VGAN: Generalizing MSE GAN and WGAN-GP for robot fault diagnosis

Ziqiang Pu, Diego Cabrera, Chuan Li, Jose Valente de Oliveira

Research output: Contribution to journalArticlepeer-review

Abstract

Generative adversarial networks (GANs) have shown their potential for data generation. However, this type of generative model often suffers from oscillating training processes and mode collapse, among other issues. To mitigate these, this work proposes a generalization of both MSE GAN and WGAN-GP, referred to as VGAN. Within the framework of conditional Wasserstein GAN with gradient penalty, VGAN resorts to the Vapnik V-matrix based criterion that generalizes MSE. Also, a novel early stopping like strategy is proposed that keeps track during training of the most suitable model. A comprehensive set of experiments on a fault diagnosis task for an industrial robot where the generative model is used as a data augmentation tool for dealing with imbalance data sets is presented. The statistical analysis of the results shows that the proposed model outperforms nine other models including vanilla GAN, conditional WGAN with and without conventional regularization, and SMOTE, a classic data augmentation technique.

Original languageEnglish
Pages (from-to)65-75
Number of pages11
JournalIEEE Intelligent Systems
Volume37
Issue number3
DOIs
StateAccepted/In press - 2022

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • conditional Wasserstein generative adversarial network
  • Data generation
  • Data models
  • fault diagnosis
  • Fault diagnosis
  • Generative adversarial networks
  • Generators
  • Intelligent systems
  • regularization
  • robotics
  • Robots
  • Training
  • V-matrix

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