Research on the Feature Selection of Rolling Bearings’ Degradation Features
The bearings’ degradation features are crucial to assess the performance degradation and predict the remaining useful life of rolling bearings. So far, numerous degradation features have been proposed. Many researchers have devoted to use dimensionality reduction methods to reduce the redundancy of...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/6450719 |
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| _version_ | 1832550929140809728 |
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| author | Yaolong Li Hongru Li Bing Wang He Yu Weiguo Wang |
| author_facet | Yaolong Li Hongru Li Bing Wang He Yu Weiguo Wang |
| author_sort | Yaolong Li |
| collection | DOAJ |
| description | The bearings’ degradation features are crucial to assess the performance degradation and predict the remaining useful life of rolling bearings. So far, numerous degradation features have been proposed. Many researchers have devoted to use dimensionality reduction methods to reduce the redundancy of those features. However, they have not considered the properties and similarity of those features. In this paper, we present a simple way to reduce dimensionality by classifying different features based on their trends. And the degradation features can be classified into two subdivisions, namely, uptrends and downtrends. In each subdivision, there exists visible trend similarity, and we have introduced two indexes to measure this similarity. By selecting the representative features of the subdivision, the multifeatures can be dimensionality reduced. Through the comparison, the root mean square and sample entropy are two good representatives of uptrend and downtrend features. This method gives an alternative way for dimensionality reduction of the rolling bearings’ degradation features. |
| format | Article |
| id | doaj-art-b9ad872ad90a41fe9853749ebbcb213a |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-b9ad872ad90a41fe9853749ebbcb213a2025-02-03T06:05:23ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/64507196450719Research on the Feature Selection of Rolling Bearings’ Degradation FeaturesYaolong Li0Hongru Li1Bing Wang2He Yu3Weiguo Wang4Army Engineering University, No. 97 Heping West Road, Shijiazhuang 050003, ChinaArmy Engineering University, No. 97 Heping West Road, Shijiazhuang 050003, ChinaShanghai Maritime University, Shanghai 200135, ChinaArmy Engineering University, No. 97 Heping West Road, Shijiazhuang 050003, ChinaArmy Engineering University, No. 97 Heping West Road, Shijiazhuang 050003, ChinaThe bearings’ degradation features are crucial to assess the performance degradation and predict the remaining useful life of rolling bearings. So far, numerous degradation features have been proposed. Many researchers have devoted to use dimensionality reduction methods to reduce the redundancy of those features. However, they have not considered the properties and similarity of those features. In this paper, we present a simple way to reduce dimensionality by classifying different features based on their trends. And the degradation features can be classified into two subdivisions, namely, uptrends and downtrends. In each subdivision, there exists visible trend similarity, and we have introduced two indexes to measure this similarity. By selecting the representative features of the subdivision, the multifeatures can be dimensionality reduced. Through the comparison, the root mean square and sample entropy are two good representatives of uptrend and downtrend features. This method gives an alternative way for dimensionality reduction of the rolling bearings’ degradation features.http://dx.doi.org/10.1155/2019/6450719 |
| spellingShingle | Yaolong Li Hongru Li Bing Wang He Yu Weiguo Wang Research on the Feature Selection of Rolling Bearings’ Degradation Features Shock and Vibration |
| title | Research on the Feature Selection of Rolling Bearings’ Degradation Features |
| title_full | Research on the Feature Selection of Rolling Bearings’ Degradation Features |
| title_fullStr | Research on the Feature Selection of Rolling Bearings’ Degradation Features |
| title_full_unstemmed | Research on the Feature Selection of Rolling Bearings’ Degradation Features |
| title_short | Research on the Feature Selection of Rolling Bearings’ Degradation Features |
| title_sort | research on the feature selection of rolling bearings degradation features |
| url | http://dx.doi.org/10.1155/2019/6450719 |
| work_keys_str_mv | AT yaolongli researchonthefeatureselectionofrollingbearingsdegradationfeatures AT hongruli researchonthefeatureselectionofrollingbearingsdegradationfeatures AT bingwang researchonthefeatureselectionofrollingbearingsdegradationfeatures AT heyu researchonthefeatureselectionofrollingbearingsdegradationfeatures AT weiguowang researchonthefeatureselectionofrollingbearingsdegradationfeatures |