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• W2012160168 abstract "Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, Alpha-stable distribution was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by Alpha-stable distribution parameters, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance." @default.
• W2012160168 created "2016-06-24" @default.
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• W2012160168 date "2014-04-01" @default.
• W2012160168 modified "2023-09-26" @default.
• W2012160168 title "Bearing fault diagnosis based on Alpha-stable distribution feature extraction and SVM classifier" @default.
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• W2012160168 doi "https://doi.org/10.1109/icmcs.2014.6911199" @default.
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