复小波分解联合SVD提取振动信号非平稳特征

针对齿轮箱故障信号的多分量多频调制特点,提出了一种基于奇异值分解的最优小波解调技术。首先,采用小波变换的最小Shannon熵作为时间尺度分辨率的度量指标,将其应用到Morlet分析小波的参数优化选择中;其次,对常规小波参数选择方法进行了改进,利用奇异值分解技术对最优小波变化尺度进行了迭代搜索。该方法可以很好地降低噪声信号,有效提取信号中的周期成分,具有较好的瞬态信息提取能力。试验结果也表明了该方法在齿轮箱故障特征提取中的重要性以及降噪方法的有效性。

The Feature Extraction Method of Non-Stationary Vibration Signal Based on SVD-Complex Analytical Wavelet Demodulation

In order to analyze the multi-component and multi-modulation characteristics of a gearbox fault signal, an optimal wavelet demodulation method based on singular value decomposition (SVD) is proposed. In this method, Morlet wavelet transform is used as an adaptive band-pass filter to extract the impact component in the geabox vibration signal. The minimum Shannon entropy is used as the wavelet time scale resolution index to optimize the Morlet wavelet parameters. Based on SVD, the optimal wavelet coefficient is utilized to determine the parameters. The new method can extract transient information better, reduce noise, effectively extract the signal period, and assure the validity of the fault feature recognition. The experimental results show that the proposed method can more accurately and effectively extract the fault characteristic hidden in the gearbox vibration signal.