Can GANs Recover Faults in Electrical Motor Sensors?

Sagar Verma, Nicolas Henwood, Marc Castella, Jean-Christophe Pesquet, Al Kassem Jebai

Keywords: GANs, Electrical Motors, Sensors

TL;DR: Resolving missing and erroneous data in industrial sensors using generative adversarial networks.

Abstract:

Electrical motors in industrial and emerging applications such as electrical automotive require high dynamic performance, robustness against parameter variation, and reliability. Recent advances in neural network-based estimators and fault detection techniques rely heavily on accurate sensor information. Due to the extreme operating conditions of electrical motors, there is always a chance of sensor failure which might lead to poor performance in downstream tasks using neural networks. This paper introduces the problem of identifying and recovering sensor faults using generative adversarial networks. We consider sensors monitoring various quantities like currents, voltages, speed, torque, temperature, and vibrations. We introduce fault model for these sensors to simulate training datasets. We use existing GAN based data imputation methods as baseline solutions.
Project Page: https://sagarverma.github.io/sensors.html