Abstract

In the current steel industry production, the detection of the mechanical properties of ferromagnetic materials relies on destructive testing, which greatly increases the cost of production. In this paper, a method is proposed to estimate the mechanical properties of ferromagnetic materials based on the pulsed eddy current (PEC) techniques. Firstly, the traditional features of the PEC signal, such as differential signal peaks and spectral amplitudes, are applied to the quantitative estimation of mechanical properties. Secondly, a method based on the empirical mode decomposition and Hilbert-Huang transform is used to extract the marginal spectral peak and marginal spectral energy as new features. Finally, BP neural network algorithm is introduced to quantitatively estimate the mechanical property parameters. The results show that the combination of both traditional and new features is suitable for mechanical properties estimation. The method has high accuracy for the quantitative estimation of mechanical performance parameters.

中文翻译：

---

基于脉冲涡流的铁磁材料力学性能估算方法

摘要

目前钢铁工业生产中，铁磁材料力学性能的检测依赖于破坏性检测，大大增加了生产成本。在本文中，提出了一种基于脉冲涡流 (PEC) 技术估计铁磁材料机械性能的方法。首先，将PEC信号的传统特征，如差分信号峰值和频谱幅度，应用于力学性能的定量估计。其次，采用基于经验模态分解和Hilbert-Huang变换的方法提取边缘谱峰和边缘谱能量作为新特征。最后，引入BP神经网络算法对力学性能参数进行定量估计。结果表明，传统特征和新特征的结合适用于力学性能估计。该方法对力学性能参数的定量估计具有较高的准确性。