Ricketts, JonathanPelham, JoniBarry, DavidGuo, Weisi2023-01-112023-01-112022-10-31Ricketts J, Pelham J, Barry D, Guo W. (2022) An NLP framework for extracting causes, consequences, and hazards from occurrence reports to validate a HAZOP study. In: 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), 18-22 September 2022, Portsmouth, Virginia, USA.978-1-6654-8608-82155-7195https://doi.org/10.1109/DASC55683.2022.9925822https://dspace.lib.cranfield.ac.uk/handle/1826/18925A 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.enAttribution-NonCommercial 4.0 Internationalhazard analysissafetyassurancesafety assessmentnatural language processingConference paper978-1-6654-8607-1