Incrementally Generative Adversarial Diagnostics Using Few-Shot Enabled One-Class Learning

Abstract

In real-world industrial scenarios, fault diagnosis often relies on a significant volume of normal data to detect faults with a few coming samples. The limited sample nature of newly introduced faults can pose challenges for deep learning-based fault diagnosis models. To address this issue, a novel framework named incrementally generative adversarial diagnostics was developed by using few-shot enabled one-class learning (FSEOCL) for effective anomaly detection and fault classification of the new-coming few samples. In the addressed method, a bi-directional generative adversarial network is first trained using only normal data to acquire an encoder for latent representation from time-series data. A few samples of each fault condition in latent space can be then identified and classified correctly through FSEOCL. The effectiveness of the proposed framework is demonstrated through fault diagnosis experiments conducted on both a benchmark bearing and an industrial robot. The results underscore the adaptability of this framework to industrial fault diagnosis scenarios, highlighting its ability to achieve accurate anomaly detection and fault classification compared to state-of-the-art peer methods.