一种新的稀疏分类融合方法及其在机车轴承故障诊断中的应用  

A Novel Sparse Classification Fusion Method and Its Application in Locomotive Bearing Fault Diagnosis

作　　者：刘小峰[1] 舒仁杰 柏林[1] 罗宏林 LIU Xiaofeng;SHU Renjie;BO Lin;LUO Honglin(The State Key Laboratory of Mechanical Transmission(Chongqing University),Shapingba District,Chongqing 400044,China)

机构地区：[1]机械传动国家重点实验室(重庆大学),重庆市沙坪坝区400044

出　　处：《中国电机工程学报》2020年第17期5675-5681,共7页Proceedings of the CSEE

基　　金：国家自然科学基金项目(51975067,51675064)。

摘　　要：针对高速列车轴箱轴承的健康状态监测中存在的故障数据不充分,单个分类器故障识别精度不高的问题,提出基于K-SVD重构残差的稀疏分类融合诊断方法。该方法利用K-SVD分解后的重构误差表征训练样本在每种故障模式下的分类趋势,根据样本的重构残差分布估计各K-SVD分类器的混淆矩阵并计算相应的可靠性矩阵,再结合D-S证据融合理论对测试样本在各K-SVD分类器下的故障识别结果进行融合分析,得到最终诊断结果。该方法在动车轴箱轴承故障试验中的应用结果表明,提出的新的稀疏分类融合方法较单一特征分类器、传统的投票融合法的识别精度有显著提高,即使是在小样本情况下其容错性、稳定性也较好,解决了高速列车轴箱轴承故障诊断中单传感器检测精度低、单一域特征信息不足即证据冲突情况下分类信息难以有效融合的问题。Aiming at the problems of insufficient fault data and low accuracy of fault diagnosis in the health monitoring of high-speed train axle box bearing, a sparse classification fusion diagnosis method based on K-SVD reconstruction residual was proposed. In this method, the reconstruction error of K-SVD was used to characterize the classification trend of samples in each fault mode. The confusion matrix of each K-SVD classifier was estimated and the corresponding reliability matrix was calculated according to the distribution of the reconstruction residual of the samples. Then the fault identification results of the tested samples using each K-SVD classifier are fused based on the D-S evidence fusion theory to get the final diagnosis results. The experimental results of EMU axle box bearing showed that the recognition accuracy of proposed method is significantly improved compared with the single feature classifier and the traditional voting fusion method. Even in the case of small sample, its fault tolerance and stability are better. It solves the problems of low detection accuracy of single sensor, insufficient feature information of single domain and difficulty to effectively integrate the classification information in the fault diagnosis of the axle box bearing of the high-speed train.

关 键 词：轴箱轴承 K-SVD稀疏分类器 混淆矩阵 融合诊断 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TH133.3[自动化与计算机技术—控制科学与工程]