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A hierarchical approach for discrete-event model identification incorporating expert knowledge

机译：分离事件模型识别的分层方法，包括专家知识

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摘要

Recently, a new technique for identification of Discrete-Event Systems (DES) with the aim of fault detection has been proposed in the literature, where a model is obtained from the observation of the fault-free system behavior. In some cases, the system may execute different tasks in a sequential order to perform the complete operation of the system. In these cases, the number of observed paths representing the complete system operation may grow exponentially with the number of tasks. In addition, by using black-box identification methods, it is possible that the sequential order that the tasks must be performed is not represented in the model, reducing the fault detection capability or delaying the fault detection. In this paper, a two-level hierarchical approach for DES identification is proposed. In the higher level of the model hierarchy, the system is described by using some basic knowledge of its functioning provided by an expert, and in the lower level of the hierarchy, the behavior is described by black-box identified models for the system tasks. The modeling framework proposed in this paper reduces the number of observed paths needed for system identification and increases the fault detection capability. A practical example is used to illustrate the results of the paper.
机译：最近，在文献中提出了一种用于识别具有故障检测的离散事件系统（DES）的新技术，其中可以从无故障系统行为的观察中获得模型。在一些情况下，系统可以以顺序执行不同的任务以执行系统的完整操作。在这些情况下，表示完整系统操作的观察路径的数量可以以任务数量呈指数增长。此外，通过使用黑匣子识别方法，可以在模型中表示任务的顺序排序，降低故障检测能力或延迟故障检测。本文提出了一种用于DES识别的两级分层方法。在模型层次结构的较高级别中，通过使用专家提供的其功能的一些基本知识来描述系统，并且在层次结构的较低级别中，该行为由Black-Box识别的系统任务进行描述。本文提出的建模框架减少了系统识别所需的观察路径的数量，并提高了故障检测能力。实际的例子用于说明纸张的结果。

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