Fv-AD: F-AnoGAN Based Anomaly Detection in Chromate Process for Smart Manufacturing

Anomaly detection for quality prediction has recently become important, as data collection has increased in various fields, such as smart factories and healthcare systems. Various attempts have been made in the existing manufacturing process to improve discrimination accuracy due to data imbalance in the anomaly detection model. Predicting the quality of a chromate process has a significant influence on the completeness of the process, and anomaly detection is important. Furthermore, obtaining image data, such as monitoring during the manufacturing process, is difficult, and prediction is challenging owing to data imbalance. Accordingly, the model employs an unsupervised learning-based Generative Adversarial Networks (GAN) model, performs learning with only normal data images, and augments the Fast Unsupervised Anomaly Detection with GAN (F-AnoGAN) base with a visualization component to provide a more intuitive judgment of defects with chromate process data. In addition, anomaly scores are calculated based on mapping in the latent space, and new data are applied to confirm anomaly detection and the corresponding location values. As a result, this paper presents a GAN architecture to detect anomalies through chromate facility data in a smart manufacturing environment. It proved meaningful performance and added visualization parts to provide explainable interpretation. Data experiments on the chromate process show that the loss value, anomaly score, and anomaly position are accurately distinguished from abnormal images.