发表机构
York University（约克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对IIoT故障传播，提出无拓扑的轻量级RCA框架SFlexRCA，通过紧凑正交表示与共享线性建模，在三个数据集上优于10种基线，并实现边缘设备高效部署。
AI 中文摘要
工业物联网（IIoT）系统从相互连接的组件中生成高维传感器遥测数据，故障可能在系统中传播。为应对这些挑战，我们提出了SFlexRCA（可扩展且灵活的根因分析），一种面向资源受限IIoT环境的无拓扑RCA框架。SFlexRCA将多变量遥测数据转换为紧凑的正交表示，并应用共享的轻量级线性建模，避免了显式图构建、消息传递以及逐变量或逐滞后的参数增长。我们在三个公开可用的IIoT数据集（BATADAL、SWaT和WADI）上评估了SFlexRCA，这些数据集涵盖了不同数量的监测变量、时间特征和训练数据模式。我们将SFlexRCA与10种统计、因果和非因果基线在RCA准确性、训练效率、推理延迟和内存消耗方面进行了比较。此外，在Raspberry Pi 3和Raspberry Pi 5上评估了推理效率和内存消耗，并在Raspberry Pi 5上额外测量了能耗。我们进一步研究了时间上下文敏感性、架构和损失组件以及替代表示。值得注意的是，尽管BATADAL的正常运行训练数据有限，SFlexRCA仍保持了强大的定位性能，而其紧凑的共享架构避免了与因果和图方法相关的参数增长。其轻量级共享架构进一步实现了在资源受限的IIoT边缘设备上的高效部署。SFlexRCA代码可在https URL Analysis-Correlation-Attentive-Modeling获取。
英文摘要
Industrial Internet of Things (IIoT) systems generate high-dimensional sensor telemetry from interconnected components, where faults can propagate across the system. To address these challenges, we propose SFlexRCA (Scalable and Flexible Root Cause Analysis), a topology-free RCA framework designed for resource-constrained IIoT environments. SFlexRCA transforms multivariate telemetry into compact orthogonal representations and applies shared lightweight linear modeling, avoiding explicit graph construction, message passing, and per-variable or lag-specific parameter growth. We evaluate SFlexRCA on three publicly available IIoT datasets, BATADAL, SWaT, and WADI, spanning different numbers of monitored variables, temporal characteristics, and training-data regimes. SFlexRCA is compared with 10 statistical, causal, and non-causal baselines in terms of RCA accuracy, training efficiency, inference latency, and memory consumption. In addition, inference efficiency and memory consumption are evaluated on Raspberry Pi 3 and Raspberry Pi 5, while energy consumption is additionally measured on Raspberry Pi 5. We further investigate temporalcontext sensitivity, architectural and loss components, and alternative representations. Notably, SFlexRCA maintains strong localization performance on BATADAL despite its limited normal-operation training data, while its compact shared architecture avoids the parameter growth associated with causal and graph-based approaches. Its lightweight shared architecture further enables efficient deployment on resource-constrained IIoT edge devices. The SFlexRCA code is available at https://github.com/theamrzaki/RootCause- Analysis-Correlation-Attentive-Modeling.
