CAJ | 학술논문

针对流形学习故障诊断中非敏感特征干扰和邻域大小难以确定的问题，提出了基于 DSmT 多准则融合特征选择和局部集聚系数自适应邻域的流形学习故障诊断方法。利用多种特征评价准则对原始高维特征进行排序，通过 DSmT证据理论对各评价序列进行融合，再根据融合序列选择敏感特征构成优化高维特征集；采用基于局部集聚系数的自适应正交邻域保持嵌入流形学习进行维数约简，得到低维特征集；最后输入到K最近邻分类器进行故障识别。轴承振动故障实验表明了本文所提方法的有效性。
침대류형학습고장진단중비민감특정간우화린역대소난이학정적문제，제출료기우 DSmT 다준칙융합특정선택화국부집취계수자괄응린역적류형학습고장진단방법。이용다충특정평개준칙대원시고유특정진행배서，통과 DSmT증거이론대각평개서렬진행융합，재근거융합서렬선택민감특정구성우화고유특정집；채용기우국부집취계수적자괄응정교린역보지감입류형학습진행유수약간，득도저유특정집；최후수입도K최근린분류기진행고장식별。축승진동고장실험표명료본문소제방법적유효성。
In order to solve interference of non-sensitive features and the neighborhood size of the manifold learning, in the present paper, a novel manifold learning method for mechanical fault diagnosis based on feature selection with Dezert-marandache theory (DSmT) and adaptive neighborhood with local cluster coefficient is proposed. Multi feature evaluation criterias are used to sort the original high-dimensional features, a fusion sequence by DSmT is used to extract optimal subset. The adaptive neighbor-hood of orthogonal neighborhood preserving embedding (ONPE) with local cluster coefficient is used to reduce the high-dimensional set to the low-dimensional compressed sensitive feature subset. Then, fault is identified with feeding the feature subset into the k nearest neighbor classification (KNNC). At last, the validity of the proposed method is verified with fault diagno-sis tests of bearings.