一种基于改进干扰属性投影的超声电机退化特征估计方法
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摘要
压电陶瓷部件开裂是超声电机的主要故障模式之一,通过监测孤极信号能够有效地提取退化特征,然而运转工况的不同会给退化特征提取带来干扰,干扰属性投影(Nuisance Attribute Projection, NAP)在移除干扰成分时存在"二分式"的缺陷,不可避免地删除部分有价值信息.将协方差矩阵的特征值离散度量化估计干扰源干扰程度的方法引入干扰属性投影算法,提出一种改进干扰属性投影(Improved Nuisance Attribute Projection, INAP)的退化特征估计方法,该方法能够有效地削弱由于工况影响对退化特征提取的干扰成分,得到的退化特征能够更加有效地反映超声电机的退化状态.利用退化特征集建立标准退化模式矩阵,根据灰关联度的大小判定超声电机的退化状态,最后通过对比分析,验证了该方法的有效性.
The cracking of piezoelectric ceramic components is one of the main failure pattern of ultrasonic motors. The degradation characteristics can be extracted effectively by monitoring the voltage signal of piezoelectric sensor. However, the operating conditions will bring some interference components for the degradation feature extraction. Nuisance attribute projection(NAP) exist the defect that the element in weight matrix is either 0 or 1 when interfering components are removed. The "two fraction" can delete some valuable information inevitably. The dispersion degree of eigenvalue of covariance matrix is introduced into NAP in this paper, and a method of degradation feature estimation based on improved nuisance attribute projection(INAP) is proposed. The proposed method can effectively waken the interference components caused by the influence of the operating conditions. The degradation feature that remove interference components can reflect degradation state of ultrasonic motor more effectively. Standard degradation pattern matrix is established with degradation feature set. Degradation state of ultrasonic motor is determined by grey correlation degree. Finally, the validity of the method is verified by comparing analysis.
The cracking of piezoelectric ceramic components is one of the main failure pattern of ultrasonic motors. The degradation characteristics can be extracted effectively by monitoring the voltage signal of piezoelectric sensor. However, the operating conditions will bring some interference components for the degradation feature extraction. Nuisance attribute projection(NAP) exist the defect that the element in weight matrix is either 0 or 1 when interfering components are removed. The "two fraction" can delete some valuable information inevitably. The dispersion degree of eigenvalue of covariance matrix is introduced into NAP in this paper, and a method of degradation feature estimation based on improved nuisance attribute projection(INAP) is proposed. The proposed method can effectively waken the interference components caused by the influence of the operating conditions. The degradation feature that remove interference components can reflect degradation state of ultrasonic motor more effectively. Standard degradation pattern matrix is established with degradation feature set. Degradation state of ultrasonic motor is determined by grey correlation degree. Finally, the validity of the method is verified by comparing analysis.
引文
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[19]孙健,李洪儒,田再克.基于敏感分量融合的液压泵退化特征提取方法[J].仪器仪表学报,2016,37(6):1290-1298.Sun Jian,Li Hongru,Tian Zaike.A degradation feature extraction method for hydraulic pumps based on fusion of sensitive components[J].Chinese Journal of Scientific Instrument,2016,37(6):1290-1298.
[20]程哲.直升机传动系统行星轮系损伤建模与故障预测理论及方法研究[D].长沙:国防科学技术大学,2011.Cheng Zhe.Theory and method on damage modeling and prognostics for planetary gear set of helicopter transmission system[D].Changsha:National University of Defense Technology,2011.
[21]王余奎.液压泵退化状态识别与退化趋势预测方法研究[D].石家庄:军械工程学院,2015.Wang Yukui.Research on degradation state identification and degradation trend prediction methods of hydraulic pump[D].Shijiazhuang:Ordnance Engineering College,2015.
[2]Medjaher K,Skima H,Zerhouni N.Condition assessment and fault prognostics of microelectromechanical systems[J].Microelectronics Reliability,2014,54:143-151.
[3]Shi Jingzhuo,You Dongmei.Characteristic model of travelling wave ultrasonic motor[J].Ultrasonics,2014,54:725-730.
[4]胡敏强,王心坚,金龙,等.行波超声波电机瞬态特性的测试及分析[J].中国电机工程学报,2006,26(23):120-125.Hu Minqiang,Wang Xinjian,Jin Long,et al.Measurement and analysis on transient response of traveling-wave ultrasonic motors[J].Proceedings of the CSEE,2006,26(23):120-125.
