In this paper, a new technique is proposed in order to detect, locate, as well as approximate the fluid leaks in a straight pipeline (without branching) by taking into consideration the pressure and flow evaluations at the ends of pipeline on the basis of data fusion from two methods: a steady-state approximation and Second-order Extended Kalman Filter (SEKF). The SEKF is on the basis of the second-order Taylor expansion of a nonlinear system unlike to the more popular First-order Extended Kalman Filter (FEKF). The suggested technique in this paper deals with just pressure head and flow rate evaluations at the ends of pipeline that has intrinsic sensor as well as process noise. A simulation example is given for demonstrating the validity of the proposed technique. It shows that the extended Kalman particle filter algorithm on the basis of the second-order Taylor expansion is effective and performs well in decreasing systematic deviations as well as running time.
|Number of pages||6|
|Publication status||Published - 6 Dec 2019|
|Event||13th IFAC Workshop on Adaptive and Learning Control Systems, ALCOS 2019 - Winchester, United Kingdom|
Duration: 4 Dec 2019 → 6 Dec 2019
- Leakage detection
- Second-order Extended Kalman Filter