A reliability estimation method based on combination of failure mechanism and ANN supported wiener processes
For some engineering application, accurately estimating reliability only depend on the history data or failure mechanism is difficult to implement, due to the lack of data and imperfect theory of failure mechanism. Namely, both history data and failure mechanism should be utilized to improve the rel...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024022618 |
| _version_ | 1797267709904289792 |
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| author | Di Liu Yajing Qiao Shaoping Wang Siming Fan Dong Liu Cun Shi Jian Shi |
| author_facet | Di Liu Yajing Qiao Shaoping Wang Siming Fan Dong Liu Cun Shi Jian Shi |
| author_sort | Di Liu |
| collection | DOAJ |
| description | For some engineering application, accurately estimating reliability only depend on the history data or failure mechanism is difficult to implement, due to the lack of data and imperfect theory of failure mechanism. Namely, both history data and failure mechanism should be utilized to improve the reliability estimation accuracy for engineering applications. Hence, we construct a reliability estimation method by fusing the failure mechanism and artificial neural network (ANN) supported Wiener processes for utilizing both history data and failure mechanism. ANN and failure mechanism are integrated into Wiener process with random effects, respectively. Bayesian model averaging (BMA) method is adapted to combine the failure mechanism with ANN supported Wiener processes, as well as to update the model parameters by fusing data. Based on a typical aviation hydraulic pump's actual dataset, we illustrate the advantages of our approach by comparing to Wiener process supported only by ANN or failure mechanism in engineering practices. The proposed method shows superiorities on reliability estimation considering the estimation accuracies comparing the other two models. |
| first_indexed | 2024-03-08T00:01:18Z |
| format | Article |
| id | doaj.art-f5bb58e66d314193a9c7ea3ab96bcc6d |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-25T01:20:54Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-f5bb58e66d314193a9c7ea3ab96bcc6d2024-03-09T09:27:38ZengElsevierHeliyon2405-84402024-02-01104e26230A reliability estimation method based on combination of failure mechanism and ANN supported wiener processesDi Liu0Yajing Qiao1Shaoping Wang2Siming Fan3Dong Liu4Cun Shi5Jian Shi6School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China; State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China; Tianmushan Laboratory, Xixi Octagon City, Yuhang District, Hangzhou 310023, China; Key Laboratory of Flight Techniques and Flight Safety, CAAC, Guanghan 618307, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China; Tianmushan Laboratory, Xixi Octagon City, Yuhang District, Hangzhou 310023, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China; Corresponding author.For some engineering application, accurately estimating reliability only depend on the history data or failure mechanism is difficult to implement, due to the lack of data and imperfect theory of failure mechanism. Namely, both history data and failure mechanism should be utilized to improve the reliability estimation accuracy for engineering applications. Hence, we construct a reliability estimation method by fusing the failure mechanism and artificial neural network (ANN) supported Wiener processes for utilizing both history data and failure mechanism. ANN and failure mechanism are integrated into Wiener process with random effects, respectively. Bayesian model averaging (BMA) method is adapted to combine the failure mechanism with ANN supported Wiener processes, as well as to update the model parameters by fusing data. Based on a typical aviation hydraulic pump's actual dataset, we illustrate the advantages of our approach by comparing to Wiener process supported only by ANN or failure mechanism in engineering practices. The proposed method shows superiorities on reliability estimation considering the estimation accuracies comparing the other two models.http://www.sciencedirect.com/science/article/pii/S2405844024022618Reliability estimationWiener processFailure mechanismANNModel combination |
| spellingShingle | Di Liu Yajing Qiao Shaoping Wang Siming Fan Dong Liu Cun Shi Jian Shi A reliability estimation method based on combination of failure mechanism and ANN supported wiener processes Heliyon Reliability estimation Wiener process Failure mechanism ANN Model combination |
| title | A reliability estimation method based on combination of failure mechanism and ANN supported wiener processes |
| title_full | A reliability estimation method based on combination of failure mechanism and ANN supported wiener processes |
| title_fullStr | A reliability estimation method based on combination of failure mechanism and ANN supported wiener processes |
| title_full_unstemmed | A reliability estimation method based on combination of failure mechanism and ANN supported wiener processes |
| title_short | A reliability estimation method based on combination of failure mechanism and ANN supported wiener processes |
| title_sort | reliability estimation method based on combination of failure mechanism and ann supported wiener processes |
| topic | Reliability estimation Wiener process Failure mechanism ANN Model combination |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024022618 |
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