引入稀疏原子特征融合的滑动轴承摩擦故障状态监测  

Condition monitoring of friction fault of plain bearings by introducing sparse atoms feature fusion

作　　者：张峻宁[1] 张培林[1] 华春蓉[2] 李兵[1] 陈彦龙[1] 

机构地区：[1]军械工程学院车辆与电气工程系,石家庄050003 [2]西南交通大学机械工程学院,成都610031

出　　处：《航空动力学报》2017年第10期2476-2483,共8页Journal of Aerospace Power

基　　金：国家自然科学基金(51205405;51305454)

摘　　要：从信息融合理论出发,将特征的稀疏表达作为特征融合参数,提出一种结合K奇异值分解(KSVD)和最大相关最小冗余准则(mRMR)的轴承摩擦故障特征融合算法。该算法采用KSVD对信号稀疏化,将稀疏系数对应的字典原子作为特征融合的参数,用以表达非线性故障信息;针对字典原子集的优化选择问题,基于互信息的mRMR提出一种确定最优原子集的原子数目的准则;最后,通过最大化原则融合稀疏系数,提取故障状态监测的有效信息。轴承摩擦故障模拟实验的结果表明,所提方法能够更好地融合不同特征的故障信息,相比于单特征和其他融合特征方法,提高了约12%的故障识别率。Starting from the theory of information fusion,a algorithm of bearing friction fault feature fusion was proposed based on K-means singular value decomposition（KSVD）and maximum relevance minimum redundancy（mRMR）principle.First,in order to represent the nonlinear fault information,the algorithm uses KSVD to sparse the signals,and the dictionary atoms corresponding to the sparse coefficients were used as the parameters of the feature fusion.Second,in order to optimize the selection of dictionary atomic set,a criterion based on mutual information mRMR was proposed to determine the number of atoms in the optimal atomic set.Finally,the sparse coefficients were fused by maximizing the principle to extract the valid information for fault condition monitoring.The results of simulation experi-ment of bearing friction fault show that the proposed method can better integrate the feature information of the redundancy and complementarity.Compared with the single feature and other fusion method,the proposed method can improve fault recognition rate about 12%.

关 键 词：滑动轴承 状态监测 信息融合 稀疏表示 字典原子 互信息 

分 类 号：V229.2[航空宇航科学与技术—飞行器设计] TH117.2[机械工程—机械设计及理论]