基于局部特征尺度分解谱熵和VPMCD的液压泵退化状态识别

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摘要

针对液压泵故障信号的非平稳特性以及其退化状态难以识别的问题，结合局部特征尺度分解与信息熵理论，提出了局部特征尺度分解谱熵的退化特征提取方法，并将基于变量预测模型的模式识别(Variable Predictive Model based Class Discriminate, VPMCD)方法引入到液压泵的退化状态识别。对不同程度故障的液压泵振动信号进行局部特征尺度分解，从得到的内禀尺度分量中提取振动信号的复杂度和随机性度量指标能谱熵、奇异谱熵和包络谱熵，以其作为液压泵的退化特征向量，通过建立VPMCD退化状态识别模型实现液压泵的退化状态识别。仿真信号分析结果验证了本文提出的局部特征尺度分解谱熵具有较好的表征液压泵故障退化状态的能力。通过对实测液压泵松靴和滑靴磨损两种故障模式下的退化状态振动信号进行分析验证了提出方法的有效性。

关键词：液压泵，退化状态识别，局部特征尺度分解，谱熵，VPMCD

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	王余奎
	李洪儒
	魏晓斌
	许葆华

关键词 ：液压泵')" href="#">

液压泵, 退化状态识别, 局部特征尺度分解, 谱熵, VPMCD

Abstract：Aiming at the problem that the degradation state of hydraulic pump is difficult to identification and also the non-stationary characteristic which result in the difficult increase of degradation feature extraction. The local characteristic scale decomposition spectrum entropy as a novel degradation feature extraction method is proposed based on the combination of local characteristic scale decomposition arithmetic (LCD) and information entropy theory. And the variable predictive model based class discriminate method is adopted as innovative degradation state identification method. The local characteristic scale decomposition is performed to the hydraulic pump vibration signal in different fault levels. The power spectrum entropy, singular spectrum entropy and the envelope spectrum entropy are extracted from the obtained intrinsic scale components, and they are used as the degradation features of pump. Then the degradation state identification model are founded based on the VPMCD method and the degradation features, the degradation state of pump can be identified. The analysis result of simulation signal verified the prefer performance of proposed local characteristic scale decomposition spectrum entropy to reflect the degradation state of pump. The analysis result of practical vibration signal of pump with different fault levels with loose boot fault and piston shoe wear fault demonstrate the availability of the proposed method.

收稿日期: 2015-04-29 出版日期: 2016-06-15

引用本文:

王余奎, 李洪儒, 魏晓斌，许葆华. 基于局部特征尺度分解谱熵和VPMCD的液压泵退化状态识别[J]. 振动与冲击, 2016, 35(12): 188-195.
WANG Yukui, LI Hongru, WEI Xiaobin, XU Baohua. Degradation state identification of hydraulic pump based on LCD decomposition spectrum entropy and VPMCD. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(12): 188-195.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2016/V35/I12/188

