Pipeline Fault Diagnosis System Enhancement

In order to decrease the false alarm rate and improve the sensitivity of pipeline fault diagnosis system, three artificial intelligence based methods are first proposed. Neural networks with the input matrix composed by stress wave characteristics in time domain or frequency domain is proposed to classify various situations of the pipeline, in order to detect the leakage from pipeline online running data. Context-free grammar of symbolic representation of the negative wave form is used and a negative wave form parsing system with application to syntactic pattern recognition based on the representation is described. New complex thermal and hydraulic models, in which the flow regime, viscosity-temperature characteristics, density-temperature characteristics and specific heat-temperature characteristics, etc., of the running fluid in the pipelines are set up for non-isothermal pipeline carrying higher temperature fluid or in ambient environment.