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Time-frequency methods for vibration-based gearbox faults detection have beenconsidered the most efficient method. Among these methods, continuous wavelettransform (CWT) as one of the best time-frequency method has been used for bothstationary and transitory signals. Some deficiencies of CWT are problem ofoverlapping and distortion ofsignals. In this condition, a large amount ofredundant information exists so that it may cause false alarm ormisinterpretation of the operator. In this paper a modified method called ExactWavelet Analysis is used to minimize the effects of overlapping and distortionin case of gearbox faults. To implement exact wavelet analysis, Particle SwarmOptimization (PSO) algorithm has been used for this purpose. This method havebeen implemented for the acceleration signals from 2D acceleration sensoracquired by Advantech PCI-1710 card from a gearbox test setup in AmirkabirUniversity of Technology. Gearbox has been considered in both healthy andchipped tooth gears conditions. Kernelized Support Vector Machine (SVM) withradial basis functions has used the extracted features from exact waveletanalysis for classification. The efficiency of this classifier is thenevaluated with the other signals acquired from the setup test. The results showthat in comparison of CWT, PSO Exact Wavelet Transform has better ability infeature extraction in price of more computational effort. In addition, PSOexact wavelet has better speed comparing to Genetic Algorithm (GA) exactwavelet in condition of equal population because of factoring mutation andcrossover in PSO algorithm. SVM classifier with the extracted features ingearbox shows very good results and its ability has been proved.

Gearbox Fault Detection through PSO Exact Wavelet Analysis and SVM Classifier