Hazard and operability analysis (HAZOP) is the paradigm of industrial safety
that can reveal the hazards of process from its node deviations, consequences,
causes, measures and suggestions, and such hazards can be considered as hazard
events (HaE). The classification research on HaE has much irreplaceable
pragmatic values. In this paper, we present a novel deep learning model termed
DLF through multifractal to explore HaE classification where the motivation is
that HaE can be naturally regarded as a kind of time series. Specifically,
first HaE is vectorized to get HaE time series by employing BERT. Then, a new
multifractal analysis method termed HmF-DFA is proposed to win HaE fractal
series by analyzing HaE time series. Finally, a new hierarchical gating neural
network (HGNN) is designed to process HaE fractal series to accomplish the
classification of HaE from three aspects: severity, possibility and risk. We
take HAZOP reports of 18 processes as cases, and launch the experiments on this
basis. Results demonstrate that compared with other classifiers, DLF classifier
performs better under metrics of precision, recall and F1-score, especially for
the severity aspect. Also, HmF-DFA and HGNN effectively promote HaE
classification. Our HaE classification system can serve application incentives
to experts, engineers, employees, and other enterprises. We hope our research
can contribute added support to the daily practice in industrial safety.

本文提出了一种新的深度学习模型 DLF 通过多重分形来分类 Hazard Event，利用 BERT 将事件向量化，提出了一种新的多重分形分析方法，最后使用分形序列进行分类。实验结果表明该模型在工业安全领域的严重程度、可能性和风险方面具有更好的分类性能。

基于深度学习和多分形的新灾害事件分类模型

A new hazard event classification model via deep learning and multifractal

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.

此研究通过使用 DLGM 模型，利用深度学习对危害进行分级，并应用于 18 个工业过程进行实验，结果表明其具有卓越的应用前景，为工业安全实践提供了有价值的支持。