Joint Domain Adaptation via Cluster Centers and Thermal Imaging for Detecting Leakages in Different Pneumatic Components

Pneumatic components are essential for precise control and automation in mechanical manufacturing. Leakage faults in pneumatic components can seriously undermine the reliability of manufacturing processes. When high-pressure gas leaks and expands, heat transfer between the gas and the component wall creates a localized low-temperature area. This thermal anomaly can be detected using thermal imaging techniques. However, thermal images captured from different pneumatic components exhibit distinct distribution patterns, which can significantly degrade the accuracy of existing detection methods. Conventional domain adaptation methods typically use the sample mean to represent feature distributions, neglecting intraclass dispersion and the interdependence among feature learning, classifier learning, and pseudolabel learning. To address these limitations, we propose a joint unsupervised domain adaptation method via cluster centers and thermal imaging for leakage detection in different pneumatic components (JCC-LDC). Specifically, JCC-LDC accounts for the fact that samples of a single class may disperse into multiple clusters, and it integrates feature learning, classifier learning, and pseudolabel learning into a unified framework based on cluster centers. Experimental results demonstrate that JCC-LDC increases the average detection accuracy from 71.5% to 82.3%, effectively enhancing the generalization capability of thermal-imaging-based leakage detection in pneumatic components.