Unified Modelling Language–Adaptive Neuro-Fuzzy Inference System Models for Understanding the Design Complexity of Oil/Gas Pipeline Intrusion Detection System

Abstract

Oil/gas pipeline vandalism is a common and regular occurrence in the oilproducing regions of Nigeria. To ameliorate on the efforts so far made to combat this menace, a proactive system that can detect and identify damage-causing forces on oil/gas pipelines becomes very imperative. Through the use of the Unified Modelling Language (UML), this paper presents models that describe a real-time, intelligent, but complex system that detects, discriminates between varied vibration signatures of oil/gas distribution pipelines, and identifies the signatures due to intrusion on the pipes by vandals. To enhance understanding of the nature and interconnection of its constituent subcomponents, dynamics and design complexities, the system under study is modelled by use of hierarchical activity and state transition diagrams from the Unified Modelling Language (UML) domain.Because of the qualitative nature of its inputs, the system is integrated with Adaptive Neuro-Fuzzy Inference System (ANFIS) to intelligently handle the imprecision in the representation of the input data in human language. Implementation of the system modelled in this study would significantly reduce oil theft/spillage, destruction of properties, and loss of human lives hitherto experienced in the oil-producing regions of Nigeria.