Vibration signature analysis of a faulted gear transmission system

Abstract

A comprehensive procedure in predicting faults in gear transmission systems under normal operating conditions is presented. Experimental data was obtained from a spiral bevel gear fatigue test rig at NASA Lewis Research Center. Time synchronous averaged vibration data was recorded throughout the test as the fault progressed from a small single pit to severe pitting over several teeth, and finally tooth fracture. A numerical procedure based on the WignerVille distribution was used to examine the time averaged vibration data. Results from the Wigner-Ville procedure are compared to results from a variety of signal analysis techniques which include time domain analysis methods and frequency analysis methods. Using photographs of the gear tooth at various stages of damage, the limitations and accuracy of the various techniques are compared and discussed. Conclusions are drawn from the comparison of the different approaches as well as the applicability of the Wigner-Ville method in predicting gear faults .

<title>Health monitoring and damage detection of a rotorcraft planetary geartrain system using piezoelectric sensors</title>

“…The expansion in equation 1 assumes that the wavelet basis functions are real. However, like the Fourier series expansion given by f(t) = i: Fjei23t (4) the basis functions for the DHWT are complex. The result is that the real part represents an even wavelet and the imaginary part represents an odd wavelet.…”

Vibration Signature Analysis and Parameter Extractions on Damages in Gears and Rolling Element Bearings

“…One of the advanced fault identification procedures commonly used is the condition-based vibration signature analysis [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Acquired machine vibration/acoustic signals are compared with ones obtained from the healthy machines allowing the detection of component abnormalities from the signals.…”

“…Others use time and frequency method combined with statistical approach [5][6][7][8][9][10], which provides very good comparisons in between present and past vibrations and a definite indication for damages in the system. In addition, the use of joint time-frequency domain methods based on the Wigner-Ville Distribution (WVD) as well as the Continuous Wavelet Transform (CWT) [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] have also been applied extensively to detect gear and bearing failures in transmission systems. The joint time-frequency domain methods provide an instantaneous frequency spectrum of the system at various time instances of the rotor rotation and can be used to pinpoint accurately the location of the damage in a gear transmission system.…”

Early Detection of Gear Tooth Cracking Using the Resonance Demodulation Technique

“…Statistical measures of the residual signal are the most commonly used methods. These include residual kurtosis (FM4), variance, and other parameters such as the ratio of the maximum peak-to-peak amplitude to the sum of amplitudes at the meshing harmonics (FM0) and the quasi-normalised residual kurtosis (NA4) [5].…”

“…With the demodulated signal, both the amplitude (envelope signal) and the phase (phase-modulating signal) can be calculated. The kurtosis of the demodulated signal (such as quasi-normalised envelope kurtosis*NB4) appears to be a good indicator of gear cracks at their early stage [5]. It is argued that the phase-modulating signal may be more e!ective for the detection of gear cracks than the envelope signal [6].…”