基于主轴故障诊断的微弱信号特征提取技术

Technology of Weak Signal Feature Extraction Based on Spindle Fault Diagnosis

微弱信号特征提取对于主轴系统早期故障诊断有着重要意义。从抑制噪声和利用噪声达到提高信噪比的角度出发,基于信号处理领域的研究成果,列举了可用于主轴系统微弱信号特征的提取方法。这些方法包括抑制噪声提高信噪比的信号处理方法和利用噪声增强微弱信号的方法,抑制噪声提高信噪比的信号处理方法有谱峭度、小波变换、经验模式分解、循环平稳理论、盲源分离、流形学习等;利用噪声增强微弱信号的方法有随机共振和总体平均经验模式分解。为主轴故障诊断研究提供了参考。

The feature extraction of weak signal is of important significance for early stage fault diagnosis in spindle system. Started from the aspects of suppressing and adding noise to improve the signal to noise ratio, feature extraction methods with weak signal of spindle system using were listed based on the research results of signal processing field. These methods of signal processing are included, to improve the ratio of signal to noise to suppressing noise and using noise to strengthen weak signal. The methods of suppressing noise are Spectral Kurtosis, Empirical Mode Decomposition, Wavelet Transform, Cyclostationarity, Blind Source Separation, and Manifold Learning etc. The methods of strengthening weak signal by adding noise are Stochastic Resonance and Ensemble Empirical Mode Decomposition. References are provided for research of spindle fault diagnosis.