Extracting the region of interest from MFL signals

Abstract

Abstract:In this paper, we study the magnetic flux leakage (MFL) signals for detection of defects in ferromagnetic materials. MFL signals consist of a background that is not constant and is combined with noise. Since there are slight variations because of noise, any large distortion shows a defect. Here the estimation of the background and then the determination of a threshold to distinguish defects from noise have been used for locating defects. In this method, precise evaluation of these two parameters has a vital role on the defect detection. The concept of histograms has been employed for eliminating the effect of defects in computing background signal. Results show that this algorithm is fast enough and yields detection of more defects that have lower amplitude. In the next step, an appropriate value for the threshold is determined by considering a trade-off between defect detection rate and noise separation rate.

Prospect of Using Artificial Intelligence for Microwave Nondestructive Testing Technique: A Review

“….Sadr and Okhovat [40] presented the concept of the histogram for canceling the effects of a defect noise in computing background signal to inspect the pipeline's surface. The common techniques of defect detection based on the use of a simple threshold to distinguish between the defected and background regions which depend on the average of the acquired signal.…”

Development of magnetic flux leakage device as a non-destructive method for structural reinforcement detection

“…In conclusion, the defects at the center of the rail head could be identified at speeds above 80 km/h and they stated the distance between the poles of the magnetizing system should be more than 3 m or some alternative like MFL remote field analysis should be used. Sadr and Okhovat (23) studied MFL signals for detection of defects in ferromagnetic materials. They used histograms to eliminate effect of defects.…”