SRAD-CLF: Squeak and Rattle Anomaly Detection via Contrastive Learning Framework on Real Industrial Noise Recordings

Vehicle manufacturing monitoring places significant emphasis on special noises from vehicles, encompassing Squeaking and Rattling (SR) noises as a pivotal aspect. Existing SR Anomaly Detection (SR-AD) studies still suffer from several challenges under actual industrial scenarios: lengthy and diverse real-recordings, costly manual labelling, and subtle distinction in road track noises. For the first time, we proposed SRAD-CLF, a novel framework that leverages contrastive learning on Real Industrial Noise Recordings (RINR) to tackle these challenges. Specifically, we initiate the process by segmenting a complete audio sample into distinct road-surface-type audio clips, subsequently assigning a surface label to each clip. Then, a contrastive learning-based framework employing encoder perturbations is implemented to enhance representation, effectively mitigating risks associated with data augmentation, like context loss and the incorporation of irrelevant augmentation details into the features. To enhance the efficacy of SRAD-CLF, we integrate a classifier based on road surface-type information within a self-supervised learning framework, fortifying the performance of the SR-AD task. Our proposed SRAD-CLF achieves promising results for SR-AD on real-world industrial datasets collected from the Ford Manutacturing Plant in Valencia (Spain).