[5]Li Hongru,Wang Yukui,Wang Bing,et al.The application of a generalized mathematical morphological particle as a novel indicator for the performance degradation assessment of a bearing[J].Mechanical Systems and Signal Processing,2017,82:490-502.
[6]Solomonoff A,Campbell W M,Quillen C.Nuisance attribute projection[C]//Speech Communication,2007:1-73.
[7]truc V,Vesnicer B,Miheliˇc F,et al.Removing illumination artifacts from face images using the nuisance attribute projection[C]//Proceedings of the 2010IEEE International Conference on Acoustics Speech and Signal Processing(ICASSP),IEEE,2010:846-849.
[8]Yifrach Ariel,Eitan Novoselsky,Yosef A Solewicz,et al.Improved nuisance attribute projection for face recognition[J].Pattern Anal Applic,2016,19:69-78.
[9]范彬,胡雷,胡茑庆.变工况下旋转机械故障跟踪的相空间曲变方法[J].物理学报,2013,62(16):1-8.Fan Bin,Hu Lei,Hu Niaoqing.Fault tracking of rotating machinery under variable operation based on phase space warping[J].Acta Physica Sinica,2013,62(16):1-8.
[10]Mishra C,Samantaray A K,Chakraborty G.Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet denoised estimate[J].Mechanical Systems and Signal Processing,2016,72-73:206-222.
[11]姜战伟,郑近德,潘海洋,等.POVMD与包络阶次谱的变工况滚动轴承故障诊断[J].振动、测试与诊断,2017,37(3):609-616.Jiang Zhanwei,Zheng Jinde,Pan Haiyang,et al.Fault diagnosis under variable conditions based on parameter optimized variational mode decomposition and envelope order spectrum[J].Journal of Vibration,Measurement&Diagnosis,2017,37(3):609-616.
[12]Jiang Huiming,Chen Jin,Dong Guangming.Hidden Markov model and nuisance attribute projection based bearing performance degradation assessment[J].Mechanical Systems and Signal Processing,2016,72-73:184-205.
[13]Gopinath R,Santhosh Kumar C,Ramachandran K I,et al.Intelligent fault diagnosis of synchronous generators[J].Expert Systems with Applications,2016,45:142-149.
[14]Dragomiretskiy K,Zosso D.Variational mode decomposition[J].IEEE Tran on Signal Processing,2014,62(3):531-544.
[15]Chang Huai You,Li Haizhou,Lee Kong Aik.Relevance factor of maximum a posteriori adaptation for GMM-NAP-SVM in speaker and language recognition[J].Computer Speech and Language,2015,30:116-134.
[16]刘伟伟,吉立新,李邵梅,等.基于区分加权干扰属性投影的语种识别方法[J].中文信息学报,2012,26(6):59-64.Liu Weiwei,Ji Lixin,Li Shaomei,et al.Language recognition based on discriminating weighted nuisance attribute projection[J].Journal of Chinese Information Processing,2012,26(6):59-64.
[17]焦卫东,蒋永华,施继忠,等.一种旋转机械系统耦合故障诊断的新方法[J].仪器仪表学报,2016,37(7):1449-1456.Jiao Weidong,Jiang Yonghua,Shi Jizhong,et al.New approach for diagnosis on coupled faults of rotating machine system[J].Chinese Journal of Scientific Instrument,2016,37(7):1449-1456.
[18]王冰,李洪儒,许葆华.数学形态学分形维数广义估算方法及其应用[J].振动工程学报,2014,27(6):951-959.Wang Bing,Li Hongru,Xu Baohua.Generalized estimation method of fractal dimension based on mathematical morphology and its application in bearing degradation status identification[J].Journal of Vibration Engineering,2014,27(6):951-959.
[19]孙健,李洪儒,田再克.基于敏感分量融合的液压泵退化特征提取方法[J].仪器仪表学报,2016,37(6):1290-1298.Sun Jian,Li Hongru,Tian Zaike.A degradation feature extraction method for hydraulic pumps based on fusion of sensitive components[J].Chinese Journal of Scientific Instrument,2016,37(6):1290-1298.
[20]程哲.直升机传动系统行星轮系损伤建模与故障预测理论及方法研究[D].长沙:国防科学技术大学,2011.Cheng Zhe.Theory and method on damage modeling and prognostics for planetary gear set of helicopter transmission system[D].Changsha:National University of Defense Technology,2011.
[21]王余奎.液压泵退化状态识别与退化趋势预测方法研究[D].石家庄:军械工程学院,2015.Wang Yukui.Research on degradation state identification and degradation trend prediction methods of hydraulic pump[D].Shijiazhuang:Ordnance Engineering College,2015.