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.

翻译：危险与可操作性分析（HAZOP）是工业安全的范例，能够从其节点偏差、后果、原因、措施和建议中揭示工艺过程的危险，此类危险可视为危险事件（HaE）。对危险事件进行分类研究具有不可替代的实用价值。本文提出一种通过多重分形实现的新型深度学习模型DLF，用于探索危险事件分类，其动机在于危险事件可自然视为一种时间序列。具体而言，首先利用BERT将危险事件向量化得到危险事件时间序列；其次，提出一种新的多重分形分析方法HmF-DFA，通过分析危险事件时间序列获得危险事件分形序列；最后，设计一种新型层次化门控神经网络（HGNN）处理危险事件分形序列，从严重性、可能性和风险三个维度完成危险事件分类。我们以18个工艺过程的HAZOP报告为案例开展实验。结果表明，与其他分类器相比，DLF分类器在精确率、召回率和F1值指标上表现更优，尤其在严重性方面。同时，HmF-DFA和HGNN有效促进了危险事件分类。我们的危险事件分类系统可为专家、工程师、员工及其他企业提供应用激励，希望该研究能为工业安全的日常实践提供额外支持。