Interpretable Sensor Change Detection via Conditional Cauchy-Schwarz Divergence

Detecting distributional changes in multivariate sensor networks is a fundamental task for monitoring complex systems such as industrial processes, structural health monitoring, and large-scale Internet of Things infrastructures. Despite significant progress, most existing change-point detection methods either operate on high-dimensional observations in a black-box manner or provide limited insight into how inter-sensor dependencies evolve over time, thereby restricting their practical utility in safety-critical applications. In this work, we propose an interpretable change detection framework based on the Cauchy-Schwarz (CS) divergence. By extending CS divergence to conditional distributions over sensor variables, the proposed method detects distributional shifts through changes in sensor-wise conditional relationships. This design enables reliable change detection while simultaneously providing transparent sensor-level explanations of detected changes. Extensive experiments on synthetic data, generic multivariate sensor time series, and a large-scale industrial process benchmark demonstrate that the proposed method achieves competitive or superior detection performance compared to representative baselines, while offering fine-grained interpretability for practical sensor monitoring systems.