhen-kun.Monveling fault diagnosis method of rolling bearing based on LMD and SVDD[J].Measurement and Control Automation,2012,28(10):81-82.)
[6]王涛,李艾华,王旭平.基于SVDD与距离测度的齿轮泵故障诊断方法研究[J].振动与冲击,2013,32(11):62-65.(Wang Tao,Li Ai-hua,Wang Xu-ping.Study on gear pump fault diagnosis method based on SVDD and distance measure[J].Journal of Vibration and Shock,2013,32(11):62-65.)
[7]Jun-sheng Cheng,Mei-li Shi,Yu Yang.A roller bearing fault diagnosis method based on improved LMD and SVM[J].Journal of Measurement Science and Instrumentation,2011,2(1):1-5.
[8]石瑞敏,杨兆建.基于LMD能量特征的滚动轴承故障诊断方法[J].振动、测试与诊断,2015,35(5):832-836.(Shi Rui-min,Yang Zhao-jian.Monveling fault diagnosis method of rolling bearing based on LMD energy characteristics[J].Journal of Vibration,Measurement and Diagnosis,2015,35(5):832-836.)
[9]崔春英,段礼祥,张来斌.基于LMD和FCM的滚动轴承故障诊断方法[J].科学技术与工程,2013,13(7):1764-1767.(Cui Chun-ying,Duan Li-xiang,Zhang Lai-bin.Methods for rolling bearing fault diagnosis based on LMD and FCM[J].Science Technology and Engineering,2013,13(7):1764-1767.)
[10]刘震坤.基于支持向量数据描述的滚动轴承故障诊断方法研究[D].长沙:湖南大学,2012.(Liu Zhen-kun.Study on fault diagnosis method of rolling bearing based on support vector data[J].Changsha:Journal of Hunan University of Science and Technology,2012.)
[11]王昆.支持向量数据描述在故障诊断中的应[D].郑州:郑州大学,2009.(Wang Kun.Effects of support vector data in fault diagnosis[D].Zhengzhou:Zhengzhou University,2009.)
[12]杨宇,王欢欢,程军圣.基于LMD的包络谱特征值在滚动轴承故障诊断中的应用[J].航空动力学报,2012,27(5):1153-1157.(Yang Yu,Wang Huan-huan,Cheng Jun-sheng.Application of characteristic spectral eigenvalues based on LMD in rolling bearing fault diagnosis[J].Journal of Aeronautics and Astronautics,2012,27(5):1153-1157.)
[13]王力敏.基于小波分析和SVDD的工程机械故障诊断研究[D].长沙:湖南大学,2012.(Wang Li-min.Study on fault diagnosis of construction machinery based on wavelet analysis and SVDD[D].Changsha:Hunan University,2012.)
[14]钟鑫,刘文彬,杨剑锋.基于逻辑回归的滚动轴承性能退化评估[J].科技信息,2010(16):504-505.(Zheng Xin,Liu Wen-bin,Yang Jian-feng.Research on performance degradation of rolling bearing based on logistic regression[J].Science and Technology Information,2010(16):504-505.)