  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.
