A new intelligent methodology in bearing condition diagnosis analysis has been proposed to predict the status of rolling bearing based on vibration signals by multi class support vector machine (MSVM), a classification algorithm. Wavelet packet transform (WPT) is used for signal processing and standard statistical feature extraction process. Feature reduction is a method used to deselect the irrelevant features acquired from the large dataset. Recent survey shows feature reduction is used widely in the field of machine learning to discover the knowledge with reduced features. Rough set is hybridized with particle swarm optimization (PSO), an population based stochastic optimization technique, to reduce the features. The efficiency of classification algorithm is compared based on their classification accuracy before and after feature reduction. Four states of bearing health conditions such as normal, defective inner race, defective outer race and defective ball conditions are simulated and used in this proposed work.

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Early fault detection methodology in gear box diagnosis has been proposed to find the status of the gear based on vibration signals obtained from the experimental test rig. Signal processing categorized to time-frequency domain such as continues wavelet transform is used in the proposed work for statistical feature extraction. Feature selection method is used for selecting the extensive useful features among the extracted features to reduce the processing time. A famous optimization technique, Genetic algorithm (GA) and rough set based approach is used to select the best input features to reduce the computation burden. The efficiency of this feature selection method is evaluated based on the classification accuracy obtained from the proposed algorithms: back propagation neural network (BPNN) a famous artificial neural network algorithm and C4.5.Performance of classifiers are evaluated with the different signals acquired from the experimental test rig for different states of gears.

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