精细复合多尺度波动散布熵在液压泵故障诊断中的应用

PDF(2140 KB)

振动与冲击 ›› 2022, Vol. 41 ›› Issue (8) : 7-16.

论文

姜万录1,2，赵亚鹏1,2，张淑清3，李满1,2

作者信息 +

Application of refined composite multiscale fluctuation dispersion entropy in hydraulic pumps fault diagnosis

JIANG Wanlu1,2，ZHAO Yapeng1,2，ZHANG Shuqing3，LI Man1,2

Author information +

文章历史 +

摘要

液压泵振动信号具有非线性、非平稳性的特点，熵算法在该类信号分析方面有着独到的优势，但传统的熵算法在液压泵振动信号特征提取中有计算速度慢、熵值不准确、不稳定等不足，为了更有效地提取故障特征信息并提高故障诊断准确性，将精细复合多尺度波动散布熵(refined composite multiscale fluctuation dispersion entropy ，RCMFDE)引入到液压泵的故障特征提取中，提出了一种基于RCMFDE和粒子群优化支持向量机结合的液压泵故障诊断方法。计算不同故障振动信号的RCMFDE，并选取合适尺度下的多个RCMFDE值作为特征向量形成特征样本，输入粒子群优化支持向量机中进行故障分类识别。通过仿真信号和液压泵故障实测信号进行分析，并将所提出的方法与基于多尺度样本熵(multiscale sample entropy ，MSE)、多尺度排列熵(multiscale permutation entropy ，MPE)、多尺度符号动态熵(multiscale symbolic dynamic entropy ，MSDE)、多尺度散布熵(multiscale dispersion entropy ，MDE)、精细复合多尺度散布熵(refined composite multiscale dispersion entropy ，RCMDE)、多尺度波动散布熵(multiscale fluctuation dispersion entropy ，MFDE)的故障特征提取方法进行对比。试验结果表明，该方法能够更加准确地识别多类液压泵故障并能对液压泵性能退化程度进行有效评估。

Abstract

The vibration signal of hydraulic pump has the characteristics of non-linearity and non-stationarity. Entropy algorithms have a unique advantage in this kind of signal analysis. However, the traditional entropy algorithms still have shortcomings of slow calculation speed, inaccurate entropy value and unstable entropy value in hydraulic pump vibration signal feature extraction. To extract fault feature information more effectively and improve fault diagnosis accuracy, the refined composite multiscale fluctuation dispersion entropy(RCMFDE) is introduced into the fault feature extraction of hydraulic pumps. A hydraulic pump fault diagnosis method based on RCMFDE and particle swarm optimization support vector machine(PSO-SVM) algorithm is proposed. Firstly, the RCMFDE values of different fault vibration signals are calculated and the multi-RCMFDE values are selected at appropriate scales as feature vectors to form feature samples. Then the feature samples are input to PSO-SVM for fault diagnosis. Through analyzing the simulation signals and hydraulic pump experiments signals, the proposed method is compared with the fault diagnosis methods based on multiscale sample entropy(MSE), multiscale permutation entropy(MPE), multiscale symbolic dynamic entropy(MSDE), multiscale dispersion entropy(MDE), refined composite multiscale dispersion entropy (RCMDE) and multiscale fluctuation dispersion entropy(MFDE). Experimental results show that the proposed method can accurately identify multiple types of hydraulic pump faults and effectively evaluate the performance degradation degree of hydraulic pump.

