将风险评估与故障预测结合，用贝叶斯网络实现更精准的设备健康预测。
Risk-Based Prognostics and Health Management
- 基于连续时间贝叶斯网络，打通风险评估与故障预测的关联。
- 从数据中构建模型，支持决策辅助和性能导向的后勤管理。
- 适合从事设备健康管理、工业预测性维护的研究者和工程师。
风险评估与故障预测常被视为独立任务。本文提出一种基于风险的预测方法，通过连续时间贝叶斯网络实现两者的紧密耦合。文中介绍了从数据中构建此类模型的技术，并展示了其在决策支持和性能导向后勤中的实际应用。本工作旨在综述近期风险驱动型预测技术的发展，为读者提供入门指导，助力相关技术的落地应用。
原文摘要 · Abstract (English)
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.
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