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- W4309227250 abstract "In rotating machinery systems, the bearing is the most common and easily damaged part of the mechanical system. Correct and quick bearing fault detection is important for machinery systems. Traditionally, we first extract the features from raw process data, then diagnose the faults and further apply classification methods. However, these traditional methods lack the dynamic information from raw processed data and also abide the expertise of feature extraction. The classification of rolling bearing failures is proposed in this research using a new model based on the Support Vector Machine (SVM) algorithm. Raw process data can be directly classified by the fault diagnosis method without performing the feature extraction. The model can learn dynamic information from raw process data in an adaptable manner. The ability of the proposed model to correctly classify bearing conditions at any running speed as Normal and different types of fault using raw vibration has been tested and results are compared with existing techniques. The SVM method produces better outcomes than the previous research. Keywords-Support Vector Machine, Intelligent Fault Classification, Roller Bearings Vibration" @default.
- W4309227250 created "2022-11-24" @default.
- W4309227250 creator A5052620896 @default.
- W4309227250 date "2022-09-23" @default.
- W4309227250 modified "2023-09-25" @default.
- W4309227250 title "Intelligent Fault Classification of Motor Bearing by using Support Vector Machine" @default.
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- W4309227250 doi "https://doi.org/10.1109/globconpt57482.2022.9938197" @default.
- W4309227250 hasPublicationYear "2022" @default.
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