CAJ | 학술논문

联合收获机中零部件繁多及滚珠滑失等因素，导致监测信号中轴承组件的特征频率并非总能找到，进而影响了故障诊断的正确率。为了解决该问题，提出了一种基于不完全信息的轴承故障聚类识别方法。该方法将特征频率显著的样本作为先验信息，利用这些信息进行相关成分分析，从而给相关程度高的特征赋予大的权重，然后利用改进的半监督聚类算法对所有样本进行聚类识别。其中，提出了近邻扩展方法对先验信息进行扩充，增加了目标函数惩罚环节对聚类过程予以指导。将所提方法应用于联合收获机的轴承滚珠和外圈故障识别，与其它几种聚类方法相比，故障识别率提高了2．78％～7．22％。
연합수획궤중령부건번다급곤주활실등인소，도치감측신호중축승조건적특정빈솔병비총능조도，진이영향료고장진단적정학솔。위료해결해문제，제출료일충기우불완전신식적축승고장취류식별방법。해방법장특정빈솔현저적양본작위선험신식，이용저사신식진행상관성분분석，종이급상관정도고적특정부여대적권중，연후이용개진적반감독취류산법대소유양본진행취류식별。기중，제출료근린확전방법대선험신식진행확충，증가료목표함수징벌배절대취류과정여이지도。장소제방법응용우연합수획궤적축승곤주화외권고장식별，여기타궤충취류방법상비，고장식별솔제고료2．78％～7．22％。
Due to the reasons of too many components in combine harvester and the skid of rolling balls, the characteris-tic frequencies of bearing assembly in monitoring signals are not always clearly existing, which causes the low accuracy of fault diagnosis.Hence, a clustering approach based on partial information is proposed to tackle this problem.This ap-proach sets these samples with clearly characteristic frequencies as priori information, and then uses them to make rele-vant component analysis to high weights to relevant dimensions.This approach also design an advanced clustering algo-rithm to recognize all the samples, wherein an extension strategy based on neighborhood is presented to obtain more priori information, and a penalty step is added to the objective function to guiding the clustering.The fault data on ball and outer race of bearing of a combine harvester is used to validate the proposed approach.The results show that our proposed approach works better than others, where the recognition accuracy is higher than others from 2.78%to 7.22%.