Pub Date : 2021-05-17DOI: 10.1007/978-3-030-77348-9_7
S. Vettori, Emilio DiLorenzo, B. Peeters, E. Chatzi
{"title":"Virtual Sensing for Wind Turbine Blade Full Field Response Estimation in Operational Modal Analysis","authors":"S. Vettori, Emilio DiLorenzo, B. Peeters, E. Chatzi","doi":"10.1007/978-3-030-77348-9_7","DOIUrl":"https://doi.org/10.1007/978-3-030-77348-9_7","url":null,"abstract":"","PeriodicalId":278140,"journal":{"name":"Model Validation and Uncertainty Quantification, Volume 3","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122494993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-17DOI: 10.1007/978-3-030-77348-9_16
R. Barthorpe, A. Hughes, P. Gardner
{"title":"A Forward Model Driven Structural Health Monitoring Paradigm: Damage Detection","authors":"R. Barthorpe, A. Hughes, P. Gardner","doi":"10.1007/978-3-030-77348-9_16","DOIUrl":"https://doi.org/10.1007/978-3-030-77348-9_16","url":null,"abstract":"","PeriodicalId":278140,"journal":{"name":"Model Validation and Uncertainty Quantification, Volume 3","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130820687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-17DOI: 10.1007/978-3-030-77348-9_18
Kyle L. Hom, H. Beigi, R. Betti
{"title":"Application of Speaker Recognition x-Vectors to Structural Health Monitoring","authors":"Kyle L. Hom, H. Beigi, R. Betti","doi":"10.1007/978-3-030-77348-9_18","DOIUrl":"https://doi.org/10.1007/978-3-030-77348-9_18","url":null,"abstract":"","PeriodicalId":278140,"journal":{"name":"Model Validation and Uncertainty Quantification, Volume 3","volume":"65 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132473968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-17DOI: 10.1007/978-3-030-77348-9_5
L. Horta, M. Reaves, Clay W. Fulcher
{"title":"Error Localization Examples: Looking for a Needle in a Haystack","authors":"L. Horta, M. Reaves, Clay W. Fulcher","doi":"10.1007/978-3-030-77348-9_5","DOIUrl":"https://doi.org/10.1007/978-3-030-77348-9_5","url":null,"abstract":"","PeriodicalId":278140,"journal":{"name":"Model Validation and Uncertainty Quantification, Volume 3","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133289232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-17DOI: 10.1007/978-3-030-77348-9_19
R. Nayek, K. Worden, E. Cross
{"title":"Equation Discovery Using an Efficient Variational Bayesian Approach with Spike-and-Slab Priors","authors":"R. Nayek, K. Worden, E. Cross","doi":"10.1007/978-3-030-77348-9_19","DOIUrl":"https://doi.org/10.1007/978-3-030-77348-9_19","url":null,"abstract":"","PeriodicalId":278140,"journal":{"name":"Model Validation and Uncertainty Quantification, Volume 3","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115750279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-17DOI: 10.1007/978-3-030-77348-9_13
Lara J. Edington, N. Dervilis, P. Gardner, D. Wagg
{"title":"An Initial Concept for an Error-Based Digital Twin Framework for Dynamics Applications","authors":"Lara J. Edington, N. Dervilis, P. Gardner, D. Wagg","doi":"10.1007/978-3-030-77348-9_13","DOIUrl":"https://doi.org/10.1007/978-3-030-77348-9_13","url":null,"abstract":"","PeriodicalId":278140,"journal":{"name":"Model Validation and Uncertainty Quantification, Volume 3","volume":"17 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120874845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-31DOI: 10.1007/978-3-030-12075-7_25
R. Viala, V. Placet, S. L. Conte, S. Vaiedelich, S. Cogan
{"title":"Model-Based Decision Support Methods Applied to the Conservation of Musical Instruments: Application to an Antique Cello","authors":"R. Viala, V. Placet, S. L. Conte, S. Vaiedelich, S. Cogan","doi":"10.1007/978-3-030-12075-7_25","DOIUrl":"https://doi.org/10.1007/978-3-030-12075-7_25","url":null,"abstract":"","PeriodicalId":278140,"journal":{"name":"Model Validation and Uncertainty Quantification, Volume 3","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114944411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-31DOI: 10.3929/ETHZ-B-000315814
