Magnetic Hall Sensor-Generated MFL-Based Improved Metal Loss Detection and SVM-Based Quantification for Ferromagnetic Structural Components

This study focuses on the assessment of magnetic Hall sensors' orientation-based application through magnetic flux leakage excitation data from a sample target ferromagnetic structure. Initially, we designed a prototype of a rectangular sensor head with ten channel compartments, each equipped with Hall magnetic sensors, to conduct contact-based experiments on a ferromagnetic structural component. The target specimen, a steel plate, was subjected to a strong magnetic field using a Neodymium-35 magnet. Various-sized metal loss anomalies were deliberately induced in the target plate structure. Upon scanning the target specimen at a constant speed, the Hall magnetic sensor collected and stored data on both intact and damaged magnetic flux. To enhance accuracy and extract essential features, the raw flux leakage input underwent sequential signal adjustment steps. Signal averaging was employed to improve the overall data gathered from repeated experiments. The magnetic induction signal curve was analyzed to observe the nature of flux leakage in the magnetic Hall sensors. Frequency analysis and sorted amplitude analysis were conducted to gain a deeper understanding of significant features. Following box plot-based outlier identification, a low-pass filter and fast Fourier transform-based filtering were applied to eliminate unwanted flux leakage. Data detrending and signal-to-noise ratio analysis were employed to assess the baseline variation filtering results for improving the original flux leakage data. To allocate defects, a three-step iteration-based wavelet decomposition was proposed, successfully determining the defect region. Anomaly size estimation was carried out using a support vector machine, yielding promising results.