中文摘要：

研究核聚类方法在机械故障检测中的应用问题,将基于半监督学习的模糊核聚类方法用于齿轮箱离群故障的检测。机械故障早期检测的难点是故障特征微弱、样本差异小,基于半监督学习的核聚类方法利用少量已知模式的样本,结合大量未知模式的样本进行半监督学习,得到较好的识别效果。进行齿轮箱正常运行和齿轮轻微点蚀的故障试验,比较基于半监督学习的核聚类方法与无监督学习核聚类方法的检测效果。试验结果表明,基于半监督学习的核聚类方法性能更优越。

英文摘要：

Kernel clustering is investigated in mechanical fault detection, and a semi-supervised kernel-based fuzzy clustering method is presented for gear fault early detection. The difficulty in early detection of mechanical incipient fault is to extract the weak fault information in noises. The semi-supervised kernel clustering method utilizes a few of known samples, combined with a larger amount of unknown samples to perform semi-supervised learning, and obtains good efficiency. The experiments are conducted on a gearbox, where a surface defect of tooth pitting is introduced .The result of semi-supervised kernel clustering is compared with that of unsupervised kernel clustering, which demonstrates the superiority of the semi-supervised method for gear failure detection.