1. Data-Driven Autoregressive Model Identification for Structural Health Monitoring in an Anisotropic Composite Plate
- Author
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da Silva, Samuel, Paixao, Jessé, Rebillat, Marc, Mechbal, Nazih, Universidade Estadual Paulista Júlio de Mesquita Filho = São Paulo State University (UNESP), Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM), Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM), Sao Paulo Research Foundation (FAPESP) grants 2017/15512-8 and 2018/15671-1 and the Brazilian National Council forScientific and Technological Development (CNPq) grant number 307520/2016-1., and Administrateur Ensam, Compte De Service
- Subjects
Extrapolated Model ,[SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph] ,[SPI.ACOU] Engineering Sciences [physics]/Acoustics [physics.class-ph] ,Quantification ,AR Models ,Prognosis ,Multiple Models - Abstract
International audience; A simple data-driven AutoRegressive (AR) model may be used to assess a model to describeand to predict the time-series outputs of the PZT sensors receiving Lamb waves for different operatingconditions in composite structures. Thus, this paper presents the potentiality of the use of a set of ARmodels to detect, locate, and, manly, to extrapolate a damage sensitive index based on changes in onestep-ahead prediction errors. To illustrate this proposal, an aeronautical composite panel with bondedpiezoelectric elements, that act both as sensors and actuators, is used to study the relationship betweenthe variation of the parameters of the identified model and the presence of various simulated damage.A damage progression evaluation by extrapolating the AR parameters is also suggested and examinedbased on cubic spline functions to verify the future state and to observe how the damage could evolute,based on some simplified assumptions. This step could help to make a decision about a possible requiredrepair without adopting a complicated and costly physical model.
- Published
- 2019