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.
Highlights
The magnetic Barkhausen noise (MBN) as a non-destructive evaluation method is mainly used to detect stress of ferromagnetic materials [1,2,3]
Previous methods for extracting MBN features require the choice of a suitable threshold so that these features can have good linearity and low dispersion
(4) The results of the multivariable linear regression (MLR) and multilayer perceptron (MLP) models show that the adaptive pulse count feature (x5 ) has a lower RMSE, which contributes to stress detection
Summary
The magnetic Barkhausen noise (MBN) as a non-destructive evaluation method is mainly used to detect stress of ferromagnetic materials [1,2,3]. Feature extraction of MBN signals is important for stress detection of ferromagnetic materials. MBN signals, a large amount of redundant and irrelevant information will lead to a prolonged time spent on training the model and using the model for stress detection, and will reduce measurement accuracy. How to extract MBN features is critical to the results of stress detection. Previous methods for extracting MBN features require the choice of a suitable threshold so that these features can have good linearity and low dispersion. It was found that new features had a good linear correlation with residual stress [1]. The linearity and dispersion of the pulse count feature are closely related to the choice of threshold
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