改进MOMEDA方法及其在滚动轴承故障特征增强中的应用
Aiming at the shortcomings of multipoint optimal minimum entropy deconvolution adjusted（ MOMEDA） method,which cannot automatically identify the fault impulse period and shorten the length of deconvolved signal when enhancing bearing fault features,an improved MOMEDA（ IMOMEDA） method is proposed.The...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2021-01-01
|
| Series: | Jixie qiangdu |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.01.001 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841535892038090752 |
|---|---|
| author | 陈丙炎 宋冬利 张卫华 程尧 |
| author_facet | 陈丙炎 宋冬利 张卫华 程尧 |
| author_sort | 陈丙炎 |
| collection | DOAJ |
| description | Aiming at the shortcomings of multipoint optimal minimum entropy deconvolution adjusted（ MOMEDA） method,which cannot automatically identify the fault impulse period and shorten the length of deconvolved signal when enhancing bearing fault features,an improved MOMEDA（ IMOMEDA） method is proposed.The autocorrelation spectrum of square envelope of the vibration signal is used to adaptively identify the fault period,and the estimated impulse period is used to deconvolve the vibration signal to enhance the periodic impulse features.Then the signal waveform extension method is used to extend the deconvolved signal to make its length consistent with the original signal.Finally,the obtained filtered signal is deconvolved for a certain number of times to effectively enhance the periodic features of the original signal.The analysis results of simulated bearing fault signal and railway bearing experiment signals and the comparisons with Kurtogram method show that the improved MOMEDA method can automatically identify the fault impulse period and effectively enhance the fault characteristics of rolling bearing. |
| format | Article |
| id | doaj-art-c47ac6d176024ea8b2c895a3e8a64f93 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-c47ac6d176024ea8b2c895a3e8a64f932025-01-15T02:26:31ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692021-01-011830609741改进MOMEDA方法及其在滚动轴承故障特征增强中的应用陈丙炎宋冬利张卫华程尧Aiming at the shortcomings of multipoint optimal minimum entropy deconvolution adjusted（ MOMEDA） method,which cannot automatically identify the fault impulse period and shorten the length of deconvolved signal when enhancing bearing fault features,an improved MOMEDA（ IMOMEDA） method is proposed.The autocorrelation spectrum of square envelope of the vibration signal is used to adaptively identify the fault period,and the estimated impulse period is used to deconvolve the vibration signal to enhance the periodic impulse features.Then the signal waveform extension method is used to extend the deconvolved signal to make its length consistent with the original signal.Finally,the obtained filtered signal is deconvolved for a certain number of times to effectively enhance the periodic features of the original signal.The analysis results of simulated bearing fault signal and railway bearing experiment signals and the comparisons with Kurtogram method show that the improved MOMEDA method can automatically identify the fault impulse period and effectively enhance the fault characteristics of rolling bearing.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.01.001 |
| spellingShingle | 陈丙炎 宋冬利 张卫华 程尧 改进MOMEDA方法及其在滚动轴承故障特征增强中的应用 Jixie qiangdu |
| title | 改进MOMEDA方法及其在滚动轴承故障特征增强中的应用 |
| title_full | 改进MOMEDA方法及其在滚动轴承故障特征增强中的应用 |
| title_fullStr | 改进MOMEDA方法及其在滚动轴承故障特征增强中的应用 |
| title_full_unstemmed | 改进MOMEDA方法及其在滚动轴承故障特征增强中的应用 |
| title_short | 改进MOMEDA方法及其在滚动轴承故障特征增强中的应用 |
| title_sort | 改进momeda方法及其在滚动轴承故障特征增强中的应用 |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.01.001 |
| work_keys_str_mv | AT chénbǐngyán gǎijìnmomedafāngfǎjíqízàigǔndòngzhóuchénggùzhàngtèzhēngzēngqiángzhōngdeyīngyòng AT sòngdōnglì gǎijìnmomedafāngfǎjíqízàigǔndòngzhóuchénggùzhàngtèzhēngzēngqiángzhōngdeyīngyòng AT zhāngwèihuá gǎijìnmomedafāngfǎjíqízàigǔndòngzhóuchénggùzhàngtèzhēngzēngqiángzhōngdeyīngyòng AT chéngyáo gǎijìnmomedafāngfǎjíqízàigǔndòngzhóuchénggùzhàngtèzhēngzēngqiángzhōngdeyīngyòng |