Abstract
Different features of Magnetic Barkhausen Noise (MBN) represent distinctive characteristics of material stress states, microstructures and domain wall (DW) behaviors. However, a group of features could be selected and fused for the evaluation of specific characters such as hardness and stress. This paper analyzes multiple features of MBN and their reflections on different material and health status influences and proposes a new feature selection and fusion method for hardness evaluation through multiple feature relationships and different material influence. Principal component analysis (PCA) algorithm combined with feature correlation analysis method is utilized for feature selection by reducing redundant features and multiple parameter influences. Finally, we apply multivariate linear regression (MLR) with selected features to build a statistical linear model of MBN feature fusion for various material hardness prediction. The effectiveness of this feature selection is validated by various MLR models with different MBN features.
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