Non-Destructive Testing of Baked Anodes Based on Modal Analysis and Principal Component Analysis

2017 ◽  
pp. 1289-1298 ◽  
Author(s):  
Moez Ben Boubaker ◽  
Donald Picard ◽  
Carl Duchesne ◽  
Jayson Tessier ◽  
Houshang Alamdari ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Modal Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive

scholarly journals Comparison of Image Analysis Methods on the Example of Ultrasonic Thermography of an Aramid Composite

2019 ◽  
Vol 26 (2) ◽  
pp. 145-150
Author(s):  
Waldemar Świderski
Keyword(s):  
Principal Component Analysis ◽  
Image Analysis ◽  
Wavelet Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Thermal Excitation ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
The Impact ◽  
Non Destructive

Abstract Infrared thermography using ultrasound thermal excitation of the tested material is one of the most effective methods in non-destructive testing of a multi-layer aramid composite. This type of material is very popular in the construction of light ballistic armours. Typical defects are delamination between layers of aramid fabric joined by resin. They are usually filled with air. Delamination located deep under the surface of the test generates very weak temperature signals. They are often at the level of noise. To reduce the impact of noise on the detection of a defect, special methods of image analysis (thermograms) are used. Such methods include principal component analysis and wavelet analysis. Principal Component Analysis is a relatively new procedure of statistical data treatment, which is becoming increasingly popular in non-destructive testing. Mathematically, it is often regarded as implementation of the so-called singular values decomposition technique, which allows extracting of spatial information from a matrix of source data. The wavelet analysis is an integral transform, which represents the convolution of an analysed process with a special mother function called wavelet. Wavelets are characterized by two parameters: scale and shift. The paper presents a comparison of the efficacy of these methods in the detection of defects in the multilayer composite reinforced aramid fibre.

Review on Non-Destructive Testing Technique of Eddy Current Pulsed Thermography

2015 ◽  
Vol 742 ◽  
pp. 128-131 ◽  
Author(s):  
Jian Min Zhou ◽  
Jun Yang ◽  
Qi Wan
Keyword(s):  
Eddy Current ◽  
Signal Reconstruction ◽  
Infrared Image ◽  
Principal Component ◽  
Research Progress ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Pulsed Thermography ◽  
Pulsed Phase Thermography ◽  
Non Destructive

This paper introduces the theory of eddy current pulsed thermography and expounds the research status of eddy current pulsed thermography in application and information extraction. Thermographic signal reconstruction, pulsed phase thermography, principal component analysis were introuduced in this paper and listed some fusion multiple methods to acquire information from infrared image. At last, it summarizes research progress, existing problem and deelopment of eddy current pulsed thermography.

Microsis: The Method and Equipment for Non‐Destructive Testing of Foundations and Concrete Constructions Based on Modal Analysis

2002 ◽  
Author(s):  
France Goupil ◽  
Boris Konioukhov ◽  
Jean‐Luc Arsenault ◽  
Réjean Paul ◽  
Yannick Machabée
Keyword(s):  
Modal Analysis ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive

scholarly journals Infrared Non-Destructive Testing via Semi-Nonnegative Matrix Factorization

Proceedings ◽  
2019 ◽  
Vol 27 (1) ◽  
pp. 13 ◽  
Author(s):  
Yousefi ◽  
Ibarra-Castanedo ◽  
Maldague
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix ◽  
Principal Component ◽  
Computational Time ◽  
Detection Accuracy ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive ◽  
Non Negative Matrix Factorization ◽  
Subsurface Defects

Detection of subsurface defects is undeniably a growing subfield of infrared non-destructive testing (IR-NDT). There are many algorithms used for this purpose, where non-negative matrix factorization (NMF) is considered to be an interesting alternative to principal component analysis (PCA) by having no negative basis in matrix decomposition. Here, an application of Semi non-negative matrix factorization (Semi-NMF) in IR-NDT is presented to determine the subsurface defects of an Aluminum plate specimen through active thermographic method. To benchmark, the defect detection accuracy and computational load of the Semi-NMF approach is compared to state-of-the-art thermography processing approaches such as: principal component thermography (PCT), Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT), Sparse PCT, Sparse NMF and standard NMF with gradient descend (GD) and non-negative least square (NNLS). The results show 86% accuracy for 27.5s computational time for SemiNMF, which conclusively indicate the promising performance of the approach in the field of IR-NDT.

