2014
DOI: 10.1016/j.jsv.2014.05.012
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Robust methods of inclusive outlier analysis for structural health monitoring

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Cited by 56 publications
(70 citation statements)
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“…Dervilis et al [136] assessed the performance of the MSD, MCD, and MVEE algorithms on the Z-24 Bridge that included environmental variations. Their study showed that the MCD outperformed the MSD in terms of damage sensitivity and resolution; however, the MVEE produced significantly better results than both.…”
Section: Advancements To Machine Learning Methodologies For Damage Inmentioning
confidence: 99%
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“…Dervilis et al [136] assessed the performance of the MSD, MCD, and MVEE algorithms on the Z-24 Bridge that included environmental variations. Their study showed that the MCD outperformed the MSD in terms of damage sensitivity and resolution; however, the MVEE produced significantly better results than both.…”
Section: Advancements To Machine Learning Methodologies For Damage Inmentioning
confidence: 99%
“…For instance, Chang & Kim's [77] use of the Mahalanobis-Taguchi System, which combines multiple modal-based DSFs at once to enhance their collective damage sensitivity and performance consistency when applied to a steel truss bridge subjected to vehicle induced excitation. Dervilis et al [98,136] demonstrated the need for robust methods of outlier detection for supervised learning techniques to ensure that training datasets are not contaminated with outliers, by incorporating the Minimum Covariance Determinate and the Minimum Volume Enclosing Ellipsoid into standard outlier detection methods on the Z-24 Bridge.…”
mentioning
confidence: 99%
“…One of the standard references is Barnett and Lewis (1984). Some recent advances regarding robust outlier detection can be found in Dervilis et al (2014bDervilis et al ( , 2015, Hubert and Debruyne (2010), Schyns et al (2010), Attar et al (2013), Rousseeuw and Hubert (2013), Nurunnabi et al (2012), Verdonck et al (accepted), Fritsch et al (2011), andVariyath andVattathoor (2013).…”
Section: Problem Descriptionmentioning
confidence: 99%
“…Detecting EOVs is difficult because they manifest as multiple outliers -this requires robust methods of outlier/novelty detection. Unfortunately, this applies to both EOVs and data from a damaged system and a two-stage procedure is needed before monitoring: identify EOVs in training data and remove EOVs by subtraction or projection (Dervilis et al, 2014b(Dervilis et al, , 2015.…”
Section: Introductionmentioning
confidence: 99%
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