Deployment of an Anomaly Detection Methodology Based on Generative Adversarial Network

The automotive sector is undergoing a significant transformation driven by the proliferation of interconnected sensors and advancements in data processing techniques. Among these, anomaly detection for sensor fault diagnosis in sensor operations is crucial due to the critical nature of the application. The rise of Artificial Neural Networks, particularly Deep Autoencoders, has enabled effective anomaly detection using time series data without relying on physical redundancies or complex methods. Generative Adversarial Networks (GANs), widely known for synthetic image generation, have also shown promise in anomaly detection but have been scarcely applied to measurement sensors. This study explores the preliminary implementation of GAN-based anomaly detection on a motorcycle suspension stroke sensor. Initial results indicate an improvement in detection performance compared to traditional autoencoders.