CAJ | 학술논문

针对传统流形学习算法不具有增量学习能力；故难以处理新增数据与大规模海量数据集的问题，由此，提出一种用于机械转子故障数据集降维的增量局部切空间的排列算法(ILTSA)。该算法首先采用局部切空间排列算法对原始训练样本进行降维处理，获得其低维流形结构，然后通过增量学习算法对新增样本进行处理。得到所有数据的低维嵌入坐标，最后通过转子故障数据集验证了该方法的有效性，取得了良好的分类效果，有利于实时动态故障监测与诊断。
침대전통류형학습산법불구유증량학습능력；고난이처리신증수거여대규모해량수거집적문제，유차，제출일충용우궤계전자고장수거집강유적증량국부절공간적배렬산법(ILTSA)。해산법수선채용국부절공간배렬산법대원시훈련양본진행강유처리，획득기저유류형결구，연후통과증량학습산법대신증양본진행처리。득도소유수거적저유감입좌표，최후통과전자고장수거집험증료해방법적유효성，취득료량호적분류효과，유리우실시동태고장감측여진단。
The traditional learning algorithm does not have incremental learning ability, so it is unlikely to deal with ad-ditional new data and large data sets. In this paper, an incremental local tangent space alignment (LTSA) algorithm for me-chanical rotor fault diagnosis was put forward. In this method, the LTSA algorithm was used for dimension reduction of the original training samples, and the corresponding low-dimension configuration was obtained. Then, using the incremental learning algorithm, the additional new samples were processed, and the embedded low-dimensional coordinates of the data were obtained. Finally, the rotor fault datasets verified the feasibility of the method, and a good classification effect was ob-tained.