PCA+CHMM在设备性能退化状态识别中的应用研究
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摘要
为了准确识别机械设备当前所处的退化状态,研究了一种基于PCA(主成分分析)和CHMM(连续型隐马尔可夫模型)结合的性能退化状态识别方法。首先提取设备振动信号全寿命周期的时域、频域、时频域的特征,经过初步筛选后组成新的特征集,使用PCA方法对其进行降维处理;然后利用降维后的数据,训练一个全寿命周期CHMM用来确定退化状态数目,再针对每个退化状态训练一个CHMM,通过比较观测序列处于各个模型下的似然概率值判断设备当前所处的退化状态;最后通过实验对比了PCA+CHMM和PCA+SVM、PCA+KNN、PCA+CART方法的各退化状态识别准确率,结果表明PCA+CHMM的平均识别准确率最高、识别效果较好,适用于设备退化状态的识别。
In order to accurately identify the degradation state of mechanical equipment,this paper researched a recognition method of performance degradation state based on PCA( principal component analysis) and CHMM( continuous hidden Markov model). Firstly,it extracted the vibration signal's time domain,frequency domain and time-frequency domain features in full life cycle,then constructed a new feature set by screening the features,then performed PCA dimensionality reduction for this set. Secondly,it trained a full life cycle CHMM to determine the number of degraded states by using of reduced dimension feature data,and then trained a CHMM for each degraded state,judged the degradation state of the device by comparing the likelihood probability of the observation sequence under each model. Finally,it compared the accuracy of PCA + CHMM and PCA + CHM,PCA + KNN and PCA + CART methods that to identify each degraded state. The results show that the average recognition accuracy of PCA + CHMM is the highest,the recognition effect is good,and it is suitable for the identification of device degraded state.
In order to accurately identify the degradation state of mechanical equipment,this paper researched a recognition method of performance degradation state based on PCA( principal component analysis) and CHMM( continuous hidden Markov model). Firstly,it extracted the vibration signal's time domain,frequency domain and time-frequency domain features in full life cycle,then constructed a new feature set by screening the features,then performed PCA dimensionality reduction for this set. Secondly,it trained a full life cycle CHMM to determine the number of degraded states by using of reduced dimension feature data,and then trained a CHMM for each degraded state,judged the degradation state of the device by comparing the likelihood probability of the observation sequence under each model. Finally,it compared the accuracy of PCA + CHMM and PCA + CHM,PCA + KNN and PCA + CART methods that to identify each degraded state. The results show that the average recognition accuracy of PCA + CHMM is the highest,the recognition effect is good,and it is suitable for the identification of device degraded state.
引文
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[8]李巍华,戴炳雄,张绍辉.基于小波包熵和高斯混合模型的轴承性能退化评估[J].振动与冲击,2013,32(21):35-40.(Li Weihua,Dai Bingxiong,Zhang Shaohui. Bearing performance degradation assessment based on wavelet packet entropy and Gaussian mixture model[J]. Journal of Vibration and Shock,2013,32(21):35-40.)
[9]魏永合,王明华.基于EEMD和SVM的滚动轴承退化状态识别[J].计算机集成制造系统,2015,21(9):2475-2483.(Wei Yonghe,Wang Minghua. Based on EEMD and SVM of rolling element degradation state recognition[J]. Computer Integrated Manufacturing Systems,2015,21(9):2475-2483.)
[10]姜万录,杨凯,董克岩,等.基于CHMM的轴承性能退化程度综合评估方法研究[J].仪器仪表学报,2016,37(9):2014-2021.(Jiang Wanlu,Yang Kai,Dong Keyan,et al. Research on comprehensive evaluation method of deterioration degree of bearing performance based on CHMM[J]. Chinese Journal of Scientific Instrument,2016,37(9):2014-2021.)
[11]Pearson K. On lines and planes of closest fit to systems of points in space[J]. Philosophical Magazine,1901,6(2):559-572.
