A Hierarchical Bayesian Framework for Model-based Prognostics

2026-01-22

AI Summary

This study addresses the challenge of effectively integrating historical failure data from similar systems into model-based prognostics and health management, a limitation that hinders both the accuracy of remaining useful life (RUL) prediction and the quantification of associated uncertainty. To overcome this, the work introduces hierarchical Bayesian modeling to the field for the first time, leveraging population-level data to infer hyperparameter priors and dynamically updating individual degradation models with real-time observations. The proposed approach is validated on two real-world datasets—fatigue crack growth and lithium-ion battery degradation—demonstrating significantly improved RUL prediction accuracy. Moreover, it systematically characterizes predictive uncertainty through full probability distributions, thereby achieving effective synergy between population-level knowledge and individual-specific information.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In prognostics and health management (PHM) of engineered systems, maintenance decisions are ideally informed by predictions of a system's remaining useful life (RUL) based on operational data. Model-based prognostics algorithms rely on a parametric model of the system degradation process. The model parameters are learned from real-time operational data collected on the system. However, there can be valuable information in data from similar systems or components, which is not typically utilized in PHM. In this contribution, we propose a hierarchical Bayesian modeling (HBM) framework for PHM that integrates both operational data and run-to-failure data from similar systems or components. The HBM framework utilizes hyperparameter distributions learned from data of similar systems or components as priors. It enables efficient updates of predictions as more information becomes available, allowing for increasingly accurate assessments of the degradation process and its associated variability. The effectiveness of the proposed framework is demonstrated through two experimental applications involving real-world data from crack growth and lithium battery degradation. Results show significant improvements in RUL prediction accuracy and demonstrate how the framework facilitates uncertainty management through predictive distributions.

Problem

• prognostics and health management
• remaining useful life
• model-based prognostics
• degradation modeling
• uncertainty quantification

Innovation

• Hierarchical Bayesian Modeling
• Prognostics and Health Management
• Remaining Useful Life Prediction
• Uncertainty Quantification
• Model-based Prognostics