Can GANs recover faults in electrical motor sensors? - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2022

Can GANs recover faults in electrical motor sensors?

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.
Fichier principal
Vignette du fichier
45_can_gans_recover_faults_in_ele.pdf (254.41 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03887143 , version 1 (06-12-2022)

Identifiers

  • HAL Id : hal-03887143 , version 1

Cite

Sagar Verma, Nicolas Henwood, Marc Castella, Jean-Christophe Pesquet, Al Kassem Jebai. Can GANs recover faults in electrical motor sensors?. ICLR 2022 - International Conference on Learning Representations (ICLR ), Apr 2022, Virtual, France. ⟨hal-03887143⟩
86 View
52 Download

Share

Gmail Mastodon Facebook X LinkedIn More