4.6 Article Proceedings Paper

A Contactless Insulator Contamination Levels Detecting Method Based on Infrared Images Features and RBFNN

Journal

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
Volume 55, Issue 3, Pages 2455-2463

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2018.2889835

Keywords

Color moment features; contamination levels identification; gradient descent algorithm; hidden center parameters; image processing; infrared image; insulators; radial basis function neural network (RBFNN); random number control factor

Funding

  1. Natural Science Foundation of Hunan Province, China

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A contactless method uses infrared image features and radial basis function neural network (RBFNN) to detect contamination levels for porcelain insulators is proposed in this paper. First, theory evidence for contamination levels detection by infrared images is inferred. Then, the denoising and image segmentation is implemented to suppress the image noise and eliminate the affection of the background. Nine color moment features related to the contamination levels are extracted from the insulator images. Finally, an RBFNN is constructed to identify the contamination levels. The images features and ambient relative humidity are taken as the inputs of the RBFNN. For improve the precision of the detection, a new method based on the statistical probability of the values of each contamination feature component is proposed to select the initial hidden center parameters for RBFNN hidden nodes. An improved learning algorithm combined with the gradient descent algorithm and a random number control factor are proposed to modify the hidden center parameters and the weights vectors. Testing results show that the selected color moment features are effective on the contamination levels representation and the constructed RBFNN performs better than back propagation neural network (BPNN) and generalized regression neural network (GRNN) on contamination levels identification.

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