Research on Detecting Method of Submarine Oil Pipelines Corrosion Degree Based on Chaos Genetic Algorithm Neural Network

Considering the issues that the relationship between the submarine oil pipelines corrosion degree and detection information of sensor is a complicated and nonlinear and the chaos genetic algorithm neural network has the ability of strong nonlinear function approach and the feature of global optimization, in this paper, a detection method of submarine oil pipelines corrosion degree based on chaos genetic algorithm neural network is presented. It got the original data by sensors. We made multiscale wavelet transform and frequency analysis to multichannels original data, extracted and selected the key attribute parameters. We construct the structure of chaos genetic algorithm neural network that used for the corrosion degree detection of oil pipelines. The method can truly detect the corrosion degree of oil pipelines by learning the detection information of sensors. The detection results show that this method is feasible and effective.