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Automatic component abstraction for Model-Based Diagnosis on relational models

机译：用于基于模型的关系模型诊断的自动组件抽象

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摘要

In the present paper, we address the problem of automatically synthesizing component abstractions by taking into account the level of observability of the system as well as restrictions on its operating conditions. Compared with previous work, the proposed approach can be applied to a significantly wider class of systems, namely those whose nominal and faulty behaviors can be modeled with finite-domain relations. The computed abstractions are specifically tailored for the Model-Based Diagnosis task, with the main goal of getting fewer and more informative diagnoses through the use of abstract models. To this end, we define a spectrum of indiscriminabilitv relations among the states of subsystems, and formally prove that respecting indiscriminability is both a necessary and sufficient condition for abstracting the original model without losing any relevant diagnostic information. We present an algorithm for the computation of abstractions that implements two specially important cases of indiscriminability, namely local and global-indiscriminability. The implemented system is exploited to collect experimental results that confirm the benefits of using the abstractions for diagnosis, in terms of both the number of returned diagnoses and the computational cost.
机译：在本文中，我们通过考虑系统的可观察性水平及其对操作条件的限制来解决自动综合组件抽象的问题。与以前的工作相比，所提出的方法可以应用于更广泛的一类系统，即可以用有限域关系对名义和错误行为进行建模的系统。计算的抽象是专门为基于模型的诊断任务量身定制的，其主要目标是通过使用抽象模型来获得越来越少的信息性诊断。为此，我们在子系统状态之间定义了不加区分的关系，并正式证明尊重不加区分性是抽象化原始模型且不丢失任何相关诊断信息的必要和充分条件。我们提出了一种用于抽象计算的算法，该算法实现了不可区分性的两个特别重要的情况，即局部不可区分性和全局不可区分性。利用已实现的系统来收集实验结果，这些实验结果从返回的诊断数和计算成本两方面确认了使用抽象进行诊断的好处。

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