Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering
Health monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/1/2 |
| _version_ | 1811308301106806784 |
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| author | Giovanni Capellari Saeed Eftekhar Azam Stefano Mariani |
| author_facet | Giovanni Capellari Saeed Eftekhar Azam Stefano Mariani |
| author_sort | Giovanni Capellari |
| collection | DOAJ |
| description | Health monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is linked to the computational costs, two remedies are jointly considered: first, an order-reduction of the numerical model used to track the structural dynamics, enforced with proper orthogonal decomposition; and, second, an improved particle filter, which features an extended Kalman updating of each evolving particle before the resampling stage. The former remedy can reduce the number of effective degrees-of-freedom of the structural model to a few only (depending on the excitation), whereas the latter one allows to track the evolution of damage and to locate it thanks to an intricate formulation. To assess the effectiveness of the proposed procedure, the case of a plate subject to bending is investigated; it is shown that, when the procedure is appropriately fed by measurements, damage is efficiently and accurately estimated. |
| first_indexed | 2024-04-13T09:20:46Z |
| format | Article |
| id | doaj.art-31bdeda35bce4746a6940d3ba54c1274 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T09:20:46Z |
| publishDate | 2015-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-31bdeda35bce4746a6940d3ba54c12742022-12-22T02:52:36ZengMDPI AGSensors1424-82202015-12-01161210.3390/s16010002s16010002Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman FilteringGiovanni Capellari0Saeed Eftekhar Azam1Stefano Mariani2Politecnico di Milano, Dipartimento di Ingegneria Civile e Ambientale, Piazza L. da Vinci 32, 20133 Milano, ItalyUniversity of Thessaly, Department of Mechanical Engineering, Leoforos Athinon, Pedion Areos, 38334 Volos, GreecePolitecnico di Milano, Dipartimento di Ingegneria Civile e Ambientale, Piazza L. da Vinci 32, 20133 Milano, ItalyHealth monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is linked to the computational costs, two remedies are jointly considered: first, an order-reduction of the numerical model used to track the structural dynamics, enforced with proper orthogonal decomposition; and, second, an improved particle filter, which features an extended Kalman updating of each evolving particle before the resampling stage. The former remedy can reduce the number of effective degrees-of-freedom of the structural model to a few only (depending on the excitation), whereas the latter one allows to track the evolution of damage and to locate it thanks to an intricate formulation. To assess the effectiveness of the proposed procedure, the case of a plate subject to bending is investigated; it is shown that, when the procedure is appropriately fed by measurements, damage is efficiently and accurately estimated.http://www.mdpi.com/1424-8220/16/1/2structural health monitoringreduced-order modelingproper orthogonal decompositionparticle-Kalman filteringinertial sensors |
| spellingShingle | Giovanni Capellari Saeed Eftekhar Azam Stefano Mariani Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering Sensors structural health monitoring reduced-order modeling proper orthogonal decomposition particle-Kalman filtering inertial sensors |
| title | Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering |
| title_full | Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering |
| title_fullStr | Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering |
| title_full_unstemmed | Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering |
| title_short | Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering |
| title_sort | damage detection in flexible plates through reduced order modeling and hybrid particle kalman filtering |
| topic | structural health monitoring reduced-order modeling proper orthogonal decomposition particle-Kalman filtering inertial sensors |
| url | http://www.mdpi.com/1424-8220/16/1/2 |
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