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Data-Driven Automation: Machine Learning in Industrial Control Systems

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Data-Driven Automation: Machine Learning in Industrial Control SystemsThe accelerating pace of digital transformation in the industrial sector is reshaping how control systems are designed, operated, and evolved. At the heart of this transformation lies a powerful convergence between traditional control engineering and modern machine learning techniques. Data-Driven Automation: Machine Learning in Industrial Control Systems explores this convergence in depth, presenting a comprehensive vision of how intelligent, adaptive, and data-informed systems are redefining industrial automation.Industrial Control Systems (ICS) have long formed the backbone of manufacturing, energy, utilities, and other critical infrastructure domains. Historically engineered for stability, determinism, and reliability, these systems operated within tightly controlled environments. Today, however, the proliferation of sensors, real-time data streams, edge and cloud computing, and advanced analytics has fundamentally altered the role of data within control architectures. Data is no longer a passive byproduct of industrial operations—it has become a strategic asset and a core driver of system intelligence and performance.This book is written for engineers, automation professionals, data scientists, researchers, and advanced practitioners who seek to understand both the theoretical foundations and practical realities of integrating machine learning into industrial control environments. It provides a structured and rigorous exploration of how data-driven methods can be embedded into existing and next-generation industrial workflows to enable predictive maintenance, process optimization, anomaly detection, adaptive control, and enhanced human–machine interaction.Bridging the gap between academic research and real-world implementation, each chapter progresses from foundational concepts to applied use cases. Readers are guided through core principles of control systems and machine learning, followed by detailed discussions on data acquisition, preprocessing, model integration, and deployment within operational constraints. The book further examines advanced topics such as digital twins, prescriptive analytics, autonomous decision-making, and the role of AI in improving resilience and efficiency across industrial systems.Given the mission-critical nature of ICS, special attention is devoted to cybersecurity, system robustness, and safety-aware AI design. The book also addresses emerging challenges related to scalability, explainability, governance, and ethics—ensuring that intelligent automation is deployed responsibly and sustainably.The interdisciplinary scope of this work reflects a broader shift within engineering sciences, where boundaries between software, hardware, and human expertise are dissolving in favor of adaptive cyber-physical systems capable of learning, self-optimizing, and evolving over time. By combining technical depth with forward-looking insight, this book equips readers with the knowledge needed to navigate both the current landscape and the future trajectory of intelligent industrial automation.Ultimately, Data-Driven Automation serves not only as a technical reference, but also as a catalyst for innovation, critical thinking, and dialogue at the intersection of machine learning and industrial control systems - where the future of intelligent industry is being shaped.

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Data-Driven Automation: Machine Learning in Industrial Control Systems

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