An NLP framework for extracting causes, consequences, and hazards from occurrence reports to validate a HAZOP study

A substantial amount of effort and resource is applied to the design of aircraft systems to reduce risk to life and improve safety. This is often applied through a variety of safety assessment methods, one of which being Hazard and Operability (HAZOP) Studies. Once an air system is in-service, it is common for flight data to be collected and analysed to validate the original safety assessment. However, the operator of the air system generates and stores a substantial amount of safety knowledge within free-text occurrence reports. These allow maintainers and aircrew to report occurrences, often describing hazards and associated detail revealing consequences and causes. A lack of resource means it is difficult for safety professionals to manually review these occurrences and although occurrences are classified against a set taxonomy (e.g., birdstrike, technical failure) this lacks the granularity to apply to a specific safety analysis. To resolve this, the paper presents the development of a novel Natural Language Processing (NLP) framework for extracting causes, consequences, and hazards from free-text occurrence reports in order to validate and inform an aircraft sub-system HAZOP study. Specifically using a combination of rule-based phrase matching with a spaCy Named Entity Recognition (NER) model. It is suggested that the framework could form a continual improvement process whereby the findings drive updates to the HAZOP, in turn updating the rules and model, therefore improving accuracy and hazard identification over time.