Advances in Wireless Damage Detection for Structural Health Monitoring

Janne Toivola

Advances in Wireless Damage Detection for Structural Health Monitoring

Julkaisun otsikon käännös: Edistysaskelia vaurioiden langattomaan ilmaisemiseen rakenteiden kunnonvalvonnassa

Janne Toivola

Tutkimustuotos: Doctoral Thesis › Collection of Articles

Abstrakti

Eräs rakenteiden kunnonvalvonnan keskeisistä tehtävistä on tuottaa merkityksellistä informaatiota rakenteesta, kuten sillasta tai nostokurjesta, ja tehdä tilastollisia päätelmiä mahdollisten vaurioiden olemassaolosta. Langattomien anturiverkkojen tekniikan kehitys on tarjonnut uusia mahdollisuuksia rakenteiden kunnonvalvontaan liittyvän tiedon automaattiseen mittaamiseen ja käsittelemiseen. Tämän väitöskirjan tarkoituksena on tarkastella erilaisia tiedonkäsittelymenetelmiä aiemmin havaitsemattomien poikkeamien ilmaisemiseen kiihtyvyysanturimittauksissa, rakenteiden luonnolliseen värähtelyyn perustuen. Osa käsittelystä on suunniteltu suoritettavaksi resurssirajoitetuissa langattomissa antureissa, tavoitteena vähentää mittausten kustannuksia. Ehdotettujen ilmaisujärjestelmien tiedonkäsittely on jaettu seuraaviin yleisen tason vaiheisiin: piirreirrotus, ulotteisuuden vähentäminen, poikkeavuuden ilmaisu ja suorituskyvyn arviointi. Useita kullekin vaiheelle ehdotettuja menetelmiä vertaillaan keskitettyinä eräajoina suoritetuissa kokeissa usealla kiihtyvyysanturiaineistolla. Menetelminä ovat mm. Goertzelin algoritmi, satunnaisprojektiot, tensorihajotelma, yhteisöllinen suodatus, lähimmän naapurin luokittelu ja ilmaisutarkkuuden arviointi toimintaominaiskäyrien avulla. Antureilta lähetetyn ja tilastollisille luokittimille syötetyn datan määrässä saavutetaan merkittäviä säästöjä, säilyttäen samalla osa luokittelutarkkuudesta. Vertailussa täysin keskitettyihin, anturiverkossa käsittelemättömään tietoon perustuviin menetelmiin nähden, ilmaisun herkkyys ja spesifisyys kuitenkin heikkenee. Työssä on ehdotettu ja arvioitu useita langattoman vaurionilmaisun tiedonkäsittelyvaiheiden yhdistelmiä. Satunnaista ilmaisua parempi tarkkuus on saavutettavissa hyvinkin pienellä määrällä mittauksia kiihtyvyysanturia kohden, mutta käytännön sovelluksien vaatiman spesifisyyden saavuttaminen jää haasteeksi.

Julkaisun otsikon käännös	Edistysaskelia vaurioiden langattomaan ilmaisemiseen rakenteiden kunnonvalvonnassa
Alkuperäiskieli	Englanti
Pätevyys	Tohtorintutkinto
Myöntävä instituutio	Aalto-yliopisto
Valvoja/neuvonantaja	Rousu, Juho, Vastuuprofessori Hollmen, Jaakko, Ohjaaja
Kustantaja	Aalto University
Painoksen ISBN	978-952-60-5712-5
Sähköinen ISBN	978-952-60-5713-2
Tila	Julkaistu - 2014
OKM-julkaisutyyppi	G5 Artikkeliväitöskirja

Tutkimusalat

kiihtyvyysmittaus
langaton anturiverkko
ulotteisuuden vähentäminen
yhteisöllinen suodatus
poikkeavuuden ilmaisu
rakenteiden kunnonvalvonta

Pääsy asiakirjaan

http://urn.fi/URN:ISBN:978-952-60-5713-2Lisenssi: Määrittelemätön

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@phdthesis{7e75d3bfe8654ff69a5590076678662d,

title = "Advances in Wireless Damage Detection for Structural Health Monitoring",

abstract = "One of the fundamental tasks in structural health monitoring is to extract relevant information about a structure, such as a bridge or a crane, and reach statistical decisions about the existence of damages in the structure. Recent advances in wireless sensor network technology has offered new possibilities for acquiring and processing structural health monitoring data automatically. The purpose of this dissertation is to explore various data processing methods for detecting previously unobserved deviation in measurements from accelerometer sensors, based on natural vibration of structures. Part of the processing is projected to be performed on resource constrained wireless sensors to ultimately reduce the cost of measurements. Data processing in the proposed detection systems is divided into following general stages: feature extraction, dimensionality reduction, novelty detection, and performance assessment. Several methods in each of the stages are proposed and benchmarked in offline experiments with multiple accelerometer data sets. The methods include, for example, the Goertzel algorithm, random projection, tensor decomposition, collaborative filtering, nearest neighbor classification, and evaluating detection accuracy in terms of receiver operating characteristic curves. Significant reductions are achieved in the amount of data transmitted from sensors and input to statistical classifiers, while maintaining some of the classification accuracy. However, the sensitivity and specificity in detection are worse than those of centralized methods operating on raw sensor data. The work proposed and evaluated several combinations of data processing stages for wireless damage detection. While better than random detection accuracy can be achieved with very small amount of data per accelerometer sensor, challenges remain in reaching specificity required in practical applications.",

