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Predictive maintenance smart system based on ultrasonic guided waves and data mining

Abstract : In the Industry 4.0 era, predictive maintenance and internet-of-things are gaining ground. This kind of maintenance does not include yet structural health monitoring (SHM) by guided ultrasonic waves (UGW) in particular. The final objective of the thesis is to develop a tool to enhance this type of maintenance. SHM is an emerging approach that allows continuous monitoring of the structural health of a given structure. It is generally done in three main steps: data acquisition, defect detection and localization (diagnosis) and estimation of the residual life (prognosis). The first step requires the use of non-destructive testing systems such as that of UGW in this thesis. However, these systems were designed to perform spot checks and require the intervention of qualified operators. In this thesis, a system of generation and acquisition of UGW data, allowing among other things to connect the structure to be monitored to a cellular network, has been developed. This allows the construction of databases (which can be heterogeneous) in an automatic and low-cost way. Moreover, a particular attention was paid to the optimization of its power supply to guarantee the most autonomy possible. The second step consists in exploiting these data in order to detect the defect and to localize it. Three approaches have been proposed, depending on the required computing power and the degree of non-stationarity of the data (i.e. due to the instability of the environment of the structure and of the said measurement system). All three approaches are based on the novelty detection technique. In the case where a defect is detected, prediction algorithms of its evolution in time can be used to estimate the residual life of the structure, which is the last monitoring step. Here, a methodology based on a hybrid algorithm, using the empirical mode decomposition technique and an integrated moving average autoregressive model, has been developed. The results obtained on laboratory and in-situ structures show the relevance of the proposed monitoring methodology. Nevertheless, further work is needed to improve the technological maturation of the developed system.
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Submitted on : Wednesday, April 27, 2022 - 10:29:43 AM
Last modification on : Thursday, April 28, 2022 - 3:35:51 AM


  • HAL Id : tel-03652853, version 1


Marwen Aouini. Predictive maintenance smart system based on ultrasonic guided waves and data mining. Automatic. Université de Lorraine, 2021. English. ⟨NNT : 2021LORR0228⟩. ⟨tel-03652853⟩



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