Modern condition monitoring of rotating machinery became intelligent for enhanced reliability, productivity, and safety. Signal processing has been collaboratively implemented with several machine learning approaches to increase the effectiveness of the fault diagnosis. This paper explores fundamental bearing frequencies withdrawn from a vibration response as novel extracted features. Experimentally obtained vibration data at diverse operating conditions have been analyzed and supplied to a supervised machine learning algorithm K nearest neighbor network (KNN) for fault classification. The result shows that the KNN algorithm based on the novel features provides 98.5 fault classification accuracy and feels promising for condition monitoring of industrial rotating machines.
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