Tarcsay Bálint Levente, Bárkányi Ágnes, Németh Sándor, Chován Tibor, Lovas László, Egedy Attila
Department of Process Engineering, University of Pannonia, 8200 Veszprém, Hungary.
Hungarian Gas Storage Ltd., 1138 Budapest, Hungary.
Sensors (Basel). 2024 May 29;24(11):3511. doi: 10.3390/s24113511.
In this article, the authors focus on the introduction of a hybrid method for risk-based fault detection (FD) using dynamic principal component analysis (DPCA) and failure method and effect analysis (FMEA) based Bayesian networks (BNs). The FD problem has garnered great interest in industrial application, yet methods for integrating process risk into the detection procedure are still scarce. It is, however, critical to assess the risk each possible process fault holds to differentiate between non-safety-critical and safety-critical abnormalities and thus minimize alarm rates. The proposed method utilizes a BN established through FMEA analysis of the supervised process and the results of dynamical principal component analysis to estimate a modified risk priority number () of different process states. The is used parallel to the FD procedure, incorporating the results of both to differentiate between process abnormalities and highlight critical issues. The method is showcased using an industrial benchmark problem as well as the model of a reactor utilized in the emerging liquid organic hydrogen carrier (LOHC) technology.
在本文中，作者重点介绍了一种基于风险的故障检测（FD）混合方法，该方法使用动态主成分分析（DPCA）以及基于失效模式与效应分析（FMEA）的贝叶斯网络（BNs）。故障检测问题在工业应用中引起了极大关注，但将过程风险纳入检测程序的方法仍然很少。然而，评估每个可能的过程故障所具有的风险对于区分非安全关键型和安全关键型异常情况并从而将报警率降至最低至关重要。所提出的方法利用通过对受监督过程进行FMEA分析建立的贝叶斯网络以及动态主成分分析的结果来估计不同过程状态的修正风险优先数（ ）。该 与故障检测程序并行使用，结合两者的结果来区分过程异常并突出关键问题。使用一个工业基准问题以及新兴的液体有机氢载体（LOHC）技术中使用的反应堆模型展示了该方法。
