CULPRIT: Localizing Mechanism Shifts from Shared Sensors under Varying Conditions

Root cause analysis (RCA) for industrial systems must name the faulty component from sensor data. Because sensors typically sit between components, ranking them as root causes mistakes the site of measurement for the site of failure. Our key insight is that a fault is a mechanism shift on a component-level latent variable. Under this view, a system's sensor-to-component association and its operating condition together can localize that shift from sensor data. We introduce CULPRIT, an unsupervised approach to pinpoint the root cause in industrial systems under varying conditions. It matches each fault sample to healthy samples at its operating point and projects the matched residuals onto one latent per component through the association. It then separates root causes from downstream effects along the component edges. We evaluate CULPRIT against 15 baselines on six benchmarks, where it holds the best average Top-1 accuracy of 0.52. We further show that operating-condition adjustment and the sensor-to-component association are each necessary. Matching at the operating point lifts Top-1 accuracy from 0.35 to 0.68 on N-CMAPSS, and one latent per component, instead of a sensor maximum, lifts the average Top-1 from 0.42 to 0.52. Code is available at https://anonymous.4open.science/r/culprit-DC73.