keywords = "accelerometer data, wireless sensor network, dimensionality reduction, collaborative filtering, novelty detection, structural health monitoring, kiihtyvyysmittaus, langaton anturiverkko, ulotteisuuden v{\"a}hent{\"a}minen, yhteis{\"o}llinen suodatus, poikkeavuuden ilmaisu, rakenteiden kunnonvalvonta, accelerometer data, wireless sensor network, dimensionality reduction, collaborative filtering, novelty detection, structural health monitoring",

author = "Janne Toivola",

year = "2014",

language = "English",

isbn = "978-952-60-5712-5",

series = "Aalto University publication series DOCTORAL DISSERTATIONS",

publisher = "Aalto University",

number = "80",

address = "Finland",

school = "Aalto University",

}

TY - BOOK

T1 - Advances in Wireless Damage Detection for Structural Health Monitoring

AU - Toivola, Janne

PY - 2014

Y1 - 2014

N2 - One of the fundamental tasks in structural health monitoring is to extract relevant information about a structure, such as a bridge or a crane, and reach statistical decisions about the existence of damages in the structure. Recent advances in wireless sensor network technology has offered new possibilities for acquiring and processing structural health monitoring data automatically. The purpose of this dissertation is to explore various data processing methods for detecting previously unobserved deviation in measurements from accelerometer sensors, based on natural vibration of structures. Part of the processing is projected to be performed on resource constrained wireless sensors to ultimately reduce the cost of measurements. Data processing in the proposed detection systems is divided into following general stages: feature extraction, dimensionality reduction, novelty detection, and performance assessment. Several methods in each of the stages are proposed and benchmarked in offline experiments with multiple accelerometer data sets. The methods include, for example, the Goertzel algorithm, random projection, tensor decomposition, collaborative filtering, nearest neighbor classification, and evaluating detection accuracy in terms of receiver operating characteristic curves. Significant reductions are achieved in the amount of data transmitted from sensors and input to statistical classifiers, while maintaining some of the classification accuracy. However, the sensitivity and specificity in detection are worse than those of centralized methods operating on raw sensor data. The work proposed and evaluated several combinations of data processing stages for wireless damage detection. While better than random detection accuracy can be achieved with very small amount of data per accelerometer sensor, challenges remain in reaching specificity required in practical applications.

AB - One of the fundamental tasks in structural health monitoring is to extract relevant information about a structure, such as a bridge or a crane, and reach statistical decisions about the existence of damages in the structure. Recent advances in wireless sensor network technology has offered new possibilities for acquiring and processing structural health monitoring data automatically. The purpose of this dissertation is to explore various data processing methods for detecting previously unobserved deviation in measurements from accelerometer sensors, based on natural vibration of structures. Part of the processing is projected to be performed on resource constrained wireless sensors to ultimately reduce the cost of measurements. Data processing in the proposed detection systems is divided into following general stages: feature extraction, dimensionality reduction, novelty detection, and performance assessment. Several methods in each of the stages are proposed and benchmarked in offline experiments with multiple accelerometer data sets. The methods include, for example, the Goertzel algorithm, random projection, tensor decomposition, collaborative filtering, nearest neighbor classification, and evaluating detection accuracy in terms of receiver operating characteristic curves. Significant reductions are achieved in the amount of data transmitted from sensors and input to statistical classifiers, while maintaining some of the classification accuracy. However, the sensitivity and specificity in detection are worse than those of centralized methods operating on raw sensor data. The work proposed and evaluated several combinations of data processing stages for wireless damage detection. While better than random detection accuracy can be achieved with very small amount of data per accelerometer sensor, challenges remain in reaching specificity required in practical applications.

KW - accelerometer data

KW - wireless sensor network

KW - dimensionality reduction

KW - collaborative filtering

KW - novelty detection

KW - structural health monitoring

KW - kiihtyvyysmittaus

KW - langaton anturiverkko

KW - ulotteisuuden vähentäminen

KW - yhteisöllinen suodatus

KW - poikkeavuuden ilmaisu

KW - rakenteiden kunnonvalvonta

KW - accelerometer data

KW - wireless sensor network

KW - dimensionality reduction

KW - collaborative filtering

KW - novelty detection

KW - structural health monitoring

M3 - Doctoral Thesis

SN - 978-952-60-5712-5

T3 - Aalto University publication series DOCTORAL DISSERTATIONS

PB - Aalto University

ER -

Advances in Wireless Damage Detection for Structural Health Monitoring

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Tutkimusalat

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