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