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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.11031 |
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| _version_ | 1866916900088315904 |
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| author | Sheppard, John W. |
| author_facet | Sheppard, John W. |
| contents | It is often the case that risk assessment and prognostics are viewed as related but separate tasks. This chapter describes a risk-based approach to prognostics that seeks to provide a tighter coupling between risk assessment and fault prediction. We show how this can be achieved using the continuous-time Bayesian network as the underlying modeling framework. Furthermore, we provide an overview of the techniques that are available to derive these models from data and show how they might be used in practice to achieve tasks like decision support and performance-based logistics. This work is intended to provide an overview of the recent developments related to risk-based prognostics, and we hope that it will serve as a tutorial of sorts that will assist others in adopting these techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11031 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Risk-Based Prognostics and Health Management Sheppard, John W. Systems and Control Artificial Intelligence Applications It is often the case that risk assessment and prognostics are viewed as related but separate tasks. This chapter describes a risk-based approach to prognostics that seeks to provide a tighter coupling between risk assessment and fault prediction. We show how this can be achieved using the continuous-time Bayesian network as the underlying modeling framework. Furthermore, we provide an overview of the techniques that are available to derive these models from data and show how they might be used in practice to achieve tasks like decision support and performance-based logistics. This work is intended to provide an overview of the recent developments related to risk-based prognostics, and we hope that it will serve as a tutorial of sorts that will assist others in adopting these techniques. |
| title | Risk-Based Prognostics and Health Management |
| topic | Systems and Control Artificial Intelligence Applications |
| url | https://arxiv.org/abs/2508.11031 |