  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.
