  Time-frequency methods for vibration-based gearbox faults detection have been
considered the most efficient method. Among these methods, continuous wavelet
transform (CWT) as one of the best time-frequency method has been used for both
stationary and transitory signals. Some deficiencies of CWT are problem of
overlapping and distortion ofsignals. In this condition, a large amount of
redundant information exists so that it may cause false alarm or
misinterpretation of the operator. In this paper a modified method called Exact
Wavelet Analysis is used to minimize the effects of overlapping and distortion
in case of gearbox faults. To implement exact wavelet analysis, Particle Swarm
Optimization (PSO) algorithm has been used for this purpose. This method have
been implemented for the acceleration signals from 2D acceleration sensor
acquired by Advantech PCI-1710 card from a gearbox test setup in Amirkabir
University of Technology. Gearbox has been considered in both healthy and
chipped tooth gears conditions. Kernelized Support Vector Machine (SVM) with
radial basis functions has used the extracted features from exact wavelet
analysis for classification. The efficiency of this classifier is then
evaluated with the other signals acquired from the setup test. The results show
that in comparison of CWT, PSO Exact Wavelet Transform has better ability in
feature extraction in price of more computational effort. In addition, PSO
exact wavelet has better speed comparing to Genetic Algorithm (GA) exact
wavelet in condition of equal population because of factoring mutation and
crossover in PSO algorithm. SVM classifier with the extracted features in
gearbox shows very good results and its ability has been proved.
