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Automatic model separation and application for diagnosis in industrial automation systems

机译：自动模型分离及其在工业自动化系统中的诊断应用

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In this paper, automatic separation of hybrid system models for industrial automation systems is considered. The proposed method facilitates efficient separation of systemlevel models into component-level models. Such component-level models allow for model-based diagnosis, since a close relation exists between anomalies on a component-level and fault causes. The approach is based on the concept of separation variables, which relate models for components such as electric drives to system modes, i.e. phases of continuous system behaviour. For automation systems, the system modes are defined by sequences of discrete control events. Separation variables determine active components for each system mode, which contribute to the overall output signal on the system-level. System modes and separation variables are automatically learned from training data with normal system behaviour. The proposed method allows both model-based diagnosis and efficient model learning.
机译：在本文中，考虑了用于工业自动化系统的混合系统模型的自动分离。所提出的方法有助于将系统级模型有效地分离为组件级模型。这样的组件级模型允许基于模型的诊断，因为组件级的异常与故障原因之间存在密切的关系。该方法基于分离变量的概念，分离变量将诸如电驱动器之类的组件的模型与系统模式，即连续系统行为的各阶段相关联。对于自动化系统，系统模式由离散控制事件序列定义。分离变量确定每种系统模式的活动组件，这些组件对系统级的总体输出信号有贡献。系统模式和分离变量会从训练数据中以正常系统行为自动获知。所提出的方法允许基于模型的诊断和有效的模型学习。

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