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    The analysis concludes that AI-based HVAC FDD, despite its higher accuracy and reduced reliance on expert knowledge, has garnered considerable research interest compared to physics-based methods However, it still encounters difficulties in dynamic and time-varying environments and achieving FDD resolution
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    AI-based fault detection models achieve an average accuracy of 85% to 95%, reducing false alarms by 50% and minimizing power restoration times by up to 60% The integration of IoT sensors with real-time analytics has improved anomaly detection rates by 28%, while digital twin technology has enhanced predictive maintenance efficiency, reducing
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    Artificial Intelligence (AI) techniques, particularly machine learning and data analytics, have emerged as powerful tools for automating fault detection and prediction processes
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    This study investigates the integration of customization and personalization approaches in aviation maintenance through Aviation Technical Support as a Service (ATSaaS) platform Through a comprehensive survey of 86 small and medium-sized airlines, combined with mathematical modeling of fault detection systems, the study develops and validates a hybrid framework that integrates traditional
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    As the manufacturing industry advances with sensor integration and automation, the opaque nature of deep learning models in machine learning poses a significant challenge for fault detection and diagnosis And despite the related predictive insights Artificial Intelligence (AI) can deliver, advanced machine learning engines often remain a black box This paper reviews the eXplainable AI (XAI
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    By consolidating insights from a large body of literature, this study provides a detailed understanding of the potential and limitations of AI-driven data analytics and automation, serving as a foundational resource for researchers, practitioners, and policymakers aiming to leverage AI for innovation and efficiency across industries





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