Structural health monitoring using statistical learning methods : Application on tubular structures

Abstract : To ensure better working conditions of civil and engineering structures, inspections must be made on a regular basis. However, these inspections could be labor-intensive and cost-consuming. In this context, structural health monitoring (SHM) systems using permanently attached transducers were proposed to ensure continuous damage diagnostic of these structures. In SHM, damage detection is generally based on comparison between the healthy state signals and the current signals. Nevertheless, the environmental and operational conditions will have an effect on the healthy state signals. If these effects are not taken into account they would result in false indication of damage (false alarm). In this thesis, classical machine learning methods used for damage detection have been applied in the case of pipelines. The effects of some measurements parameters on the robustness of these methods have been investigated. Afterthat, two approaches were proposed for damage diagnostic depending on the database of reference signals. If this database contains large variation of these EOCs, a sparse estimation of the current signal is calculated. Then, the estimation error is used as an indication of the presence of damage. Otherwise, if this database is acquired at limited range of EOCs, moving window PCA can be applied to update the model of the healthy state provided that the EOCs show slow and continuous variation. In both approaches, damage localization was ensured using a sliding window over the damaged pipe signal.
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Theses
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http://docnum.univ-lorraine.fr/ulprive/DDOC_T_2019_0047_EL_MOUNTASSIR.pdf
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https://hal.univ-lorraine.fr/tel-02332316
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Submitted on : Thursday, October 24, 2019 - 5:06:34 PM
Last modification on : Friday, October 25, 2019 - 1:53:26 AM

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  • HAL Id : tel-02332316, version 1

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Mahjoub El Mountassir. Structural health monitoring using statistical learning methods : Application on tubular structures. Automatic. Université de Lorraine, 2019. English. ⟨NNT : 2019LORR0047⟩. ⟨tel-02332316⟩

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