Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process
In this article, a fast krill herd algorithm is developed for prognosis of hybrid mechatronic system using the improved Wiener degradation process. First, the diagnostic hybrid bond graph is used to model the hybrid mechatronic system and derive global analytical redundancy relations. Based on the g...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/10/9/213 |
| _version_ | 1797520701749460992 |
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| author | Ming Yu Haotian Lu Hai Wang Chenyu Xiao Dun Lan Junjie Chen |
| author_facet | Ming Yu Haotian Lu Hai Wang Chenyu Xiao Dun Lan Junjie Chen |
| author_sort | Ming Yu |
| collection | DOAJ |
| description | In this article, a fast krill herd algorithm is developed for prognosis of hybrid mechatronic system using the improved Wiener degradation process. First, the diagnostic hybrid bond graph is used to model the hybrid mechatronic system and derive global analytical redundancy relations. Based on the global analytical redundancy relations, the fault signature matrix and mode change signature matrix for fault and mode change isolation can be obtained. Second, in order to determine the true faults from the suspected fault candidates after fault isolation, a fault estimation method based on adaptive square root cubature Kalman filter is proposed when the noise distributions are unknown. Then, the improved Wiener process incorporating nonlinear term is developed to build the degradation model of incipient fault based on the fault estimation results. For prognosis, the fast krill herd algorithm is proposed to estimate unknown degradation model coefficients. After that, the probability density function of remaining useful life is derived using the identified degradation model. Finally, the proposed methods are validated by simulations. |
| first_indexed | 2024-03-10T08:00:21Z |
| format | Article |
| id | doaj.art-be9bfba417654ec0a56f06fa7de4d7b7 |
| institution | Directory Open Access Journal |
| issn | 2076-0825 |
| language | English |
| last_indexed | 2024-03-10T08:00:21Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj.art-be9bfba417654ec0a56f06fa7de4d7b72023-11-22T11:32:55ZengMDPI AGActuators2076-08252021-08-0110921310.3390/act10090213Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener ProcessMing Yu0Haotian Lu1Hai Wang2Chenyu Xiao3Dun Lan4Junjie Chen5School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaDiscipline of Engineering and Energy, Murdoch University, Perth, WA 6150, AustraliaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaIn this article, a fast krill herd algorithm is developed for prognosis of hybrid mechatronic system using the improved Wiener degradation process. First, the diagnostic hybrid bond graph is used to model the hybrid mechatronic system and derive global analytical redundancy relations. Based on the global analytical redundancy relations, the fault signature matrix and mode change signature matrix for fault and mode change isolation can be obtained. Second, in order to determine the true faults from the suspected fault candidates after fault isolation, a fault estimation method based on adaptive square root cubature Kalman filter is proposed when the noise distributions are unknown. Then, the improved Wiener process incorporating nonlinear term is developed to build the degradation model of incipient fault based on the fault estimation results. For prognosis, the fast krill herd algorithm is proposed to estimate unknown degradation model coefficients. After that, the probability density function of remaining useful life is derived using the identified degradation model. Finally, the proposed methods are validated by simulations.https://www.mdpi.com/2076-0825/10/9/213diagnostic hybrid bond graphhybrid mechatronic systemadaptive square root cubature Kalman filterimproved Wiener processfast krill herd algorithm |
| spellingShingle | Ming Yu Haotian Lu Hai Wang Chenyu Xiao Dun Lan Junjie Chen Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process Actuators diagnostic hybrid bond graph hybrid mechatronic system adaptive square root cubature Kalman filter improved Wiener process fast krill herd algorithm |
| title | Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process |
| title_full | Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process |
| title_fullStr | Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process |
| title_full_unstemmed | Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process |
| title_short | Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process |
| title_sort | computational intelligence based prognosis for hybrid mechatronic system using improved wiener process |
| topic | diagnostic hybrid bond graph hybrid mechatronic system adaptive square root cubature Kalman filter improved Wiener process fast krill herd algorithm |
| url | https://www.mdpi.com/2076-0825/10/9/213 |
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