A new MFL imaging and quantitative nondestructive evaluation method in wire rope defect detection

As a reliable and flexible loading tool, wire rope has been widely applied in various of industries and practical applications. Nondestructive testing and evaluation for wire rope defect inspection have become necessary methods in safety guaranteeing. However, traditional one-dimensional (1D) and two-dimensional (2D) magnetic flux leakage (MFL) signal processing methods for wire rope nondestructive evaluation (NDE) may lead to miss and false detection, especially under complex operation conditions with strong noise interference. Thus, a new MFL imaging and quantitative defect recognition method is proposed. Based on the reshaped sine function, wavelet transformation and grid entropy matrix reconstruction, five different MFL imaging algorithms are presented and compared. Besides, three MFL image processing methods through the Gabor filtering, morphological filtering, and small object feature extraction as well as the quantitative centroid distance calculation are also investigated. Finally, the quantitative evaluation model for wire rope defect inspection is built and tested in the case study and performance comparison, where six groups of wire rope testing signal recognition results not only verify the feasibility and validity of the proposed quantitative methods, but also demonstrate its great application promise in wire rope defect evaluation under sophisticated conditions. Additionally, advantages and disadvantages of the proposed method as well as the future wok are discussed.