• Corpus ID: 8825240

Visualisation and dimension reduction of high-dimensional data for damage detection

  title={Visualisation and dimension reduction of high-dimensional data for damage detection},
  author={Keith Worden and Graeme Manson},
Important developments have occurred recently in the field of damage identification as a result of the import of numerous techniques from the disciplines of multivariate statistics and pattern recognition. One problem in the application of these methods is the curse of dimensionality, which can complicate and sometimes invalidate the use of certain techniques if the data under examination has too high a dimension. The object of this paper is to illustrate the use of some established methods of… 

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This paper introduces a new approach to detect structural damage using independent component analysis combined with a k-mean clustering algorithm. Starting with the vibration history measurements for

A Review of Vibration Based Inverse Methods for Damage Detection and Identification in Mechanical Structures Using Optimization Algorithms and ANN

The Structural Health Monitoring (SHM) technique is today the principle approach to manage the discovery and recognizable proof of damage in the most various designing areas. The need to monitor

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A novel structural damage assessment technique based on the principal component analysis (PCA) and the flexibility matrix approach is proposed in this paper. The technique is a model free method and

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From the point of view of structural health monitoring, it is extremely important to discriminate alteration in structural behavior/response attribute due to damage from that due to environmental and

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A robust-SHM methodology that combines three technologies to manage uncertainty and examine how the uncertainty space changes in time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator is proposed.




The concept of discordancy from the statistical discipline of outlier analysis is used to signal deviance from the norm in a statistical method for damage detection.

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This report contains a review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural

A Nonlinear Mapping for Data Structure Analysis

  • J. Sammon
  • Mathematics, Computer Science
    IEEE Transactions on Computers
  • 1969
An algorithm for the analysis of multivariate data is presented along with some experimental results. The algorithm is based upon a point mapping of N L-dimensional vectors from the L-space to a

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An alternative approach is explored in which a description of normality is attempted using the large number of available mammograms which do not show any evidence of mass-like structures to try and identify candidate masses in previously unseen images analysis and interpretation.

Nonlinear principal component analysis using autoassociative neural networks

The NLPCA method is demonstrated using time-dependent, simulated batch reaction data and shows that it successfully reduces dimensionality and produces a feature space map resembling the actual distribution of the underlying system parameters.

Multivariate Density Estimation, Theory, Practice and Visualization

Representation and Geometry of Multivariate Data. Nonparametric Estimation Criteria. Histograms: Theory and Practice. Frequency Polygons. Averaged Shifted Histograms. Kernel Density Estimators. The

Novelty detection and neural network validation

This paper provides a quantitative procedure for measuring novelty, and its performance is demonstrated using an application involving the monitoring of oil flow in multi-phase pipelines.


The method of novelty detection is applied to diagnose damage in a simple simulated lumped-parameter mechanical system. It is shown that the system transmissibility provides a sensitive feature for

Neural networks for pattern recognition

This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.