A digital twin solution for fault detection in time-critical IIoT applications

Amish Ranpariya and Sangeeta Sharma

Journal of Simulation, 2025, vol. 19, issue 4, 441-454

Abstract: IIoT sensor data plays a pivotal role in monitoring the industrial system’s health and identifying potential faults. However, traditional fault detection approaches often face challenges such as network latency, limited accuracy, and resource-intensive processing. This paper introduces an end-to-end Digital Twin solution that enhances fault detection for IIoT systems. The solution is powered by two key innovations: the integration of a Digital Twin architecture that leverages a collaborative cloud-edge approach for real-time monitoring, and the use of a lightweight two-phased machine-learning ensemble model optimized for resource-constrained environments. The great performance achieved across various fault scenarios demonstrates the effectiveness of the proposed approach. The model provides an average accuracy of 99.71% with a mere 4.8 ms of average estimation delay. These advancements ensure both high accuracy and rapid response times, providing a robust solution for proactive fault detection in dynamic industrial environments.

Date: 2025
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DOI: 10.1080/17477778.2025.2453725

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