In this article, a diagnosis approach for partially observed labelled Petri nets is developed based on building a set of analytical redundancy relationships on a progressive horizon. A nominal model is used for fault detection based on a set of relationships linking the known data of the nominal behaviour. A fault model is used for fault isolation by establishing a set of relationships for each fault transition connecting the known data of the fault behaviour. The above-mentioned analytical redundancy relationships are established offline by eliminating unknown variables from the considered model. The proposed online procedure for fault diagnosis is polynomial with respect to the number of unobservable events.

中文翻译：

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渐进式分析冗余关系生成用于标记 Petri 网的故障诊断

在本文中，基于在渐进范围内建立一组分析冗余关系，开发了一种用于部分观察到的标记 Petri 网的诊断方法。标称模型用于基于链接标称行为的已知数据的一组关系进行故障检测。故障模型通过为连接故障行为的已知数据的每个故障转换建立一组关系来进行故障隔离。上述分析冗余关系是通过从所考虑的模型中消除未知变量来离线建立的。所提出的故障诊断在线程序是关于不可观察事件数量的多项式。