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

Nathan T. Moja; Andrew J. Willis

doi:10.1117/12.279582

30 October 1997 Classification of thermally condition-monitored components using statistical and neural network techniques

Nathan T. Moja, Andrew J. Willis

Author Affiliations +

Proceedings Volume 3164, Applications of Digital Image Processing XX; (1997) https://doi.org/10.1117/12.279582
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

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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Nathan T. Moja and Andrew J. Willis "Classification of thermally condition-monitored components using statistical and neural network techniques", Proc. SPIE 3164, Applications of Digital Image Processing XX, (30 October 1997); https://doi.org/10.1117/12.279582

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