Generative adversarial network to alleviate information insufficiency in intelligent fault diagnosis by generating continuations of signals

Abstract

This paper introduces Con-GAN, an innovative improvement of GAN-based data augmentation designed to address data insufficiency in fault diagnosis methodologies. Distinctly different from traditional GAN-based data augmentation, Con-GAN aims to generate realistic continuations of existing signals and subsequently integrates these continuations with the original signals. This ’real-fake-mixed’ strategy fully leverages the existing signals and results in high-quality new signals for data augmentation, making our approach both safer and more effective. With rigorous validation across multiple datasets, which include both artificially induced and test-caused real faults, Con-GAN’s consistent effectiveness has been substantiated. Compared to other GANs, Con-GAN presents distinct advantages, yielding new signals with better validity and variety. In summary, Con-GAN introduces a novel and feasible paradigm in GAN-based data augmentation, demonstrating practical value for industrial. In the future, we plan to evaluate Con-GAN’s performance under conditions with high noise levels, exploring its robustness and adaptability across a wider range of scenarios.