[12]Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition[J]. Proceedings of the IEEE,1989,77(2):257-286.
[13]董绍江.基于优化支持向量机的空间滚动轴承寿命预测方法研究[D].重庆:重庆大学,2012.(Dong Shaojiang. Research on life prediction method of space rolling bearing based on optimized support vector machine[D]. Chongqing:Chongqing University,2012.)
[2]王建利.滚动轴承性能退化评价与趋势预测研究[D].大连:大连理工大学,2013.(Wang Jianli. Research on performance degradation evaluation and trend prediction of rolling bearings[D].Dalian:Dalian University of Technology,2013.)
[3]汪万紫.对交互特征和自适应聚类算法的研究[D].西安:陕西师范大学,2011.(Wang Wanzi. Research on interactive features and adaptive clustering algorithms[D]. Xi’an:Shaanxi Normal University,2011.)
[4]夏丽莎,方华京,罗贞.基于隐马尔可夫模型的故障诊断与预报综述[C]//中国控制与决策会议论文集. 2013:4881-4884.(Xia Lisha,Fang Huajing,Luo Zhen. Overview of fault diagnosis and prediction based on hidden Markov models[C]//Proc of the China Control and Decision Conference. 2013:4881-4884.)
[5]黄家善,张平均.基于PCA与DHMM的发动机故障诊断研究[J].厦门大学学报:自然科学版,2010,49(5):617-621.(Huang Jiashan,Zhang Pingjun. Research on engine fault diagnosis based on PCA and DHMM[J]. Journal of Xiamen University:Natural Science,2010,49(5):617-621.)
[6]张金春,张继军,曹彪.基于HMM的机载设备状态健康评估方法研究[J].计算机测量与控制,2015,23(2):491-493.(Zhang Jinchun,Zhang Jijun,Cao Biao. Research on health assessment method of airborne equipment based on HMM[J]. Computer Measurement&Control,2015,23(2):491-493.)
[7]肖文斌,陈进,周宇,等.小波包变换和隐马尔可夫模型在轴承性能退化评估中的应用[J].振动与冲击,2011,30(8):32-35.(Xiao Wenbin,Chen Jin,Zhou Yu,et al. Application of wavelet packet transform and hidden Markov model in evaluation of bearing performance degradation[J]. Journal of Vibration and Shock,2011,30(8):32-35.)
[8]李巍华,戴炳雄,张绍辉.基于小波包熵和高斯混合模型的轴承性能退化评估[J].振动与冲击,2013,32(21):35-40.(Li Weihua,Dai Bingxiong,Zhang Shaohui. Bearing performance degradation assessment based on wavelet packet entropy and Gaussian mixture model[J]. Journal of Vibration and Shock,2013,32(21):35-40.)
[9]魏永合,王明华.基于EEMD和SVM的滚动轴承退化状态识别[J].计算机集成制造系统,2015,21(9):2475-2483.(Wei Yonghe,Wang Minghua. Based on EEMD and SVM of rolling element degradation state recognition[J]. Computer Integrated Manufacturing Systems,2015,21(9):2475-2483.)
[10]姜万录,杨凯,董克岩,等.基于CHMM的轴承性能退化程度综合评估方法研究[J].仪器仪表学报,2016,37(9):2014-2021.(Jiang Wanlu,Yang Kai,Dong Keyan,et al. Research on comprehensive evaluation method of deterioration degree of bearing performance based on CHMM[J]. Chinese Journal of Scientific Instrument,2016,37(9):2014-2021.)
[11]Pearson K. On lines and planes of closest fit to systems of points in space[J]. Philosophical Magazine,1901,6(2):559-572.
[12]Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition[J]. Proceedings of the IEEE,1989,77(2):257-286.
[13]董绍江.基于优化支持向量机的空间滚动轴承寿命预测方法研究[D].重庆:重庆大学,2012.(Dong Shaojiang. Research on life prediction method of space rolling bearing based on optimized support vector machine[D]. Chongqing:Chongqing University,2012.)