Supervised Contrastive Learning for Multisensor Signals Classification in Automobile Engine Manufacturing

Classification of multisensor signals is an important problem in maintaining stable process operations, particularly in advancing predictive modeling for early detection of abnormal states. Self-supervised learning methods, one of the representation learnings, have been widely studied. However, they have focused on using unlabeled data. In this study, we aim to address the challenge of effectively utilizing fully labeled data for modeling multisensor signals. We introduce supervised contrastive learning (SCL) for the classification of multisensor signals. Our training framework involves a two-step process: SCL for encoder pretraining with time-series data augmentations, and classifier training with the pretrained encoder. Our method exhibits superior performance, outperforming traditional supervised learning approaches by a substantial margin. Furthermore, we demonstrate the practical applicability of our approach for early prediction problems through experiments conducted with real-process data obtained from automobile engine manufacturing. Our work offers a promising method for multisensor signal analysis and early fault detection in manufacturing industries.