Model-based structural damage identification using vibration measurements

Antti Huhtala

Model-based structural damage identification using vibration measurements

Julkaisun otsikon käännös: Mallipohjainen rakenteiden vauriontunnistus värähtelymittauksiin perustuen

Antti Huhtala

Matematiikan ja systeemianalyysin laitos

Tutkimustuotos: Doctoral Thesis › Collection of Articles

Abstrakti

Rakenteiden kunnonvalvonnassa rakennetta tarkkaillaan jatkuvasti siihen upotetuin anturein. Vauriontunnistus on rakenteiden kunnonvalvonnan osa, jossa rakenteen vauriotila päätellään mittauksista. Tarkemmin sanottuna vaurion läsnäolo tunnistetaan, ja sen paikka ja suuruus arvioidaan. Vaikka mitatun suureen tiedettäisiin olevan herkkä vauriolle, vaurion rekonstruointi mittauksesta ei yleisesti ole hyvin määrätty, koska moni merkittävästi toisistaan poikkeava vauriotila voi tuottaa hyvin samanlaisia mittaustuloksia. Vauriontunnistus on siis käänteisongelma. Tässä väitöstyössä otetaan mallipohjainen lähestymistapa käyttäen värähtelymittauksia. Rakenteen värähtelyä mitataan useilla antureilla, jotka voivat olla esimerkiksi venymäliuskoja, gyroskooppeja tai kiihtyvyysantureita. Rakenteen mallilla, joka sisältää mallit vaurion vaikutuksista sekä anturien toiminnasta, tuotetaan simuloituja mittauksia. Vauriontunnistus tehdään etsimällä uskottava vauriotila mallille siten, että sen tuottamat simuloidut mittaukset vastaavat todellisia mittauksia. Suurin osa työstä tässä väitöskirjassa käsittelee vauriontunnistusta käyttäen bayesiläistä päättelyä, kun mittaukset ovat rakenteen mooditaajuuksia ja -muotoja. Mittausmalliin lisätään monimuuttuja-normaalijakautunut kohinatermi, joka mahdollistaa mittavirheen sekä suuren osan mallivirhettä huomioon ottamisen. Tieto uskottavista vauriotiloista kirjoitetaan priorijakaumana, joka yhdistetään mittauksessa saatuun uuteen tietoon käyttäen bayesiläistä päättelyä. Työssä tarkastellaan myös muita lähestymistapoja ongelmaan. Kalman-suodinta voi käyttää vauriontunnistukseen lisäämällä värähtelevän rakenteen tilavektoriin vauriotilan muuttujat. Vauriotilan saa tällöin selville tila-arviosta muiden tilakomponenttien ohessa. Vaikka tämä lähestymistapa on herkempi mallivirheille, sitä voisi käyttää reaaliaikaisessa vauriontunnistuksessa jatkuvasti toimivana arviona vauriotilasta. Sigma-algebrat tasa-arvokuvauksilla -menetelmä (SACOM) käyttää samaa kohinajakaumaa kuin bayesiläinen lähestymistapa, ja kuten bayesiläinen lähestymistapa se myös antaa todennäköisyysjakauman vauriotilalle. Tässä lähestysmistavassa kuitenkin jakauma saadaan kuvaamalla kohinajakauma rakenteen mallin joukkoarvoisen käänteiskuvauksen läpi. Lopulta esitetään lyhyt tarkastelu mahdollisuudesta esittää vauriontunnistusongelma käänteislähdeongelmana. Koska tällä tavalla muodostuva ongelma on lineaarinen, sen matemaattiseen analyysiin on parempi mahdollisuus.

