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.
An NLP framework for extracting causes, consequences, and hazards from occurrence reports to validate a HAZOP study
2022-09-18
1041071 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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