Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification

3rd International Conference on Mechanical Engineering and Automation Science

机械制造;无线电电子学

Kai, Li^1 ; Bo, Luo^1 ; Tao, Ma^1 ; Xuefeng, Yang^1 ; Guangming, Wang^1

1037 Luoyu Road, Hongshan District, Wuhan, Hubei Province

430074, China^1

关键词: High-accuracy;    Intelligent fault diagnosis;    Novel clustering;    Number of clusters;    Rolling bearings;    Rolling Element Bearing;    Supervised learning methods;    Training sample;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/280/1/012013/pdf
DOI  :  10.1088/1757-899X/280/1/012013

来源: IOP

【 摘 要 】

To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.