Fault diagnosis of rolling bearing based on EMD-ICA de-noising
Author(s): CAI Jian-hua, HU Wei-wen, WANG Xian-chun, Information Institute, Hunan University of Arts and Science
Pages: 17-23
Year: 2015 Issue: 1
Journal: Journal of Machine Design
Keyword: empirical mode decomposition; independent component analysis; rolling bearing; fault diagnosis; de-noising;
Abstract: A method of fault diagnosis was proposed which combined empirical mode decomposition with independent component analysis based on the facts that the fault signal of rolling bearings was affected easily by environment noise and difficult to be separated from noise.The principle and steps of the method were given and the de-noising effect was evaluated by some parameters.The simulated signal and actual fault signals of the bearing ball,the inner and outer race were analyzed and diagnosed.The results show that the proposed method can suppress the noise interference greatly.Fault characteristics extracted from the de-noised signal can distinguish the state of bearing and type of faults obviously.The accuracy of fault diagnosis of the bearing is improved effectively.
Pages: 17-23
Year: 2015 Issue: 1
Journal: Journal of Machine Design
Keyword: empirical mode decomposition; independent component analysis; rolling bearing; fault diagnosis; de-noising;
Abstract: A method of fault diagnosis was proposed which combined empirical mode decomposition with independent component analysis based on the facts that the fault signal of rolling bearings was affected easily by environment noise and difficult to be separated from noise.The principle and steps of the method were given and the de-noising effect was evaluated by some parameters.The simulated signal and actual fault signals of the bearing ball,the inner and outer race were analyzed and diagnosed.The results show that the proposed method can suppress the noise interference greatly.Fault characteristics extracted from the de-noised signal can distinguish the state of bearing and type of faults obviously.The accuracy of fault diagnosis of the bearing is improved effectively.
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