Abstract
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.
Highlights
Thermal non-destructive testing (TNDT) is widely used in various fields of industry due to its contactlessness, high performance and effectiveness
This study focuses on the technique of thermogram processing and describing the process of conducting research using neural networks and comparing the results with the wavelet analysis method and the principal components analysis (PCA) method, since previous studies have demonstrated their advantages over other methods of thermograms processing
This study indicates that the shape of signals in thermal testing is smoothed, whereas wavelet analysis is used mainly in the tasks of detecting and processing short-term radio pulses
Summary
Thermal non-destructive testing (TNDT) is widely used in various fields of industry due to its contactlessness, high performance and effectiveness. The results are influenced by parameters of the infrared radiation detector, preferences of testing, external conditions, thermophysical characteristics of the object. In this regard, thermal images are characterized by high levels of noise. The artificial thermogram sequence with a high level of noise is obtained for the object of testing. The advantages of the neural network processing of digital data in thermal non-destructive testing have been established and proved in this article. 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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