Online leak diagnosis in pipelines using an EKF-based and steady-state mixed approach


== Abstract ==

This paper proposes a methodology for leak detection and isolation (LDI) in pipelines based on data fusion from two approaches: a steady-state estimation and an Extended Kalman Filter (EKF). The proposed method considers only pressure head and flow rate measurements at the pipeline ends, which contain intrinsic sensor and process noise. The LDI system is tested in real-time by using an USB data acquisition device that is implemented in MATLAB environment. The effectiveness of the method is analyzed by considering: online detection, location as well as quantification of non-concurrent leaks at different positions. The leak estimation error average is less than 1% of the flow rate and less than 3% in the leakage position. Furthermore, the incorporation of a steady-state estimation shows that the solution of the LDI problem has improved significantly with respect to the one that only considers the EKF estimation. An experimental analysis was also performed on the effectiveness of the proposed approach for different sampling rates and for different leakage positions

Peer Reviewed


== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.1016/j.conengprac.2018.09.006 http://dx.doi.org/10.1016/j.conengprac.2018.09.006]

* [http://hdl.handle.net/2117/130336 http://hdl.handle.net/2117/130336] under the license http://creativecommons.org/licenses/by-nc-nd/3.0/es/

* [https://api.elsevier.com/content/article/PII:S0967066118305094?httpAccept=text/xml https://api.elsevier.com/content/article/PII:S0967066118305094?httpAccept=text/xml],
: [https://api.elsevier.com/content/article/PII:S0967066118305094?httpAccept=text/plain https://api.elsevier.com/content/article/PII:S0967066118305094?httpAccept=text/plain],
: [http://dx.doi.org/10.1016/j.conengprac.2018.09.006 http://dx.doi.org/10.1016/j.conengprac.2018.09.006] under the license https://www.elsevier.com/tdm/userlicense/1.0/

* [https://www.sciencedirect.com/science/article/pii/S0967066118305094 https://www.sciencedirect.com/science/article/pii/S0967066118305094],
: [https://upcommons.upc.edu/handle/2117/130336 https://upcommons.upc.edu/handle/2117/130336],
: [https://academic.microsoft.com/#/detail/2892233124 https://academic.microsoft.com/#/detail/2892233124]