形态谱和LCD法提取超声波电动机陶瓷故障特征

doi:1004-7018-47-2-18

微特电机 ›› 2019, Vol. 47 ›› Issue (2): 18-23.doi: 1004-7018-47-2-18

形态谱和LCD法提取超声波电动机陶瓷故障特征

安国庆^1,²,杨少锐²,安孟宇²,刘庆瑞²,李洪儒¹

^1. 陆军工程大学,石家庄 050003
^2. 河北科技大学,石家庄 050018

收稿日期:2018-08-27 出版日期:2019-02-28 发布日期:2019-02-22
作者简介:安国庆(1982—),男,博士,副教授。
基金资助:
国家自然科学基金项目(51877070);中国博士后科学基金项目(2017M623404);河北省自然科学基金项目(E2017208086);河北省高等学校科学技术研究青年基金项目(QN2017329)

Fault Feature Extraction of Piezoelectric Ceramics in Ultrasonic Motor Based on Morphological Spectrum and LCD Method

AN Guo-qing^1,²,YANG Shao-rui²,AN Meng-yu²,LIU Qing-rui²,LI Hong-ru¹

^1. Army Engineering University,Shijiazhuang 050003,China
^2. Hebei University of Science and Technology,Shijiazhuang 050018,China

Received:2018-08-27 Online:2019-02-28 Published:2019-02-22

摘要/Abstract

摘要：

压电陶瓷开裂是导致超声波电动机失效的主要原因之一。针对超声波电动机定转子机械耦合过程中的噪声影响,研究利用多尺度下的形态谱信息,重构孤极电压故障信号的方法。对重构信息进行LCD分解(Local Characteristic-scale Decomposition)并计算能谱熵,作为故障特征反映超声波电动机压电陶瓷片的开裂程度。在不同噪声等级的仿真信号下,对故障特征在噪声环境的适用性进行了分析。实验结果验证了该方法在超声波电动机压电陶瓷开裂故障特征提取上的可行性和有效性。

关键词: 超声波电动机, 压电陶瓷, 形态谱, 局部特征尺度分解, 能谱熵

Abstract:

Piezoelectric ceramic cracking is one of the main causes of failure of ultrasonic motors. Aiming at the influence of noise in the mechanical coupling process between the stator and rotor, a method to reconstruct fault signal based on the solitary voltage via the morphological spectrum information under multi-scale was presented. Through the local characteristic-scale decomposition (LCD) of restructured signal, entropy of energy spectrum was taken as the fault feature of piezoelectric ceramics cracking in ultrasonic motor. The anti-interference performance of the fault feature was analyzed under different simulated noise environments. The experimental results verify the feasibility and effectiveness of this method in the fault features extraction of piezoelectric ceramic cracking of ultrasonic motor.

Key words: ultrasonic motor, piezoelectric ceramics, morphological spectrum, local characteristic-scale decomposition (LCD), energy spectrum entropy

中图分类号:

TM359.9

安国庆,杨少锐,安孟宇,刘庆瑞,李洪儒. 形态谱和LCD法提取超声波电动机陶瓷故障特征[J]. 微特电机, 2019, 47(2): 18-23.

ANGuo-qing,YANGShao-rui,ANMeng-yu,LIUQing-rui,LIHong-ru. Fault Feature Extraction of Piezoelectric Ceramics in Ultrasonic Motor Based on Morphological Spectrum and LCD Method[J]. Small & Special Electrical Machines, 2019, 47(2): 18-23.