Julkaisun otsikon käännös	Mallipohjainen rakenteiden vauriontunnistus värähtelymittauksiin perustuen
Alkuperäiskieli	Englanti
Pätevyys	Tohtorintutkinto
Myöntävä instituutio	Aalto-yliopisto
Valvoja/neuvonantaja	Stenberg, Rolf, Vastuuprofessori Bossuyt, Sven, Vastuuprofessori
Kustantaja	Aalto University
Painoksen ISBN	978-952-60-6110-8
Sähköinen ISBN	978-952-60-6109-2
Tila	Julkaistu - 2015
OKM-julkaisutyyppi	G5 Artikkeliväitöskirja

Tutkimusalat

rakenteiden kunnonvalvonta
vauriontunnistus
käänteisongelmat
bayesiläinen päättely
elementtimenetelmä

Pääsy asiakirjaan

http://urn.fi/URN:ISBN:978-952-60-6109-2Lisenssi: Määrittelemätön

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@phdthesis{23a20e41e5e24cb2b29d54d6c3e39d45,

title = "Model-based structural damage identification using vibration measurements",

abstract = "In structural health monitoring (SHM), a structure is continuously monitored with a set of embedded sensors. Damage identification is the part of a SHM system, in which the damage state of the structure is determined from obtained measurements. More specifically, the presence of damage is detected, and its location and severity are estimated. Even if the measured quantities are known to be sensitive to damage, the reconstruction of damage from the measurement is generally not well-posed, since significantly different damage states may still produce similar measurement results. Damage identification is thus an inverse problem. In this thesis, a model-based approach using vibration measurements is taken. The vibration of the structure is measured using several sensors, which can be for instance strain gauges, gyroscopes or accelerometers. A model of the structure, including a model of how the damage affects the structure and a model of the measurement sensors, is then used to simulate the measurements. Damage identification is achieved through finding a plausible damage state of the model which reproduces the actual measurements as simulated measurements. Most of the work in this thesis is on damage identification using Bayesian inference, while taking the measurements as mode frequencies and mode shapes of the structure. A multivariate normal distributed noise term is included in the measurement model, which allows taking into account the measurement error and also a large part of the model error. The knowledge of plausible damage states is described using a prior distribution, which is merged with the information obtained through measurement using Bayesian inference. Other approaches to the damage identification problem are also discussed in the work. The Kalman filter can be used for damage identification by augmenting the state vector of the vibrating structure with parameters of the damage state. The state estimate then gives the damage parameters along with the other state components. While this approach is more sensitive to model errors, it could be used for real-time damage identification for a continuous assessment of the damage state. The method of sigma algebras on contour maps (SACOM) uses the same noise distribution as the Bayesian approach, and like the Bayesian approach also gives a probability distribution for the damage state. However, in this approach the distribution is obtained by mapping the noise distribution through the set-valued inverse of the structure model. Finally, a brief discussion is given on the possibility of formulating the damage identification problem as an inverse source problem. As the resulting problem is linear, it gives greater opportunity for a thorough mathematical analysis.",

keywords = "structural health monitoring, damage identification, inverse problems, Bayesian inference, finite element method, rakenteiden kunnonvalvonta, vauriontunnistus, k{\"a}{\"a}nteisongelmat, bayesil{\"a}inen p{\"a}{\"a}ttely, elementtimenetelm{\"a}, structural health monitoring, damage identification, inverse problems, Bayesian inference, finite element method",

