This paper presents a leak isolation methodology using a fitting loss coefficient calibration. Two stages are considered for this purpose: First, the equivalent straight length (ESL) is fixed by an model‐base observer designed as an extended Kalman filter. Once the leak is detected, the previous observer is stopped and the second system, based on an algebraic observer, is started with the ESL value fixed by the previous observer. Finally, the estimated leak position is recovered in original coordinates since the observer deal with ESL coordinates. In order to tackle the friction variations problem, the so‐called Swamee‐Jain equation is embedded explicitly, instead of a constant parameter as in other studies. The approach assumes only flow and pressure sensors at the ends of the duct. Experimental results with data obtained from a plastic pipeline prototype are presented to assess the method efficiency.

This paper addresses the two simultaneous leak diagnosis problem in pipelines based on a state vector reconstruction as a strategy to improve water shortages in large cities by only considering the availability of the flow rate and pressure head measurements at both ends of the pipeline. The proposed algorithm considers the parameters of both leaks as new state variables with constant dynamics, which results in an extended state representation. By applying a suitable persistent input, an invertible mapping in x can be obtained as a function of the input and output, including their time derivatives of the third-order. The state vector can then be reconstructed by means of an algebraic-like observer through the computation of time derivatives using a Numerical Differentiation with Annihilatorsconsidering its inherent noise rejection properties. Experimental results showed that leak parameters were reconstructed with accuracy using a test bed plant built at Cinvestav Guadalajara.

The aim of this paper is to compare two analytic detection algorithms for pipeline leaks in order to assess their effectivity. Parameters such as precision and velocity to detect and locate a leak, algorithm complexity, and application facilities are evaluated, with the purpose of finding the most useful algorithm for a future real-time implementation on a Mexican aqueduct.

This work deals with the Leak Detection and Isolation (LDI) problem in water pipelines based on some heuristic method and assuming only flow rate and pressure head measurements at both ends of the duct. By considering the single leak case at an interior node of the pipeline, it has been shown that observability is indeed satisfied in this case, which allows designing an observer for the unmeasurable state variables, i.e., the pressure head at leak position. Relying on the fact that the origin of the observation error is exponentially stable if all parameters (including the leak coefficients) are known and uniformly ultimately bounded otherwise, the authors propose a bank of observers as follows: taking into account that the physical pipeline parameters are well-known, and there is only uncertainty about leak coefficients (position and magnitude), a pair of such coefficients is taken from a search space and is assigned to an observer. Then, a Genetic Algorithm (GA) is exploited to minimize the integration of the square observation error. The minimum integral observation error will be reached in the observer where the estimated leak parameters match the real ones. Finally, some results are presented by using real-noisy databases coming from a test bed plant built at Cinvestav-Guadalajara, aiming to show the potentiality of this method.