An NLP framework for extracting causes, consequences, and hazards from occurrence reports to validate a HAZOP study
| dc.contributor.author | Ricketts, Jonathan | |
| dc.contributor.author | Pelham, Joni | |
| dc.contributor.author | Barry, David | |
| dc.contributor.author | Guo, Weisi | |
| dc.date.accessioned | 2023-01-11T09:39:16Z | |
| dc.date.available | 2023-01-11T09:39:16Z | |
| dc.date.issued | 2022-10-31 | |
| dc.description.abstract | 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. | en_UK |
| dc.description.sponsorship | Whitworth Senior Scholarship Award: Institution of Mechanical Engineers. QinetiQ. Royal Air Force. | en_UK |
| dc.identifier.citation | Ricketts 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. | en_UK |
| dc.identifier.eisbn | 978-1-6654-8607-1 | |
| dc.identifier.isbn | 978-1-6654-8608-8 | |
| dc.identifier.issn | 2155-7195 | |
| dc.identifier.uri | https://doi.org/10.1109/DASC55683.2022.9925822 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/18925 | |
| dc.language.iso | en | en_UK |
| dc.publisher | IEEE | en_UK |
| dc.rights | Attribution-NonCommercial 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | * |
| dc.subject | hazard analysis | en_UK |
| dc.subject | safety | en_UK |
| dc.subject | assurance | en_UK |
| dc.subject | safety assessment | en_UK |
| dc.subject | natural language processing | en_UK |
| dc.title | An NLP framework for extracting causes, consequences, and hazards from occurrence reports to validate a HAZOP study | en_UK |
| dc.type | Conference paper | en_UK |
| dcterms.dateAccepted | 2022-06-01 |