MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings
In order to extract fault features of rolling bearings to characterize their operation state effectively, an improved method, based on modified variational mode decomposition (MVMD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), is proposed. Firstly, the MVMD method is intro...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-03-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/21/4/331 |
| _version_ | 1828276554849845248 |
|---|---|
| author | Zhuorui Li Jun Ma Xiaodong Wang Jiande Wu |
| author_facet | Zhuorui Li Jun Ma Xiaodong Wang Jiande Wu |
| author_sort | Zhuorui Li |
| collection | DOAJ |
| description | In order to extract fault features of rolling bearings to characterize their operation state effectively, an improved method, based on modified variational mode decomposition (MVMD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), is proposed. Firstly, the MVMD method is introduced to decompose the vibration signal into intrinsic mode functions (IMFs), and then calculate the energy ratio of each IMF component. The IMF component is selected as the effective component from high energy ratio to low in turn until the total energy proportion <i>E<sub>sum</sub></i>(<i>t</i>) ≥ 90%. The IMF effective components are reconstructed to obtain the subsequent analysis signal <i>x_<sub>new</sub></i>(<i>t</i>). Secondly, the MOMEDA method is introduced to analyze <i>x_<sub>new</sub></i>(<i>t</i>), extract the fault period impulse component <i>x_<sub>cov</sub></i>(<i>t</i>), which is submerged by noise, and demodulate the signal <i>x_<sub>cov</sub></i>(<i>t</i>) by Teager energy operator demodulation (TEO) to calculate Teager energy spectrum. Thirdly, matching the dominant frequency in the spectrum with the fault characteristic frequency of rolling bearings, the fault feature extraction of rolling bearings are completed. Finally, the experiments have compared MVMD-MOEDA-TEO with MVMD-TEO and MOMEDA-TEO based on two different data sets to verify the superiority of the proposed method. The experimental results show that MVMD-MOMEDA-TEO method has better performance than the other two methods, and provides a new solution for condition monitoring and fault diagnosis of rolling bearings. |
| first_indexed | 2024-04-13T07:00:04Z |
| format | Article |
| id | doaj.art-a710c9b326be4cddbaa748f5226c7e94 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T07:00:04Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-a710c9b326be4cddbaa748f5226c7e942022-12-22T02:57:09ZengMDPI AGEntropy1099-43002019-03-0121433110.3390/e21040331e21040331MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling BearingsZhuorui Li0Jun Ma1Xiaodong Wang2Jiande Wu3Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650000, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650000, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650000, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650000, ChinaIn order to extract fault features of rolling bearings to characterize their operation state effectively, an improved method, based on modified variational mode decomposition (MVMD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), is proposed. Firstly, the MVMD method is introduced to decompose the vibration signal into intrinsic mode functions (IMFs), and then calculate the energy ratio of each IMF component. The IMF component is selected as the effective component from high energy ratio to low in turn until the total energy proportion <i>E<sub>sum</sub></i>(<i>t</i>) ≥ 90%. The IMF effective components are reconstructed to obtain the subsequent analysis signal <i>x_<sub>new</sub></i>(<i>t</i>). Secondly, the MOMEDA method is introduced to analyze <i>x_<sub>new</sub></i>(<i>t</i>), extract the fault period impulse component <i>x_<sub>cov</sub></i>(<i>t</i>), which is submerged by noise, and demodulate the signal <i>x_<sub>cov</sub></i>(<i>t</i>) by Teager energy operator demodulation (TEO) to calculate Teager energy spectrum. Thirdly, matching the dominant frequency in the spectrum with the fault characteristic frequency of rolling bearings, the fault feature extraction of rolling bearings are completed. Finally, the experiments have compared MVMD-MOEDA-TEO with MVMD-TEO and MOMEDA-TEO based on two different data sets to verify the superiority of the proposed method. The experimental results show that MVMD-MOMEDA-TEO method has better performance than the other two methods, and provides a new solution for condition monitoring and fault diagnosis of rolling bearings.https://www.mdpi.com/1099-4300/21/4/331modified variational mode decompositionmultipoint optimal minimum entropy deconvolution adjustedTeager energy operator demodulationfault feature extractionrolling bearings |
| spellingShingle | Zhuorui Li Jun Ma Xiaodong Wang Jiande Wu MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings Entropy modified variational mode decomposition multipoint optimal minimum entropy deconvolution adjusted Teager energy operator demodulation fault feature extraction rolling bearings |
| title | MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings |
| title_full | MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings |
| title_fullStr | MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings |
| title_full_unstemmed | MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings |
| title_short | MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings |
| title_sort | mvmd momeda teo model and its application in feature extraction for rolling bearings |
| topic | modified variational mode decomposition multipoint optimal minimum entropy deconvolution adjusted Teager energy operator demodulation fault feature extraction rolling bearings |
| url | https://www.mdpi.com/1099-4300/21/4/331 |
| work_keys_str_mv | AT zhuoruili mvmdmomedateomodelanditsapplicationinfeatureextractionforrollingbearings AT junma mvmdmomedateomodelanditsapplicationinfeatureextractionforrollingbearings AT xiaodongwang mvmdmomedateomodelanditsapplicationinfeatureextractionforrollingbearings AT jiandewu mvmdmomedateomodelanditsapplicationinfeatureextractionforrollingbearings |