A digital twin solution for fault detection in time-critical IIoT applications
Amish Ranpariya & Sangeeta Sharma
What the paper says
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.
3 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.32 × 0.4 = 0.13 |
| M · momentum | 0.57 × 0.15 = 0.09 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.