Physics-Infused Fuzzy Generative Adversarial Network for Robust Failure Prognosis

TL;DR

This paper introduces a novel hybrid prognostics model called Physics-Infused Fuzzy GAN, combining fuzzy logic, physics-based models, and GANs to improve system health prediction accuracy, especially under data irregularities.

Contribution

It presents a new hybrid modeling approach that integrates physics-based models with fuzzy GANs for more accurate and realistic system prognosis.

Findings

Enhanced prognosis accuracy on bearing data

Physics-based constraints improve GAN performance

Hybrid model outperforms traditional methods

Abstract

Prognostics aid in the longevity of fielded systems or products. Quantifying the system's current health enable prognosis to enhance the operator's decision-making to preserve the system's health. Creating a prognosis for a system can be difficult due to (a) unknown physical relationships and/or (b) irregularities in data appearing well beyond the initiation of a problem. Traditionally, three different modeling paradigms have been used to develop a prognostics model: physics-based (PbM), data-driven (DDM), and hybrid modeling. Recently, the hybrid modeling approach that combines the strength of both PbM and DDM based approaches and alleviates their limitations is gaining traction in the prognostics domain. In this paper, a novel hybrid modeling approach for prognostics applications based on combining concepts from fuzzy logic and generative adversarial networks (GANs) is outlined. The FuzzyGAN based method embeds a physics-based model in the aggregation of the fuzzy implications. This technique constrains the output of the learning method to a realistic solution. Results on a bearing problem showcases the efficacy of adding a physics-based aggregation in a fuzzy logic model to improve GAN's ability to model health and give a more accurate system prognosis.