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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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uncertainty and dynamic conditions. In complex electronic systems, ensuring reliability and minimizing downtime are critical challenges. AI-driven fault diagnosis and self-healing electronics offer innovative
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. Experimental validation will be used to refine simulation accuracy and ultimately establish a reliable toolset for testing and developing fusion-relevant materials. Cranfield University is internationally
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supported by collaborations with industry giants including Boeing, Rolls-Royce, Thales, and UKRI, this research offers a unique platform to contribute to the advancement of secure, reliable, and transparent
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into areas such as AI-driven verification, predictive maintenance, and compliance assurance, aiming to enhance system reliability and safety. Situated within the esteemed IVHM Centre and supported by
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. While autonomy is becoming more integrated into modern mobility, the reliability of Position, Navigation and Timing (PNT) systems—especially in environments where GNSS signals are denied or degraded
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, reliability, and environmental resilience. The proliferation of intelligent systems has led to increased energy consumption, raising concerns about sustainability and operational costs. Energy-efficient
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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noise from other mechanical components such as gears, screws, etc., fault diagnosis using such signals is not an easy task. Having a robust and reliable CM system for low-speed bearings will have
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable