标题：Fault diagnosis of rolling bearings based on undirected weighted graph
作者：Wang, Teng; Lu, Guoliang; Yan, Peng
作者机构：[Wang, Teng; Lu, Guoliang; Yan, Peng] Shandong Univ, Natl Demonstrat Ctr Expt Mech Engn Educ, Sch Mech Engn, Key Lab High Efficiency & Clean Mech Manu 更多
会议名称：Prognostics and System Health Management Conference in Paris (PHM Paris)
会议日期：MAY 02-05, 2019
来源：2019 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-PARIS)
关键词：Bearing fault diagnosis; Periodogram; Graph model; K-Nearest Neighbor
摘要：One of the main functions of rolling bearing condition monitoring is to diagnosis the type of fault that is occurred during its continuous operations. This paper presents a new method for rolling bearing fault diagnosis based on the graph model. Concretely, through Fourier transform, the periodogram is computed from the condition monitoring (CM) signal and then modeled into an undirected weighted graph. This graph is subsequently fed to K-Nearest Neighbor (KNN) Classifier for fault type diagnosis. In particular, to perform KNN upon graph model, a robust graph distance metric so-called sum of the difference in edge-weight values (SDEWV) is adopted via investigating four candidate metrics existed in the literature. Based on experimental results in the publicly-available database, we demonstrate exciting results of the proposed method in bearing fault diagnosis, indicating its great potentials in real engineering applications.