Charilaos Mylonas, I. Abdallah, E. Chatzi
In this work we are addressing the problem of statistical modeling of the joint distribution of data collected from wind turbines interacting due to collective effect of their placement in a wind-farm, the wind characteristics (speed/orientation) and the turbine control. Operating wind turbines extract energy from the wind and at the same time produce wakes on the down-wind turbines in a park, causing reduced power production and increased vibrations, potentially contributing in a detrimental manner to fatigue life. This work presents a Variational Auto-Encoder (VAE) Neural Network architecture capable of mapping the high dimensional correlated stochastic variables over the wind-farm, such as power production and wind speed, to a parametric probability distribution of much lower dimensionality. We demonstrate how a trained VAE can be used in order to quantify levels of statistical deviation on condition monitoring data. Moreover, we demonstrate how the VAE can be used for pre-training an inference model, capable of predicting the power production of the farm together with bounds on the uncertainty of the predictions.
{"title":"Deep Unsupervised Learning for Condition Monitoring and Prediction of High Dimensional Data with Application on Windfarm SCADA Data","authors":"Charilaos Mylonas, I. Abdallah, E. Chatzi","doi":"10.3929/ETHZ-B-000315814","DOIUrl":"https://doi.org/10.3929/ETHZ-B-000315814","url":null,"abstract":"In this work we are addressing the problem of statistical modeling of the joint distribution of data collected from wind turbines interacting due to collective effect of their placement in a wind-farm, the wind characteristics (speed/orientation) and the turbine control. Operating wind turbines extract energy from the wind and at the same time produce wakes on the down-wind turbines in a park, causing reduced power production and increased vibrations, potentially contributing in a detrimental manner to fatigue life. This work presents a Variational Auto-Encoder (VAE) Neural Network architecture capable of mapping the high dimensional correlated stochastic variables over the wind-farm, such as power production and wind speed, to a parametric probability distribution of much lower dimensionality. We demonstrate how a trained VAE can be used in order to quantify levels of statistical deviation on condition monitoring data. Moreover, we demonstrate how the VAE can be used for pre-training an inference model, capable of predicting the power production of the farm together with bounds on the uncertainty of the predictions.","PeriodicalId":278140,"journal":{"name":"Model Validation and Uncertainty Quantification, Volume 3","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131263213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-31DOI: 10.1007/978-3-030-12075-7_34
K. Worden, E. Cross, P. Gardner, R. Barthorpe, D. Wagg
{"title":"On Digital Twins, Mirrors and Virtualisations","authors":"K. Worden, E. Cross, P. Gardner, R. Barthorpe, D. Wagg","doi":"10.1007/978-3-030-12075-7_34","DOIUrl":"https://doi.org/10.1007/978-3-030-12075-7_34","url":null,"abstract":"","PeriodicalId":278140,"journal":{"name":"Model Validation and Uncertainty Quantification, Volume 3","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132733540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-31DOI: 10.1007/978-3-030-12075-7_30
D. Wagg, P. Gardner, R. Barthorpe, K. Worden
{"title":"On Key Technologies for Realising Digital Twins for Structural Dynamics Applications","authors":"D. Wagg, P. Gardner, R. Barthorpe, K. Worden","doi":"10.1007/978-3-030-12075-7_30","DOIUrl":"https://doi.org/10.1007/978-3-030-12075-7_30","url":null,"abstract":"","PeriodicalId":278140,"journal":{"name":"Model Validation and Uncertainty Quantification, Volume 3","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117185617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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