Xiao Cuntao, Liu Fuchun
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China; School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou, China.
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China.
ISA Trans. 2025 Mar;158:242-255. doi: 10.1016/j.isatra.2025.01.034. Epub 2025 Jan 28.
Fault diagnosis and prognosis play important roles in the community of discrete event systems (DESs) and have garnered significant interest from researchers. Despite their close relationship, these concepts are typically formalized and studied independently. This paper introduces a novel concept, known as feature recognition of DESs, which unifies fault diagnosis and prognosis into one framework based on ω-language. For any infinite faulty ω-string, feature string is defined as its some finite prefix by which the faulty behavior can be distinguished from all normal language, and fault diagnosis and prognosis can be decided by the type of feature strings (normal or faulty). Then the problem of feature recognizability is converted to verify the existence of feature strings with respect to every faulty ω-string. Compared with fault diagnosis and prognosis, the notion of feature recognition is more general because it relaxes the restriction of uniformity on reaction bound and helps to understand the essence of fault diagnosis and prognosis more intuitively. More importantly, online recognition algorithms can be designed straightforward according to the definition of feature recognition and online decision can be realized as soon as possible. A necessary and sufficient condition for verifying feature recognizability is concluded and an online recognizer that meets timeliness condition is also constructed to execute fault diagnosis and prognosis synchronously.
故障诊断与预测在离散事件系统（DESs）领域中发挥着重要作用，并引起了研究人员的极大兴趣。尽管它们关系密切，但这些概念通常是分别形式化和研究的。本文引入了一个新颖的概念，即离散事件系统的特征识别，它基于ω语言将故障诊断与预测统一到一个框架中。对于任何无限故障ω串，特征串被定义为其某个有限前缀，通过该前缀可以将故障行为与所有正常语言区分开来，并且可以根据特征串的类型（正常或故障）来确定故障诊断与预测。然后，将特征可识别性问题转化为验证相对于每个故障ω串是否存在特征串。与故障诊断和预测相比，特征识别的概念更具一般性，因为它放宽了对反应界限一致性的限制，并有助于更直观地理解故障诊断和预测的本质。更重要的是，可以根据特征识别的定义直接设计在线识别算法，并尽快实现在线决策。得出了验证特征可识别性的充要条件，并构造了一个满足及时性条件的在线识别器，以同步执行故障诊断和预测。
