Title
Customized multiwavelets for planetary gearbox fault detection based on vibration sensor signals.
Abstract
Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox.
Year
DOI
Venue
2013
10.3390/s130101183
SENSORS
Keywords
Field
DocType
planetary gearbox,fault detection,vibration sensor signals,customized multiwavelets,redundant symmetric lifting schemes,improved neighboring coefficients
Noise reduction,Data mining,Lifting scheme,Fault detection and isolation,Algorithm,Electronic engineering,Gear train,Communications satellite,Vibration,Engineering,Periodic graph (geometry),Kurtosis
Journal
Volume
Issue
ISSN
13
1.0
1424-8220
Citations 
PageRank 
References 
4
0.65
5
Authors
6
Name
Order
Citations
PageRank
Hailiang Sun140.99
Yanyang Zi226825.13
Zhengjia He337231.85
Jing Yuan440.65
Xiaodong Wang540.65
Lue Chen641.33