Fault detection and diagnosis are crucial to reducing risks and costs in any process. The identification of the propagation path and the variables responsible for faulty operating conditions is also vital. This paper presents a causal network-based approach to detect, diagnose, and identify root causes in multivariate processes. We discuss aspects such as complexity and rules related to modeling such network approaches. The proposed strategy is established on statistical justifications. The introduced decision rules deal with unknown faults and offer new perspectives to data-driven methods for fault diagnosis. The proposed approach is evaluated and demonstrated using the well-known Tennessee Eastman Process (TEP) benchmark.

故障检测和诊断对于降低任何流程中的风险和成本至关重要。传播路径和导致故障操作条件的变量的识别也很重要。本文提出了一种基于因果网络的方法来检测、诊断和识别多变量过程中的根本原因。我们讨论了与建模此类网络方法相关的复杂性和规则等方面。拟议的战略是建立在统计理由的基础上的。引入的决策规则处理未知故障，并为数据驱动的故障诊断方法提供了新的视角。使用著名的田纳西伊士曼过程 (TEP) 基准对所提出的方法进行了评估和演示。