Abstract

The study considers contemporary approaches used in the analysis of infrared thermography results of a test object aimed at identifying structural features for defect map formation. Emphasis is placed on the advantages of employing statistical analysis methods in thermogram analysis, involving the quantitative assessment of signal-to-noise ratio and optimizing the thermographic analysis procedure through global extremum search of the objective function. According to the developed methodology, a set of four possible outcomes resulting from the thermographic analysis of a test object is examined, including true positive, false positive, true negative, and false negative results. The analysis of thermographic procedure results for the detection of a potential defect in an individual thermogram element is determined through the probability distribution function of temperature, which is compared with a similar function for a homogeneous section of the test object. In the basic methodology, probability distribution parameters for a homogeneous section of the test object and a section with structural features differ in terms of mean and variance. By dividing the temperature probability distribution area with a threshold value, four zones are formed: a zone where a defect is guaranteed to be absent, a zone where a defect is not detected according to the chosen threshold value, a zone where a defect is detected according to the chosen threshold value, and a zone where a defect is guaranteed to be present. The basic approach, basedon statistical methods, allows determining the accuracy of thermographic analysis for an individual thermogram element by calculating the signal-to-noise ratio based on the fundamental indicators of mean and variance of the temperature probability distribution. Within the extended scheme of statistical analysis of thermographic investigation results, a z-value is determined, based on the number of neighboring thermogram elements corresponding to a homogeneous section and a section with potential structural features.

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