On an Application of Generative Adversarial Networks on Remaining Lifetime Estimation

TL;DR

This paper introduces a generative adversarial network-based model for predicting damage evolution and remaining useful life in structures, effectively handling uncertainties and multiple past states within a population-based SHM framework.

Contribution

It presents a novel generative model that improves damage prognosis by incorporating uncertainties and multiple past states in population-based structural health monitoring.

Findings

Model accurately predicts damage evolution outcomes.

Provides confident remaining useful life estimations.

Effective in simulated damage scenarios.

Abstract

A major problem of structural health monitoring (SHM) has been the prognosis of damage and the definition of the remaining useful life of a structure. Both tasks depend on many parameters, many of which are often uncertain. Many models have been developed for the aforementioned tasks but they have been either deterministic or stochastic with the ability to take into account only a restricted amount of past states of the structure. In the current work, a generative model is proposed in order to make predictions about the damage evolution of structures. The model is able to perform in a population-based SHM (PBSHM) framework, to take into account many past states of the damaged structure, to incorporate uncertainties in the modelling process and to generate potential damage evolution outcomes according to data acquired from a structure. The algorithm is tested on a simulated damage evolution example and the results reveal that it is able to provide quite confident predictions about the remaining useful life of structures within a population.