基于改进KFDA独立特征选择的故障诊断
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摘要
为了有效利用故障特征集中对故障敏感的特征进行故障诊断,对核Fishier判别分析(KFDA)进行改进,提出基于改进KFDA独立特征选择的故障诊断方法。该方法首先从多个角度提取故障振动信号的故障特征,构建原始高维多域混合故障特征集。然后,采用改进的核Fisher特征选择方法为每两类故障状态独立选择敏感特征集。最后,采用"一对一"的方法训练多个二分类相关向量机(RVM),将得到的敏感特征集输入多分类故障诊断模型进行识别。齿轮故障诊断实例表明,所提方法具备较高的诊断准确率。
In order to diagnose fault effectively by using sensitive features contained in the feature set, KFDA was improved in this paper and a fault diagnosis method based on improved KFDA individual feature selection was proposed. Firstly, the mixed feature of the fault vibration signal was extracted from different angels, and the original high-dimensional and multi-domain feature set was constructed. Then, an improved kernel Fisher feature selection method was proposed and used to select individual sensitive feature subset for each pair of class. Finally, a one-against-one approach was applied to train several relevance vector machine(RVM) binary classifiers, and sensitive feature was input into the multi-class fault diagnosis model for recognizing the fault types. The experimental results of gear indicate that the proposed method is of high diagnostic accuracy.
In order to diagnose fault effectively by using sensitive features contained in the feature set, KFDA was improved in this paper and a fault diagnosis method based on improved KFDA individual feature selection was proposed. Firstly, the mixed feature of the fault vibration signal was extracted from different angels, and the original high-dimensional and multi-domain feature set was constructed. Then, an improved kernel Fisher feature selection method was proposed and used to select individual sensitive feature subset for each pair of class. Finally, a one-against-one approach was applied to train several relevance vector machine(RVM) binary classifiers, and sensitive feature was input into the multi-class fault diagnosis model for recognizing the fault types. The experimental results of gear indicate that the proposed method is of high diagnostic accuracy.
引文
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[9] 周勇,何创新.基于独立特征选择与相关向量机的变载荷轴承故障诊断 [J].振动与冲击,2012,31(3):157-161.ZHOU Yong,HE ChuangXin.Bearing fault diagnosis under varying load condition based on individual feature selection and relevance vector machine [J].Journal of Vibration and Shock,2012,31(3):157-161 (In Chinese).
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[11] 张良,张前图.基于LCD模糊熵和流行学习的故障特征提取方法[J].机械强度,2016,38(2):225-230ZHANG Liang,ZHANG QianTu.Fault feature extraction method based on LCD fuzzy entropy and manifold learning[J].Journal of Mechanical Strength,2016,38(2):225-230 (In Chinese).
[2] 李锋,汤宝平,陈法法.基于线性局部切空间排列维数化简的故障诊断[J].振动与冲击,2012,31(13):36-40.LI Feng,TANG BaoPing,CHEN FaFa.Fault diagnosis model based on dimension reduction using linear local tangent space alignment [J].Journal of Vibration and Shock,2012,31(13):36-40 (In Chinese).
[3] 李峰,王家序,杨荣松.有监督不相关正交局部保持映射故障辨识[J].仪器仪表学报,2013,34(5):1113-1120.LI Feng,WANG JiaXu,YANG RongSong.Fault identification method based on supervised uncorrelated orthogonal locality preserving projection[J].Chinese Journal of Scientific Instrument,2013,34(5):1113-1120 (In Chinese).
[4] ZHAI L D,DING Z Y,JIA Y,et al.A word positionrelated LDA model [J].International Journal of Pattern Recognition and Artificial Intelligence,2011,25(6):909-925.
[5] MIKA Sebastian.Fisher Discriminant Analysis With Kernels [J].Neural Networks for Signal Processing,1999(9):41-48.
[6] Wang Lei.Feature Selection with Kernel Class Separability [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(9):1534-1546.
[7] 王广斌,刘义伦,黄良沛.基于核schur正交局部Fisher判别的转子故障诊断 [J].仪器仪表学报,2010,31(5):1005-1009.WANG GuangBin,LIU YiLun,HUANG LiangPei.Rotor fault diagnosis based on kernel schurorthogonal local Fisher discriminant [J].Chinese Journal of Scientific Instrument,2010,31(5):1005-1009 (In Chinese).
[8] Ye LV,Huizhong YANG.A Multimodel Approach for Soft Sensor Development Based on Feature Extraction Using Weighted Kernel Fisher Criterion [J].Chinese Journal of Chemical Engineering,2014,22(2):146-152.
[9] 周勇,何创新.基于独立特征选择与相关向量机的变载荷轴承故障诊断 [J].振动与冲击,2012,31(3):157-161.ZHOU Yong,HE ChuangXin.Bearing fault diagnosis under varying load condition based on individual feature selection and relevance vector machine [J].Journal of Vibration and Shock,2012,31(3):157-161 (In Chinese).
[10] Hsu C W,Lin C J.A comparison of methods for multiclass support vector machines [J].IEEE Transactions on Neural Networks,2002,13(4):10-26.
[11] 张良,张前图.基于LCD模糊熵和流行学习的故障特征提取方法[J].机械强度,2016,38(2):225-230ZHANG Liang,ZHANG QianTu.Fault feature extraction method based on LCD fuzzy entropy and manifold learning[J].Journal of Mechanical Strength,2016,38(2):225-230 (In Chinese).