钢绳芯输送带的磁记忆信号特征提取方法研究

Research on Signal Feature Extraction Method on Steel-cord Belt with Metal Magnetic Memory Testing

利用金属磁记忆(MMM)技术进行故障检测时,较弱的故障信号提取成为检测准确度高低的关键。采用小波分析和奇异值分解相结合的方法,对金属磁记忆信号经行故障特征提取。通过小波分析将故障信号分解为不同尺度的分量,以形成初始向量特征矩阵,并对该矩阵进行奇异值分解,选择代表特征信号的奇异值分量重构,从而实现对故障信号的特征提取。经过实验证明该方法有效。

The detection accuracy of metal magnetic memory （MMM） technology level was decided by extraction of weak fault signal. The fault characteristic signal of MMM was extracted with method of combining wavelet analysis and singular value decomposition （SVD）. The fault signal was decomposed into different scale components with wavelet analysis. And SVD was applied to decompose characteristic matrix, which was composed with different scale components, into different subspace. With reconstruction of singular vectors, the signal features were extracted effectively. The experiments had shown that method was effective.