位置优化Fisher测度在轴承故障特征选择中的应用  

Application of position optimized Fisher measure in bearing fault feature selection

作　　者：刘浩炜 刘韬[1] 涂文涛 陈庆[1] Liu Haowei;Liu Tao;Tu Wentao;Chen Qing(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650504,China)

机构地区：[1]昆明理工大学机电工程学院,昆明650504

出　　处：《电子测量与仪器学报》2020年第8期124-132,共9页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(51675251);云南省重点项目(201601PE00008)资助。

摘　　要：为了提高滚动轴承故障诊断率,充分利用时域、频域及时频域特征对轴承运行状态的识别能力的差异性,并考虑到特征之间易出现不相关、冗余干扰等问题以及实际工程对简单、快速、有效的特征评估方法的需求,在构建轴承混合域特征集的基础上,提出了一种位置优化Fisher测度(POFM)方法并将其应用于轴承故障特征选择。该方法基于Fisher准则,引入中值法通过多类样本的位置关系修正特征对状态分离聚合敏感程度的评估系数,从而筛选出能抑制状态间重合度的特征。此外,针对智能诊断模型确定最优特征集效率低的问题,提出了多维空间测度-Fisher的特征集评估方法,通过计算不同维数候选特征集在多维空间中的距离测度指标,基于极大值原则筛选出最优特征集。最后,通过轴承故障实验对所提算法进行验证,实验结果表明,提出方法得到的最优低维特征集可以有效诊断轴承故障,在特征组合数为3时支持向量机分类器诊断正确率达到了99.17%。In order to improve the fault diagnosis rate of rolling bearings, make full use of the difference in the recognition ability of the bearing operating state by the time domain, frequency domain and frequency domain features, and take into account that the features are prone to irrelevance, redundant interference and other issues, as well as the simple, fast and effective feature evaluation of the actual project method needs. Position optimized Fisher distance measure(POFDM) method is proposed and applied to bearing fault characteristic select. The method is based on Fisher’s criterion, and the positional relationship between multi-class samples is used to correct the evaluation coefficient by the median method, which could reflect sensitivity of the state separation and aggregation. Thus, the features that can suppress the degree of state coincidence are selected. In addition, aiming at the problem that the intelligent diagnosis model is inefficient in seeking optimal feature set, feature set evaluation method based on multi-dimensional spatial measure-Fisher is proposed. The optimal feature set is selected based on the maximum value principle by calculating the distance measure index of different dimension candidate feature sets in multidimensional space. Finally, the proposed algorithm is verified by the bearing fault experiment. The experimental results show that the optimal low-dimensional feature set obtained by the proposed method achieves 99.17% diagnostic accuracy of the SVM classifier when the number of feature combinations is 3, which can effectively diagnose bearing faults.

关 键 词：混合域 位置关系修正 位置优化Fisher测度 多维空间距离测度-Fisher 

分 类 号：TH165.3[机械工程—机械制造及自动化] TN911[电子电信—通信与信息系统]