ItemArticle

DIGITAL TWINBASED INTELLIGENT MONITORING OF INDUSTRIAL SYSTEMS USING EXPLAINABLE AI

Title
DIGITAL TWINBASED INTELLIGENT MONITORING OF INDUSTRIAL SYSTEMS USING EXPLAINABLE AI
Creator
Kousalya, R.; Radhika, V.; Thangamani, C.; Deepa, V.; Dharmannavar, Laxmi Basappa
Description
Industrial systems increasingly rely on Industrial Internet of Things (IIoT) sensors for real-time monitoring and predictive maintenance. However, most existing digital twinbased monitoring solutions depend on static or black-box machine learning models, limiting interpretability, operator trust, and safe deployment in safety-critical environments. In response to these challenges, the author develops the Adaptive Hybrid Digital Twin with Causality-Aware Explainable Artificial Intelligence (HADT-C-XAI) framework to offer transparency and intelligence in industrial monitoring. The framework describes three integrated layers: (i) acquisition of real-time sensors, (ii) continually synchronized hybrid digital twin modeling, which is the integration of physics and data hybrid modeling and (iii) an intelligent analysis layer where LSTM-based anomaly detection is ungraded with explainable feature attribution. A closed-loop learning mechanism updates the model dynamically to adapt to operational drift while generating interpretable fault causes for operator decision support. Experiments were conducted on a multi-sensor industrial testbed containing 120 hours of vibration, temperature, acoustic, and rotational data. The implemented system shows a 94.8% detection accuracy, 95.4% recall, and a 4.1% low false alarm rate, which surpasses standard LSTM (88.5%) and threshold-based monitoring (82.9%). With edge-level inference, detection latency has been reduced to 26-30 ms, which allows for real-time deployment. Results demonstrate that integrating adaptive digital twins with explainable AI improves reliability, transparency, and fault diagnosis while maintaining computational efficiency. The proposed framework provides a scalable and trustworthy solution for predictive maintenance, Industry 4.0 applications, and cyberphysical system monitoring. 2025, Technical institute of Bijeljina. All rights reserved.
Source
Archives for Technical Sciences;Volume;3;Issue;34;pp.1256-1272
Date
01-01-2025
Publisher
Technical institute of Bijeljina
Subject
adaptive modeling; causality analysis; cyberphysical systems; digital twins; explainable artificial intelligence; industry 4.0.1; intelligent industrial monitoring; predictive maintenance
Coverage
Kousalya R., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Radhika V., Sankara college of science and commerce, Tamil Nadu, Coimbatore, India; Thangamani C., Sri Ramakrishna College of Arts & Science, Tamil Nadu, Coimbatore, India; Deepa V., Department of Information Technology, Sri Ramakrishna college of Arts & Science, Tamil Nadu, Coimbatore, India; Dharmannavar L.B., Department of Statistics and Data Science, CHRIST (Deemed to be University), Karnataka, Bangalore, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 18404855;
Format
online
Language
English
Type
Article

Citation

Kousalya, R.; Radhika, V.; Thangamani, C.; Deepa, V.; Dharmannavar, Laxmi Basappa, “DIGITAL TWINBASED INTELLIGENT MONITORING OF INDUSTRIAL SYSTEMS USING EXPLAINABLE AI,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23753.

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