scholarly journals Torsional vibration signal analysis as a diagnostic tool for planetary gear fault detection

2018 ◽  
Vol 100 ◽  
pp. 706-728 ◽  
Author(s):  
Song Xue ◽  
Ian Howard
Keyword(s):  
Fault Detection ◽  
Torsional Vibration ◽  
Diagnostic Tool ◽  
Signal Analysis ◽  
Planetary Gear ◽  
Vibration Signal ◽  
Gear Fault ◽  
Vibration Signal Analysis

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
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Detection Method ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Vibration Signal Analysis ◽  
Processing Techniques ◽  
Signal Processing Techniques

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.

Diagnosis for Engine Misfire Fault Based on Torsional Vibration and Neural-Network Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 7240-7246
Author(s):  
Can Yi Du ◽  
Kang Ding ◽  
Zhi Jian Yang ◽  
Cui Li Yang
Keyword(s):  
Neural Network ◽  
Torsional Vibration ◽  
Bp Neural Network ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Correction Method ◽  
Operating Conditions ◽  
Diagnostic Model ◽  
Vibration Signal Analysis

Misfire is a common fault which affects the engine performances. Because the signal-to-noise ratio of torsional vibration signal is high, torsional vibration test and analysis for the engine were performed in a variety of operating conditions, including healthy condition and single-cylinder misfire condition. In order to improve the accuracy of analysis, energy centrobaric correction method was used to correct the amplitude. Taking the corrected amplitude of main order as the fault feature, and then a BP neural-network diagnostic model can be established for misfire diagnosis. The result shows that the method of combining torsional vibration signal analysis and neural-network can diagnose engine misfire fault correctly.

Comparison of Vibration Signal Analysis Methods for Effective Gear Fault Detection

2014 ◽  
Vol 709 ◽  
pp. 456-459
Author(s):  
Woong Yong Lee ◽  
Dong Hyong Lee ◽  
Hae Young Ji
Keyword(s):  
Signal Analysis ◽  
High Speed ◽  
Vibration Signal ◽  
Spur Gear ◽  
High Speed Train ◽  
Crest Factor ◽  
Gear Fault ◽  
Specimen Test ◽  
Vibration Signal Analysis ◽  
Crack Condition

Reduction unit for high-speed train is an important component. However if faults of reduction unit occurred, the damages such as material and human damage have been caused. To prevent the damage, it is necessary to study reduction unit monitoring for high-speed train. We conducted spur gear specimen test which was crack, breakage and pitting tests and analyzed FFT, Sideband energy ratio (SER), RMS, crest factor, and kurtosis. There was not distinct difference between no-fault and pitting condition at RMS, crest factor and kurtosis. But SER increased depending on crack condition. In breakage test, all parameters had difference between no-fault and breakage condition.

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