Abstract
Conventional methods (i.e. time, frequency and cepstrum) can routinely be used to reveal fault-indicating information in the vibration signal. In recent years, wavelet analysis, which can lead to a clear identification of the nature of faults, is widely used to describe rotating machine condition. The capability of this method in the detection of any abnormality can be further improved when its low-order frequency moments are considered. This paper presents the use of the fast kurtogram in the early detection and condition monitoring of pitting fault. For this purpose, a dynamic model of a simple stage gearbox (with and without defects) is used. Then, the pinion’s vibration displacement is analyzed by using a fast kurtogram method. This method is suitable for such diagnosis and gives valuable information about the presence and effects of the pitting tooth defect.
Highlights
Gear systems are used to transfer rotation or power from one shaft to another in desired ratios and high efficiency
This paper presents the use of fast kurtogram in the early detection and advancement monitoring of gear tooth pitting fault
Kurtogram was first introduced by Antoni and Randall [21], which comes from spectral kurtosis (SK) [21]
Summary
Gear systems are used to transfer rotation or power from one shaft to another in desired ratios and high efficiency. Various time– frequency methods such as Short Time Fourier Transform (STFT) [8, 9], Wigner–Ville distribution (WV) [10], Choi-Williams distribution (CWD) [11], Instantaneous Power Spectrum distribution (IPS) [12, 13], Smoothed Instantaneous Power Spectrum distribution (SIPS) [13] and Continuous Wavelet Transform (CWT) [14,15,16,17,18,19], have been used extensively to analyze vibration signals and extract useful diagnostic information. This paper presents the use of fast kurtogram in the early detection and advancement monitoring of gear tooth pitting fault. For this purpose, a dynamic model of one stage gear box system (with and without defects) is proposed. It is a measure of the peakiness of the probability density function ky(f )
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