Abstract
This paper presents comparative studies of Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) as several advanced time-frequency analysis methods for diagnosing an early stage of spur gear tooth failure. An incipient fault of a chipped tooth was investigated in this work using vibration measurements from a spur gearbox test rig. Time Synchronous Averaging was implemented for the analysis to enhance the clarity of fault feature from the gear of interest. Based on the experimental results and analysis, it was shown that FFT method could identify the location of the faulty gear with sufficient accuracy. On the other hand, Short Time Fourier Transform method could not provide the angular location information of the faulty gear. It was found that the Continuous Wavelet Transform method offered the best representation of angle-frequency representation. It was not only able to distinguish the difference between the normal and faulty gearboxes from the joint angle-frequency results but could also provide an accurate angular location of the faulty gear tooth in the gearbox.
Highlights
Vibration signals generated from a gearbox contain abundant information about the operating condition and health of the gearbox
In terms of radial acceleration, from a healthy and faulty gearboxes are presented in Figs. 4 and 5
This can be explained by the fact that at the early stage of the gear fault, relatively small impulse responses are generated. This makes the amplitude of vibration signal from the faulty gearbox almost indistinguishable compared to that of the healthy gearbox
Summary
Vibration signals generated from a gearbox contain abundant information about the operating condition and health of the gearbox. FFT only could provide the frequency content of the TSA signals and the angular information of the faulty gear is lost. To overcome this drawback, the use of STFT and CWT were proposed in this work to further analyze the TSA signal for accurate diagnostic purposes. The use of STFT and CWT were proposed in this work to further analyze the TSA signal for accurate diagnostic purposes This analysis can reveal both the frequency content of the TSA signals and indicate how the frequency content evolves with respect to the angular location of the gear.
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