A deep adversarial approach based on multi-sensor fusion for remaining useful life prognostics

TL;DR

This paper presents a deep adversarial learning framework utilizing multi-sensor data fusion for accurate remaining useful life prediction of machinery, demonstrating improved performance on turbofan engine datasets.

Contribution

It introduces a novel deep adversarial approach with variational inference for RUL prognostics, advancing multi-sensor fusion techniques in asset management.

Findings

Enhanced RUL prediction accuracy with the proposed method

Effective application to turbofan engine data set

Demonstrated superiority over existing methods

Abstract

Multi-sensor systems are proliferating the asset management industry and by proxy, the structural health management community. Asset managers are beginning to require a prognostics and health management system to predict and assess maintenance decisions. These systems handle big machinery data and multi-sensor fusion and integrate remaining useful life prognostic capabilities. We introduce a deep adversarial learning approach to damage prognostics. A non-Markovian variational inference-based model incorporating an adversarial training algorithm framework was developed. The proposed framework was applied to a public multi-sensor data set of turbofan engines to demonstrate its ability to predict remaining useful life. We find that using the deep adversarial based approach results in higher performing remaining useful life predictions.