Enforcement of Diagnosability in Labeled Petri Nets via Optimal Sensor Selection
Enforcement of Diagnosability in Labeled Petri Nets via Optimal Sensor Selection
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DOI：
10.1109/tac.2018.2874020
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发表时间：
2019-07
影响因子：
6.8
通讯作者：
Ning Ran;A. Giua;C. Seatzu
中科院分区：
文献类型：
--
作者：
Ning Ran;A. Giua;C. Seatzu
在本文中，我们处理的问题，加强可诊断性标记Petri网（PN）适当地添加新的传感器。我们表明，解决整数线性规划问题，可以选择相对于给定目标函数（例如，传感器的成本）。该解决方案是基于两个概念，已经介绍了作者在以前的作品，即基础标记和展开验证。这允许以相对于文献中的其他方法更有效的方式来解决所考虑的问题。最后，我们提出了一个算法来计算$K$的最小值，使得PN系统是$K$-诊断下的新的标签功能，这意味着故障可以检测到最多$K$观察后，他们的发生。
In this paper, we deal with the problem of enforcing diagnosability to labeled Petri nets (PNs) appropriately adding new sensors. We show that, solving an integer linear programming problem, it is possible to select a solution that is optimal with respect to a given objective function (e.g., the cost of sensors). The solution is based on two notions, already introduced by the authors in previous works, namely basis marking and unfolded verifier. This allows to solve the considered problem in a more efficient way with respect to other approaches in the literature. Finally, we propose an algorithm to compute the smallest value of $K$ such that the PN system is $K$-diagnosable under the new labeling function, which implies that faults can be detected in at most $K$ observations after their occurrence.