Real-time online inversion of GA-PSO-BP flux leakage defects based on information fusion: Numerical simulation and experimental research

Currently, defect inversion is always a difficult problem in magnetic flux leakage (MFL) detection, and all kinds of algorithms cannot solve this problem effectively. Back propagation (BP) neural network is widely used in the reconstruction of MFL. However, BP neural network has problems such as slow training speed, low recognition accuracy, and easy to fall into local minima. In this study, an information fusion method combining fuzzy set theory and neural network is studied to eliminate outliers caused by vibration, so as to improve the reliability of data in vibration environment. And an improved particle swarm optimization (PSO) algorithm (GA-PSO-BP) is applied to invert the defect size from the flux leakage signal. The traditional particle swarm optimization method has strong dependence on the initial value and can only obtain the local optimal solution. In this paper, the crossover and mutation operations in the genetic algorithm are used to improve the population diversity and global search ability of the particle swarm algorithm, and the weights and thresholds of the network are adjusted to make the predicted output continuously approach the expected output. The defect size is estimated using an inversion technique based on GA-PSO-BP. The results show that the inversion method based on GA-PSO-BP can improve the average error accuracy of defect depth by 5.83% and the average error accuracy of defect length by 4.87%. Thus, the inversion method based on GA-PSO-BP is superior to the BP neural network inversion technology. Besides, the improved algorithm can improve the defect inversion speed and meet the requirements of real-time online detection in a vibration environment.