中文摘要：

针对D-S证据理论很难确定基本概率分配（BPA）及支持向量机（SVM）的硬判决难获得概率输出的缺陷,融合D-S证据理论和SVM算法提出了一种多数据融合故障诊断新方法：利用＂一对一＂多类SVM分配了BPA,引入基于矩阵分析的融合算法解决了证据理论存在的计算瓶颈问题。对液压泵进行了试验,首先,采集了柱塞泵松靴、缸体与配流盘磨损等故障信号,应用小波包对采集的信号进行了预处理,提取了12个时频特征量;最后,用所提出的基于SVM和证据理论的多数据融合新方法进行了诊断。试验结果表明,新方法故障确诊率高,诊断有效。

英文摘要：

Aiming at the difficulty that evidence theory can hardly determine basic probability assignment （BPA） and SVM can hardly obtain probability output,a new multi-data fusion fault diagnosis method is proposed,which is based on SVM and D-S evidence theory.In the method,BPA is assigned based on one-versus-one multi-class SVM;a fusion arithmetic based on matrix analysis is presented to solve the calculation bottleneck problem of evidence theory.The method is tested on a hydraulic pump;at first,the fault signals of slipper looseness and wearing between cylinder body and valve plate are collected;the measured signals are preprocessed using wavelet cluster;12 fault features are picked up in time domain and frequency domain;at last,the fault is diagnosed using the proposed new method.Experiment results show that the new method features high correct diagnosis rate and is effective in fault diagnosis.