Reverse twin plant for efficient diagnosability testing and optimizing
Reverse twin plant for efficient diagnosability testing and optimizing
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DOI：
10.1016/j.engappai.2014.10.007
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发表时间：
2015-02
期刊：
影响因子：
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通讯作者：
Boyu Li;Ting Guo;Xingquan Zhu;Zhanshan Li
中科院分区：
文献类型：
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作者：
Boyu Li;Ting Guo;Xingquan Zhu;Zhanshan Li
离散事件系统基于模型的诊断是故障诊断领域的一个重要研究课题，其中可诊断性在诊断引擎的构建中起着重要的作用。为了提高可诊断性求解的效率，提出了一种新的技术来解决可诊断性测试和优化问题。我们提出了一个新的概念，反向孪生工厂，它是从DESS的最终状态向后生成的，因此不需要生成DES模型的完整副本来确定可诊断性。这样的设计使得我们的测试算法比现有的方法要快得多。文中还提出了一种有效的优化算法，通过对DES模型的一部分进行操作来扩展最小可观测空间，从而使不可诊断系统成为可诊断的。算例和理论研究证明了所提设计的性能。
Model-based diagnosis in discrete event systems (DESs) is a major research topic in failure diagnosis, where diagnosability plays an important role in the construction of the diagnosis engine. To improve the solution efficiency for diagnosability, this paper proposes novel techniques to solve the problems of testing and optimizing for diagnosability. We propose a new concept, reverse twin plant, which is generated backwards from the final states of the DESs so there is no need to generate a complete copy of the DES model to determine the diagnosability. Such a design makes our testing algorithm much faster than existing methods. An efficient optimizing algorithm, which makes a non-diagnosable system diagnosable, is also proposed in the paper by expanding the minimal observable space with operation on just a part of the DES model. Examples and theoretical studies demonstrate the performance of the proposed designs.