Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kousalya, R.; Radhika, V.; Thangamani, C.; Deepa, V.; Dharmannavar, Laxmi Basappa Title A name given to the resource DIGITAL TWINBASED INTELLIGENT MONITORING OF INDUSTRIAL SYSTEMS USING EXPLAINABLE AI Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Archives for Technical Sciences;Volume;3;Issue;34;pp.1256-1272 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.70102/afts.2025.1834.1256" target="_blank" rel="noreferrer noopener">https://doi.org/10.70102/afts.2025.1834.1256</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030119874?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030119874?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 &amp; Science, Tamil Nadu, Coimbatore, India; Deepa V., Department of Information Technology, Sri Ramakrishna college of Arts &amp; Science, Tamil Nadu, Coimbatore, India; Dharmannavar L.B., Department of Statistics and Data Science, CHRIST (Deemed to be University), Karnataka, Bangalore, India Description An account of the resource 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. Subject The topic of the resource adaptive modeling; causality analysis; cyberphysical systems; digital twins; explainable artificial intelligence; industry 4.0.1; intelligent industrial monitoring; predictive maintenance Publisher An entity responsible for making the resource available Technical institute of Bijeljina Relation A related resource ISSN: 18404855; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online