On an Application of Generative Adversarial Networks on Remaining Lifetime Estimation

by   G. Tsialiamanis, et al.

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.


page 1

page 2

page 3

page 4


CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection

The accelerated advancements in the data science field in the last few d...

Online Subspace Tracking for Damage Propagation Modeling and Predictive Analytics: Big Data Perspective

We analyze damage propagation modeling of turbo-engines in a data-driven...

Generative Adversarial Networks for Data Generation in Structural Health Monitoring

Structural Health Monitoring (SHM) has been continuously benefiting from...

On generative models as the basis for digital twins

A framework is proposed for generative models as a basis for digital twi...

On an application of graph neural networks in population based SHM

Attempts have been made recently in the field of population-based struct...

On generating parametrised structural data using conditional generative adversarial networks

A powerful approach, and one of the most common ones in structural healt...

Statistical guided-waves-based SHM via stochastic non-parametric time series models

Damage detection in active-sensing, guided-waves-based Structural Health...

Please sign up or login with your details

Forgot password? Click here to reset