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- W2470850335 abstract "In allusion to more indeterminate information and higher speed request characteristic in fault diagnosis system, according to the intelligence complementary strategy, a new fault diagnosis(SWPSO-BPN) model based on combining improved particle swarm optimization (PSO) algorithm and Back-propagation(BP) neural network is proposed in this paper. In the SWPSO-BPN method, an improved PSO (SWPSO) algorithm is proposed to optimize the parameters of BP neural network in order to overcome the shortcomings of slow learning speed and being easy to fall into local minimum, and obtain the optimal values of parameter combination in the BP neural network(SWPSOBPN) model. Then proposed SWPSO-BPN model is applied to diagnose the fault in order to obtain a new fault diagnosis (SWPSO-BPN-FD) method. Finally, the proposed SWPSO-BPN-FD method is used to test the data from bearing data center of CWRU. The experimental results show that the proposed SWPSO-BPN-FD method can accurately and effectively realize high precision fault diagnosis of rolling bearing. And this method takes on strong robustness and generalization ability." @default.
- W2470850335 created "2016-07-22" @default.
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- W2470850335 date "2016-06-30" @default.
- W2470850335 modified "2023-09-25" @default.
- W2470850335 title "Study on a New Fault Diagnosis Method Based on Combining Intelligent Technologies" @default.
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- W2470850335 doi "https://doi.org/10.14257/ijmue.2016.11.6.06" @default.
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