Remaining Useful Life Analysis for Two‐Stage Nonlinear Inverse Gaussian Process With Random Effects.
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| Title: | Remaining Useful Life Analysis for Two‐Stage Nonlinear Inverse Gaussian Process With Random Effects. |
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| Authors: | Yan, Zai‐Zai1 (AUTHOR), Sun, Li‐Jun1 (AUTHOR) sunlj1015@163.com, Liang, Yu‐Ying1 (AUTHOR) |
| Source: | Quality & Reliability Engineering International. Jun2025, Vol. 41 Issue 4, p1447-1460. 14p. |
| Subjects: | Remaining useful life, Markov chain Monte Carlo, Probability density function, Gaussian processes, Gibbs sampling |
| Abstract: | Evaluating the reliability of high‐reliability, long‐life products remains a central challenge in the realm of reliability engineering. The intricate internal structure of these products results in a performance degradation process that is both nonlinear and multi‐staged. Distinguished from the traditional single‐stage degradation model, the two‐stage degradation model requires consideration of the degradation state at a critical transition point. This complexity significantly complicates the task of modeling and predicting the remaining useful life (RUL) for products exhibiting two‐stage nonlinear degradation. To address this challenge, this paper introduces a novel two‐stage inverse Gaussian (IG) degradation process model. This model builds upon and extends the traditional nonlinear IG degradation process model, incorporating a two‐stage approach to account for the uncertainty at the degradation transition point and randomizing the drift coefficients to better reflect the stochastic nature of the degradation process. The analytical expressions of probability density functions (PDF) and reliability functions are derived based on the definition of first hitting time (FHT). The unknown parameters of the model are estimated using the Gibbs sampling method within the Markov Chain Monte Carlo (MCMC) framework. The applicability and effectiveness of our proposed method are substantiated through a simulation example and by analyzing real‐world degradation data from a cabin door lock mechanism. [ABSTRACT FROM AUTHOR] |
| Copyright of Quality & Reliability Engineering International is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 184927274 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Remaining Useful Life Analysis for Two‐Stage Nonlinear Inverse Gaussian Process With Random Effects. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yan%2C+Zai‐Zai%22">Yan, Zai‐Zai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Li‐Jun%22">Sun, Li‐Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sunlj1015@163.com</i><br /><searchLink fieldCode="AR" term="%22Liang%2C+Yu‐Ying%22">Liang, Yu‐Ying</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Quality+%26+Reliability+Engineering+International%22">Quality & Reliability Engineering International</searchLink>. Jun2025, Vol. 41 Issue 4, p1447-1460. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Remaining+useful+life%22">Remaining useful life</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+chain+Monte+Carlo%22">Markov chain Monte Carlo</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+density+function%22">Probability density function</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+processes%22">Gaussian processes</searchLink><br /><searchLink fieldCode="DE" term="%22Gibbs+sampling%22">Gibbs sampling</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Evaluating the reliability of high‐reliability, long‐life products remains a central challenge in the realm of reliability engineering. The intricate internal structure of these products results in a performance degradation process that is both nonlinear and multi‐staged. Distinguished from the traditional single‐stage degradation model, the two‐stage degradation model requires consideration of the degradation state at a critical transition point. This complexity significantly complicates the task of modeling and predicting the remaining useful life (RUL) for products exhibiting two‐stage nonlinear degradation. To address this challenge, this paper introduces a novel two‐stage inverse Gaussian (IG) degradation process model. This model builds upon and extends the traditional nonlinear IG degradation process model, incorporating a two‐stage approach to account for the uncertainty at the degradation transition point and randomizing the drift coefficients to better reflect the stochastic nature of the degradation process. The analytical expressions of probability density functions (PDF) and reliability functions are derived based on the definition of first hitting time (FHT). The unknown parameters of the model are estimated using the Gibbs sampling method within the Markov Chain Monte Carlo (MCMC) framework. The applicability and effectiveness of our proposed method are substantiated through a simulation example and by analyzing real‐world degradation data from a cabin door lock mechanism. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Quality & Reliability Engineering International is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/qre.3729 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1447 Subjects: – SubjectFull: Remaining useful life Type: general – SubjectFull: Markov chain Monte Carlo Type: general – SubjectFull: Probability density function Type: general – SubjectFull: Gaussian processes Type: general – SubjectFull: Gibbs sampling Type: general Titles: – TitleFull: Remaining Useful Life Analysis for Two‐Stage Nonlinear Inverse Gaussian Process With Random Effects. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yan, Zai‐Zai – PersonEntity: Name: NameFull: Sun, Li‐Jun – PersonEntity: Name: NameFull: Liang, Yu‐Ying IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 07488017 Numbering: – Type: volume Value: 41 – Type: issue Value: 4 Titles: – TitleFull: Quality & Reliability Engineering International Type: main |
| ResultId | 1 |
