Title:Study on a Novel Fault Diagnosis Method of Rolling Bearing in Motor
VOLUME: 9 ISSUE: 2
Author(s):Xiumei Li, Wu Deng, Huimin Zhao and Guanghai Zheng
Affiliation:Software Institute, Dalian Jiaotong University, Huanghe Road, Shahekou District, Dalian, 116028, P.R. China.
Keywords:Fast ICA algorithm, fault diagnosis, Hilbert transform, principal component analysis, resonance-based sparse decomposition,
rolling bearing, vibration signal.
Abstract:It is important to diagnose the faults of rolling bearings, because they may lead to the failure
of motor, and even the entire operating system-related disorders and failures. In order to diagnose
the early faults of bearings, a novel method for early diagnosis of rolling bearing faults based on resonance-based sparse
signal decomposition and principal component analysis was proposed in the present paper. Firstly, the vibration signals
produced from a faulty rolling bearing were split into high and low resonance components using resonance-based sparse
signal decomposition. Secondly, the principal components were extracted using principal component analysis, in order to
transform the signals into frequency domain. Finally, the results were compared with the theoretical fault frequencies to
locate the faulty elements. The proposed method was applied in the experimental data. The experimental results show that
the proposed fault diagnosis method can quickly discern the faulty elements of rolling bearings, improve the diagnostic
accuracy and provide an overview of the early fault diagnosis of rolling bearings. In this article, recent patents have been
discussed.
