Feature Extraction Method for Hydraulic Pump Fault Signal Based on Improved Empirical Wavelet Transform
1
College of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, China
2
National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
*
Authors to whom correspondence should be addressed.
Processes 2019, 7(11), 824; https://doi.org/10.3390/pr7110824
Received: 8 October 2019
/
Revised: 31 October 2019
/
Accepted: 31 October 2019
/
Published: 6 November 2019
(This article belongs to the Special Issue Smart Flow Control Processes in Micro Scale)
There are many interference components in Fourier amplitude spectrum of a contaminated fault signal, and thus the segment obtained based on the spectrum can lead to serious over-decomposition of empirical wavelet transform (EWT). Aiming to resolve the above problems, a novel method named improved empirical wavelet transform (IEWT) is proposed. Because the power spectrum is less sensitive to the contaminated interference and manifests the presence of fault feature information, IEWT replaces the Fourier amplitude spectrum of EWT with power spectrum in segment acquirement, and threshold processing is also introduced to eliminate the bad influence on the acquirement, and thus the best decomposition result of IEWT can be obtained based on feature energy ratio (FER). The loose slipper fault signal of hydraulic pump is tested and verified. The result demonstrates that the proposed method is superior and can extract the fault feature information accurately.
View Full-Text
Keywords:
hydraulic pump; fault signal; feature extraction; empirical wavelet decomposition; power spectrum density; feature energy ratio
▼
Show Figures
Figure 1
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
Zheng, Z.; Wang, Z.; Zhu, Y.; Tang, S.; Wang, B. Feature Extraction Method for Hydraulic Pump Fault Signal Based on Improved Empirical Wavelet Transform. Processes 2019, 7, 824. https://doi.org/10.3390/pr7110824
AMA Style
Zheng Z, Wang Z, Zhu Y, Tang S, Wang B. Feature Extraction Method for Hydraulic Pump Fault Signal Based on Improved Empirical Wavelet Transform. Processes. 2019; 7(11):824. https://doi.org/10.3390/pr7110824
Chicago/Turabian StyleZheng, Zhi, Zhijun Wang, Yong Zhu, Shengnan Tang, and Baozhong Wang. 2019. "Feature Extraction Method for Hydraulic Pump Fault Signal Based on Improved Empirical Wavelet Transform" Processes 7, no. 11: 824. https://doi.org/10.3390/pr7110824
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