参考文献

[1]	STEPANENKO D A, MINCHENYA V T .Development and study of novel non-contact ultrasonic motor based on principle of structural asymmetry[J]. Ultrasonics, 2012(7), 866-872.
[2]	LI X,YAO Z,LV Q .Dynamic modeling and characteristics analysis of a modal-independent linear ultransonic motor[J]. Ultrasonics, 2016(12):117-127.
[3]	王理停, 黄宜坚 . 关联维数的超声波电机故障诊断[J].华侨大学学报:自然科学版, 2011(4):368-372.
[4]	陈智博, 黄宜坚 . 基于AR 双谱的超声波电机故障诊断 [J].中南大学学报, 2011(12):3728-3734.
[5]	刘漫漫 . 基于神经网络的行波超声电机的摩擦材料故障诊断[D]. 哈尔滨:哈尔滨工业大学, 2013.
[6]	SUN J LI H,TIAN Z. Degradation feature extraction of hydraulic pump based on LCD-DCS fusion algorithm[J]. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 2018,22(8):1460-1470.
[7]	LI Y LIANG X,XU W. Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform[J]. Mechanical Systems and Signal Processing, 2017,31(3):204-223.
[8]	SINGH J DARPE A K,SINGH S P.Rolling element bearing fault diagnosis based on over-complete rational dilation wavelet transform and auto-correlation of analytic energy operator[J]. Mechanical Systems and Signal Processing, 2018,32(2):662-693.
[9]	IMAOUCHEN Y KEDADOUCHE M,ALKAMA R.A frequency-weighted energy operator and complementary ensemble empirical mode decomposition for bearing fault detection[J]. Mechanical Systems and Signal Processing, 2017,31(1):103-116.
[10]	LV Y YUAN R, SONG G.Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing[J]. Mechanical Systems and Signal Processing, 2016,30(12):219-234.
[11]	SUN J LI H, XU B.The morphological undecimated wavelet decomposition-discrete cosine transform composite spectrum fusional gorithm and its application on hydraulic pumps[J]. Measurement, 2016,30(12):794-805.
[12]	DONG Y LIAO M,ZHANG X.Faults diagnosis of rolling element bearings based on modified morphological method[J]. Mechanical Systems and Signal Processing, 2011,25(5):1276-1286 .
[13]	YU D,WANG M,CHENG X..A method for the compound fault diagnosis of gearboxes based on morphological component analysis[J]. Measurement, 2016(9):519-531.
[14]	LI Y ZUO M,LIN J.Fault detection method for railway wheel flat using an adaptive multiscale morphological filter[J]. Mechanical Systems and Signal Processing, 2017,31(2):642-658.
[15]	LI Y LIANG X,ZUO M.Diagonal slice spectrum assisted optimal scale morphological filter for rolling element bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2017,31(2):146-161.
[16]	LI Y LIANG X,LIN J.Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter[J]. Mechanical Systems and Signal Processing, 2018,32(2):435-448.
[17]	张亢, 程军圣, 杨宇 . 基于局部均值分解与形态谱的旋转机械故障诊断方法 [J]. 振动与冲击, 2013,32(9):135-140
[18]	LI B MI S, LIU P,et al.Classification of time-frequency representations using improved morphological pattern spectrum for engine fault diagnosis[J]. Journal of Sound and Vibration, 2013,332(13):3329-3337.
[19]	ZHAO Z JIA M,WANG F .Intermittent chaos and sliding window symbol sequence statistics-based early fault diagnosis for hydraulic pump on hydraulic tube tester[J]. Mechanical Systems and Signal Processing, 2009,23(5):1573-1585.
[20]	郑近德, 程军圣, 杨宇 . 部分集成局部特征尺度分解:一种新的基于噪声辅助数据分析方法[J]. 电子学报, 2013,41(5):1030-1035.
[21]	郑近德, 程军圣, 杨宇 . 基于LCD和排列熵的滚动轴承故障诊断[J]. 振动、测试与诊断, 2014,34(5):802-806.
[22]	田再克, 李洪儒, 谷宏强 , 等. 基于局部特征尺度分解和JRD距离的液压泵性能退化状态识别方法[J]. 振动与冲击, 2016,35(20):54-59.
[23]	CHAI M, ZHANG Z, DUAN Q . A new qualitative acoustic emission parameter based on Shannon’s entropy for damage monitoring[J]. Mechanical Systems and Signal Processing, 2018,32(2):617-629 .
[24]	AI Y, GUAN J, FEI C , et al. Fusion information entropy method of rolling bearing fault diagnosis based on n-dimensional characteristic parameter distance[J]. Mechanical Systems and Signal Processing, 2017,31(5):123-136.
[25]	郝如江, 卢文秀, 褚福磊 . 滚动轴承故障信号的多尺度形态学分析[J]. 机械工程学报, 2008,44(11):160-165.

形态谱和LCD法提取超声波电动机陶瓷故障特征

Fault Feature Extraction of Piezoelectric Ceramics in Ultrasonic Motor Based on Morphological Spectrum and LCD Method

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