SFlexRCA enables efficient fault detection on low power IIoT edge devices
SFlexRCA: Lightweight, Scalable, and Flexible Root Cause Analysis for IIoT Edge Systems
Machine LearningArtificial Intelligence
Summary
Industrial IoT systems have many sensors producing lots of data, which makes finding the real cause of problems difficult and resource-heavy. The authors propose SFlexRCA, a lightweight method that summarizes sensor data and uses a shared model to pinpoint fault causes without complex system maps. They tested it on different datasets and small computers, showing it uses less memory and runs faster while still accurately identifying faults. This method helps engineers monitor and troubleshoot industrial systems, even on simple, resource-limited devices.
What this means in practice
- •For industrial system operators: Detect and locate faults quickly on IIoT edge devices with limited computing power to reduce downtime and maintenance costs.
- •For smart building maintenance teams: Use lightweight fault analysis on sensor networks in buildings to identify issues without needing powerful central servers.
Authors
Amr M. Zaki, Farhoud Jafari, Honggeun Ji, Komal Sarda, Marin Litoiu
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.