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

DOI: 10.5402/2011/402928

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Abstract:

This paper is to analyze and identify damage in gear teeth and rolling element bearings by establishing pattern feature parameters from vibration signatures. In the present work, different damage scenarios involving different combinations of gear tooth damage, bearing damage are considered. Each of the damage scenarios are studied and compared in the time domain, the frequency domain, and the joint time-frequency domain using the FM0 technique, the Fourier Transform, the Wigner-Ville Transform, and the Continuous Wavelet Transform, respectively. Results obtained from the three different signal domains are analyzed to develop indicative parameters and visual presentations that measure the integrity and wellness of the bearing and gear components. The joint time-frequency domain obtained from the continuous wavelet transform has shown to be a superior technique for providing clear visual examination solution for different types of component damages as well as for feature extractions used for computer-based machine health monitoring solution. 1. Introduction In the aerospace industry, where both weight-to-load factor and efficiency are pushed to their design limits, one of the major concerns is the fracture and fatigue failures in the gear transmission systems. Such failures often result from excessive gear tooth or bearing damage, which in turn leads to premature failures. Presently, the prevention and management of the premature equipment failures has become a vital part of the maintenance program. One of the advanced fault identification procedures commonly used is the condition-based vibration signature analysis [1–15]. Acquired machine vibration/acoustic signals are compared with ones obtained from the healthy machines allowing the detection of component abnormalities from the signals. This procedure does not require machinery shutdown and can be used as an online diagnostic and trend-monitoring tool. Traditional signature analysis procedures using both time signal and frequency analysis [1–4] showed considerable success using the zero-order figure of merit (FM0) technique by detecting relative vibration level change to the variations of particular frequency energy in the transmission system. Others use time and frequency method combined with statistical approach [5–10], 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)

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