The second order statistics for cyclostationary signals were introduced, and their performance were discussed. It especially researched the time lag characteristic of the cyclic autocorrelation function and spectral correlation characteristic of spectral correlation density function. It was pointed out that those functions can be available to extract the time vary information of the kind of non stationary signals. Using the relations of time lag cyclic frequency and frequency cyclic frequency independently, vibration signals of a rolling element bearing measured on test bed were analyzed. The results indicate that the second order cyclostationary statistics might provide a powerful tool for the feature extracting and fault diagnosis of rolling element bearing.
The vibration signals of rolling element bearing are produced by a combination of periodic and random processes due to the machine’s rotation cycle and interaction with the real world. The combination of such components can give rise to signals, which have periodically time-varying ensemble statistical and are best considered as cyclostationary. When the early fault occurs, the background noise is very heavy, it is difficult to disclose the latent periodic components successfully using cyclostationary analysis alone. In this paper the degree of cyclostationarity is combined with wavelet filtering for detection of rolling element bearing early faults. Using the proposed entropy minimization rule. The parameters of the wavelet filter are optimized. This method is shown to be effective in detecting rolling element bearing early fault when cyclostationary analysis by itself fails.
