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• W2022243308 abstract "Abstract This study presents a novel procedure based on ensemble empirical mode decomposition (EEMD) and optimized support vector machine (SVM) for multi-fault diagnosis of rolling element bearings. The vibration signal is adaptively decomposed into a number of intrinsic mode functions (IMFs) by EEMD. Two types of features, the EEMD energy entropy and singular values of the matrix whose rows are IMFs, are extracted. EEMD energy entropy is used to specify whether the bearing has faults or not. If the bearing has faults, singular values are input to multi-class SVM optimized by inter-cluster distance in the feature space (ICDSVM) to specify the fault type. The proposed method was tested on a system with an electric motor which has two rolling bearings with 8 normal working conditions and 48 fault working conditions. Five groups of experiments were done to evaluate the effectiveness of the proposed method. The results show that the proposed method outperforms other methods both mentioned in this paper and published in other literatures." @default.
• W2022243308 created "2016-06-24" @default.
• W2022243308 creator A5006387641 @default.
• W2022243308 creator A5014135403 @default.
• W2022243308 date "2013-12-01" @default.
• W2022243308 modified "2023-10-01" @default.
• W2022243308 title "Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines" @default.
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• W2022243308 doi "https://doi.org/10.1016/j.ymssp.2013.07.006" @default.
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