Abstract
This study offers a novel intelligent detection method of rolling bearings that applied: the Common Spatial Pattern (CSP) method for feature extraction of bearing faults. CSP maximizes the variance ratio of the two class signal matrices coming from different sources. Different from the frequency features, characteristic vibration features from each measurement that can distinguish between the faulty and the healthy bearings were extracted. Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbour (k-NN) methods were tested on two different data sets. An 88.5% accuracy was obtained with ANN for two-class fault detection and 93.5% for fault classification. The classification results were compared with classical time domain feature sets. CSP features have achieved higher accuracies in all two-class and multiclass cases. Therefore, the CSP features can be a possible means for condition monitoring of bearings through vibration data.
Published Version
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