In data-driven fault diagnosis for turbo-generator sets,the fault samples are usually expensive to obtain,and inevitably with noise,which will both lead to an unsatisfying identification performance of diagnosis model...In data-driven fault diagnosis for turbo-generator sets,the fault samples are usually expensive to obtain,and inevitably with noise,which will both lead to an unsatisfying identification performance of diagnosis models.To address these issues,this paper proposes a fault diagnosis model for turbo-generator sets based on Weighted Extension Neural Network(W-ENN).WENN is a novel neural network which has three types of connection weights and an improved correlation function.The performance of the proposed model is validated against Extension Neural Network(ENN),Support Vector Machine(SVM),Relevance Vector Machine(RVM)and Extreme Learning Machine(ELM)based models.The results indicate that,on noisy small sample sets,the proposed model is superior to the other models in terms of higher identification accuracy with fewer samples and strong noise-tolerant ability.The findings of this study may serve as a powerful fault diagnosis model for turbo-generator sets on noisy small sample sets.展开更多
The cashmere yarns were set in steam of 120℃ for 5 minutes after they had been extended to about 3% and wrapped onto the glass mandrels, which was relevant to the industrial setting processes. The effects of the stea...The cashmere yarns were set in steam of 120℃ for 5 minutes after they had been extended to about 3% and wrapped onto the glass mandrels, which was relevant to the industrial setting processes. The effects of the steaming on the tensile mechanical properties of cashmere fiber are investigated. The extension in ' yield region' and the extension at rupture of the set cashmere fiber are obviously decreased.展开更多
基金the National Natural Science Foundation of China(No.51775272,No.51005114)The Fundamental Research Funds for the Central Universities,China(No.NS2014050)。
文摘In data-driven fault diagnosis for turbo-generator sets,the fault samples are usually expensive to obtain,and inevitably with noise,which will both lead to an unsatisfying identification performance of diagnosis models.To address these issues,this paper proposes a fault diagnosis model for turbo-generator sets based on Weighted Extension Neural Network(W-ENN).WENN is a novel neural network which has three types of connection weights and an improved correlation function.The performance of the proposed model is validated against Extension Neural Network(ENN),Support Vector Machine(SVM),Relevance Vector Machine(RVM)and Extreme Learning Machine(ELM)based models.The results indicate that,on noisy small sample sets,the proposed model is superior to the other models in terms of higher identification accuracy with fewer samples and strong noise-tolerant ability.The findings of this study may serve as a powerful fault diagnosis model for turbo-generator sets on noisy small sample sets.
文摘The cashmere yarns were set in steam of 120℃ for 5 minutes after they had been extended to about 3% and wrapped onto the glass mandrels, which was relevant to the industrial setting processes. The effects of the steaming on the tensile mechanical properties of cashmere fiber are investigated. The extension in ' yield region' and the extension at rupture of the set cashmere fiber are obviously decreased.
