MATERO-RCA：面向工业根因分析的模式感知轨迹级基于能量的根集优化
MATERO-RCA: Mode-Aware Trajectory-Level Energy-Based Root-Set Optimization for Industrial Root Cause Analysis
AI总结：
本文提出MATERO-RCA方法，通过联合优化根集等，结合CompatNet与RepairNet，经混合整数线性规划搜索最优根因，在工业时间序列RCA任务上性能优于基线。
AI中文摘要：
针对工业时间序列中上下文异常的根因分析（RCA）颇具挑战性，因为响应同时依赖控制指令、运行状态及耦合物理变量。响应可能边缘正常但违反运行上下文，事件可能涉及多个根因与告警，每个根因被分配仅在其记录轨迹内的仅观测效应，或对后代的物理传播效应。本文提出面向根因分析的模式感知轨迹级基于能量的根集优化（MATERO-RCA），其联合优化根集、根效应模式及辅助反事实轨迹。其全图目标结合告警消解与局部因果关系间的时间兼容性。时间兼容性网络（CompatNet）将父条件轨迹似然映射为校准后的兼容性能量。反事实修复网络（RepairNet）为目标导向的梯度细化初始化模式感知反事实轨迹。精确混合整数线性规划最小化残差覆盖下界，使固定内部求解器下能在有限可接受根-模式空间中进行经认证的最优边界搜索。在模拟及真实工业数据集上的实验表明，MATERO-RCA相较于代表性基线展现出更优的RCA性能。
英文摘要：
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.
