A Hierarchical Bayesian Framework for Model-based Prognostics

TL;DR

This paper introduces a hierarchical Bayesian framework for prognostics that combines operational and similar-system data to improve remaining useful life predictions and uncertainty quantification.

Contribution

It presents a novel hierarchical Bayesian approach that integrates data from similar systems to enhance model-based prognostics accuracy and adaptability.

Findings

Improved RUL prediction accuracy in real-world applications.

Effective uncertainty management through predictive distributions.

Demonstrated benefits in crack growth and battery degradation cases.

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.