Physical insights into principal component thermography

2020 ◽  
Vol 62 (5) ◽  
pp. 277-280 ◽  
Author(s):  
K Kaur ◽  
A Sharma ◽  
A Rani ◽  
V Kher ◽  
R Mulaveesala
Keyword(s):  
Principal Component ◽  
Steel Specimen ◽  
Vital Role ◽  
Empirical Orthogonal Functions ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Orthogonal Functions ◽  
Pulsed Thermography ◽  
Physical Insight ◽  
Non Destructive

Among widely used non-destructive testing (NDT) methods, infrared thermography (IRT) has gained importance due to its fast, whole-field, remote and quantitative inspection capabilities for the evaluation of various materials. Being fast and easy to implement, pulsed thermography (PT) plays a vital role in the infrared thermographic community. This paper provides a physical insight into the selection of empirical orthogonal functions obtained from principal component pulsed thermography for the detection of subsurface defects located inside a mild steel specimen.

scholarly journals PENERAPAN ALGORITMA DISKRIMINASI MENGGUNAKAN METODE PRINCIPAL COMPONENT ANALYSIS (PCA) DAN Vis-SWNIR SPECTROSCOPY PADA BUAH CABAI RAWIT DOMBA BERBAGAI TINGKAT KEMATANGAN

2021 ◽  
Vol 4 (1) ◽  
pp. 40-46
Author(s):  
Ine Elisa Putri ◽  
Kusumiyati Kusumiyati ◽  
Agus Arip Munawar
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Absorbance Spectrum ◽  
Pepper Fruit ◽  
Destructive Measurement ◽  
Non Destructive ◽  
Destructive Methods ◽  
Spectrum Data

Cayenne pepper fruit can be used for health because it is a source of antioxidants. Detection of quality fruit can use non-destructive methods as an alternative method. Visible short wavelength near infrared (Vis-SWNIR) spectroscopy is non-destructive measurement. This method can be used to discriminate fruit by using the principal component analysis (PCA). This research aimed to discriminate between Cayenne pepper with various maturity by using Vis-SWNIR spectroscopy with a wavelength of 300-1065 nm and principal component analysis (PCA). Cayenne pepper fruit was devided into three groups, namely green, orange and red. The spectrum used the absorbance spectrum data (original). The research was carried out from March to June 2020. The result showed that the use of Vis-SWNIR and PCA were able to discriminate various maturity of cayenne pepper with a 100% success rate.

scholarly journals An online and real-time adaptive operational modal parameter identification method based on fog computing in Internet of Things

2020 ◽  
Vol 16 (2) ◽  
pp. 155014772090361 ◽  
Author(s):  
Cheng Wang ◽  
Haiyang Huang ◽  
Jianwei Chen ◽  
Wei Wei ◽  
Tian Wang
Keyword(s):  
Principal Component Analysis ◽  
Internet Of Things ◽  
Modal Analysis ◽  
Real Time ◽  
Fog Computing ◽  
Principal Component ◽  
Component Analysis ◽  
Operational Modal Analysis ◽  
Smart Devices ◽  
Analysis Method

A large number of smart devices make the Internet of Things world smarter. However, currently cloud computing cannot satisfy real-time requirements and fog computing is a promising technique for real-time processing. Operational modal analysis obtains modal parameters that reflect the dynamic properties of the structure from the vibration response signals. In Internet of Things, the operational modal analysis method can be embedded in the smart devices to achieve structural health monitoring and fault detection. In this article, a four-layer framework for combining fog computing and operational modal analysis in Internet of Things is designed. This four-layer framework introduces fog computing to solve tasks that cloud computing cannot handle in real time. Moreover, to reduce the time and space complexity of the operational modal analysis algorithm and support the real-time performance of fog computing, a limited memory eigenvector recursive principal component analysis–based operational modal analysis approach is proposed. In addition, by examining the cumulative percent variance of principal component analysis, this article explains the reasons behind the identified modal order exchange. Finally, the time-varying operational modal identification results from non-stationary random response signals of a cantilever beam whose density changes slowly indicate that the limited memory eigenvector recursive principal component analysis–based operational modal analysis method requires less memory and runtime and has higher stability and identification effect.

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