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Temperature measurement by infrared thermography is a powerful technique for predictive maintenance. To make accurate diagnoses, errors associated with temperature measurement results should be minimized because the diagnosis is obtained by applying temperature difference criteria in consecutive measurements or in similar equipment. Temperature measurement by infrared thermography is susceptible to errors caused by many things. We study and assess the effectiveness obtained in use of experimental models for error correction in temperature measurement by thermal imagers due to the influence of the field of view of the imager's lens, combined with varying viewing angles between the analyzed object and the imager. The results indicate that these factors can introduce errors that compromise the diagnosis. Experimental models based on artificial neural network and on regression were developed to correct such errors.
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Muniz et al. (2013) studied this question.
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