导出引用

姜万录1,2，赵亚鹏1,2，张淑清3，李满1,2. 精细复合多尺度波动散布熵在液压泵故障诊断中的应用[J]. 振动与冲击, 2022, 41(8): 7-16

JIANG Wanlu1,2，ZHAO Yapeng1,2，ZHANG Shuqing3，LI Man1,2. Application of refined composite multiscale fluctuation dispersion entropy in hydraulic pumps fault diagnosis[J]. Journal of Vibration and Shock, 2022, 41(8): 7-16

参考文献

[1] Pincus S M. Approximate entropy as a measure of system complexity[J]. Proceedings of the National Academy of Sciences of the United States of America， 1991, 88(6): 2297-2301.
[2] Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy[J]. American Journal of Physiology-Heart and Circulatory Physiology. 2000, 278(6): H2039-H2049.
[3] Bandt C, Pompe B. Permutation entropy: a natural complexity measure for time series[J]. Physical Review Letters ，2002, 88(17): 174102.
[4] Li Y, Yang Y, Li G, et al. A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection[J]. Mechanical Systems and Signal Processing. 2017, 91: 295-312.
[5] Rostaghi M, Azami H. Dispersion Entropy: A Measure for Time-Series Analysis[J]. IEEE Signal Processing Letters. 2016, 23(5): 610-614.
[6] 姜万录,董克岩,朱勇,等.基于多尺度熵偏均值的液压泵故障特征识别[J]. 液压与气动, 2016,(07): 12-17.
JIANG Wanlu, DONG Keyan, ZHU Yong, et al. Fault Feature Identification Based on Partial Mean of Multiscale Entropy for Hydraulic Pump[J]. Chinese Hydraulics & Pneumatics. 2016, (7):12-17.
[7] Costa M, Goldberger A L, Peng A C K. Multiscale Entropy Analysis of Complex Physiologic Time Series[J]. Physical Review Letters， 2002，89： 068102.
[8] Morabito F C, Labate D, La Foresta F, et al. Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG[J]. Entropy. 2012, 14(7): 1186-1202.
[9] 王余奎,李洪儒,叶鹏. 基于多尺度排列熵的液压泵故障识别[J]. 中国机械工程. 2015(4): 518-522, 523.
Wang Yukui, Li Hongru, Ye Peng. Fault Identification of Hydraulic Pump Based on Multi-scale Permutation Entropy[J]. China Mechanical Engineering, 2015, 26(4): 518-523.
[10] Wu S, Wu P, Wu C, et al. Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine[J]. Entropy (Basel, Switzerland). 2012, 14(8): 1343-1356.
[11] Azami H. Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals[J]. IEEE Transactions on Biomedical Engineering. 2017, 64(12): 2872-2879.
[12] Javier E, Hamed A. Coarse-Graining Approaches in Univariate Multiscale Sample and Dispersion Entropy[J]. Entropy. 2018, 20(2): 138.
[13] 乔新勇，顾程，韩立军. 基于VMD多尺度散布熵的柴油机故障诊断方法[J]. 汽车工程. 2020, 42(8): 1139-1144.
Qiao Xinyong, Gu Cheng, Han Lijun. Diesel Engine Fault Diagnosis Method Based on VMD and Multi-Scale Dispersion Entropy[J]. Automotive Engineering. 2020, 42(8): 1139-1144.
[14] 吴守军，冯辅周，吴春志，等. 基于VMD-DE的坦克行星变速箱故障诊断方法研究[J]. 振动与冲击. 2020, 39(10): 170-179.
WU Shoujun, FENG Fuzhou, WU Chunzhi, et al. Research on fault diagnosis method of tank planetary gearbox based on VMD-DE[J]. JOURNAL OF VIBRATION AND SHOCK. 2020, 39(10): 170-179.
[15] 付文龙，谭佳文，王凯. 基于VMD散布熵与改进灰狼优化SVDD的轴承半监督故障诊断研究[J]. 振动与冲击. 2019, 38(22): 190-197.
FU Wenlong, TAN Jiawen, WANG Kai. Semi-supervised fault diagnosis of bearings based on the VMD dispersion entropy and improved SVDD with modified grey wolf optimizer[J]. JOURNAL OF VIBRATION AND SHOCK. 2019, 38(22): 190-197.
[16] 柯赟，宋恩哲，姚崇，等. 层次离散熵及其在高压共轨喷油器故障诊断中的应用[J]. 振动与冲击. 2021, 40(02): 72-80.