author = "Antti Huhtala",

year = "2015",

language = "English",

isbn = "978-952-60-6110-8",

series = "Aalto University publication series DOCTORAL DISSERTATIONS",

publisher = "Aalto University",

number = "30",

address = "Finland",

school = "Aalto University",

}

TY - BOOK

T1 - Model-based structural damage identification using vibration measurements

AU - Huhtala, Antti

PY - 2015

Y1 - 2015

N2 - In structural health monitoring (SHM), a structure is continuously monitored with a set of embedded sensors. Damage identification is the part of a SHM system, in which the damage state of the structure is determined from obtained measurements. More specifically, the presence of damage is detected, and its location and severity are estimated. Even if the measured quantities are known to be sensitive to damage, the reconstruction of damage from the measurement is generally not well-posed, since significantly different damage states may still produce similar measurement results. Damage identification is thus an inverse problem. In this thesis, a model-based approach using vibration measurements is taken. The vibration of the structure is measured using several sensors, which can be for instance strain gauges, gyroscopes or accelerometers. A model of the structure, including a model of how the damage affects the structure and a model of the measurement sensors, is then used to simulate the measurements. Damage identification is achieved through finding a plausible damage state of the model which reproduces the actual measurements as simulated measurements. Most of the work in this thesis is on damage identification using Bayesian inference, while taking the measurements as mode frequencies and mode shapes of the structure. A multivariate normal distributed noise term is included in the measurement model, which allows taking into account the measurement error and also a large part of the model error. The knowledge of plausible damage states is described using a prior distribution, which is merged with the information obtained through measurement using Bayesian inference. Other approaches to the damage identification problem are also discussed in the work. The Kalman filter can be used for damage identification by augmenting the state vector of the vibrating structure with parameters of the damage state. The state estimate then gives the damage parameters along with the other state components. While this approach is more sensitive to model errors, it could be used for real-time damage identification for a continuous assessment of the damage state. The method of sigma algebras on contour maps (SACOM) uses the same noise distribution as the Bayesian approach, and like the Bayesian approach also gives a probability distribution for the damage state. However, in this approach the distribution is obtained by mapping the noise distribution through the set-valued inverse of the structure model. Finally, a brief discussion is given on the possibility of formulating the damage identification problem as an inverse source problem. As the resulting problem is linear, it gives greater opportunity for a thorough mathematical analysis.

AB - In structural health monitoring (SHM), a structure is continuously monitored with a set of embedded sensors. Damage identification is the part of a SHM system, in which the damage state of the structure is determined from obtained measurements. More specifically, the presence of damage is detected, and its location and severity are estimated. Even if the measured quantities are known to be sensitive to damage, the reconstruction of damage from the measurement is generally not well-posed, since significantly different damage states may still produce similar measurement results. Damage identification is thus an inverse problem. In this thesis, a model-based approach using vibration measurements is taken. The vibration of the structure is measured using several sensors, which can be for instance strain gauges, gyroscopes or accelerometers. A model of the structure, including a model of how the damage affects the structure and a model of the measurement sensors, is then used to simulate the measurements. Damage identification is achieved through finding a plausible damage state of the model which reproduces the actual measurements as simulated measurements. Most of the work in this thesis is on damage identification using Bayesian inference, while taking the measurements as mode frequencies and mode shapes of the structure. A multivariate normal distributed noise term is included in the measurement model, which allows taking into account the measurement error and also a large part of the model error. The knowledge of plausible damage states is described using a prior distribution, which is merged with the information obtained through measurement using Bayesian inference. Other approaches to the damage identification problem are also discussed in the work. The Kalman filter can be used for damage identification by augmenting the state vector of the vibrating structure with parameters of the damage state. The state estimate then gives the damage parameters along with the other state components. While this approach is more sensitive to model errors, it could be used for real-time damage identification for a continuous assessment of the damage state. The method of sigma algebras on contour maps (SACOM) uses the same noise distribution as the Bayesian approach, and like the Bayesian approach also gives a probability distribution for the damage state. However, in this approach the distribution is obtained by mapping the noise distribution through the set-valued inverse of the structure model. Finally, a brief discussion is given on the possibility of formulating the damage identification problem as an inverse source problem. As the resulting problem is linear, it gives greater opportunity for a thorough mathematical analysis.

KW - structural health monitoring

KW - damage identification

KW - inverse problems

KW - Bayesian inference

KW - finite element method

KW - rakenteiden kunnonvalvonta

KW - vauriontunnistus

KW - käänteisongelmat

KW - bayesiläinen päättely

KW - elementtimenetelmä

KW - structural health monitoring

KW - damage identification

KW - inverse problems

KW - Bayesian inference

KW - finite element method

M3 - Doctoral Thesis

SN - 978-952-60-6110-8

T3 - Aalto University publication series DOCTORAL DISSERTATIONS

PB - Aalto University

ER -

Model-based structural damage identification using vibration measurements

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Tutkimusalat

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