Digital Twin-Enabled Anomaly Detection for Industrial IoT Using Explainable AI

A hybrid approach is then introduced in this paper to combine the DT technology with XAI to detect the anomaly in IIoT environment in real time.The system also integrates highfidelity simulation models with sensor data in order to increase the accuracy of detection and decrease the number of false positives.It leverages SHAP-based explanations, counterfactual deliberation, and natural language normalization to render the system interpretable for the engineers or operators in charge of decision making.Experimental results on real industrial datasets achieve a detection accuracy of 95.3% and 78% of reduction in false positives with respect to the state of the art.The promising performance of XAI-DT integration with a decision-supported mechanism demonstrates its application value for reliable and transparent predictive maintenance in industrial domain.