Bearing fault feature detection based on parameter identification of transient impulse response

Localized defects in rotating machinery parts tend to result in impulse response in vibration signal, and the parameter identification of which provides a potential approach for localized fault diagnosis. This paper reviewed the Laplace wavelet correlation filtering and then, based on which, proposed Cyclic Laplace Wavelet Correlation Filtering (CLWCF) for the identification of not only the impulse response parameter but also the cyclic period. Simulation study with cyclic impulse response signal with different SNR shows that CLWCF is effective in detecting the impulse response parameter and the cyclic period. Applications in bearing vibration parameter identification for localized fault diagnosis showed that the CLWCF in bearing vibration signal is effective in finding the period that represents the outer race localized fault at different fault seriousness, and thus provide a potential feature detection method for bearing fault diagnosis.