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To improve the accuracy of fault diagnosis for motor bearing with non-stationary and nonlinear characteristic, an ensemble fault diagnosis approach based on EEMD (Ensemble Empirical Mode Decomposition), KPCA (Kernel Principal Component Analysis) and IGSABP(Improved Gravitational Search Algorithm for Back Propagation neural network) is proposed. Firstly, EEMD extracts the non-stationary vibration signal...
For the sake of surmount problems of batch process precision of single fault diagnosis methods and low efficiency of the traditional, a new ensemble approach based on multi-way fast independent component analysis (MFICA) and recursive least squares support vector machines with forgetting factor (FFRLSSVM) is proposed. Firstly, MFICA is used to abstract rapid information which belongs to non-Gaussian...
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