Title: Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems,
Abstract: Fault detection problem for closed loop uncertain dynamical systems, is
investigated in this paper, using different deep learning based methods.
Traditional classifier based method does not perform well, because of the
inherent difficulty of detecting system level faults for closed loop dynamical
system. Specifically, acting controller in any closed loop dynamical system,
works to reduce the effect of system level faults. A novel Generative
Adversarial based deep Autoencoder is designed to classify datasets under
normal and faulty operating conditions. This proposed network performs
significantly well when compared to any available classifier based methods, and
moreover, does not require labeled fault incorporated datasets for training
purpose. Finally, this aforementioned network's performance is tested on a high
complexity building energy system dataset.