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When localized fault occurs in a bearing, the periodic impulsive feature of the vibration signal appears in time domain and the corresponding Bearing Characteristic Frequencies (BCFs) emerge in frequency domain. The common technique of Fast Fourier Transforms (FFT) and Envelope Detection (ED) are always used to identify faults occurring at the BCFs. In the early stage of bearing failures, the BCFs contain very little energy and are often overwhelmed by noise and higher-level macro-structural vibrations. In order to extract the weak fault information submerged in strong background noise of the gearbox vibration signal, an effective signal processing method would be necessary to remove such corrupting noise and interference. Optimal Morlet Wavelet Filter and Envelope Detection (ED) are applied in this paper.

In this paper, a fault in rolling bearing is diagnosed using time waveform analysis. In order to verify the ability of time waveform analysis in fault diagnosis of rolling bearing, an artificial fault is introduced in vehicle gearbox bearing: an orthogonal placed groove on the inner race with the initial width of 0.6 mm approximately. The faulted bearing is a roller bearing located on the gearbox input shaft - on the clutch side. An optimal Morlet Wavelet Filter and autocorrelation enhancement are applied in this paper. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, autocorrelation enhancement is applied to the filtered signal.

Bearing and gear condition monitoring are important to improve a mechanical system reliability and performance. In the early stage of bearing failures, the Bearing Characteristic Frequencies (BCFs) contain very little energy and are often overwhelmed by noise and higher-level macro-structural vibrations, an effective signal processing method would be necessary to eliminate such corrupting noise and interference. Referring to the non-stationary characteristics of roller bearing fault vibration signals, a roller bearing condition monitoring method based on Envelope Process to raw time-domain vibration signal and Autocorrelation enhancement to the residual signal is put forward in this paper. The concept of Envelope and Autocorrelation techniques and its implementation for defect identification are discussed. Also, distinction of bearing fault signal as cyclostationary from periodic signal for gear fault.