Abstract
Vibration analysis has been demonstrated to be one of the best tools to detect faults in a gearbox by providing abundant information about the operating condition of a gearbox. However, a gearbox generates complex vibration signals, which makes it difficult to diagnose when a fault occurs. There are several fault diagnosis methods that can be utilized to analyze the underlying signals. The time-frequency method has been used and showed some promising results. On the other hand, it also has its drawback when it is applied to a complex mechanical system such as gearboxes. This paper thus attempts to examine the effectiveness of several diagnosis methods to detect faults in a gearbox from vibration measurements. The results show that the cepstrum method can provide a more accurate indication of a faulty gearbox compared to other diagnosis methods.
Highlights
Introduction harmonicsThe spacing of the sidebands indicates the location of the faulty gear [4]
The This work compares several methods to diagnose a failure of the gear mechanism affects the entire operation faulty gearbox and suggests the most effective way to of the machinery, which can cause significant loss in diagnose the fault
The time-frequency domain, e.g Continuous Wavelet Transform (CWT), can provide a reliable diagnosis for gearboxes operating under a non-stationary condition [1,2]
Summary
As mentioned in the Introduction, localized fault in the gearbox will give rise to sidebands in the vicinity of the gearmesh frequency and its harmonics. Randal mentioned that there are 3 main applications of cepstrum analysis in gear diagnostics [4], author believe that in Condition Monitoring of a gearbox, the main application of the Cepstrum analysis is to detect the presence of such sidebands family and its harmonics [4, 7]. In the area of gearbox fault diagnosis, the Cepstrum is commonly defined as the inverse Fourier Transform of the logarithmic power spectrum as defined as equation two below. Randall [4] mentioned that it is partly because it is more logical to use the inverse transform between a function of frequency and a function of time, and partly because it is reversible to the power spectrum. The indepencent variable ߬ known as quefrency has the dimension of time(s)
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