CAJ | 학술논문

针对机械故障检测中,正常样本多、故障样本少、训练样本严重不平衡的客观情况,将小球大间隔方法引入其中,提出了一种不平衡样本下的机械故障检测方法。该方法同时使用大量的正常样本和少量的故障样本进行训练,在特征空间中构造一个包围正常样本的超球,在该超球体积最小化的同时,进一步使超球边界与故障样本之间的间隔最大化,从而显著减小将故障情况误判为正常情况的概率。将该方法应用到滚动轴承故障检测中,并与传统的支持向量机和支持向量数据描述方法进行了比较,实验结果表明,该方法在解决不平衡样本下机械故障检测问题具有优越性。
침대궤계고장검측중,정상양본다、고장양본소、훈련양본엄중불평형적객관정황,장소구대간격방법인입기중,제출료일충불평형양본하적궤계고장검측방법。해방법동시사용대량적정상양본화소량적고장양본진행훈련,재특정공간중구조일개포위정상양본적초구,재해초구체적최소화적동시,진일보사초구변계여고장양본지간적간격최대화,종이현저감소장고장정황오판위정상정황적개솔。장해방법응용도곤동축승고장검측중,병여전통적지지향량궤화지지향량수거묘술방법진행료비교,실험결과표명,해방법재해결불평형양본하궤계고장검측문제구유우월성。
In machinery fault detection, normal examples are much more than tault examples ano the training examples are highly imbalanced. Aiming at this problem, a small sphere and large margin approach was used for machinery fault detection and a machinery fault detection method for imbal- anced examples was put forward. The proposed method can use both of many normal examples and few fault examples to train. It constructed a hypersphere that contained normal examples in the fea- ture space by training, such that the volume of this sphere was as small as possible, while at the same time the margin between the surface of this sphere and the fault examples was as large as possible. This method was applied to fault detection of rolling element bearings and comparisons were conduc- ted with support vector machine and support vector data description. Experimental results validate its effectiveness in the machinery fault detection where the training examples are highly imbalanced.