Detection of Uniaxial Fatigue Stress under Magnetic Flux Leakage Signals using Morlet Wavelet

This paper demonstrates the application of continuous wavelet transform technique for magnetic flux leakage signal generated during a uniaxial fatigue test. This is a consideration as the magnetic signal is weak and susceptible to being influenced by an external magnetic field. The magnetic flux leakage signal response of API steel grade X65 is determined using Metal Magnetic Memory under cyclic load conditions ranging from 50% to 85% of the UTS. To facilitate further signal analysis, the magnetic flux gradient, the dH(y)/dx signal were converted from a length base into time series in this study. Magnetic flux leakage readings indicated a maximum UTS load of 56.5 (A/m)/mm at 85%, where a higher load resulted in a higher reading and the signal contained Morlet wavelet coefficient energy of 1.02×106 µe2/Hz. As increasing percentages of UTS loads were applied, the signal analysis revealed an increasing linear trend in the dH(y)/dx and wavelet coefficient energy. The analysis revealed a strong correlation between the wavelet coefficient energy and the dH(y)/dx amplitude, as indicated by the coefficient of determination (R2) value of 0.8572. Hence, this technique can provide critical information about magnetic flux leakage signals that can be used to detect high stress concentration zones

Assessment on recent landslide susceptibility mapping methods: A review

Landslide is a destructive natural hazard that causes severe property loss and loss of lives. Numerous researchers have developed landslide susceptibility maps in order to forecast its occurrence, particularly in hill-site development. Various quantitative approaches are used in landslide susceptibility map production, which can be classified into three categories; statistical data mining, machine learning and deterministic approach. In this paper, we choose two regular models in each category, which are Weight of Evidence (WoE) and Frequency Ratio (FR), Artificial Neutral Networks (ANN) and Support Vector Machines (SVM), Shallow Landsliding Stability Model (SHALSTAB) and YonSei-Slope (YS-Slope). Discussion and assessment on these models are based on relevant literature