Zhu Hongzhen, Liu Fuchun, Zhao Rui
School of Computers, Guangdong University of Technology, Guangzhou, 510006, China.
School of Computers, Guangdong University of Technology, Guangzhou, 510006, China.
ISA Trans. 2022 Apr;123:230-239. doi: 10.1016/j.isatra.2021.05.022. Epub 2021 May 18.
Predictability is an important property which is used to predict the failures which is not observable for the sensors straightly before they occur. In an automation system, in addition to the failure caused by a single event, there also exist pattern failures caused by event strings composed of multiple events. In order to prevent some local sites malfunction, the issue of reliable predictability of patterns is considered in this paper, where the prediction information may be distributed at physically separated sites. Our contributions are listed mainly as follows: Firstly, the k-reliable pattern copredictability in decentralized DESs is defined with formal languages. Generally speaking, for a decentralized system where there are r local sites, it is said to be k-reliably pattern copredictable (1≤k≤r) if there are at least r-k+1 local agents which can predict every occurrences of the pattern failure for every pattern failure, it indicates that the prognostication capability will be maintained while r-k local sites in malfunction state. Then two nondeterministic automata respectively named codiagnoser and coverifier from the given system are constructed in this paper, and two algorithms of verifying the reliable copredictability of pattern are presented by constructing the codiagnoser and coverifier respectively for the purpose of attain the capability of prognostication. Especially, two necessary and sufficient conditions under the codiagnoser and coverifier are proposed. Moreover, for the decentralized DESs, the verification algorithm related to the k-reliable pattern copredictability is proposed after presenting the necessary and sufficient conditions for reliable pattern copredictability. It is worth noting that a polynomial complexity algorithm is used in constructing the coverifier and verifying the k-reliable pattern copredictability.
可预测性是一项重要属性，用于预测故障，这些故障在传感器直接观测到之前就已发生。在自动化系统中，除了由单个事件导致的故障外，还存在由多个事件组成的事件串所引起的模式故障。为防止某些局部站点出现故障，本文考虑了模式可靠可预测性问题，其中预测信息可能分布在物理上分离的站点。我们的贡献主要如下：首先，用形式语言定义了分散式离散事件系统中的k可靠模式协同可预测性。一般来说，对于一个有r个局部站点的分散式系统，如果至少有r - k + 1个局部智能体能够预测每个模式故障的每次发生，那么就称其为k可靠模式协同可预测的（1≤k≤r），这表明当r - k个局部站点处于故障状态时，预测能力仍能保持。然后本文从给定系统构造了两个分别名为协同诊断器和覆盖验证器的非确定性自动机，并分别通过构造协同诊断器和覆盖验证器提出了两种验证模式可靠协同可预测性的算法，以实现预测能力。特别地，提出了协同诊断器和覆盖验证器下的两个充要条件。此外，对于分散式离散事件系统，在给出模式可靠协同可预测性的充要条件后，提出了与k可靠模式协同可预测性相关的验证算法。值得注意的是，在构造覆盖验证器和验证k可靠模式协同可预测性时使用了多项式复杂度算法。
