Fuzzy Monitoring and Diagnostic System for Main Gas Pipelines and Gas Distribution Stations Equipment

Abstract

Objective. Development of an intelligent system architecture based on fuzzy logic to enhance the operational reliability and diagnostic efficiency of gas transmission system (GTS) facilities – main gas pipelines and gas distribution stations (GDS).
Method. The theory of fuzzy sets and fuzzy inference was used as the core methodological framework. The methodology includes the stages of fuzzification of input data, application of a knowledge base of fuzzy production rules that formalize expert knowledge, aggregation of results, and defuzzification to obtain quantitative assessments of technical condition.
Result. The study developed and tested a multilevel architecture of an intelligent diagnostic system, integrating sequentially connected modules for primary data acquisition, preprocessing, fuzzy inference, and knowledge base. Within the system, fuzzy production rule bases for diagnosing the GDS filter-separator and comprehensively assessing the technical condition of main gas pipeline sections were formalized and verified. It was experimentally confirmed that the implemented approach increases the reliability of diagnostic conclusions by 25–30% through the use of mechanisms for processing noisy data and linguistic descriptions, enables early identification of incident states at the pre-failure stage, and ensures effective formalization of expert empirical knowledge. It has been established that the system's implementation creates a methodological basis for transitioning from scheduled maintenance to a condition-based maintenance (CBM) strategy.
Conclusion. The use of fuzzy logic is an effective approach for creating diagnostic and monitoring systems for technologically complex GTS facilities operating under uncertainty. Future development of the system is associated with the creation of hybrid neuro-fuzzy models for self-learning and the development of digital twins of equipment.