Damage detection in nonlinear civil structures using kernel principal component analysis

2020 ◽  
Vol 23 (11) ◽  
pp. 2414-2430
Author(s):  
Khaoula Ghoulem ◽  
Tarek Kormi ◽  
Nizar Bel Hadj Ali
Keyword(s):  
Principal Component Analysis ◽  
Damage Detection ◽  
Structural Model ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Correlated Data ◽  
Data Driven ◽  
Kernel Principal Component Analysis ◽  
Structural Systems

In the general framework of data-driven structural health monitoring, principal component analysis has been applied successfully in continuous monitoring of complex civil infrastructures. In the case of linear or polynomial relationship between monitored variables, principal component analysis allows generation of structured residuals from measurement outputs without a priori structural model. The principal component analysis has been widely used for system monitoring based on its ability to handle high-dimensional, noisy, and highly correlated data by projecting the data onto a lower dimensional subspace that contains most of the variance of the original data. However, for nonlinear systems, it could be easily demonstrated that linear principal component analysis is unable to disclose nonlinear relationships between variables. This has naturally motivated various developments of nonlinear principal component analysis to tackle damage diagnosis of complex structural systems, especially those characterized by a nonlinear behavior. In this article, a data-driven technique for damage detection in nonlinear structural systems is presented. The proposed method is based on kernel principal component analysis. Two case studies involving nonlinear cable structures are presented to show the effectiveness of the proposed methodology. The validity of the kernel principal component analysis–based monitoring technique is shown in terms of the ability to damage detection. Robustness to environmental effects and disturbances are also studied.

scholarly journals Visualization of classified data with kernel principal component analysis

2018 ◽  
Author(s):  
Toni Bakhtiar
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Feature Space ◽  
Original Data ◽  
Component Analysis ◽  
Kernel Functions ◽  
Kernel Principal Component Analysis ◽  
Gaussian Kernel ◽  
Kernel Pca ◽  
Study Results

Kernel Principal Component Analysis (Kernel PCA) is a generalization of the ordinary PCA which allows mapping the original data into a high-dimensional feature space. The mapping is expected to address the issues of nonlinearity among variables and separation among classes in the original data space. The key problem in the use of kernel PCA is the parameter estimation used in kernel functions that so far has not had quite obvious guidance, where the parameter selection mainly depends on the objectivity of the research. This study exploited the use of Gaussian kernel function and focused on the ability of kernel PCA in visualizing the separation of the classified data. Assessments were undertaken based on misclassification obtained by Fisher Discriminant Linear Analysis of the first two principal components. This study results suggest for the visualization of kernel PCA by selecting the parameter in the interval between the closest and the furthest distances among the objects of original data is better than that of ordinary PCA.

Damage Detection Using Principal Component Analysis Based on Wavelet Ridges

2013 ◽  
Vol 569-570 ◽  
pp. 916-923 ◽  
Author(s):  
Fahit Gharibnezhad ◽  
Luis Eduardo Mujica ◽  
José Rodellar ◽  
Claus Peter Fritzen
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Transform ◽  
Statistical Analysis ◽  
Damage Detection ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Vital Role ◽  
Sensors And Actuators ◽  
Aluminum Beam

Principal Component Analysis (PCA) and Wavelet Transform (WT) aretwo well-known signal processing tools that are widely used indifferent fields. PCA playsa vital role in statistical analysis as a dimensional reduction tool. Besides, WT has proven its abilityto overcome many of the limitation of the others among various time-frequencyanalyzers. The present work attempts to use the properties and advantagesof both methodologies together in damage detection. To achieve thisaim, PCA is applied on ridges of wavelet transform of measured signalsfrom the structure. The results show that the proposed combination improvesthe accuracy of detection comparing with PCA damage detection basedon original data captured from sensors. According to the result, when PCA uses the ridges of transformed data, theidentifications of damages are more clear and accurate. This work involvesexperiments with an aluminum beam using piezoelectrictransducers as sensors and actuators. Damages are introduced intothe structure as a cut in several steps enlarging the depthof cut.

scholarly journals Kernel Principal Component Analysis for Structural Health Monitoring and Damage Detection of an Engineering Structure Under Operational Loading Variations

2021 ◽  
Author(s):  
Sharafiz Abdul Rahim ◽  
Graeme Manson
Keyword(s):  
Principal Component Analysis ◽  
Damage Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Baseline Data ◽  
Kernel Principal Component Analysis ◽  
Aircraft Wing ◽  
Data Set ◽  
Loading Effects ◽  
Wing Box

AbstractThis paper highlights kernel principal component analysis (KPCA) in distinguishing damage-sensitive features from the effects of liquid loading on frequency response. A vibration test is performed on an aircraft wing box incorporated with a liquid tank that undergoes various tank loading. Such experiment is established as a preliminary study of an aircraft wing that undergoes operational load change in a fuel tank. The operational loading effects in a mechanical system can lead to a false alarm as loading and damage effects produce a similar reduction in the vibration response. This study proposes a non-nonlinear transformation to separate loading effects from damage-sensitive features. Based on a baseline data set built from a healthy structure that undergoes systematic tank loading, the Gaussian parameter is measured based on the distance of the baseline data set to various damage states. As a result, both loading and damage features expand and are distinguished better. For novelty damage detection, Mahalanobis square distance (MSD) and Monte Carlo-based threshold are applied. The main contribution of this project is the nonlinear PCA projection to understand the dynamic behavior of the wing box under damage and loading influences and to differentiate both effects that arise from the tank loading and damage severities.

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

2020 ◽  
Author(s):  
Xin Yi See ◽  
Benjamin Reiner ◽  
Xuelan Wen ◽  
T. Alexander Wheeler ◽  
Channing Klein ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
De Novo ◽  
Principal Component ◽  
Component Analysis ◽  
Catalyst Design ◽  
Data Driven ◽  
Initial Reaction ◽  
Training Set ◽  
Reaction Conditions ◽  
Component Loadings

<div> <div> <div> <p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. </p> </div> </div> </div>

scholarly journals Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach

2021 ◽  
Vol 11 (14) ◽  
pp. 6370
Author(s):  
Elena Quatrini ◽  
Francesco Costantino ◽  
David Mba ◽  
Xiaochuan Li ◽  
Tat-Hean Gan
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Water Purification ◽  
Principal Component ◽  
Temporal Correlation ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Original Dataset ◽  
Monitoring Process ◽  
Machine Health

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.

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