A Gear Chain Fault Detection Method Using an Adaptive Interference Canceling

2007 ◽  
Vol 345-346 ◽  
pp. 1303-1306
Author(s):  
Bum Won Bae ◽  
In Pil Kang ◽  
Yeon Sun Choi

A fault diagnosis method based on wavelet and adaptive interference canceling is presented for the identification of a damaged gear tooth. A damaged tooth of a certain gear chain generates impulsive signals that could be informative to fault detections. Many publications are available not only for the impulsive vibration signal analysis but the application of signal processing techniques to the impulsive signal detections. However, most of the studies about the gear fault detection using the impulsive vibration signals of a driving gear chain are limited to the verification of damage existence on a gear pair. Requirements for more advanced method locating damaged tooth in a driving gear chain should be a motivation of further studies. In this work an adaptive interference canceling combined with wavelet method is used for a successful identification of the damaged tooth location. An application of the wavelet technique provides a superior resolution for the damage detection to the traditional frequency spectrum based methods. An analysis and experiment with three pair gear chain show the feasibility of this study yielding a precise location of the damaged gear tooth.

2013 ◽  
Vol 430 ◽  
pp. 78-83 ◽  
Author(s):  
Rusmir Bajrić ◽  
Ninoslav Zuber ◽  
Safet Isić

This paper provides a review of the literature, progress and changes over the years on fault detection of gears using vibration signal processing techniques. Analysis of vibration signals generated by gear in mesh has shown its usefulness in industrial gearbox condition monitoring. Vibration measurement provides a very efficient way of monitoring the dynamic conditions of a machine such as gearbox. Various vibration analysis methods have been proposed and applied to gear fault detection. Most of the traditional signal analysis techniques are based on the stationary assumption. Such techniques can only provide analyses in terms of the statistical average in the time or frequency domain, but can not reveal the local features in both time and frequency domains simultaneously. Frequency/quefrency analysis, time/statistical analysis, time-frequency analysis and cyclostationarity analysis are reviewed in regard for stationary and nonstationary operation. The use of vibration signal processing detection techniques is classified and discussed. The capability of each technique, fundamental principles, advantages and disadvantages and practical application for gear faults detection have been examined by literature review.


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