Pipeline Leakage Detection and Localization Using a Reliable Pipeline-Mechanism Model Incorporating a Bayesian Model Updating Approach

Abstract

Pipeline transportation is widely used in industrial production and daily life. In order to reduce the waste of resources and economic losses caused by pipeline leakage, effective pipeline leakage detection and localization technology is particularly important. Among the many leakage detection methods, the model-based method for pipeline leakage detection and localization is widely used. However, the key to the method is how to obtain an accurate and reliable pipeline model to ensure and improve the detection accuracy. This paper proposes a novel method to obtain a reliable pipeline-mechanism model that fused data and mechanism models based on Bayesian theory. Moreover, in the process of Bayesian fusion, the complexity and calculations in the mechanism models were greatly reduced by establishing a surrogate model. After that, the multidimensional posterior distribution was sampled by the Markov chain Monte Carlo-differential evolution adaptive metropolis (ZS) (MCMC-DREAM (ZS)) algorithm, and the uncertainty in the model was updated to obtain a reliable pipeline-mechanism model. Subsequently, the pipeline resistance coefficient, which could be calculated based on the reliable pipeline-mechanism model, was proposed as an indicator for detecting whether the pipeline leaked or not. Finally, the pipeline leak model was used to determine the location of the leak. The reliable pipeline-mechanism model was applied in an experimental device to validate its performance. The results showed that the proposed method improved the accuracy and reliability of the mechanism model, and, in addition, the leakage could be accurately located.

Keywords

• pipeline leakage detection and localization
• mechanism model
• model reliability
• Bayesian theory
• pipeline resistance coefficient
• uncertainty quantification