Open AccessThis article is
- freely available
Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, China
* Author to whom correspondence should be addressed.
Received: 8 October 2013; in revised form: 11 November 2013 / Accepted: 22 November 2013 / Published: 9 December 2013
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
Article StatisticsClick here to load and display the download statistics.
Notes: Multiple requests from the same IP address are counted as one view.
Cite This Article
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.
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.
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.