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- W2392951313 abstract "In view of the non-stationary features of vibration signals of rolling bearings and the difficulty to collect a large number of fault samples in practice, a fault diagnosis scheme based on ensemble empirical mode decomposition(EEMD) energy entropy and least square support vector machine(LS-SVM) is proposed. First of all, original acceleration vibration signals are decomposed into several intrinsic mode functions(IMFs). Since the energy of vibration signal changes in different frequency bands when fault occurs, the fault pattern and condition can be identified by calculating and analyzing the EEMD energy entropy. The energy features extracted from several IMFs, which contain the most dominant fault information, serve as the input vector of the support vector machine to judge the technical condition of the bearing and seriousness of the fault. The diagnosis results are analyzed and compared with different kernel functions. The experimental results show that the proposed method is effective." @default.
- W2392951313 created "2016-06-24" @default.
- W2392951313 creator A5013005164 @default.
- W2392951313 date "2014-01-01" @default.
- W2392951313 modified "2023-09-25" @default.
- W2392951313 title "Fault Diagnosis Method of Rolling Bearings Based on Ensemble Empirical Mode Decomposition Energy Entropy and LS-SVM" @default.
- W2392951313 hasPublicationYear "2014" @default.
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