Morphological Component Analysis-Based Hidden Markov Model for Few-Shot Reliability Assessment of Bearing
Reliability is of great significance in ensuring the safe operation of modern industry, which mainly relies on data analysis and life tests. However, as the life of mechanical systems becomes increasingly longer with the rapid development of the manufacturing industry, the collection of historical f...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/10/6/435 |
| _version_ | 1797485071627714560 |
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| author | Yi Feng Weijun Li Kai Zhang Xianling Li Wenfang Cai Ruonan Liu |
| author_facet | Yi Feng Weijun Li Kai Zhang Xianling Li Wenfang Cai Ruonan Liu |
| author_sort | Yi Feng |
| collection | DOAJ |
| description | Reliability is of great significance in ensuring the safe operation of modern industry, which mainly relies on data analysis and life tests. However, as the life of mechanical systems becomes increasingly longer with the rapid development of the manufacturing industry, the collection of historical failure data becomes progressively more time-consuming. In this paper, a few-shot reliability assessment approach is proposed in order to overcome the dependence on historical data. Firstly, the vibration response of a bearing was illustrated. Then, based on a vibration response analysis, a morphological component analysis (MCA) method based on sparse representation theory was used to decompose vibration signals and extract impulse signals. After the impulse components’ reconstruction, their statistical indexes were utilized as the input observation vector of a Mixture of Gaussians Hidden Markov Model (MoG-HMM) for a reliability estimation. Finally, the experimental dataset of an aerospace bearing was analyzed via the proposed method. The comparison results illustrate the effectiveness of the proposed method of a few-shot reliability assessment. |
| first_indexed | 2024-03-09T23:14:35Z |
| format | Article |
| id | doaj.art-595d77e8f38e41d99dc0cdb974669495 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T23:14:35Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-595d77e8f38e41d99dc0cdb9746694952023-11-23T17:39:03ZengMDPI AGMachines2075-17022022-06-0110643510.3390/machines10060435Morphological Component Analysis-Based Hidden Markov Model for Few-Shot Reliability Assessment of BearingYi Feng0Weijun Li1Kai Zhang2Xianling Li3Wenfang Cai4Ruonan Liu5Science and Technology on Thermal Energy and Power Laboratory, Wuhan 430205, ChinaHangzhou Yineng Electric Technology Co., Ltd., Hangzhou 310000, ChinaCollege of Intelligence and Computing, Tianjin University, Tianjin 300072, ChinaScience and Technology on Thermal Energy and Power Laboratory, Wuhan 430205, ChinaHangzhou Yineng Electric Technology Co., Ltd., Hangzhou 310000, ChinaCollege of Intelligence and Computing, Tianjin University, Tianjin 300072, ChinaReliability is of great significance in ensuring the safe operation of modern industry, which mainly relies on data analysis and life tests. However, as the life of mechanical systems becomes increasingly longer with the rapid development of the manufacturing industry, the collection of historical failure data becomes progressively more time-consuming. In this paper, a few-shot reliability assessment approach is proposed in order to overcome the dependence on historical data. Firstly, the vibration response of a bearing was illustrated. Then, based on a vibration response analysis, a morphological component analysis (MCA) method based on sparse representation theory was used to decompose vibration signals and extract impulse signals. After the impulse components’ reconstruction, their statistical indexes were utilized as the input observation vector of a Mixture of Gaussians Hidden Markov Model (MoG-HMM) for a reliability estimation. Finally, the experimental dataset of an aerospace bearing was analyzed via the proposed method. The comparison results illustrate the effectiveness of the proposed method of a few-shot reliability assessment.https://www.mdpi.com/2075-1702/10/6/435few-shot reliability assessmentmorphological component analysiscondition monitoringbearing |
| spellingShingle | Yi Feng Weijun Li Kai Zhang Xianling Li Wenfang Cai Ruonan Liu Morphological Component Analysis-Based Hidden Markov Model for Few-Shot Reliability Assessment of Bearing Machines few-shot reliability assessment morphological component analysis condition monitoring bearing |
| title | Morphological Component Analysis-Based Hidden Markov Model for Few-Shot Reliability Assessment of Bearing |
| title_full | Morphological Component Analysis-Based Hidden Markov Model for Few-Shot Reliability Assessment of Bearing |
| title_fullStr | Morphological Component Analysis-Based Hidden Markov Model for Few-Shot Reliability Assessment of Bearing |
| title_full_unstemmed | Morphological Component Analysis-Based Hidden Markov Model for Few-Shot Reliability Assessment of Bearing |
| title_short | Morphological Component Analysis-Based Hidden Markov Model for Few-Shot Reliability Assessment of Bearing |
| title_sort | morphological component analysis based hidden markov model for few shot reliability assessment of bearing |
| topic | few-shot reliability assessment morphological component analysis condition monitoring bearing |
| url | https://www.mdpi.com/2075-1702/10/6/435 |
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