On an Application of Generative Adversarial Networks on Remaining Lifetime Estimation
This addresses prognosis and damage evolution for structural health monitoring, but appears incremental as it builds on existing generative adversarial network methods.
The paper tackles the problem of predicting remaining useful life in structural health monitoring by proposing a generative model that incorporates uncertainties and many past states, tested on a simulated example to provide confident predictions.
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.