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

by Mohammad Abu Kausar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 37
Year of Publication: 2025
Authors: Mohammad Abu Kausar
10.5120/ijca2025925641

Mohammad Abu Kausar . Digital Twin-Enabled Anomaly Detection for Industrial IoT using Explainable AI. International Journal of Computer Applications. 187, 37 ( Sep 2025), 47-55. DOI=10.5120/ijca2025925641

@article{ 10.5120/ijca2025925641,
author = { Mohammad Abu Kausar },
title = { Digital Twin-Enabled Anomaly Detection for Industrial IoT using Explainable AI },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2025 },
volume = { 187 },
number = { 37 },
month = { Sep },
year = { 2025 },
issn = { 0975-8887 },
pages = { 47-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number37/digital-twin-enabled-anomaly-detection-for-industrial-iot-using-explainable-ai/ },
doi = { 10.5120/ijca2025925641 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-09-23T00:35:17.165238+05:30
%A Mohammad Abu Kausar
%T Digital Twin-Enabled Anomaly Detection for Industrial IoT using Explainable AI
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 37
%P 47-55
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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 high-fidelity 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.

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Index Terms

Computer Science
Information Sciences

Keywords

Digital Twin Anomaly Detection Industrial IoT Explainable AI Predictive Maintenance

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