Modern mineral processing plants utilise fault detection and diagnosis to minimise time spent under faulty conditions. However, a fault originating in one plant section (PS) can propagate throughout the entire plant, obscuring its root cause. Causality analysis identifies the cause–effect relationships between process variables and presents them in a causality map to inform root cause identification. This paper presents a novel hierarchical approach for plant-wide causality analysis that decreases the number of nodes in a causality map, improving interpretability and enabling causality analysis as a tool for plant-wide fault diagnosis. Two causality maps are constructed in subsequent stages: first, a dimensionally reduced, plant-wide causality map used to localise the fault to a PS; second, a causality map of the identified PS used to identify the root cause. The hierarchical approach accurately identified the true root cause in a well-understood case study; its plant-wide map consisted of only three nodes compared to 15 nodes in the standard causality map and its transitive reduction. The plant-wide map required less fault-state data, time series in the order of hours or days instead of weeks or months, further motivating its application in rapid root cause analysis.

中文翻译：

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提高全厂断层识别因果图可解释性的分层方法

现代矿物加工厂利用故障检测和诊断来最大限度地减少在故障条件下花费的时间。然而，源自一个工厂部分 (PS) 的故障可能会传播到整个工厂，从而掩盖其根本原因。因果关系分析识别过程变量之间的因果关系，并将它们呈现在因果关系图中，以告知根本原因识别。本文提出了一种新的全厂因果关系分析分层方法，它减少了因果关系图中的节点数量，提高了可解释性，并使因果关系分析成为全厂故障诊断的工具。在随后的阶段构建了两个因果关系图：首先，用于将故障定位到 PS 的降维的全厂因果关系图；第二，用于识别根本原因的已识别 PS 的因果关系图。在一个易于理解的案例研究中，分层方法准确地确定了真正的根本原因；与标准因果关系图中的 15 个节点及其传递性归约相比，它的全厂图仅包含三个节点。工厂范围的地图需要较少的故障状态数据，时间序列按小时或天数而不是数周或数月，进一步推动了其在快速根本原因分析中的应用。