Corrosion Detection System for Oil Pipelines Based on Multi-sensor Data Fusion by Wavelet Neural Network

A system to detect the corrosion of submarine oil pipeline is introduced, it got the original data by 3 groups ultrasonic sensors and flux leakage sensors. We made multiscale wavelet transform and frequency analysis to multichannels original data and extracted multi-attribute parameters from time domain and frequency domain, then we selected the key attribute parameters that have bigger correlativity with the corrosion degrees of oil pipeline among of multi-attribute parameters. The wavelet neural network was used to do multisensor data fusion to detect the corrosion degrees of submarine oil transportation pipelines and those key attribute parameters were used to as input vectors of network. The experimental results show that this method is feasible and effective.