One-classification anomaly detection: Utilizing Contrastive Transfer Learning

To address the challenge of quickly developing reliable anomaly detectors with limited sensor data in industrial processes, we propose a novel single-classification anomaly detection algorithm that combines Contrastive Learning and Transfer Learning. Our algorithm leverages abundant normal data from similar devices to migrate anomaly detection rules to target devices. Using a supporting data vector description algorithm, we derive anomaly detection rules for both the source and target domain devices. Then, Transfer Learning transfers these rules from the source domain to the target domain. Furthermore, we introduce an innovative approach that forms sample pairs from two domain samples and applies Contrastive Learning to ensure a uniform distribution of sample features, even with limited samples. This approach allows for extracting discriminative feature representations and facilitates rule migration. Experimental evaluations using the IEEE PHM Challenge 2012 dataset and the Arc Furnace dataset demonstrate the superior performance of our approach compared to existing state-of-the-art algorithms.