| 刘浩炜,刘 韬,涂文涛,陈 庆.位置优化 Fisher 测度在轴承故障特征选择中的应用[J].电子测量与仪器学报,2020,34(8):124-132 |
| 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 |
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| DOI： |
| 中文关键词: 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 |
| 英文关键词:mixed domain position relationship correction position optimized Fisher measure ( POFM) multi-dimensional spatial
measure-Fisher |
| 基金项目:国家自然科学基金(51675251)、云南省重点项目(201601PE00008)资助 |
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| 摘要点击次数: 187 |
| 全文下载次数: 676 |
| 中文摘要: |
| 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%。 |
| 英文摘要: |
| 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. |
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