Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
1
School of mechanical engineering and automation, Northeastern University, Shenyang 110819, China
2
Key Laboratory of Vibration and Control of Aero-Propulsion Systems of Ministry of Education, Northeastern University, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(9), 1441; https://doi.org/10.3390/app8091441
Received: 25 July 2018 / Revised: 15 August 2018 / Accepted: 20 August 2018 / Published: 23 August 2018
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
A method is proposed to improve the feature extraction of vibration signals of rotating machinery. Firstly, the single-channel vibration signal is decomposed with ensemble empirical mode decomposition (EEMD). Then, the number of fault signals can be estimated with singular-value decomposition (SVD). Finally, the fault signals can be extracted with kernel-independent component analysis (KICA). The advantage of this method is that it can estimate the number of fault signals of single-channel vibration signals and can extract the fault features clearly. Compared with wavelets, empirical mode decomposition (EMD), variational mode decomposition (VMD) and EEMD, the better performance of this method is proven with three experimental analyses of faulty gear, a faulty rolling bearing and a faulty shaft. The results demonstrate that the proposed method is efficient to extract the fault features of single-channel vibration signals of rotating machinery.
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Keywords:
rotating machinery fault diagnosis; single-channel monitoring; EEMD-based KICA; singular-value decomposition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Fang, L.; Sun, H. Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery. Appl. Sci. 2018, 8, 1441.
AMA Style
Fang L, Sun H. Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery. Applied Sciences. 2018; 8(9):1441.
Chicago/Turabian StyleFang, Liang; Sun, Hongchun. 2018. "Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery" Appl. Sci. 8, no. 9: 1441.
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