CAJ | 학술논문

在机械设备故障诊断研究领域中，系统采集的原始监测数据经过处理得到的结果往往是数据量很大，维数很高的图像数据，因此，从高维图像中获取敏感特征是当前故障诊断领域中面临的一项关键技术。本文提出了基于多核非负矩阵分解的机械设备故障诊断方法，该方法克服了传统故障诊断需对机械设备信号进行特征提取而造成信息丢失，通过应用多核非负矩阵分解方法进行降维，然后结合多核支持向量机实现对降维后的数据直接进行识别。实验证明该方法可降低原始数据特征的维数，提高分类运算的效率以及故障诊断的识别率。
재궤계설비고장진단연구영역중，계통채집적원시감측수거경과처리득도적결과왕왕시수거량흔대，유수흔고적도상수거，인차，종고유도상중획취민감특정시당전고장진단영역중면림적일항관건기술。본문제출료기우다핵비부구진분해적궤계설비고장진단방법，해방법극복료전통고장진단수대궤계설비신호진행특정제취이조성신식주실，통과응용다핵비부구진분해방법진행강유，연후결합다핵지지향량궤실현대강유후적수거직접진행식별。실험증명해방법가강저원시수거특정적유수，제고분류운산적효솔이급고장진단적식별솔。
In the fault diagnosis field of mechanical equipment, the result of analyzing the collected monitoring data from the equipment is often the high dimensionality of images which contain mass data;so the method of extracting sensitive feature from the high?dimensional information or image is a key technology. We present a new method for fault diagnosis of mechanical equipment based on Multi?Kernel Non?negative Matrix Factorization ( MKNMF ) , which overcomes the defect that the traditional fault diagnosis of mechanical equipment requires signal feature ex?traction this defect causes loss of information; we reduce dimensions for high dimension information through applying Multi?Kernel Non?negative Matrix Factorization method and then distinguish the dimensionality reduction data with Multi?Kernel Support Vector Machine ( MKSVM) . The experiments and their analysis show preliminarily that this method can reduce the dimensions of the original monitored data and improve the recognition rate of ma?chine fault diagnosis.