With the advent of industry 4.0, many traditional industries are moving toward automation, intelligence, and large-scale. The continuous expansion of production scale also means that the structure of industrial processes and the interactions between subsystems are becoming increas- ingly complex, which also introduced potential safety risks into the actual production. Due to the complexity and danger of the production process, the process safety of the modern blast furnace (BF) ironmaking process is a prominent problem. In this paper, a novel fault detection and di- agnosis (FDD) framework is proposed, which can detect the abnormality and infer the root cause with no need of building an accurate mechanism model as the traditional methods. After a fault is detected, to better learn fault propagation mechanisms of the BF ironmaking process, a process knowledge model was proposed and integrated with the data-driven approaches for fault diagnosis. Then, to discover the causalities matrix of the faulty data generating procedure, a novel root cause analysis method based on Graph Neural Networks (GNN) was developed. Moreover, to accurately describe the causal interactions of faulty variables and solve the problem of the redundant edges in the causal discovery, the process knowledge constraint item was added to the GNN model to guarantee the discovered causal graph matches with the practical domain knowledge to strengthen its application in the practical industry. The experimental results on real BF data set and a bench- mark data set (The RT 580 data set) demonstrate that our algorithm not only obtains a significant improvement over other methods but also has a favorable application in the industrial process.

随着工业4.0的到来，许多传统行业正朝着自动化、智能化、规模化方向发展。生产规模的不断扩大也意味着工业流程的结构和子系统之间的相互作用越来越复杂，这也给实际生产带来了安全隐患。由于生产过程的复杂性和危险性，现代高炉（BF）炼铁工艺的工艺安全是一个突出问题。在本文中，提出了一种新颖的故障检测和诊断（FDD）框架，它可以检测异常并推断根本原因，而无需像传统方法那样建立准确的机制模型。检测到故障后，更好地学习BF的故障传播机制炼铁过程中，提出了一个过程知识模型，并与数据驱动的故障诊断方法相结合。然后，为了发现错误数据生成过程的因果矩阵，开发了一种基于图神经网络（GNN）的新的根本原因分析方法。此外，为了准确描述错误变量的因果相互作用，解决因果发现中的冗余边问题，在 GNN 模型中增加了过程知识约束项，以保证发现的因果图与实际领域知识相匹配，以加强其在实际工业中的应用。