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Hazards can be exposed by HAZOP as text information, and studying theirclassification is of great significance to the development of industrialinformatics, which is conducive to safety early warning, decision support,policy evaluation, etc. However, there is no research on this important fieldat present. In this paper, we propose a novel model termed DLGM via deeplearning for hazard classification. Specifically, first, we leverage BERT tovectorize the hazard and treat it as a type of time series (HTS). Secondly, webuild a grey model FSGM(1, 1) to model it, and get the grey guidance in thesense of the structural parameters. Finally, we design a hierarchical-featurefusion neural network (HFFNN) to investigate the HTS with grey guidance (HTSGG)from three themes, where, HFFNN is a hierarchical structure with four types ofmodules: two feature encoders, a gating mechanism, and a deepening mechanism.We take 18 industrial processes as application cases and launch a series ofexperiments. The experimental results prove that DLGM has promising aptitudesfor hazard classification and that FSGM(1, 1) and HFFNN are effective. We hopeour research can contribute added value and support to the daily practice inindustrial safety.

Yes, DLGM! A novel hierarchical model for hazard classification