DAMAGE DETECTION USING OUTLIER ANALYSIS

By Keith Worden, G. Manson, N. Fieller. 2000

646 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b94d455e9db2983e4bde320a4aa48910b02cba77

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In Civil and Structural Engineering

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Citations in the corpus

Damage identification using inverse methods (2007, methodology): “performed by pattern recognition methods or novelty detection (Worden, 1997; Worden et al., 2000).”
Sensor validation using principal component analysis (2005, background): “When dealing with the current data set, an alarm is issued when the monitored angle exceeds the upper control limit (UCL) defined as the mean angle plus three times its standard deviation (see outlier statistics [26, 27]).”
Effects of environmental and operational variability on structural health monitoring (2007, methodology): “That is, a simple damage classifier such as outlier analysis (Worden 1997; Worden et al. 2000) can be employed for deciding whether measurements from a system or structure indicate significant departure from the previously established normal conditions.”
Localized Damage Detection of Structures Subject to Multiple Ambient Excitations Using Two Distance Measures for Autoregressive Models (2009, methodology): “[8] considered statistical process control approach to damage detection, using the mean and variance of the residuals of the AR model to form the statistical process control charts.”
Structural Health Monitoring Framework Based on Internet of Things: A Survey (2017, methodology): “These algorithms include novelty detection [41], [42], classiﬁcation [43], and regression [44] approaches.”
A comparison of linear approaches to filter out environmental effects in structural health monitoring (2018, methodology): “A MonteCarlo simulation is used, summarised as follows [7]:”
Features for damage detection with insensitivity to environmental and operational variations (2012, methodology): “It is well know that outlier analysis is a very useful tool for novelty detection in SHM (Worden et al. 2000).”
Emerging artificial intelligence methods in structural engineering (2018, methodology): “[80] adopted the methods of outlier analysis to the problem of damage detection, for which they used the Mahalanobis distance in order to detect damage.”
A review of vibration-based structural health monitoring with special emphasis on composite materials (2006, background): “other indicators, such as temperature, pressure, flow or tribology features (Wowk (1991)).”
A comparative evaluation of outlier detection algorithms: Experiments and analyses (2018, methodology): “These techniques have also been successfully applied to fault detection on critical systems and result in improved damage control and component failure prediction [31].”
Materials that couple sensing, actuation, computation, and communication (2015, methodology): “In [172], the presence of delamination in composite plates was detected using MSD when a ultrasonic sensor was inline within 20mm of the delamination.”