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This paper presents a comprehensive bearing fault diagnosis methodology to prevent unscheduled interruptions in machinery. This method consists of fault signature extraction, discriminative fault feature selection (not only to improve diagnosis but also to reduce computational overhead), and decision making using a k-nearest neighbor classifier. Although the presented method yields very accurate classification,...
This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all...
Condition monitoring is a vital task in the maintenance of industry machines. This paper proposes a reliable condition monitoring method using a genetic algorithm (GA) which selects the most discriminate features by taking a transformation matrix. Experimental results show that the features selected by the GA outperforms original and randomly selected features using the same k-nearest neighbor (k-NN)...
We propose an efficient method to filter the incorrect matches of SIFT-based feature matching which includes correct and incorrect matches using fundamental matrix and genetic algorithm. First, best fundamental matrix is estimated by genetic algorithm whose input data are unfiltered feature matches. To design chromosome, each gene represents the index of selected matches and it has linkage value which...
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