Classification of thermally condition-monitored components using statistical and neural network techniques

Details Classification of thermally condition-monitored components using statistical and neural network techniques Classification of thermally condition-monitored components using statistical and neural network techniques

Oct 1997

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997spie.3164..573m&link_type=abstract

Proc. SPIE Vol. 3164, p. 573-581, Applications of Digital Image Processing XX, Andrew G. Tescher; Ed.

Statistics

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Scientific paper

A popular approach to the qualitative analysis of thermal patterns has been to identify anomalies through comparison of thermal images against a single baseline or reference image. However, this approach represents an oversimplification as significant variations of thermal patterns due to change in measurement position, changing equipment loading, environmental conditions and varying mechanisms of equipment deterioration are not catered for. To overcome these limitations, the use of neural net and statistically based classifiers has been investigated, in the latter case for both parametric and non parametric designs. An experimental thermal image database characterizing normal and abnormal load tap-changer operation of a 63 MVA, 22kV transformer provided the training data. The images were captured at different times, different locations and under varying loads.

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