A Stress Estimation Method for Barkhausen Signals Based on Slow Eigen Analysis

Abstract

The invention discloses a method for estimating the stress of Barkhausen signals based on slow feature analysis. It intends to use Barkhausen signals under different stresses, extract the Barkhausen slow signals through the slow feature analysis algorithm, and then calculate the Barkhausen slow signals. Extract the eigenvalues, and finally use the obtained eigenvalues ​​to fit the stress data. Since the residual stress is an inherent property of the material itself, the slow feature analysis method has higher accuracy. It includes the following steps: Step 1: Combine the Barkhausen signals under different stresses into a vector; Step 2: Perform slow feature analysis on the combined Barkhausen signals to obtain the Barkhausen slow signals; Step 3: Extract the Barkhausen signals The upper envelope of the slow signal obtains the slow-varying characteristics of the Barkhausen signal. Step 4: After averaging the slow features of the Barkhausen slow signal, the feature vector is formed. Step 5: Regression linearly the different stresses and eigenvectors to obtain the expression model of the characteristics and stress of the Barkhausen slow signal.

Description

technical field [0001] The invention relates to a Barkhausen signal stress estimation method based on slow feature analysis, which is suitable for quantitative and non-destructive evaluation of the stress of ferromagnetic materials. Background technique [0002] Ferromagnetic materials, as engineering basic materials, will be subjected to stress in different directions during manufacture and service, resulting in residual stress, which will lead to a decline in the performance of ferromagnetic materials; Fracture will affect the safety of production work. In order to ensure the safe, long-term and stable operation of industrial production, it is necessary to conduct quantitative non-destructive evaluation of the stress conditions of ferromagnetic materials in service. [0003] For the detection of residual stress, traditional detection techniques mainly include X-ray diffraction, metallographic analysis and hardness measurement methods. These traditional technologies will ...

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Problems solved by technology

The traditional eigenvalues ​​include: the average value of the Barkhausen signal, the root mean square value, the peak value of the envelope, the number of pulses, etc., but when these eigenvalues ​​are used to fit the stress, they all have problems such as poor linearity and high dispersion

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