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Enhancing Resilience and Efficiency in Industrial Control Systems Through AI-Driven Predictive Maintenance
Abstract
The research explores the integration of artificial intelligence (AI) into industrial control systems (ICS) to enhance resilience and efficiency through predictive maintenance. ICS play a crucial role in various industries by managing complex equipment networks. Traditional maintenance approaches, such as reactive and preventive maintenance, are often inefficient, leading to unnecessary downtime and high operational costs. This study focuses on AI-driven predictive maintenance, utilizing machine learning and neural networks to forecast equipment failures and optimize maintenance schedules. By leveraging real-time data, this approach reduces downtime, prolongs equipment lifespan, and lowers maintenance expenses. The research further addresses the challenges of integrating AI into existing infrastructures and provides a framework for its implementation in critical sectors like manufacturing and energy. The study contributes to advancing maintenance strategies by increasing system reliability and operational efficiency.
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