重要控制信息融合的机器调节

Uwe Mönks, V. Lohweg
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引用次数: 16

摘要

传感器与信息融合是近年来在复杂生产机械和工艺工程中进行机器诊断和调节的一个重要课题。众所周知,分布式自动化系统对机器诊断的信号处理和模式识别具有重大影响。因此,有必要研究和开发适用于资源有限的网络物理系统等分布式系统的智能诊断方法。本文提出了一种基于证据理论和社会心理决策的传感器与信息融合新方法。我们表明,即使在冲突的情况下,基于上下文的状态监测也可以实例化,并且在现实生活场景中永久存在。提出了一种简单而有效的重要性测度,用于控制系统中条件命题的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine conditioning by importance controlled information fusion
Sensor and information fusion is recently a major topic which becomes important in machine diagnosis and conditioning for complex production machines and process engineering. It is a known fact that distributed automation systems have a major impact on signal processing and pattern recognition for machine diagnosis. Therefore, it is necessary to research and develop smart diagnosis methods which are applicable for distributed systems like resource-limited cyber-physical systems. In this paper we propose an new approach for sensor and information fusion based on Evidence Theory and socio-psychological decision-making. We show that context based condition monitoring is instantiated even in conflict situations, oc-curing in real life scenarios permanently. A simple but effective importance measure is proposed which controls the significance of conditioning propositions in a system.
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