Spectral diffusion map approach for structural health monitoring of wind turbine blades using distributed sensors

dc.contributor.advisor Umesh Vaidya
dc.contributor.author Chinde, Venkatesh
dc.contributor.department Electrical and Computer Engineering
dc.date 2018-08-11T05:27:30.000
dc.date.accessioned 2020-06-30T02:59:09Z
dc.date.available 2020-06-30T02:59:09Z
dc.date.copyright Thu Jan 01 00:00:00 UTC 2015
dc.date.embargo 2001-01-01
dc.date.issued 2015-01-01
dc.description.abstract <p>Distributed Sensor Networks (DSN) has emerged as a sensing paradigms in the structural engineering field due to the application of Structural Health Monitoring (SHM) for large-scale structures which results in accurately diagnosing the health of structures and enhancing the reliability and robustness of monitoring systems. The multisensor network greatly enhances the feasibility of applying SHM and also provides awareness of structural damage. In this work, we develop data-driven method for the diagnosis of damage in mechanical structures using an array of distributed sensors. The proposed approach relies on comparing intrinsic geometry of data sets corresponding to the undamage and damage state of the system. This approach assumes no knowledge of underlying models of the different data sources. We use spectral diffusion map approach for identifying the intrinsic geometry of the data set. In particular, time series data from distributed sensors is used for the construction of diffusion map. The low dimensional embedding of the data set corresponding to different damage level is done using singular value decomposition of the diffusion map to identify the intrinsic geometry. We construct appropriate metric in diffusion space to compare the low-dimensional data set corresponding to different damage cases. The developed algorithm is applied for damage diagnosis of wind turbine blades. Towards this goal we developed a detailed nite element-based model of CX-100 blade in ANSYS using shell elements. The damage in the blade is modeled by degrading the material property which in turn results in change of stiffness. One of the main challenges in the development of health monitoring algorithms is the ability to use sensor data with relatively small signal to noise ratio. Our developed diffusion map-based algorithm is shown to be robust to the presence of sensor noise. The proposed diffusion map-based algorithm can not only account for data from different sensors but also different types of sensor in the form of sensor fusion hereby making it attractive to exploit the distributed nature of sensor array. The distributed nature of sensor array is further exploited to determine the location of damage on the wind turbine blade. Our extensive simulation results show that our proposed algorithms can not only determine the extend of damage but also the location of the damage.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/14782/
dc.identifier.articleid 5789
dc.identifier.contextkey 8329574
dc.identifier.doi https://doi.org/10.31274/etd-180810-4367
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/14782
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/28967
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/14782/Chinde_iastate_0097M_15355.pdf|||Fri Jan 14 20:26:17 UTC 2022
dc.subject.disciplines Electrical and Electronics
dc.subject.keywords Electrical Engineering
dc.subject.keywords condition assessment
dc.subject.keywords damage detection
dc.subject.keywords diagnosis
dc.subject.keywords diffusion map
dc.subject.keywords sensor networks
dc.subject.keywords structural health monitoring
dc.title Spectral diffusion map approach for structural health monitoring of wind turbine blades using distributed sensors
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
thesis.degree.level thesis
thesis.degree.name Master of Science
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