SensorDBSCAN: Semi-Supervised Active Learning Powered Method for Anomaly Detection and Diagnosis

Abstract

Fault detection and diagnosis (FDD) is a critical challenge in industrial processes aimed at minimizing risks such as safety hazards, costly downtime, and suboptimal production. Traditional supervised FDD methods offer great performance while heavily relying on large volumes of labeled data, whereas unsupervised methods do not depend on labeled data, though are inferior in performance compared to supervised ones. In this paper, we propose SensorDBSCAN, a novel semi-supervised method for anomaly detection and diagnosis. The key innovation lies in achieving good performance with minimal labeled data - less than 1% of the dataset - by leveraging active and contrastive learning techniques. The proposed approach combines a transformer-based encoder trained with a triplet-based contrastive learning objective and the classical density-based clustering algorithm DBSCAN, enabling strong feature extraction, efficient and interpretable feature space organization and simple clustering algorithm. Unlike existing methods, SensorDBSCAN eliminates the need for manual labeling large amounts of data, cluster analysis, and pre-defining cluster numbers, providing greater usability in real-world cases. We validate the effectiveness of our method on the Tennessee Eastman Process (TEP) and its advanced simulations (TEP Rieth and TEP Rieker). SensorDBSCAN demonstrates better performance on well-known and realistic datasets, reducing labeling requirements while maintaining high accuracy of fault detection and diagnostics. The code is available at https://github.com/K0mp0t/sensordbscan.