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Case Studies for Bearing Fault Diagnosis based on Adaptive Myriad Filter and Alpha Stable Model

Volume 13, Number 4, July 2017 - SC 53 - pp. 551-555
DOI: 10.23940/ijpe.17.04.p22.551555

Xinghui Zhanga, Fei Zhaob,c, and Jianshe Kanga

aMechanical Engineering College, Shijiazhuang 050003, China
bSchool of Business Administration, Northeastern University, Shenyang 110819, China
cNortheastern University at Qinhuangdao, Qinhuangdao 066004, China

(Submitted on December 7, 2014; Revised on November 18, 2016; Accepted on April 23, 2017)


Bearing fault diagnosis is a key research content of condition-based maintenance for machineries. Because of noise interference, incipient bearing fault is always difficult to be found. Traditionally, a large number of filtering algorithms used are limited to the cases of Gaussian noise or linear operation. In this paper, the adaptive Myriad filter and alpha stable model are elaborated. Myriad filter is a non-linear filter, which can be effectively applied in impulsive environment. The order of Myriad filter can be determined by alpha stable model. Finally, both laboratory fault data and real fault data from a public data set are used to verify the efficiency of the proposed method.


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