总体局部特征尺度分解的滚动轴承故障诊断
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摘要
针对滚动轴承非平稳性的振动信号,提出了基于总体局部特征尺度分解(Ensemble Local Characteristic-scale Decomposition, ELCD)的排列熵及相关向量机的滚动轴承故障诊断方法。首先,对振动信号进行ELCD分解,获得一系列内禀尺度分量(Instrinsic Scale Component, ISC);其次,根据分解后ISC分量的峭度值选取主ISC分量,计算主ISC分量的排列熵并将其组合成特征向量;最后,将特征向量输入相关向量机进行训练与测试,从而识别滚动轴承的故障类型。对实验信号的分析表明,该方法能够有效的诊断出滚动轴承不同的工作状态,且效果较局部特征尺度分解方法好。
Aiming at the no stationary characteristic of a gear fault vibration signal, it proposes a recognition method based on ELCD(Ensemble local Characteristic-scale decomposition) permutation entropy and RVM. First, the vibration signal was decomposed by ELCD, then a series of intrinsic scale components were obtained; Secondly, according to the kurtosis of ISCs, principal ISCs were selected, then, calculate the permutation entropy of principal ISCs and combined into a feature vector; Finally, the feature vector were input RVM classifier to train and test to identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnosis four kinds of working condition, and the effect is better than local Characteristic-scale decomposition method.
Aiming at the no stationary characteristic of a gear fault vibration signal, it proposes a recognition method based on ELCD(Ensemble local Characteristic-scale decomposition) permutation entropy and RVM. First, the vibration signal was decomposed by ELCD, then a series of intrinsic scale components were obtained; Secondly, according to the kurtosis of ISCs, principal ISCs were selected, then, calculate the permutation entropy of principal ISCs and combined into a feature vector; Finally, the feature vector were input RVM classifier to train and test to identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnosis four kinds of working condition, and the effect is better than local Characteristic-scale decomposition method.
引文
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[10] 郭晓鹏,杨淑霞,杨里. 基于粗糙集降维和相关向量机的长期用电需求预测方法[J]. 中南大学学报(自然科学版), 2013, 44(12): 5133-5138.GUO XiaoPeng, YANG ShuXia, YANG Li. Long-term electricity demand forecasting method based on rough set reduction and relevance vector machine [J].Journal of Central South University (Natural Science Edition), 2013,44(12):5133-5138(In Chinese).
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[13] SUN Jie-di, XIAO Qi-yang, WEN Jiang-tao, et al. Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM[J]. Measurement, 2014, 55(9):434-443.
[2] ZHANG Fan, LIU Yu, CHEN Chu-jie, et al. Fault diagnosis of rotating machinery based on kernel density estimation and Kullback-Leibler divergence [J]. Journal of Mechanical Science and Technology, 2014, 11(28):4441-4454.
[3] 季云, 王恒, 朱龙彪,等. 基于HMM的机械设备运行状态评估与故障预测研究综述[J]. 机械强度, 2017,39(3):511-517.JI Yun, WANG Heng, ZHU LongBiao, et al. Review on opearation state assessment and prognostics for mechanical equipment based on hidden markov model [J]. Journal of Mechanical Strength, 2017, 39(3):511-517(In Chinese).
[4] 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[J].振动工程学报, 2012, 25(2):215-220.CHENG JunSheng,ZHENG JinDe, YANG Yu. A nonstationary signal analysis approach-the local characteristic-scale decomposition method [J]. Journal of Vibration Engineering, 2012, 25(2):215-220(In Chinese).
[5] 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.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(5):802-806(In Chinese).
[6] 齐鹏, 范玉刚, 吴建德. 基于Morlet小波-SVD和VPMCD的故障诊断方法研究[J]. 机械强度, 2017(2):247-253.QI Peng, HUAN YuGang, WU JianDe. Study on fault diagnosis method based on morlet wavelet-SVD and VPMCD[J].Journal of Mechanical Strength, 2017,39(2):247-253(In Chinese).
[7] 杨宇,曾鸣,程军圣. 局部特征尺度分解方法及其分解能力研究[J]. 振动工程学报,2012,25(5):602-609.YANG Yu,ZENG Ming,CHENG JunSheng. Research on local characteristic-scale decomposition and its capacities [J].Journal of Vibration Engineering, 2012, 25(5):602-609(In Chinese).
[8] WANG C W, YOU W H. Boosting-SVM: effective learning with reduced data dimension [J]. Applied Intelligence, 2013, 39(3):465-474.
[9] 孙茜,曾周末,李健. 相关向量机在光纤预警系统模式识别中的应用[J]. 天津大学学报(自然科学与工程技术版),2014,47(12):1115-1120.SUN Qian,ZENG ZhouMo,LI Jian. Application of relevance vector machine in pattern recognition of optical fiber pre-warning system[J]. Journal of Tianjin University (Science and Technology), 2014, 47(12):1115-1120(In Chinese).
[10] 郭晓鹏,杨淑霞,杨里. 基于粗糙集降维和相关向量机的长期用电需求预测方法[J]. 中南大学学报(自然科学版), 2013, 44(12): 5133-5138.GUO XiaoPeng, YANG ShuXia, YANG Li. Long-term electricity demand forecasting method based on rough set reduction and relevance vector machine [J].Journal of Central South University (Natural Science Edition), 2013,44(12):5133-5138(In Chinese).
[11] WU Z H, HUANG N E. A study of the characteristics of white noise using the empirical mode decomposition method [J]. Proceedings of the Royal Society of London.Series A: Mathematical, Physical and Engineering Sciences, 2004, 460(2046):1597-1611.
[12] CHRISTOPH B, BERND P. Permutation entropy: a natural complexity measure for time series [J]. Physical Review Letters, 2002, 88(17):174102.
[13] SUN Jie-di, XIAO Qi-yang, WEN Jiang-tao, et al. Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM[J]. Measurement, 2014, 55(9):434-443.