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Taking the measured gearbox vibration signals as the analysis object,the fault feature of gear pitting is extracted. The Akaike Information Ceriterion（ AIC） is applied to evaluate the optimum Auto- regressive（ AR） model order,and then this AR model is used to pre- process the vibration signals to eliminate the linearly predictable stationary part. The Maximum Correlated Kurtosis Deconvolution（ MCKD） is applied to enhance the impact component. The fault characteristic could be identified by analyzing the envelope spectrum.And the AR- MCKD method is applied to analyze the changing trend of the vibration signals. The results proved the efficiency of the AR- MCKD method in the gear fault feature extraction and the changing procedure of the envelope spectrum of gear pitting fault.

Aiming at the facts that the modulation signal bispectrum (MSB) might only process stationary signals, a novel method for fault feature extraction of rolling bearings was proposed based on the WAEEMD and MSB. Firstly, vibration signals of roiling bearings were decomposed into a list of intrinsic mode uncrowns (IMFs) by ensemble empirical mode decomposition (EEMD). Subsequently, the IMFs were reconstructed into the WAEEMD inhered signals using the weighted average method based on Teager energy kurtosis (TEK). Finally, the MSB was used to decompose the modulated components in the WAEEMD filtered signals and extract the fault characteristic frequencies. The analysis results illustrate that the WAEEMD-MSB has a superior performance over fast kurtogram (FK) and EEMD-MSB in extracting bearing fault features. [ABSTRACT FROM AUTHOR]