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G, 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 3A (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.
References
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- Fault Detection of Bearing using Signal Processing Technique and Machine Learning Approach
Abstract Views :65 |
PDF Views:0
Authors
Affiliations
1 Research Scholar, Department of Mechanical Engineering, UVCE, Bangalore University, Karnataka, India. Assistant Professor, School of Mechanical Engineering, REVA University, Karnataka, India., IN
2 Professor, Department of Mechanical Engineering, UVCE, Bangalore University, Karnataka, India., IN
3 Department of Mechanical Engineering, NITTE (Deemed to be University), Nitte 574110, India., IN
1 Research Scholar, Department of Mechanical Engineering, UVCE, Bangalore University, Karnataka, India. Assistant Professor, School of Mechanical Engineering, REVA University, Karnataka, India., IN
2 Professor, Department of Mechanical Engineering, UVCE, Bangalore University, Karnataka, India., IN
3 Department of Mechanical Engineering, NITTE (Deemed to be University), Nitte 574110, India., IN
Source
Journal of Mines, Metals and Fuels, Vol 70, No 10A (2022), Pagination: 380-388Abstract
In large- or small-scale industries, machines have rotary element supported by bearings for accurate drive and fixed support. Fault diagnosis has gained much importance in recent times due to increased bearing failures. This demands an efficient diagnosis methodology to detect faults in bearings. In this work, fault diagnosis for the acquired vibration signals of healthy and fault seeded in rolling element bearings were investigated using signal processing technique and online machine learning approach. The research work is carried out in two phases. The first phase of research work investigates fault detection of bearing using conventional signal processing techniques such as time domain analysis and spectrum analysis. The results show that signal processing techniques may be useful for revealing post fault detection information. It was also concluded that the use of different signal processing techniques is often necessary to achieve meaningful diagnostic information from the signals. The second phase of research work describes fault diagnosis of bearing using machine learning approach. Using MATLAB, Discrete Wavelet Features (DWT), were extracted from acquired signals for different rolling element bearing conditions. J48 algorithm was implemented to extract most significant features. Extracted features were used as input to different classifiers to obtain maximum classification accuracy of rolling element bearings. The results showed that machine learning technique could be used to detect and classify the different fault sizes effectively with vibration signals.
Keywords
Spectrum Analysis, DWT, Machine Learning, J-48, Bearing Fault.References
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