Poster Abstract: Minimizing Labeling Efforts for Fault Detection and Diagnosis

We present a semi-supervised fault detection method that combines contrastive and active learning techniques to efficiently analyze sensor data in industrial systems. It uses a transformer-based encoder with rotary positional embeddings for self-supervised pre-training, which allows it to build structured representations that can be used for density-based clustering using DBSCAN. This method significantly reduces the need for manual labeling, as it only requires 20% of the data to achieve high accuracy in clustering. It outperforms unsupervised alternatives and is scalable and adaptable to evolving fault patterns. Its reduced manual intervention makes it a valuable tool for real-world industrial health monitoring.