连续小波变换的滚动轴承故障诊断

Fault Diagnosis of Rolling Bearing Based on Continuous Wavelet Transformation

提出了用连续小波变换与傅立叶变换相结合进行轴承故障识别的新方法。先通过Morlet连续小波变换对故障轴承信号进行不同尺度的分解,然后进行小波尺度-能量谱统计,再在有可能体现故障频率的尺度上对其获得的小波系数进行快速傅立叶变换来识别故障特征频率。对于非常微弱的内圈故障提出了通过共振解调法对特定尺度系数进行Hilbert包络提取故障频率的新方法。优点在于能够在强噪声背景下较为精确的识别故障。实际测试验证了新方法的正确性。

A new method to distinguish rolling bearing malfunction based on continuous wavelet transform and Fourier transform is presented in this literature. The initial signal is decomposed by morlet continuous wavelet transformation at first. Then scale - power spectrum is obtained. Consequently, the wavelet coefficients obtained at the first step on faulty scale are analyzed by FFT（fast Fourier transformation） to locate faulty frequency. A new method for faint inner- ring fault signal diagnoses is presented in this paper at the same time. The method used Hilbert envelope to analyze coefficients of original signal on a certain scale based on demodulated resonance theory. The feature of the faults can be well recognized even under strong noise background. The validity of the proposed method is verified by experiments.