Digital Twin-Driven Predictive Maintenance for Rotating Machinery Using Multimodal Sensor Fusion and Explainable AI
Keywords:
digital twin; predictive maintenance; rotating machinery; multimodal sensor fusion; explainable AI; condition monitoring; cyber-physical systems; governanceAbstract
Digital twins are increasingly positioned as a transformative infrastructure for industrial maintenance, coupling high-fidelity virtual representations with real-time operational data. In rotating machinery, predictive maintenance presents complex system-level challenges because vibration, thermal, acoustic, and lubrication signals must be interpreted under variable loading, degradation, and environmental conditions. This paper examines the architecture, deployment, and governance of digital twin-driven predictive maintenance systems that integrate multimodal sensor fusion and explainable artificial intelligence. Rather than focusing on a single algorithmic contribution, the analysis addresses structural trade-offs among data ingestion, synchronization, semantic alignment, model transparency, latency, security, and organizational accountability. Multimodal fusion strategies are discussed in relation to data quality, uncertainty propagation, and the preservation of physically meaningful features. Explainable artificial intelligence is treated as a system property rather than a post hoc visual layer, connecting anomaly detection, diagnostic reasoning, and human decision-making. The paper further considers infrastructure sustainability, computational burden, robustness under data drift, fairness in maintenance prioritization, and regulatory implications for industrial artificial intelligence. Case illustrations from rotating shaft monitoring and cross-domain comparisons clarify how digital twin fidelity and explanation depth influence trust, auditability, and operational resilience. The resulting synthesis provides a socio-technical framework for designing predictive maintenance systems that are not only accurate but also governable, sustainable, and responsive to evolving industrial policy.
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