Chapters authored
AI for Improving the Overall Equipment Efficiency in Manufacturing Industry
Industry 4.0 has emerged as the perfect scenario for boosting the application of novel artificial intelligence (AI) and machine learning (ML) solutions to industrial process monitoring and optimization. One of the key elements on this new industrial revolution is the hatching of massive process monitoring data, enabled by the cyber-physical systems (CPS) distributed along the manufacturing processes, the proliferation of hybrid Internet of Things (IoT) architectures supported by polyglot data repositories, and big (small) data analytics capabilities. Industry 4.0 paradigm is data-driven, where the smart exploitation of data is providing a large set of competitive advantages impacting productivity, quality, and efficiency key performance indicators (KPIs). Overall equipment efficiency (OEE) has emerged as the target KPI for most manufacturing industries due to the fact that considers three key indicators: availability, quality, and performance. This chapter describes how different AI and ML solutions can enable a big step forward in industrial process control, focusing on OEE impact illustrated by means of real use cases and research project results.
Part of the book: New Trends in the Use of Artificial Intelligence for the Industry 4.0
Virtual Sensors for Smart Data Generation and Processing in AI-Driven Industrial Applications
The current digitalisation revolution demonstrates the high importance and possibilities of quality data in industrial applications. Data represent the foundation of any analytical process, establishing the fundamentals of the modern Industry 4.0 era. Data-driven processes boosted by novel Artificial Intelligence (AI) provide powerful solutions for industrial applications in anomaly detection, predictive maintenance, optimal process control and digital twins, among many others. Virtual Sensors offer a digital definition of a real Internet of Things (IoT) sensor device, providing a smart tool capable to face key issues on the critical data generation side: i) Scalability of expensive measurement devices, ii) Robustness and resilience through real-time data validation and real-time sensor replacement for continuous service, or iii) Provision of key parameters’ estimation on difficult to measure situations. This chapter presents a profound introduction to Virtual Sensors, including the explanation of the methodology used in industrial data-driven projects, novel AI techniques for their implementation and real use cases in the Industry 4.0 context.
Part of the book: Industry 4.0