[1] 张龙,黄文艺,熊国良.基于多尺度熵的滚动轴承故障程度评估[J]. 振动与冲击, 2014,33(9):185-189.
ZHANG Long, HUANG Wenyi, XIONG Guoliang. Assessment of rolling element bearing fault severity using multi-scale entropy[J]. Journal of Vibration and Shock, 2014,33(9):185-189.
[2] Jun Du, Shaoping Wang, Haiyan Zhang. Layered clustering multi-fault diagnosis for hydraulic piston pump [J]. Mechanical Systems and Signal Processing, 2013,(36):487-504.
[3] Zhen Zhao, Mingxing Jia, Fuli Wang, et al. 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):1573-1585.
[4] 鞠华,沈长青,黄伟国,等. 基于支持向量回归的轴承故障定量诊断应用[J]. 振动、测试与诊断,2014,34(4):767-771.
Ju Hua, Shen Changqing, Huang Weiguo, et al. Quantitative diagnosis of bearing fault based on support vector regression[J]. Journal of Vibration, measurement & Diagnosis, 2014,34(4):767-771.
[5] 王冰,李洪儒,许葆华.基于多尺度形态分解谱熵的电机轴承预测特征提取及退化状态评估[J].振动与冲击,2013,32(22):124-128.
WANG Bing, LI Hongru, XU Baohua. Motor bearing forecast extracting and degradation status identification based on multi-scale morphological decomposition spectral entropy[J]. Journal of Vibration and Shock, 2013,32(22):124-128.
[6] Costa M,Goldberger A L, Peng C K. Multi-scale entropy analysis of biological signals[J]. Physical Review E, 2005, 71,1-18.
[7] Shuen De Wu, Chiu Wen Wu, Kung Yen Lee, et al. Modified multi-scale entropy for short-term time series analysis[J], Physical A, 2013,392:5865-5873.
[8] 向丹,葛爽. 基于EMD样本熵-LLTSA的故障特征提取方法[J]. 航空动力学报,2014,29(7):1535-1542.
XIANG Dan, GE Shuang. Method of fault feature extraction based on EMD sample entropy and LLTSA[J]. Journal of Aerospace Power, 2014,29(7):1535-1542.
[9] 孙洁娣,肖启阳,温江涛,等. 基于LMD包络谱熵及SVM的天然气管道微小泄漏孔径识别[J]. 机械工程学报,2014,50(20):18-25.
SUN Jie-di, XIAO Qi-yang, WEN Jiang-tao, et al. Gas pipeline small leak aperture classification based on local mean decomposition envelope spectrum entropy and SVM[J]. Journal of Mechanical Engineering, 2014,50(20):18-25.
[10] 程军圣,郑近德,杨宇. 一种新的非平稳信号分析方法¬¬局部特征尺度分解法[J].振动工程学报,2012,25(2):215-220.
CHENG Jun-sheng, ZHENG Jin-de, YANG Yu. A non-stationary signal analysis approach—the local characteristic scale decomposition method[J]. Journal of vibration journal, 2012,25(2):215-220.
[11] 程军圣,杨怡,杨宇. 局部特征尺度分解方法及其在齿轮故障诊断中的应用[J]. 机械工程学报,2012,48(9):64-71.
CHENG Jun-sheng, YANG Yi, YANG Yu. Local characteristic-scale decomposition method and its application to gear fault diagnosis[J]. Journal of Mechanical Engineering,2012,48(9):64-71.
[12] 吴坚,赵阳,何春. 基于支持向量机回归算法的电子机械制动传感器系统故障诊断[J].吉林大学学报, 2013,43(5):1178-1183.
WU Jian, ZHAO Yang, HE Rui. Fault detection and diagnosis of EMB sensor system based on SVR[J]. Journal of JiLin University, 2013,43(5):1178-1183.
[13] 杨宇,王欢欢,曾鸣,等. 基于变量预测模型的模式识别方法在滚动轴承故障诊断中的应用[J].湖南大学学报(自然科学版),2013,40(3): 36-40.
YANU Yu, WAND Huanhuan, ZENG Ming. Application of pattern recognition approach based on VPMCD in roller bearing fault diagnosis[J]. Journal of Hunan University(Natural Sciences),2013,40(3):36-40.
[14] Raghuraj R, Lakshminarayanan S. Variable predictive models-A new multivariate classification approach for pattern recognition application [J]. Pattern Recognition,2009,42(1):7-16.
[15] Raghuraj R, Lakshminarayanan S. Variable predictive model based classification algorithm for effective separation of protein structural classes [J]. Computational Biology and Chemistry,2008,32(4):302-306.
[16] Raghuraj R, Lakshminarayanan S. VPMCD: Variable interaction modeling approach for class discrimination in biological system [J]. FEBS Letter,2007,581(5):826-830.
[17] 苏文胜,王奉涛,张志新等. EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29(3):18-21.
SU Wen-sheng, WANG Feng-tao, ZHANG Zhi-xin, etal. Application of EMD de-noising and spectral kurtosis in early fault diagnosis of rolling element bearings[J]. Journal of Vibration and Shock, 2010,29(3):18-21.
[18] 张志刚,石晓辉,施全,等. 基于改进EMD和谱峭度法滚动轴承故障特征提取[J]. 振动、测试与诊断,2013,33(3):478-482.
ZHANG Zhigang, SHI Xiaohui, SHI Quan, et al. Fault Feature Extraction of Rolling Element Bearing Based on Improved EMD and Spectral Kurtosis[J]. Journal of Vibration, Measurement & Diagnosis, 2013,33(3): 478-482.
[19] Yukui Wang, Hongru Li, Peng Ye. Fault Feature Extraction of Hydraulic Pump Based on CNC De-noising and HHT [J]. Journal of Failure Analysis and Prevention, 2015,15(1):139-151.