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Intelligent decision support for maintenance: an overview and future trends

机译：智能决策支持维护：概述和未来趋势

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摘要

The changing nature of manufacturing, in recent years, is evident in industry's willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides a framework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable 'Human in the loop' interactions.
机译：近年来，制造业的性质不断变化，在工业愿意在工厂开发计划中采用网络连接的智能机器方面是显而易见的。许多联合公司/政府举措还描述并鼓励在生产线的运营和管理中通过人工智能（AI）。机器学习将在提供自动化和智能支持的维护决策系统中发挥重要作用。虽然电子维护实践为维护实践的互联网连接操作提供了框架，但IoT的出现已经改变了网络工作的规模，并且需要新的架构和工具。虽然近年来传感器和传感器融合技术的进步已经很大，但物联网带来的可能性在数据的规模和分析中创造了新的挑战。审计跟踪风格练习对数据集合的制定以及为其处理，分析和使用提供ACOMPreplics框架，应该是可驱动的贡献，用于解决由Internet连接设备创建的维护的新数据分析挑战。本文提出了进一步的研究，应进一步研究维护数据，允许未来的系统在循环交互中启用“人类”。

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