MATERO-RCA: Mode-Aware Trajectory-Level Energy-Based Root-Set Optimization for Industrial Root Cause Analysis

2026/07/31 by Chengyu Tao, Chunxi Huang, Runquan Xiao
Engineering · #eess.SP

paper · pdf

arxiv created 2026/07/31 · arxiv updated 2026/08/03

Abstract

Root cause analysis (RCA) for contextual anomalies in industrial time series is challenging because responses depend jointly on control commands, operating states, and coupled physical variables. A response can appear marginally normal yet violate its operating context. Events may involve multiple roots and alarms, with each root assigned an observation-only effect confined to its recorded trajectory or a physical-propagation effect on descendants. We propose Mode-Aware Trajectory-Level Energy-Based Root-Set Optimization for Root Cause Analysis (MATERO-RCA), which jointly optimizes a root set, root-effect modes, and auxiliary counterfactual trajectories. Its graph-wide objective combines alarm resolution with temporal compatibility across local causal relations. A Temporal Compatibility Network(CompatNet) maps parent-conditioned trajectory likelihoods to calibrated compatibility energies. A Counterfactual Repair Network (RepairNet) initializes mode-aware counterfactual trajectories for objective-directed gradient refinement. An exact mixed-integer linear program minimizes a residual-cover lower bound, enabling certified best-bound search over the finite admissible root--mode space under the fixed inner solver. Experiments on simulated and real industrial datasets demonstrate superior RCA performance over representative baselines.

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