基于融合模糊C均值与隐马尔科夫模型的滚动轴承的退化状态识别
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摘要
滚动轴承在长期的工作过程中其性能会出现不同程度的退化,如果能对滚动轴承的退化状态进行识别就可以做好维护措施。用自回归模型(Autoregressive model, AR)对滚动轴承全寿命周期的振动信号提取其系数及残差,用正常样本和失效样本特征建立模糊C均值模型(Fuzzy C Mean, FCM),用轴承正常样本的特征数据建立隐马尔科夫(Hidden Markov model, HMM)模型,将轴承的测试样本信号输入建立的FCM和HMM模型得到的两个退化指标,再将其作为特征矩阵输入到FCM模型,得到融合方法的性能退化曲线,结果表明该方法集中了空间统计距离模型和概率统计模型两者的优势,最后用IEEE PHM2012实验数据进行验证,表明所述方法与滚动轴承性能退化趋势保持一致并且可以提早发现早期故障。
The performance of Rolling bearings will appear degradation of different degrees which run for a long time in the service period. If the degradation state of the rolling bearing can be identified we can do maintenance measures. The paper extracts the coefficients and residuals of the vibration signals of the full life cycle of rolling bearings using autoregressive model(AR). The Fuzzy C Mean model(FCM)is established using the normal and failure samples and the Hidden Markov model(HMM)is established using the normal samples. The two degradation indicators which was obtained by imputing the under test data to FCM and HMM model are input to the FCM model as the input characteristic. Then the performance degradation curve is obtained. The method combines the advantages of spatial statistical distance model and probabilistic statistical model. Then the IEEE PHM2012 experimental data are used to verify the conclusions of this paper. The experimental analysis shows that the method is consistent with the performance degradation trend of rolling bearings and can detect early faults early.
The performance of Rolling bearings will appear degradation of different degrees which run for a long time in the service period. If the degradation state of the rolling bearing can be identified we can do maintenance measures. The paper extracts the coefficients and residuals of the vibration signals of the full life cycle of rolling bearings using autoregressive model(AR). The Fuzzy C Mean model(FCM)is established using the normal and failure samples and the Hidden Markov model(HMM)is established using the normal samples. The two degradation indicators which was obtained by imputing the under test data to FCM and HMM model are input to the FCM model as the input characteristic. Then the performance degradation curve is obtained. The method combines the advantages of spatial statistical distance model and probabilistic statistical model. Then the IEEE PHM2012 experimental data are used to verify the conclusions of this paper. The experimental analysis shows that the method is consistent with the performance degradation trend of rolling bearings and can detect early faults early.
引文
[ 1 ] ZHOU J,GUO H,ZHANG L,et al.Bearing performance degradation assessment using lifting wavelet packet symbolic entropy and SVDD[J].Shock and Vibration,2016,2016:1-10.
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[ 9 ] PURUSHOTHAM V,NARAYANAN S,PRASAD S A N.Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition[J].Ndt & E International,2005,38(8):654-664.
[10] 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.
[11] TOBON-MEJIA D A,MEDJAHER K,ZERHOUNI N,et al.A data-driven failure prognostics method based on mixture of gaussians hidden markov models[J].IEEE Transactions on Reliability,2012,61(2):491-503.
[12] LI C,CERRADA M,CABRERA D,et al.A comparison of fuzzy clustering algorithms for bearing fault diagnosis[J].Journal of Intelligent & Fuzzy Systems,2018,34(6):3365-3580.
[13] DONG S,XU X,CHEN R.Application of fuzzy C-means method and classification model of optimized K -nearest neighbor for fault diagnosis of bearing[J].Journal of the Brazilian Society of Mechanical Sciences & Engineering,2015,38(8):1-9.
[14] DONG M,HE D,BANERJEE P,et al.Equipment health diagnosis and prognosis using hidden semi-Markov models[J].International Journal of Advanced Manufacturing Technology,2005,30(7/8):738-749.
[15] 周建民,郭慧娟,张龙.基于非线性降维和模糊均值聚类的滚动轴承的性能退化在线评估方法[J].机械设计与研究,2017,33(6):86-89.
[16] NECTOUX P,GOURIVEAU R,MEDJAHER K,et al.PRONOSTIA:An experimental platform for bearings accelerated degradation tests[C].2012.
[ 2 ] ALBUGHARBEE H,TRENDAFILOVA I.Autoregressive modelling for rolling element bearing fault diagnosis[C].2015.
[ 3 ] 郭磊,李兴林,吴参,等.基于支持向量机的滚动轴承性能退化评估方法[J].轴承,2012(8):46-50.
[ 4 ] 周建民,徐清瑶,张龙,等.基于小波包Tsallis熵和FCM的滚动轴承性能退化评估[J].机械传动,2016,40(5):110-115.
[ 5 ] 吴军,郝刚,邓超,等.基于模糊C-均值聚类的轴承性能衰退评估方法[J].计算机集成制造系统,2015,21(4):1046-1050.
[ 6 ] 肖满生,文志诚,张居武,等.一种改进隶属度函数的FCM聚类算法[J].控制与决策,2015,30(12):2270-2274.
[ 7 ] 李志农,柳宝,侯娟.基于无限隐 Markov 模型的旋转机械故障诊断方法研究[J].仪器仪表学报,2016,37(10):2185-2192.
[ 8 ] 肖文斌.基于耦合隐马尔可夫模型的滚动轴承故障诊断与性能退化评估研究[D].上海:上海交通大学,2011.
[ 9 ] PURUSHOTHAM V,NARAYANAN S,PRASAD S A N.Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition[J].Ndt & E International,2005,38(8):654-664.
[10] 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.
[11] TOBON-MEJIA D A,MEDJAHER K,ZERHOUNI N,et al.A data-driven failure prognostics method based on mixture of gaussians hidden markov models[J].IEEE Transactions on Reliability,2012,61(2):491-503.
[12] LI C,CERRADA M,CABRERA D,et al.A comparison of fuzzy clustering algorithms for bearing fault diagnosis[J].Journal of Intelligent & Fuzzy Systems,2018,34(6):3365-3580.
[13] DONG S,XU X,CHEN R.Application of fuzzy C-means method and classification model of optimized K -nearest neighbor for fault diagnosis of bearing[J].Journal of the Brazilian Society of Mechanical Sciences & Engineering,2015,38(8):1-9.
[14] DONG M,HE D,BANERJEE P,et al.Equipment health diagnosis and prognosis using hidden semi-Markov models[J].International Journal of Advanced Manufacturing Technology,2005,30(7/8):738-749.
[15] 周建民,郭慧娟,张龙.基于非线性降维和模糊均值聚类的滚动轴承的性能退化在线评估方法[J].机械设计与研究,2017,33(6):86-89.
[16] NECTOUX P,GOURIVEAU R,MEDJAHER K,et al.PRONOSTIA:An experimental platform for bearings accelerated degradation tests[C].2012.