Open Access
Damage detection using robust fuzzy principal component analysis
Fahit Gharibnezhad,Luis Eduardo Mujica Delgado,José Rodellar Benedé,Claus-Peter Fritzen +3 more
- 01 Jan 2013
pp 1-6
TL;DR: It has been proved that the RFPCA method achieves better result mainly because it is more compressible than classical PCA and also carries more information, hence not only it can distinguish the healthy structure from the damaged structure much sharper than the traditional counterparts.
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Abstract: In this work Robust Fuzzy Principal Component Analysis (RFPCA) is used and
compared with comparing with classical Principal Component Analysis (PCA) to
detect and classify damages. It has been proved that the RFPCA method achieves
better result mainly because it is more compressible than classical PCA and also
carries more information, hence not only it can distinguish the healthy structure from
the damaged structure much sharper than the traditional counterparts but also in some
cases traditional PCA is incapable of discerning the pristine from damaged structure.
This work involves experimental results using pipe-like structure powered by a
piezoelectric actuators and sensors.
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Citations
Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications.
Diego Alexander Tibaduiza Burgos,Ricardo C. Gomez Vargas,Ricardo C. Gomez Vargas,Cesar Pedraza,David Agis,Francesc Pozo +5 more
TL;DR: This work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications, which covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures.
Application of artificial neural networks for compounding multiple damage indices in Lamb‐wave‐based damage detection
TL;DR: In this paper, the authors presented an approach to the problem of health monitoring of aircraft structures using Lamb waves, where piezoelectric sensors, embedded in the aircraft sheathing, generate Lamb waves with the aim to monitor the structural integrity of complex structure parts.
33
High-dimensional data analytics in structural health monitoring and non-destructive evaluation: a review paper
01 Mar 2022
TL;DR: In this paper , a review of high-dimensional data analytic (HDDA) methods for structural health monitoring (SHM) and non-destructive evaluation (NDE) applications is presented.
31
The Application of Data Mining Technology to Build a Forecasting Model for Classification of Road Traffic Accidents
TL;DR: The proposed model aims to probe into the environments of traffic accidents, as well as the relationships between the variables of road designs, rule-violation items, and accident types, and shows that the accuracy rate of classifiers FRPCA-BPNN and FR PCA-LR combined withFRPCA in classification prediction is better than that of BPNN and LR.
•Dissertation
Robust damage detection in smart structures
Fahit Gharibnezhad
- 07 Apr 2014
TL;DR: This thesis is devoted to present some novel techniques in Structural Health Monitoring based on different statistical and signal processing methods based on Principal Component Analysis and its robust counterpart, Wavelet Transform, Fuzzy similarity, Andrew plots, etc.
11
References
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Principal Component Analysis
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A review of structural health monitoring literature 1996-2001
Charles R. Farrar,Jerry J. Czarnecki,Hoon Sohn,François M. Hemez +3 more
- 07 Apr 2002
TL;DR: An updated review covering the years 1996 2001 will summarize the outcome of an updated review of the structural health monitoring literature, finding that although there are many more SHM studies being reported, the investigators, in general, have not yet fully embraced the well-developed tools from statistical pattern recognition.
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ROBPCA: A New Approach to Robust Principal Component Analysis
TL;DR: The ROBPCA approach, which combines projection pursuit ideas with robust scatter matrix estimation, yields more accurate estimates at noncontaminated datasets and more robust estimates at contaminated data.
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