Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems
This addresses fault detection for closed-loop dynamical systems, offering a practical solution for domains like building energy systems, though it is incremental as it builds on existing deep learning methods.
The paper tackles fault detection in closed-loop uncertain dynamical systems by designing a novel Generative Adversarial Network-based Autoencoder, which performs significantly better than traditional classifier-based methods and does not require labeled fault data for training.
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.