Fusion of heterogeneous data for robust degradation prognostics

TL;DR

This paper presents a novel methodology for fusing heterogeneous data sources to improve the robustness and accuracy of degradation prognostics in industrial assets, combining sensitivity analysis, Bayesian updating, and surrogate modeling.

Contribution

It introduces an iterative fusion approach that integrates kernel sensitivity analysis, Bayesian priors, and surrogate models to enhance degradation prediction accuracy.

Findings

Improved prediction robustness through data fusion.

Effective handling of computationally expensive models.

Successful application to industrial degradation scenarios.

Abstract

Assessing the degradation state of an industrial asset first requires evaluating its current condition and then to project the forecast model trajectory to a predefined prognostic threshold, thereby estimating its remaining useful life (RUL). Depending on the available information, two primary categories of forecasting models may be used: physics-based simulation codes and datadriven (machine learning) approaches. Combining both modelling approaches may enhance prediction robustness, especially with respect to their individual uncertainties. This paper introduces a methodology for fusion of heterogeneous data in degradation prognostics. The proposed approach acts iteratively on a computer model's uncertain input variables by combining kernel-based sensitivity analysis for variable ranking with a Bayesian framework to inform the priors with the heterogeneous data. Additionally, we propose an integration of an aggregate surrogate modeling strategy for computationally expensive degradation simulation codes. The methodology updates the knowledge of the computer code input probabilistic model and reduces the output uncertainty. As an application, we illustrate this methodology on a toy model from crack propagation based on Paris law as well as a complex industrial clogging simulation model for nuclear power plant steam generators, where data is intermittently available over time.