Fault diagnosis method based on sample quantile permutation entropy

PDF(724 KB)

Journal of Vibration and Shock ›› 2019, Vol. 38 ›› Issue (23) : 152-156.

DAI Hongde1, CHEN Qiangqiang2, DAI Shaowu2, ZHU Min2

Author information +

History +

Abstract

Aiming at problems of rolling bearings’ obscure fault features and difficult to extract features, a new method to measure the complexity of time series, i.e., the sample quantile permutation entropy (SQPE) method was proposed and applied in feature extraction of rolling bearing fault vibration signals.By calculating sample quantile permutation entropies of fault vibration signals, fault features of different vibration signals were effectively separated.Then, the obtained entropy values were formed into feature vectors to construct a classifier, and realize fault diagnosis of rolling bearings.The proposed method was applied to analyze test data.The results showed that SQPE method can be used to effectively extract fault features of rolling bearings; the process of calculating entropy values avoids the selecting process of embedded dimension to effectively improve the self-adaptability of entropy value calculation, and expand its application range.

Key words

rolling bearing / permutation entropy / sample quantile / fault diagnosis

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

DAI Hongde1, CHEN Qiangqiang2, DAI Shaowu2, ZHU Min2. Fault diagnosis method based on sample quantile permutation entropy[J]. Journal of Vibration and Shock, 2019, 38(23): 152-156

References

[1] Logan D, Mathew J. USING THE CORRELATION DIMENSION FOR VIBRATION FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGS—I. BASIC CONCEPTS[J]. Mechanical Systems & Signal Processing. 1996, 10(3): 241-250.
[2] 程军圣，于德介，杨宇. 基于EMD和分形维数的转子系统故障诊断[J]. 中国机械工程. 2005(12): 1088-1091.
CHEN Junsheng, YU Dejie, YANG Yu. Fault Diagnosis for Rotor System Based on EMD and Fractal Dimension[J]. China Mechanical Engineering. 2005(12): 1088-1091.
[3] 侯荣涛，闻邦椿，周飙. 基于现代非线性理论的汽轮发电机组故障诊断技术研究[J]. 机械工程学报. 2005, 41(2): 142-147.
HOU Rongtao, WEN Bangchun, ZHOU Biao. Study on fault diagnosis technique to turbo unit based on modern nonlinear theories[J]. Chinese Journal of Mechanical Engineering. 2005, 41(2): 142-147.
[4] Yan R, Gao R X. Approximate Entropy as a diagnostic tool for machine health monitoring[J]. Mechanical Systems and Signal Processing. 2007, 21(2): 824-839.
[5] Christoph B, Bernd P. Permutation entropy: a natural complexity measure for time series[J]. Physical Review Letters. 2002, 88(17): 174102.
[6] Bandt C. Permutation Entropy and Order Patterns in Long Time Series[M]. 2016.
[7] Yan R, Liu Y, Gao R X. Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines[J]. Mechanical Systems and Signal Processing. 2012, 29: 474-484.
[8] 郑近德，潘海洋，张俊，等. APEEMD及其在转子碰摩故障诊断中的应用[J]. 振动.测试与诊断. 2016, 36(02): 257-263.
ZHENG Jinde, PAN Haiyang, ZHANG Jun, et al. Adaptive partly-ensemble empirical mode decomposition and its application for rotor rubbing fault diagnosis[J]. Journal of Vibration, Measurement & Diagnosis. 2016, 36(02): 257-263.
[9] 袁明，罗志增. 基于排列组合熵的表面肌电信号特征分析[J]. 杭州电子科技大学学报. 2012, 32(1): 64-67.
YUAN Ming, LUO Zhizeng. Feature Analysis of SEMG based on Permutation Entropy[J]. Journal of Hangzhou Dianzi University. 2012, 32(1): 64-67.
[10] Aziz W, Arif M. Multiscale Permutation Entropy of Physiological Time Series[C]. 2005.
[11] 代俊习，郑近德，潘海洋，等. 基于复合多尺度熵与拉普拉斯支持向量机的滚动轴承故障诊断方法[J]. 中国机械工程. 2017, 28(11): 1339-1346.
DAI Junxi, ZHENG Jinde, PAN Haiyang, et al. Rolling Bearing Fault Diagnosis Method Based on Composite Multiscale Entropy and Laplacian SVM[J]. China Mechanical Engineering. 2017, 28(11): 1339-1346.
[12] 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(05): 802-806.
ZHENG Jinde, CHENG Junsheng, YANG Yu. A Rolling Bearing Fault Diagnosis Method Based on LCD and Permutation Entropy[J]. Journal of Vibration, Measurement & Diagnosis. 2014, 34(05): 802-806.
[13] 冯辅周，饶国强，司爱威，等. 排列熵算法的应用与发展[J]. 装甲兵工程学院学报. 2012, 26(02): 34-38.
FENG Fuzhou, RAO Guoqiang, SI Aiwei, et al. Application and Development of Permutation Entropy Algorithm[J]. Journal of Academy of Armored Force Engineering. 2012, 26(02): 34-38.
[14] 丁闯，张兵志，冯辅周，等. 局部均值分解和排列熵在行星齿轮箱故障诊断中的应用[J]. 振动与冲击. 2017, 36(17): 55-60.
DING Chuang, ZHANG Bingzhi, FENG Fuzhou, et al. Application of local mean decomposition and permutation entropy in fault diagnosis of planetary gearboxes[J]. Journal of Vibration and Shock. 2017, 36(17): 55-60.
[15] 郑近德，程军圣，杨宇. 多尺度排列熵及其在滚动轴承故障诊断中的应用[J]. 中国机械工程. 2013, 24(19): 2641-2646.
ZHENG Jinde, CHENG Junsheng, YANG Yu. Multi-scale Permutation Entropy and Its Application to Rolling Bearing Fault Diagnosis[J]. China Mechanical Engineering. 2013, 24(19): 2641-2646.
[16] 饶国强，冯辅周，司爱威，等. 排列熵算法参数的优化确定方法研究[J]. 振动与冲击. 2014, 33(01): 188-193.
RAO Guoqiang, FENG Fuzhou, SI Aiwei, et al. Method for optimal determination of parameters in permutation entropy algorithm[J]. Journal of Vibration and Shock. 2014, 33(01): 188-193.
[17] 管河山，王谦，唐德文. 基于分位数特征提取的时间序列模式分类[J]. 计算机工程. 2015, 41(3): 167-171.
Guan Heshan, Wang Qian, Tang Dewen. Time Sequence Pattern Classification Based on Quantile Feature Extraction[J]. Computer Engineering. 2015, 41(3): 167-171.
[18] 李娟，景博，羌晓清，等. 基于样本分位数的机载燃油泵故障状态特征提取及实验研究[J]. 航空学报. 2016, 37(09): 2851-2863.
LI Juan, JIN Bo, QIANG Xiaoqing, et al. Fault states feature extraction and experimental study for airborne fuel pumps based on sample quantile[J]. Acta Aeronautica et Astronautica Sinica. 2016, 37(09): 2851-2863.
[19] THE Case Western Reserve University Bearing Data Center. Bearing Data Center Fault Test Data[EB/OL].[2012-03-01].http://www.eecs.cwru.edu/laboratory/bearing.