基于奇异值分解的磁记忆信号特征提取方法

Feature Extraction Method of Magnetic Memory Signal Based on SVD

针对金属磁记忆信号容易受到环境噪声影响,使得缺陷信号可检测性降低的情况,首先,利用传统的奇异值分解方法对场桥主梁磁记忆信号进行分解和重构,发现尽管可以取得较为理想的降噪效果,但如何自适应确定重构时的奇异值个数仍存在困难;然后,将磁记忆信号按照二进递推方法构造矩阵,重复进行奇异值分解可以获得具有不同分辨率的近似信号和细节信号,从而形成多分辨奇异值分解,其中细节信号对应磁记忆中的噪声成分,近似信号为去除噪声之后的有效磁记忆信号,从而实现了磁记忆信号的降噪。将该方法用于某场桥主梁磁记忆信号的处理,有效地提高了重构信号的信噪比,准确地判断出了该主梁的应力集中区域,为评估其应力状态和早期故障诊断奠定了基础。

Since the metal magnetic memory(MMM) signal tends to be influenced by environmental noise,the testability of the detected signal is reduced.The traditional singular value decomposition(SVD)method is applied to decompose and reconstruct the MMM signal of a yard crane.It is effective in reducing the noise,but it is difficult to determine the singular value number of reconstruction.In order to solve this problem,the MMM signal matrix is constituted on the principles of dichotomy and recursion,and the multi-resolution singular value decomposition is realized through the repeated SVD method to obtain approximate signals and detail signals of different resolutions,which correspond respectively to the effective components and the noise components.The experimental results of a certain yard crane magnetic memory signal processing show that the proposed method can effectively improve the signal-to-noise ratio of the reconstructed signal,and accurately judge the stress concentration area.This will lay the foundations for stress state evaluation and early fault diagnosis.