Principal component analysis in non-destructive testing by an ultrasonic thermography method of multi-layered aramid composite

2019 ◽  
Author(s):  
Waldemar Swiderski ◽  
Monika Pracht
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive ◽  
Ultrasonic Thermography

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.

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 A Study on the Detection of Internal Defect Types for Duct Depth of Prestressed Concrete Structures Using Electromagnetic and Elastic Waves

Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 3931
Author(s):  
Young-Geun Yoon ◽  
Jae-Yun Lee ◽  
Hajin Choi ◽  
Tae-Keun Oh
Keyword(s):  
Prestressed Concrete ◽  
Elastic Waves ◽  
Principal Component ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Flexural Mode ◽  
Impact Echo ◽  
Multichannel Analysis ◽  
Non Destructive

Prestressed concrete (PSC) is widely used for the construction of bridges. The collapse of several bridges with PSC has been reported, and insufficient grout and tendon corrosion were found inside the ducts of these bridges. Therefore, non-destructive testing (NDT) technology is important for identifying defects inside ducts in PSC structures. Electromagnetic (EM) waves have limited detection of internal defects in ducts due to strong reflections from the surface of the steel ducts. Spectral analysis of the existing impact echo (IE) method is limited to specific conditions. Moreover, the flexural mode in upper defects of ducts located at a shallow depth and delamination defects inside ducts are not considered. In this study, the applicability of the elastic wave of IE was analyzed, and multichannel analysis of surface, EM, and shear waves was employed to evaluate six types of PSC structures. A procedure using EM waves, IE, and principal component analysis (PCA) was proposed for a more accurate classification of defect types inside ducts. The proposed procedure was effective in classifying upper, internal, and delamination defects of ducts under 100 mm in thickness, and it could be utilized up to 200 mm in the case of duct defect limitations.

scholarly journals Non-Destructive Classification of Fruits Based on Vis-nir Spectroscopy and Principal Component Analysis

2019 ◽  
Vol 4 (1) ◽  
pp. 89-95 ◽  
Author(s):  
Kusumiyati Kusumiyati ◽  
Yuda Hadiwijaya ◽  
Ine Elisa Putri
Keyword(s):  
Principal Component Analysis ◽  
Fruit Quality ◽  
Near Infrared ◽  
Chemical Properties ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Internal Quality ◽  
Fruit Samples ◽  
Non Destructive

Fruits are one of the sources of nutrition needed for health. Fruit quality is generally assessed by physical and chemical properties. Measurement of fruit internal quality is usually done by destructive techniques. Ultraviolet, visible and near-infrared (UV-Vis-NIR) spec-troscopy is a non-destructive technique to measure fruit quality. This technique can rapidly measure the fruit quality, the measured fruit still remains intact, and can be marketed. Besides, UV-Vis-NIR spectrosco-py can also be used to classify fruits. The study aimed to classify var-ious types of fruits using UV-Vis-NIR spectroscopy with wavelengths of 300-1041 nm and Principal Component Analysis (PCA). First de-rivative savitzky-golay with 9 smoothing points (dg1) and multiplica-tive scatter correction (MSC) were applied to correct the spectra. The results showed that the use of uv-vis-nir spectroscopy and PCA com-bined with spectra pre-treatment of the MSC method were able to clas-sify various types of fruits with 100% success rate in all fruit samples including sapodilla, ridge gourd, mango, guava, apple and zucchini. 

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