Building causal models for finding actual causes of unmanned aerial vehicle failures.

ABSTRACT: Finding actual causes of unmanned aerial vehicle (UAV) failures can be split into two main tasks: building causal models and performing actual causality analysis (ACA) over them. While there are available solutions in the literature to perform ACA, building comprehensive causal models is still an open problem. The expensive and time-consuming process of building such models, typically performed manually by domain experts, has hindered the widespread application of causality-based diagnosis solutions in practice. This study proposes a methodology based on natural language processing for automating causal model generation for UAVs. After collecting textual data from online resources, causal keywords are identified in sentences. Next, cause-effect phrases are extracted from sentences based on

SUBMITTER: Zibaei E 

PROVIDER: S-EPMC10880731 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature