AI for Improving the Overall Equipment Efficiency in Manufacturing Industry

New Trends in the Use of Artificial Intelligence for the Industry 4.0 Pub Date : 2020-03-25 DOI:10.5772/intechopen.89967

F. Bonada, L. Echeverria, X. Domingo, G. Anzaldi

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引用次数: 5

Abstract

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.

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提高制造业装备整体效率的人工智能

工业4.0已成为推动新型人工智能(AI)和机器学习(ML)解决方案在工业过程监控和优化中的应用的完美场景。这场新工业革命的关键因素之一是大规模过程监控数据的孵化，这是由分布在制造过程中的网络物理系统(CPS)、多语言数据存储库支持的混合物联网(IoT)架构的扩散以及大(小)数据分析能力所实现的。工业4.0范式是数据驱动的，其中数据的智能利用提供了大量影响生产力、质量和效率关键绩效指标(kpi)的竞争优势。整体设备效率(OEE)已经成为大多数制造业的目标KPI，因为它考虑了三个关键指标:可用性、质量和性能。本章描述了不同的人工智能和机器学习解决方案如何使工业过程控制向前迈出一大步，重点关注通过实际用例和研究项目结果说明的OEE影响。

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