A Method of Barkhausen Noise Feature Extraction Based on an Adaptive Threshold

Abstract

This paper reports on a new feature extraction method for detection of applied stress using magnetic Barkhausen noise (MBN). Some previous methods for extracting MBN features need to choose a suitable threshold so that these features can have good linearity and low dispersion, such as pulse count and full width at 25, 50 and 75% of the maximum amplitude. A new approach has been proposed for selecting the appropriate threshold for MBN features adaptively using a genetic algorithm (GA). The criterion for selecting the threshold is the lowest standard deviation of features and new proposed ‘overlap’ of features. In order to verify the effectiveness of the adaptive pulse count feature for stress detection, different modelling techniques are compared, including multivariable linear regression (MLR) and multilayer perceptron (MLP). The results obtained have proven that adaptive threshold features can effectively distinguish between different stress conditions compared with traditional MBN features.