基于多分类支持向量机和D-S证据理论的轴承故障诊断  

Fault Diagnosis of Bearings Based on Multi-class SVM and D-S Evidence Theory

作　　者：梅检民[1,2] 赵慧敏[1] 肖云魁[1] 周斌[2] 

机构地区：[1]天津大学机械工程学院,天津300072 [2]军事交通学院汽车工程系,天津300161

出　　处：《汽车工程》2015年第1期114-119,共6页Automotive Engineering

摘　　要：针对支持向量机(SVM)硬判定输出分类结果缺乏定量评价的问题,提出了一种多分类SVM后验概率建模的改进方法。通过引入D-S证据理论,得到多分类SVM在D-S证据理论识别框架下的基本概率分配,使样本在分类时同时具有定性解释和定量评价。接着,将多源信息送入SVM之后在决策级对多个SVM分类输出进行证据融合,以提高诊断精度。最后,将该方法应用于轴承故障的诊断中。结果表明,该方法能正确分类采用单源信息时所错分样本,降低识别的整体误差,显著提高故障诊断的准确性。In view of the problem that the classification results of hard decision output of support vector ma-chine （ SVM） lack of quantitative evaluation, an improved modeling method for the posterior probability of multi-class SVM is proposed. Through the introduction of D-S evidence theory, the basic probability assignment （ BPA） of multi-class SVM under the recognition frame of evidence theory is obtained to enable the samples have both qualita-tive explanation and quantitative evaluation. And then the multi-source information is delivered to SVM to conduct the evidence fusion of several SVM classification outputs for improving diagnostic accuracy. Finally the method is applied to the fault diagnosis of bearings with a result showing that the method proposed can correctly classify the samples being classified wrongly using single-source information, reduce the overall error of recognition frame, and enhance the correctness of fault diagnosis remarkably.

关 键 词：故障诊断 支持向量机 后验概率 D-S证据理论 信息融合 

分 类 号：U472.9[机械工程—车辆工程]