Image Processing Techniques to Automate Quantitative Thermography Diagnostics for the Efficient Use of Electric Motors

Abstract

A practical and non-invasive method of calculating the efficiency of electric motors could help reduce anthropogenic green house gas emissions by up to 6%. Such a method has been developed using quantitative thermography, however currently, the time required for its implementation is prohibitive. In this thesis, registration and segmentation techniques are applied to the thermograms of the above method, particularly thermograms used in the lumped capacitance method (LCM) and those used to find the average temperature of motors, reducing the time required to process thermograms. The processing of LCM thermograms was completely automated (±5% difference when compared to results obtained manually) while processing of motor thermograms required the location of the motor be provided manually the first time a motor is examined, but was completely automated for subsequent thermo- grams of the same motor (±0.9◦C and ±0.6◦C difference for non-occluded and occluded motors respectively compared to manual results).