Reliable and interpretable predictive maintenance in digital twin: a hybrid modelling approach powered by XAI

Zhao, Shanshan and Li, Shancang 2026. Reliable and interpretable predictive maintenance in digital twin: a hybrid modelling approach powered by XAI. Journal of Management Analytics 10.1080/23270012.2026.2640605

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Abstract

As industrial environments transition toward Industry 4.0, the ability to transform sensor data into actionable maintenance intelligence remains a critical managerial challenge. This paper introduces a real-time digital twin framework for predictive maintenance, leveraging a hybrid tunable LSTM architecture and physics-informed analytics. Unlike static models, our approach utilizes dynamic hyperparameter optimization to address temporal degradation and sensor noise inherent in complex industrial systems. A key contribution is the integration of SHAP-based explainability, providing maintenance managers with interpretable diagnostics that justify intervention strategies. Results from cross-industry case studies indicate a 15–30% improvement in early fault detection lead times and a 41% reduction in unplanned downtime. By providing precise remaining useful life (RUL) estimates, the framework enables a shift from reactive to strategic condition-based maintenance, offering significant cost-saving implications for large-scale manufacturing operations.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Computational & Mathematical Sciences Schools > Computer Science & Informatics
Publisher:	Taylor and Francis Group
ISSN:	2327-0012
Date of First Compliant Deposit:	29 September 2026
Date of Acceptance:	28 February 2026
Last Modified:	29 Sep 2026 10:00
URI:	https://orca.cardiff.ac.uk/id/eprint/189868

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