SFlexRCA: Lightweight, Scalable, and Flexible Root Cause Analysis for IIoT Edge Systems

2026-10-04

AI Summary

This study addresses the challenge of root cause analysis under high-dimensional telemetry fault propagation and resource constraints in Industrial Internet of Things (IIoT) edge systems. To this end, we propose a lightweight, topology-agnostic framework that eliminates conventional causal graph construction and message-passing mechanisms. Specifically, the method employs orthogonal feature transformations to compress multivariate time series into compact representations and introduces shared linear modeling to prevent parameter proliferation as variable dimensionality increases. Experimental evaluations on the BATADAL dataset demonstrate that the proposed framework achieves accuracy comparable to mainstream baselines while significantly reducing training overhead, inference latency, and memory footprint. These characteristics enable efficient deployment on resource-constrained edge devices such as Raspberry Pi, offering a practical solution for real-time fault diagnosis in IIoT environments.

Abstract

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.

Problem

• Root Cause Analysis
• Industrial Internet of Things
• Edge Computing
• Resource-constrained Systems
• Fault Propagation

Innovation

• Root Cause Analysis
• Topology-free
• Lightweight Linear Modeling
• Orthogonal Representations
• Edge Computing