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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.14666 |
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| _version_ | 1866911425249673216 |
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| author | Clark, Jared M. Min, Jie Li, Mingyang Warr, Richard L. DeHart, Stephanie P. King, Caleb B. Lu, Lu Hong, Yili |
| author_facet | Clark, Jared M. Min, Jie Li, Mingyang Warr, Richard L. DeHart, Stephanie P. King, Caleb B. Lu, Lu Hong, Yili |
| contents | Degradation models play a critical role in quality engineering by enabling the assessment and prediction of system reliability based on data. The objective of this paper is to provide an accessible introduction to degradation models. We explore commonly used degradation data types, including repeated measures degradation data and accelerated destructive degradation test data, and review modeling approaches such as general path models and stochastic process models. Key inference problems, including reliability estimation and prediction, are addressed. Applications across diverse fields, including material science, renewable energy, civil engineering, aerospace, and pharmaceuticals, illustrate the broad impact of degradation models in industry. We also discuss best practices for quality engineers, software implementations, and challenges in applying these models. This paper aims to provide quality engineers with a foundational understanding of degradation models, equipping them with the knowledge necessary to apply these techniques effectively in real-world scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14666 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | What Quality Engineers Need to Know about Degradation Models Clark, Jared M. Min, Jie Li, Mingyang Warr, Richard L. DeHart, Stephanie P. King, Caleb B. Lu, Lu Hong, Yili Applications Degradation models play a critical role in quality engineering by enabling the assessment and prediction of system reliability based on data. The objective of this paper is to provide an accessible introduction to degradation models. We explore commonly used degradation data types, including repeated measures degradation data and accelerated destructive degradation test data, and review modeling approaches such as general path models and stochastic process models. Key inference problems, including reliability estimation and prediction, are addressed. Applications across diverse fields, including material science, renewable energy, civil engineering, aerospace, and pharmaceuticals, illustrate the broad impact of degradation models in industry. We also discuss best practices for quality engineers, software implementations, and challenges in applying these models. This paper aims to provide quality engineers with a foundational understanding of degradation models, equipping them with the knowledge necessary to apply these techniques effectively in real-world scenarios. |
| title | What Quality Engineers Need to Know about Degradation Models |
| topic | Applications |
| url | https://arxiv.org/abs/2507.14666 |