Exploring industrial safety knowledge via Zipf law
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The hazard and operability analysis (HAZOP) report contains precious industrial safety knowledge (ISK) with expert experience and process nature, which is of great significance to the development of industrial intelligence. Subject to the attributes of ISK, existing researches mine them through sequence labeling in deep learning. Yet, there are two thorny issues: (1) Uneven distribution of ISK and (2) Consistent importance of ISK: for safety review. In this study, we propose a novel generative mining strategy called CRGM to explore ISK. Inspired Zipf law in linguistics, CRGM consists of common-rare discriminator, induction-extension generator and ISK extractor. Firstly, the common-rare discriminator divides HAZOP descriptions into common words and rare words, and obtains the common description and the rare description, where the latter contains more industrial substances. Then, they are operated by the induction-extension generator in the way of deep text generation, the common description is induced and the rare description is extended, the material knowledge and the equipment knowledge can be enriched. Finally, the ISK extractor processes the material knowledge and equipment knowledge from the generated description through the rule template method, the additional ISK is regarded as the supplement of the training set to train the proposed sequence labeling model. We conduct multiple evaluation experiments on two industrial safety datasets. The results show that CRGM has promising and gratifying aptitudes, greatly improves the performance of the model, and is efficient and generalized. Our sequence labeling model also shows the expected performance, which is better than the existing research. Our research provides a new perspective for exploring ISK, we hope it can contribute support for the intelligent progress of industrial safety.
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