Research on ultrasonic defect identification method of well control manifold pipeline based on IAFSA-SVM

• Perform wavelet packet decomposition on the ultrasonic defect signal waveform to obtain the energy of each sub-band and the total energy of the signal. • The improved artificial fish swarm algorithm uses particle swarm optimization algorithm to reduce the influence of step size factor in artificial fish swarm algorithm, and introduces chaos mechanism to initialize fish swarm. • Use the improved artificial fish swarm algorithm to optimize the support vector machine parameters. • Experimental result demonstrate good accuracy of the method. In view of the classification of corrosion defects of well controlled manifold pipelines, an ultrasonic defect recognition method based on the combination of support vector machine(SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed. Firstly, perform wavelet packet decomposition on the ultrasonic defect signal waveform to obtain the characteristic vector of characterizes the defect type; Then establish the support vector machine defect classification model, and use the improved artificial fish swarm algorithm to optimize the support vector machine parameters. Finally, a software and hardware experimental platform for the classification of pipeline corrosion defects of the well control manifold is built to carry out software simulation and experimental analysis. The experimental results show that the recognition rate of the defect classification model based on improved artificial fish swarm optimization support vector machine parameters is 94.67% for ultrasonic defect signals at different depths.