Next Article in Journal
Modeling and Performance Analysis of Opportunistic Link Selection for UAV Communication
Previous Article in Journal
Walking Secure: Safe Routing Planning Algorithm and Pedestrian’s Crossing Intention Detector Based on Fuzzy Logic App
Open AccessArticle

An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy

by 1,2, 1,2,*, 1,2 and 1,2
1
Fauclty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
2
Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(2), 533; https://doi.org/10.3390/s21020533
Received: 13 December 2020 / Revised: 6 January 2021 / Accepted: 10 January 2021 / Published: 13 January 2021
(This article belongs to the Section Fault Diagnosis & Sensors)
As a vital component widely used in the industrial production field, rolling bearings work under complicated working conditions and are prone to failure, which will affect the normal operation of the whole mechanical system. Therefore, it is essential to conduct a health assessment of the rolling bearing. In recent years, Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is applied to the fault feature extraction for rolling bearings. However, the algorithm still has the following problems: (1) The selection of fault period T depends on prior knowledge. (2) The accuracy of signal denoising is affected by filter length L. To solve the limitations, an improved MOMEDA (IMOMEDA) method is proposed in this paper. Firstly, the envelope harmonic-to-noise ratio (EHNR) spectrum is adopted to estimate the fault period of MOMEDA. Then, the improved grid search method with EHNR spectral entropy as the objective function is constructed to calculate the optimal filter length used in the MOMEDA. Finally, a feature extraction method based on the improved MOMEDA (IMOMEDA) and Teager-Kaiser energy operator (TKEO) is applied in the field of rolling bearing fault diagnosis. The effectiveness and generalization performance of the proposed method is verified through comparison experiment with three data sets. View Full-Text
Keywords: multipoint optimal minimum entropy deconvolution adjusted; envelope harmonic-to-noise ratio; EHNR spectral entropy; improved grid search; fault feature extraction multipoint optimal minimum entropy deconvolution adjusted; envelope harmonic-to-noise ratio; EHNR spectral entropy; improved grid search; fault feature extraction
Show Figures

Figure 1

MDPI and ACS Style

Li, Z.; Ma, J.; Wang, X.; Li, X. An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy. Sensors 2021, 21, 533. https://doi.org/10.3390/s21020533

AMA Style

Li Z, Ma J, Wang X, Li X. An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy. Sensors. 2021; 21(2):533. https://doi.org/10.3390/s21020533

Chicago/Turabian Style

Li, Zhuorui; Ma, Jun; Wang, Xiaodong; Li, Xiang. 2021. "An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy" Sensors 21, no. 2: 533. https://doi.org/10.3390/s21020533

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop