A Novel Prediction Method for Hardness Using Auto-regressive Spectrum of Barkhausen Noise

In this study, a novel method for predicting hardness of ferromagnetic alloy based on the magnetic Barkhausen noise (MBN) is proposed. A set of new frequency features of MBN and a new hardness prediction method are proposed. The new features are derived from the first and second derivative of the auto-regressive spectrum of MBN signal. The new automatic hardness prediction method include Bag-of-Words, principal component analysis and back propagate neural network optimized by ensemble learning. The experimental results of the hardness classification show that the new features are superior to the previous features—the misclassification rate using the new features is less than 0.67%, while the misclassification rate using the previous features is about 2%. The efficiency of the new method is also proved by hardness classification experiment. Compared with the traditional time-domain method and the previous frequency domain method, the misclassification rate of the new method decreased significantly from 25% to less than 1%. In addition, the new method is highly automatic, so it is more versatile than manual algorithms. The above characteristics make the proposed new method suitable for predicting the hardness of ferromagnetic alloys in practice.