Abstract
The use of vibration measurementanalysis has been proven to be effective for gearbox fault diagnosis. However, the complexity of vibration signals observed from a gearbox makes it difficult to accurately detectfaults in the gearbox. This work is based on a comparative studyof several time-frequency signal processing methods that can be used to extract information from transient vibration signals containing useful diagnostic information. Experiments were performed on a bevel gearbox test rig using vibration measurements obtained from accelerometers. Initially, thediscrete wavelet transform was implementedfor vibration signal analysis to extract the frequency content of signal from the relevant frequency region. Several time-frequency signal processing methods werethen incorporated to extract the fault features of vibration signals and their diagnostic performances were compared. It was shown thatthe Short Time Fourier Transform (STFT) could not offer a good time resolution to detect the periodicity of the faulty gear tooth due the difficulty in choosing an appropriate window length to capture the impulse signal. The Continuous Wavelet Transform (CWT), on the other hand, was suitable to detection of vibration transients generated by localized fault from a gearbox due to its multi-scale property. However, both methods still require a thorough visual inspection. In contrast, it was shown from the experiments that the diagnostic method using the Cepstrumanalysis could provide a direct indication of the faulty tooth without the need of a thorough visual inspection as required by CWT and STFT.
Highlights
Vibration signals from a faulty gearbox will consist of a series of impulses that repeat at a particular defect frequency [1].An impulsesignal is a nonstationary signal that cannot be captured by conventional steady-state spectrum analysis, such as Fast Fourier Transform (FFT) analysis, because of the main assumption of FFT analysis that the underlying signal must be stationary
The signal periodwas approximately 0.1195 second between two successive impulses. These two figures showed that Continuous Wavelet Transform (CWT) was able to distinguish the pattern between the healthy and broken gears and offered a better time resolution to detect the periodicity between two impulses, compared to Short Time Fourier Transform (STFT).Since the periodicity was inversely related to the speed of the faulty gear, so the method was able to determine which gear was generating the impulse due to faulty tooth
The aim of the study is to detect the existence of a faulty gear with a broken tooth from a bevel gearbox testrig.Several time-frequency signal processing methods were compared: FFT, STFT, CWT and Cepstrum methods.Initially, Discrete Wavelet Transform (DWT) was implemented to extract only the useful frequency content of the raw vibration signal that containedthe fault features
Summary
In order to accurately diagnose possible faults in a gearbox, theextraction of features from the impulsive signal associated with the gearbox vibration is crucial. Alarge number ofsignal processing methodshave beenproposedto identify the impulsive features from vibration signals, using time-frequency analysis such as the Wigner-Ville distribution,Cepstrum,Fast Fourier Transform (FFT), and the amplitude-phase demodulation. Compared to STFT, CWT has the flexibility in adjusting its time-frequency resolutionwhich means thatit is more suitable to analyzethe impulsive signals associated with a faulty gearbox. Cepstrum analysis has been widely used for signal periodicity detection in the frequency spectrum, especially for speech analysis that contains harmonics in its voice frequencybandwidth.Cepstrum can be considered as the frequency spectrum of a frequency spectrum.In gearbox vibration signals, harmonics in the form of sidebands spaced at an integer multiple of carrier frequencies or gearmesh frequencies. Cepstrum can be used to identify the periodicity observed from the harmonics associated with certain gear faults.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
