Detecting defective electrical components in heterogeneous infra-red images by spatial control charts

Abstract

Distribution network components connect machines and other loads to electrical sources. If resistance or current of any component is more than specified range, its temperature may exceed the operational limit which can cause major problems. Therefore, these defects should be found and eliminated according to their severity. Although infra-red cameras have been used for inspection of electrical components, maintenance prioritization of distribution cubicles is mostly based on personal perception and lack of training data prevents engineers from developing image processing methods. New research on the spatial control chart encouraged us to use statistical approaches instead of the pattern recognition for the image processing. In the present study, a new scanning pattern which can tolerate heavy autocorrelation among adjacent pixels within infra-red image was developed and for the first time combination of kernel smoothing, spatial control charts and local robust regression were used for finding defects within heterogeneous infra-red images of old distribution cubicles. This method does not need training data and this advantage is crucially important when the training data is not available. AimsDeveloping a new method to detect defective electrical components in the power distribution cubicles. Place and duration of studyTehran province, Iran, 2011–2013. MethodologyCombination of kernel smoothing, spatial control charts and local robust regression used for finding defects within heterogeneous infra-red image of old distribution cubicles. ResultsThis study showed that the IM-R control chart that plots forecasting residual of local robust regression and EWMA control chart with proper λ parameter with proper scan window size are powerful control charts which can be used to finding defected components in the power distribution cubicles. ConclusionIn some applications like analyzing thermal images of the old power distribution cubicles, it is not possible to train a sophisticated model like artificial neural network to identified defects. Therefore, spatial control chart that does not need training data is a valuable tool for these applications.