Fault Identification Using System-Level Insights and Multi-Layered Classification

This paper presents a novel fault detection framework for IoT device fault detection, combining meta-algorithmic decision logic with a neural network-based classifier to enable efficient, scalable failure analysis. Leveraging system-level data, the methodology adopts a multi-layered architecture grounded in systems thinking to classify devices as failed or non-failed and then identify the root cause domain—hardware, software, or firmware. The first layer implements a meta-classifier that integrates multiple lightweight algorithms weighted by application-specific criteria such as accuracy, precision, or recall. This ensemble approach capitalizes on the strengths of diverse classifiers to enhance fault detection performance using high-level system metrics. The second layer introduces a neural network trained on subsystem-specific features—such as power metrics, SoC diagnostics, and LTE module health—to infer the most probable root cause category. This structure not only boosts classification accuracy but also captures interaction effects across subsystems through derived features. Demonstrated on real-world telematics devices that collect GPS and vehicle diagnostic data over cellular networks, the framework addresses the need for scalable diagnostic methods in high-volume, low-failure-rate environments. By minimizing unnecessary returns and streamlining corrective action workflows, this approach delivers practical value in field operations. The modular nature of the two-tiered architecture enables adaptability to a range of device types and fault modes, while future work will explore model generalization across varied deployments. The integration of neural networks for subsystem-level classification offers a pathway to more nuanced root cause analysis, reinforcing the importance of structured, data-driven approaches to operational reliability.