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Fault diagnosis method of bearing utilizing GLCM and MBASA-based KELM - Scientific Reports

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Fault diagnosis method of bearing utilizing GLCM and MBASA-based KELM - Scientific Reports

In this study, fault diagnosis method of bearing utilizing gray level co-occurrence matrix (GLCM) and multi-beetles antennae search algorithm (MBASA)-based kernel extreme learning machine (KELM) is presented. In the proposed method, feature extraction of time–frequency image based on GLCM is proposed to extract the features of the bearing vibration signal, and multi-beetles antennae search algorithm-based KELM (MBASA-KELM) is presented to recognize the states of bearing.

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Fault diagnosis method of bearing utilizing GLCM and MBASA-based KELM - Scientific Reports

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Fault diagnosis method of bearing utilizing GLCM and MBASA-based KELM - Scientific Reports

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