Abstract
Hazard and operability analysis (HAZOP) is a remarkable representative in industrial safety engineering, the HAZOP report contains a great storehouse of industrial safety knowledge (ISK). In order to unlock the value of ISK and improve HAZOP efficiency, a novel knowledge graph development for industrial safety (ISKG) is proposed. Firstly, according to the international standard IEC61882, we use the top-down approach to disintegrate HAZOP into hazard events with multi-level information, which constructs the ontology library. Secondly, using the bottom-up approach and natural language processing technology, we present an ingenious information extraction model termed HAINEX based on hybrid deep learning. Briefly, the HAINEX consists of the following modules: an improved industrial bidirectional encoder for extracting semantic features, a bidirectional long short-term memory network for obtaining the context representation, and a decoder based on conditional random field with an improved industrial loss function. Finally, the constructed HAZOP triples are imported into the graph database. Experiments show that HAINEX is advanced and reliable. We take the indirect coal liquefaction process as a case study to develop ISKG. ISKG oriented applications, such as ISK visualization, ISK retrieval, auxiliary HAZOP and hazard propagation reasoning, can mine the potential of ISK and improve HAZOP efficiency, which is of great significance in strengthening industrial safety. What is more, the ISKG based question-answering system can be applied to teaching guidance to popularize the safety knowledge and enhance prevention awareness for non-professionals.
Abstract (translated)
URL
https://arxiv.org/abs/2111.13854
