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G, Mr. Manjunatha
- Bearing Fault Classification Using Statistical Features and Machine Learning Approach
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Authors
Affiliations
1 Research Scholar, Department of Mechanical Engineering, UVCE, Bangalore University, Assistant Professor, School of Mechanical Engineering, REVA University, IN
2 Assistant Professor, School of Mechanical Engineering, REVA University, Professor, Department of Mechanical Engineering, UVCE, Bangalore University, Karnataka, IN
1 Research Scholar, Department of Mechanical Engineering, UVCE, Bangalore University, Assistant Professor, School of Mechanical Engineering, REVA University, IN
2 Assistant Professor, School of Mechanical Engineering, REVA University, Professor, Department of Mechanical Engineering, UVCE, Bangalore University, Karnataka, IN
Source
Journal of Mines, Metals and Fuels, Vol 70, No 4 (2022), Pagination: 104-107Abstract
Bearing degradation is the most common source of faults in machines. In this context, this work presents a monitoring scheme to diagnose bearing faults using machine learning approach. In this approach classification of healthy and faulty conditions of the bearing is carried out using artificial neural network (ANN). A set of statistical features are extracted from the acquired vibration signals. The decision tree technique is used to select significant features out of all statistical extracted features. The selected features were classified using different classifiers. Based on the various classifier results obtained, the ANN classifier achieve the maximum classification accuracy which is recommended for online monitoring and fault diagnosis of the bearing in various machines.Keywords
Bearing fault, diagnosis, ANN, classifiers, statistical features.
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