Zhao, Shanshan and Li, Shancang
2026.
Reliable and interpretable predictive maintenance in digital twin: A hybrid modeling 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 > Computer Science & Informatics |
| Subjects: | A General Works > AI Indexes (General) |
| Publisher: | Taylor and Francis Group |
| ISSN: | 2327-0012 |
| Date of First Compliant Deposit: | 3 July 2025 |
| Date of Acceptance: | 28 February 2026 |
| Last Modified: | 20 Oct 2026 09:19 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/179031 |
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