Abstract
The work is devoted to the development of methodology for cluster analysis of indicators of the state of pipeline transport systems, taking into account the characteristics of technical systems and potential threats to management decision-making at various stages of the life cycle of a hazardous production facility for pipeline transport of oil and gas. The proposed methodology is based on the use of artificial intelligence and digitalization for the collection and analysis of databases. The possibilities of modern application software systems for performing cluster analysis and identifying potentially dangerous sections of the pipeline using 3D models are presented and analyzed.
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