论文标题
是的，DLGM！危险分类的新型分层模型
Yes, DLGM! A novel hierarchical model for hazard classification
论文作者
论文摘要
Hazop可以将危害作为文本信息暴露，研究其分类对于工业信息学的发展具有重要意义，这有利于安全预警，决策支持，政策评估等。但是，目前尚无对这一重要领域的研究。在本文中，我们提出了一种通过深度学习危害分类的新型模型，称为DLGM。具体而言，首先，我们利用BERT将危险矢量化并将其视为时间序列（HTS）。其次，我们构建了一个灰色模型FSGM（1，1）来对其进行建模，并从结构参数的意义上获取灰色指导。最后，我们设计了一个分层功能融合神经网络（HFFNN），以从三个主题中使用灰色引导（HTSGG）研究HTS，其中HFFNN是一种具有四种模块的层次结构：两种特征编码器，两种特征编码器，一个门控机制和深层机制。我们将18个工业流程作为应用程序案例，并启动一系列实验。实验结果证明，DLGM有望成为危险分类的才能，FSGM（1，1）和HFFNN具有有效性。我们希望我们的研究能为工业安全的日常实践贡献价值和支持。
Hazards can be exposed by HAZOP as text information, and studying their classification is of great significance to the development of industrial informatics, which is conducive to safety early warning, decision support, policy evaluation, etc. However, there is no research on this important field at present. In this paper, we propose a novel model termed DLGM via deep learning for hazard classification. Specifically, first, we leverage BERT to vectorize the hazard and treat it as a type of time series (HTS). Secondly, we build a grey model FSGM(1, 1) to model it, and get the grey guidance in the sense of the structural parameters. Finally, we design a hierarchical-feature fusion neural network (HFFNN) to investigate the HTS with grey guidance (HTSGG) from three themes, where, HFFNN is a hierarchical structure with four types of modules: two feature encoders, a gating mechanism, and a deepening mechanism. We take 18 industrial processes as application cases and launch a series of experiments. The experimental results prove that DLGM has promising aptitudes for hazard classification and that FSGM(1, 1) and HFFNN are effective. We hope our research can contribute added value and support to the daily practice in industrial safety.
