Quantitative sizing of compound location defects based on PECT-EMAT hybrid testing methods. (1st October 2022)

• Record Type: Journal Article
• Authors: Duan, Zhirong; Xie, Shejuan; Huang, Lei; Zhao, Ruixiang; Tian, Mingming; Liu, Tianhao; Pei, Cuixiang; Chen, Zhenmao
• Is Part Of: Mechanical systems and signal processing. Volume 178(2022)
• Publication Date: 2022-10-01
• Subjects: PECT-EMAT hybrid testing method -- Compound location defects -- Defect reconstruction -- Genetic algorithm -- Neural network algorithm; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109267
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: The simulation program and experimental system of the hybrid method are developed. The correlation between the signal features and the defect parameters is established. Two inversion procedures for the hybrid method are developed based on GA and NN. The effects of the two inverse methods are compared and analyzed. Abstract: Metal structures are widely used in modern industrial fields, in which various compound location defects are inevitably produced from manufacture to service. Facing such complex conditions, a single electromagnetic nondestructive testing method usually cannot meet all needs. For metal structure with surface crack and bottom thinning defect, a novel PECT-EMAT hybrid testing method is proposed in this study. The relevant simulation and experimental method are developed to realize the compound location defects detection. The proposed method employs only one set of equipment to realize the compound location defects detection, which greatly reduces the complexity of the experimental system and improves the detection efficiency. In addition, the correlation between the extracted signal features and the defect parameters is established through simulation, and further verified by experiment. Furthermore, in order to estimate the damage level of the defects, the inversion calculation procedure of the hybrid NDT method are developed based on genetic algorithm and neural network algorithm, respectively. The reconstruction of the compound location defects using the above two inverse algorithm are carried out using simulation data and experimental data. Finally, the effects of the two inverse methods are compared and the reasons for their advantages and disadvantages are analyzed.