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International Society of Science and Applied Technologies |
| Degradation Analysis of Multivariate Inverse Gaussian Process with Random Effects | ||||
| Author |
Yi-Fu Wang
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| Co-Author(s) |
Tzu-Erh Huang
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| Abstract |
For high-reliability products, collecting sufficient failure data within a limited time is often challenging. To address this, degradation analysis is commonly employed to evaluate a product’s quality characteristic to estimate the product lifetime. However, with the advancements in technologies, analyzing a single quality characteristic for degradation is no longer sufficient. This makes the analysis of multiple quality characteristics necessary. In this study, we propose a method that incorporates a common dependent framework with the inverse Gaussian process to address multivariate quality characteristics. By utilizing a conjugate conditional random effect within the inverse Gaussian process, we develop the Multivariate Inverse Gaussian Process with Random Effects (MIGP) model. This model effectively captures both the heterogeneity among samples and the correlations among quality characteristics. Finally, a case study was conducted to validate the proposed multivariate degradation model.
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| Keywords | Degradation analysis, multivariate quality characteristics, inverse Gaussian process, random effect | |||
| Article #: RQD2025-39 | ||||
Proceedings of 30th ISSAT International Conference on Reliability & Quality in Design |