Risk-Based Prognostics and Health Management

2025-08-14

AI Summary

Current risk assessment and fault prediction are typically treated as disjoint tasks, limiting their effectiveness in supporting dynamic decision-making and performance-driven operations management. To address this, we propose a risk-informed Prognostics and Health Management (PHM) framework that unifies these two functions for the first time. Our approach centers on a Continuous-Time Bayesian Network (CTBN) to jointly model system degradation dynamics, integrating data-driven learning with rigorous risk quantification. This enables real-time, probabilistic risk inference and multi-step fault prediction under continuous time. Compared to conventional decoupled methods, our framework significantly improves health state estimation accuracy and temporal interpretability—providing verifiable, actionable insights for predictive maintenance scheduling, maintenance resource optimization, and performance-oriented logistics decision-making.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Integrating risk assessment with fault prediction for tighter coupling
• Using Bayesian networks for risk-based prognostics modeling framework
• Applying data-derived models for decision support and logistics

Innovation

• Continuous-time Bayesian network modeling framework
• Tighter coupling between risk assessment and fault prediction
• Data-derived models for decision support and logistics