Analysis of Methods for Digital Processing of Thermograms

Galagan, R.M.; Momot, A.S.

doi:10.20535/1970.55(1).2018.135849

Cited by 2 publications

(2 citation statements)

References 7 publications

(1 reference statement)

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“…The process of heating and subsequent cooling of OT is recording using a thermal imager. The resulting sequence of thermograms reflects the change in the temperature field on the OT surface in time [4]. Considering the temperature dynamics in separate points of thermograms (pixels) corresponding to the coordinates of the surface of OT, it is possible to construct a temperature profile -the dependence of temperature changes in time for the given area.…”

Section: Task Reviewmentioning

confidence: 99%

Statistical Analysis of Thermal Nondestructive Testing Data

Galagan

Momot

2019

A.I.S.

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The features of processing of active thermal nondestructive testing results are considered. Proved the necessity of search and introduction of new informative parameters in evaluation of thermograms in order to improve the reliability of control. Task of detecting and estimating the relationships between defect parameters and optimal testing time and the maximum value of temperature signal is set. Computer simulation of active thermal testing of two samples with artificial defects with known characteristics was performed. Also obtained the sequences of thermograms and formed the sets of initial data during simulation for correlation, regression and dispersion analysis of testing results. The method of dynamic thermal tomography was used to determine the levels of maximum differential temperature signal and optimal testing time. The estimates of correlation coefficient for various informational parameters of thermal control obtained. There is a high level of relations between the optimal control time and depth of defects. A high correlation also observed between the maximum value of temperature signal and depth of defects. The nature of relationships between various informative parameters of active thermal control established by the regression analysis. A one-factor dispersion analysis of the influence of defect parameters on optimal testing time and maximum value of the temperature signal was performed. High degree of mutual influence of all informative parameters is established. The conclusion made on the necessity of developing new modern methods for analysis the data of thermal testing. Revealed patterns in relationships between data show low efficiency of traditional statistical methods in tasks of active thermal testing. Alternatively, proposed to use the artificial intelligence technologies, in particular, neural networks.

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Section: Task Reviewmentioning

confidence: 99%

Statistical Analysis of Thermal Nondestructive Testing Data

Galagan

Momot

2019

A.I.S.

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“…The use of artificial neural networks in thermal testing is also promising [3,4]. A comparative analysis of different methods of thermogram processing was made in [5] and some recommendations are given. However, still an important issue is the assessment of efficiency and the choice of the most optimal and versatile modern method of thermogram processing.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Application of Neural Networks to Improve the Reliability of Active Thermal NDT

Galagan

Momot

2019

KPI SN

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Background. The relevant question of increasing the informative content and reliability of the thermal non-destructive testing is considered in this article. The most promising algorithms of digital processing of sequences of thermograms are given. Objective. The main aim of this research is to determine the advantages and disadvantages of the application of each considered method of digital processing of thermograms. Secondary, the possibilities of testing automation with the use of the selected methods of digital processing of thermograms are analyzed in this article. Methods. Computer simulation software was used to obtain the artificial sequence of the thermograms. Methods of wavelet analysis, principal components analysis and neural networks were used to process the received data. Results. The simulation of active thermal testing process is carried out in this research. The artificial thermogram sequence with a high level of noise is obtained for the object of testing. In order to quantify the results of application of considered methods, relative errors of determining the area of defects were calculated. Also values of Tanimoto criterion are obtained. The advantages of the neural network processing of digital data in thermal non-destructive testing have been established and proved in this article. Shape of defects on a binary map built by the neural network was closest to true compared with principal components analysis method. The effectiveness of neural networks is also confirmed by quantitative estimates. Conclusions. The method of wavelet transformation has a high sensitivity. This method is ineffective in the conditions of uneven heating and high noise. The principal components analysis method allows increasing the SNR and improving the visual perception of thermograms, but does not provide complete separation of information about defects and noises caused by uneven heating. Methods of artificial neural networks theory provide the best reproduction of the shape and size of the defects, but the training process requires significant time and computing resources.

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Analysis of Methods for Digital Processing of Thermograms

Cited by 2 publications

References 7 publications

Statistical Analysis of Thermal Nondestructive Testing Data

Statistical Analysis of Thermal Nondestructive Testing Data

Analysis of Application of Neural Networks to Improve the Reliability of Active Thermal NDT

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