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Recursive Scheme for Sequential Leaks’ Identification

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Modeling and Monitoring of Pipelines and Networks

Part of the book series: Applied Condition Monitoring ((ACM,volume 7))

Abstract

This chapter deals with the problem of detection and identification of multi-leaks in a single, horizontal pipeline assuming that only flow and pressure sensors at the ends of the pipeline are available. A characteristic of this monitoring scenario with simultaneous leaks is that the events are undistinguishable in steady state. This means the leaks could only be identified during transient behaviors. A monitoring problem, close to the simultaneous leaks’ issue, is the leaks’ scenario appearing in sequence. It is shown here that the isolation task is feasible with this scenario in the framework of model-based methods. Thus, a general recursive scheme which is formatted with three coupled nonlinear input–output equivalent models in steady state is proposed. Since the scheme is based on the equivalence model condition between one and multiple leaks’, previous to the presentation of the scheme, the static relation between equivalent models for one and multiple leaks is derived by considering the friction as a function of the flows. The interrelated three input-output models have the property to retain the data of the past leaks’, which allows an on-line identification of the new event during a time window for any arbitrary number of leaks. In particular the identifiers are implemented by using extended Kalman filters. The algorithm is tested with synthetic data simulated with Pipeline Studio software for a sequential set of three leaks, and it shows successful results.

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Acknowledgements

This work is supported by the Mexican Government Scholarship Program for International Students, DGAPA-UNAM IT100716, II-UNAM and CONACYT.

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Correspondence to Cristina Verde .

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Verde, C., Rojas, J. (2017). Recursive Scheme for Sequential Leaks’ Identification. In: Verde, C., Torres, L. (eds) Modeling and Monitoring of Pipelines and Networks. Applied Condition Monitoring, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-55944-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-55944-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55943-8

  • Online ISBN: 978-3-319-55944-5

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