Study on Corrosion Prediction Method Based on CO_2 Corrosive Morphology

The grey level data matrix statistics,wavelet transform and binarization were employed to extract surface morphology information from oil pipelines corroded by carbon dioxide.Using binary image extraction algorithm and pixel set,the pitting area expressed by numbers of pixels was obtained accurately,including the pitting number.The characteristic value of grey level data matrix statistics was taken to reflect the complexities of pitting surface.Having multiplayer feed forward neural networks considered,a pitting velocity diagnosis model was developed based on two corrosion type criterions,which including the anisotropic energy parameter of corrosion images and the image energy parameter after wavelet transform.The diagnosis results agree well with the testing results.