# Interpretable Sensor Change Detection via Conditional Cauchy–Schwarz Divergence

## TL;DR

This paper introduces a new method for detecting changes in sensor data that also explains which sensors are involved, making it more useful for safety-critical systems.

## Contribution

The novelty is using conditional Cauchy–Schwarz divergence to provide interpretable change detection in multivariate sensor networks.

## Key findings

- The method achieves competitive or better detection performance than existing baselines.
- It provides transparent explanations of changes at the sensor level.
- It works well on synthetic, generic, and industrial sensor data.

## Abstract

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.