Robust methods of inclusive outlier analysis for structural health monitoring

Dervilis, Nikolaos; Cross, Elizabeth J.; Barthorpe, R. J.; Worden, Keith

doi:10.1016/j.jsv.2014.05.012

Cited by 56 publications

(70 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

2017

View full text Add to dashboard Cite

Abstract:Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature.

show abstract

Section: Advancements To Machine Learning Methodologies For Damage Inmentioning

confidence: 99%

mentioning

confidence: 99%

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

2017

View full text Add to dashboard Cite

show abstract

“…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%

“…Furthermore, a combined approach of robust regression and robust multivariate statistics can be exploited as a means of characterising and distinguishing the influence of environmental and operational conditions on the structural response. It will be shown via the successful implementation of robust regression analysis that environmental and operational conditions can be made to manifest themselves differently compared to the damage condition (Dervilis et al, 2014b(Dervilis et al, , 2015. This paper will discuss the performance of robust outlier and regression analysis for SHM purposes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust methods for outlier detection and regression for SHM applications

Dervilis

Antoniadou

Barthorpe

et al. 2015

IJSMSS

View full text Add to dashboard Cite

Abstract:In this paper, robust statistical methods are presented for the data-based approach to structural health monitoring (SHM). The discussion initially focuses on the high level removal of the 'masking effect' of inclusive outliers. Multiple outliers commonly occur when novelty detection in the form of unsupervised learning is utilised as a means of damage diagnosis; then benign variations in the operating or environmental conditions of the structure must be handled very carefully, as it is possible that they can lead to false alarms. It is shown that recent developments in the field of robust regression can provide a means of exploring and visualising SHM data as a tool for exploring the different characteristics of outliers, and removing the effects of benign variations. The paper is not, in any sense, a survey; it is an overview and summary of recent work by the authors.

show abstract

Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring

Jagadeeshwar

Kanakambaran

Kalyani

et al. 2021

Struct Control Health Monit

View full text Add to dashboard Cite

Summary We propose an outlier detection‐based statistical approach to identify and locate a defect in composite plates using far fewer number of sensing points compared to conventional imaging techniques. The key steps involved in this computationally inexpensive approach are the random sparse selection of the sensing points through Poisson disk sampling, followed by a two‐step outlier detection process based on thresholding and computation of median absolute deviation. The robustness of the proposed technique is explored through extensive simulations involving different defect sizes, random locations on flat plate structures, and various values of signal to noise ratio (SNR). We experimentally demonstrate the feasibility of detection of delamination, whose size is comparable to the ultrasonic wavelength with probability of detection (PoD) better than 90% using <1% of the total number of samples required for conventional imaging, even under conditions wherein the SNR is as low as 5 dB.

show abstract

Robust methods of inclusive outlier analysis for structural health monitoring

Cited by 56 publications

References 25 publications

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

Robust methods for outlier detection and regression for SHM applications

Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring

Contact Info

Product

Resources

About