Digital Twin Technology for Real-Time Risk Management in Industrial IOT Systems

Abstract

Introduction:
Industry-critical systems in the IIoT have become so complex that intelligent approaches must manage avoidable issues alongside unexpected operational abnormalities. Threshold-based traditional monitoring methods do not provide real-time information while failing to adjust to evolving behaviors so new artificial intelligence-based monitoring systems need to be developed.

Objectives:
The research project strives to develop an expandable dual digital twin system which enables continuous risk detection. The proposed research aims to achieve two primary objectives of building predictive and behavioral twins that serve specific purposes in industrial applications.

Methods:
The designers employed XGBoost supervised learning from the AI4I 2020 Predictive Maintenance Dataset to develop their Twin A implementation. The unsupervised anomaly detection system of Twin B used Isolation Forest and analyzed flattened sensor logs extracted from the hydraulic test rig operational data. The simulated real-time dashboard received predictions through 1.5-second intervals to mimic industrial operational conditions while integrating both twins.

Results:
The predictive twin demonstrated 97% accuracy in classifying multi-class failures. The predictive model grouped risk states into three categories: Normal, Caution and Alert through probability analysis. The behavioral twin detected a 3.17% anomaly rate through which localized sensor drift appeared in particular pressure readings. A live dashboard showed the system could perform real-time inference procedures while displaying visual information thus proving its readiness for operational deployment.

Conclusions:
IIoT risk monitoring through dual digital twins provides extensive coverage of predictive and emergent fault detection across two architectures. The system base of intelligent manufacturing systems functions because of its real-time performance along with modular structure and adjustable capabilities. The proposed system can benefit from future development of model explainability methods along with increased human-machine interface capabilities.