Next Article in Journal
Previous Article in Journal
Sensors 2013, 13(12), 16950-16964; doi:10.3390/s131216950
Article

Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition

* ,
,
 and
Received: 8 October 2013; in revised form: 11 November 2013 / Accepted: 22 November 2013 / Published: 9 December 2013
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [443 KB, updated 21 June 2014; original version uploaded 21 June 2014]   |   Browse Figures
Abstract: The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decomposition (EEMD) was proposed accordingly. EEMD is able to reduce the mode mixing to some extent. The performance of EEMD, however, depends on the parameters adopted in the EEMD algorithms. In most of the studies on EEMD, the parameters were selected artificially and subjectively. To solve the problem, a new adaptive ensemble empirical mode decomposition method is proposed in this paper. In the method, the sifting number is adaptively selected, and the amplitude of the added noise changes with the signal frequency components during the decomposition process. The simulation, the experimental and the application results demonstrate that the adaptive EEMD provides the improved results compared with the original EEMD in diagnosing rotating machinery.
Keywords: adaptive ensemble empirical mode decomposition; fault diagnosis; sifting number; added noise; rotating machinery adaptive ensemble empirical mode decomposition; fault diagnosis; sifting number; added noise; rotating machinery
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.

Export to BibTeX |
EndNote


MDPI and ACS Style

Lei, Y.; Li, N.; Lin, J.; Wang, S. Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition. Sensors 2013, 13, 16950-16964.

AMA Style

Lei Y, Li N, Lin J, Wang S. Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition. Sensors. 2013; 13(12):16950-16964.

Chicago/Turabian Style

Lei, Yaguo; Li, Naipeng; Lin, Jing; Wang, Sizhe. 2013. "Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition." Sensors 13, no. 12: 16950-16964.



Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert