Advanced fuzzy-based leak detection and size estimation for pipelines

The design of an effective procedure for leak detection, estimation, and leak size classification is necessary to maintain the healthy and safe operations of pipelines for conveying fluids and gas from one place to another. The complexities of nonlinear and uncertain behavior inherent in a pipeline lead to difficulty of detection, estimation, and leak size estimation. Hence, a robust hybrid leak detection and size estimation method based on the back stepping hyperbolic Takagi-Sugeno (T-S) fuzzy sliding mode extended ARX-Laguerre Proportional Integral (PI) observer for pipelines is presented. Because of the effects of gases and fluids in pipelines, accurate physical modeling of a pipeline is difficult. Consequently, the ARX-Laguerre technique is used for pipeline modeling in this study. Early detection of leaks is important to avoid product loss and other severe damage. To address this issue, the extended ARX-Laguerre PI observer is utilized to detect and estimate a leak. In addition, a T-S fuzzy technique is applied to an extended ARX-Laguerre PI observer to improve leak estimation in the presence of uncertainties. Thus, the T-S fuzzy sliding mode extended ARX-Laguerre PI observer adaptively improves the reliability, robustness, and estimation accuracy of leak detection and estimation. To leak size classification in the presence of uncertainties, the hyperbolic differential equations are governed by the T-S fuzzy extended ARX-Laguerre PI observer to find the exact solution for the kernels of a backstepping-based leak boundary. The leak estimation convergence error shows that the leak size estimation can be calculated independent of the location of the leak, which is the main contribution of this research. It is assumed that pressure and flowmeter sensors are available at the inlet and outlet of the pipeline. The effectiveness of the proposed robust backstepping hyperbolic T-S fuzzy sliding mode extended ARX-Laguerre PI observer was tested over an experimental dataset. According to the results, the proposed technique improved the leak detection, estimation, and size estimation.