KE Yun, SONG Enzhe, YAO Chong，DONG Quan, et al. Hierarchical dispersion entropy and its application in fault diagnosis of high pressure common rail injectors[J]. JOURNAL OF VIBRATION AND SHOCK. 2021, 40(2): 72-80.
[17] 李颖,王金东,赵海洋, 等. 基于参数优化VMD和MDE的往复压缩机轴承故障诊断方法[J]. 组合机床与自动化加工技术. 2019(04): 120-123.
Li Ying, WANG Jindong, ZHAO Haiyang, et al. Fault Diagnosis Method of Reciprocating Compressor Bearing Based on Parameter Optimization VMD and MDE[J]. Modular Machine Tool ＆ Automatic Manufacturing Technique. 2019(04): 120-123.
[18] 李从志，郑近德，潘海洋，等. 基于精细复合多尺度散布熵与支持向量机的滚动轴承故障诊断方法[J]. 中国机械工程. 2019, 30(14): 1713-1719, 1726.
LI Congzhi, ZHENG Jinde, PAN Haiyang, et al. Fault Diagnosis Method of Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Support Vector Machine[J]. China Mechanical Engineering, 2019, 30(14): 1713-1719,1726.
[19] Azami H, Kinney-Lang E, Ebied A, et al. Multiscale dispersion entropy for the regional analysis of resting-state magnetoencephalogram complexity in Alzheimer's disease[C]. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Seogwipo, South Korea, 2017.
[20] Li Y, Liang X, Wei Y, et al. A method based on refined composite multi-scale symbolic dynamic entropy and ISVM-BT for rotating machinery fault diagnosis[J]. Neurocomputing (Amsterdam). 2018, 315: 246-260.
[21] 丁闯，冯辅周，张兵志，等. 改进多尺度符号动力学信息熵及其在行星变速箱特征提取中的应用[J]. 振动与冲击. 2020, 39(13): 97-102.
DING Chuang, FENG Fuzhou, ZHANG Bingzhi, et al. MMSDE and its application in feature extraction of a planetary gearbox[J]. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(13): 97-102
[22] Azami H, Escudero J. Amplitude- and Fluctuation-Based Dispersion Entropy[J]. Entropy. 2018, 20(3): 210.
[23] Azami H, Arnold S E, Sanei S, et al. Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases[J]. IEEE Access. 2019, 7: 68718-68733.
[24] Gan X, Lu H, Yang G. Fault Diagnosis Method for Rolling Bearings Based on Composite Multiscale Fluctuation Dispersion Entropy[J]. Entropy. 2019, 21(3): 290.
[25] 姜万录，吴胜强. 基于SVM和证据理论的多数据融合故障诊断方法[J]. 仪器仪表学报. 2010, 31(8): 1738-1743.
JIANG Wanlu, WU Shengqiang. Multi-data fusion fault diagnosis method based on SVM and evidence theory[J]. Chinese Journal of Scientific Instrument. 2010, 31(8): 1738-1743.
[26] Kennedy J, Eberhart R. Particle Swarm Optimization[C]. WA, Australia: Proceedings of ICNN’95-International Conference on Neural Networks, 1995.
[27] Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis of biological signals[J]. Physical review. E, Statistical, nonlinear, and soft matter physics. 2005, 71(2): 21906.
[28] Castiglioni P, Coruzzi P, Bini M, et al. Multiscale Sample Entropy of Cardiovascular Signals: Does the Choice between Fixed- or Varying-Tolerance among Scales Influence Its Evaluation and Interpretation?[J]. Entropy. 2017, 19(11): 590.
[29] 姜万录，孔德田，李振宝，等. 基于完备总体经验模态分解和模糊熵结合的液压泵退化特征提取方法[J]. 计量学报. 2020, 41(02): 202-209.
JIANG Wanlu, KONG Detian, LI Zhenbao, et al. Degradation Feature Extraction Method of Hydraulic Pump Based on Integrated Complete Ensemble Empirical Mode Decomposition and Fuzzy Entropy[J]. Acta Metrologica Sinica. 2020, 41(02): 202-209.