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Predictive Maintenance Platform Based on Integrated Strategies for Increased Operating Life of Factories

机译:基于集成策略的预测性维护平台可延长工厂的使用寿命

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Process output and profitability of the operations are mainly determined by how the equipment is being used. The production planning, operations and machine maintenance influence the overall equipment effectiveness (OEE) of the machinery, resulting in more 'good parts' at the end of the day. The target of the predictive maintenance approaches in this respect is to increase efficiency and effectiveness by optimizing the way machines are being used and to decrease the costs of unplanned interventions for the customer. To this end, development of ad-hoc strategies and their seamless integration into predictive maintenance systems is envisaged to bring substantial advantages in terms of productivity and competitiveness enhancement for manufacturing systems, representing a leap towards the real implementation of the Industry 4.0 vision. Inspired by this challenge, the study provides an approach to develop a novel predictive maintenance platform capable of preventing unexpected-breakdowns based on integrated strategies for extending the operating life span of production systems. The approach and result in this article are based on the development and implementation in a large collaborative EU-funded H2020 research project entitled Z-Bre4k, i.e. Strategies and predictive maintenance models wrapped around physical systems for zero-unexpected-breakdowns and increased operating life of factories.
机译:流程的产出和运营的盈利能力主要取决于设备的使用方式。生产计划,操作和机器维护会影响机器的整体设备效率(OEE),最终导致更多的“优质零件”。在这方面,预测性维护方法的目标是通过优化机器的使用方式来提高效率和有效性,并为客户减少计划外干预的成本。为此,可以设想开发临时策略并将其无缝集成到预测性维护系统中,从而在生产系统的生产率和竞争力增强方面带来实质性优势,这代表着向真正实现工业4.0愿景迈出的一步。受这一挑战的启发,该研究提供了一种开发新的预测性维护平台的方法,该平台基于可延长生产系统使用寿命的集成策略来防止意外故障。本文中的方法和结果基于由欧盟资助的一项大型合作研究项目H2020的开发和实施,该项目名为Z-Bre4k,即围绕物理系统的策略和预测性维护模型,以实现零意外故障和延长运行寿命。工厂。

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