This study identifies relevant innovations and discusses value creation in the aviation industrybetween 2000and 2019.Aviation expertswithexperiencein innovation were selected and invited to complete a survey identifyi...This study identifies relevant innovations and discusses value creation in the aviation industrybetween 2000and 2019.Aviation expertswithexperiencein innovation were selected and invited to complete a survey identifying the leading innovations in the industry.This study contributes to recent aviation history by offering a list of innovations and a discussion of technological path dependency and value proposition with examples.This overview is helpful to academics and practitioners to verify how these innovations have shaped the industry worldwide,making it more efficient,agile,sustainable,and safe.The innovations selected comprise consolidated technologies and emerging advances introduced in the timeframe proposed.33 innovations primarily related to incremental and technical typologies that add value to products were mapped.In addition,this study provides insightful findings by classifying the value created for the aviation sector into five innovation clusters:(1)aircraft technology,adding value in terms of efficiency and sustainability;(2)innovation in passenger services,creating more personalized services and enhancing the customer experience;(3)innovation in flying,adding value in terms of safety and the security environment;(4)business and operational management,improving procedures and revenue;(5)and general applications,adding value in terms of Aviation 4.o(increases in automation and data exchange,including cyber-physical systems,the Internet of Things(IOT)and cloud computing).展开更多
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
文摘This study identifies relevant innovations and discusses value creation in the aviation industrybetween 2000and 2019.Aviation expertswithexperiencein innovation were selected and invited to complete a survey identifying the leading innovations in the industry.This study contributes to recent aviation history by offering a list of innovations and a discussion of technological path dependency and value proposition with examples.This overview is helpful to academics and practitioners to verify how these innovations have shaped the industry worldwide,making it more efficient,agile,sustainable,and safe.The innovations selected comprise consolidated technologies and emerging advances introduced in the timeframe proposed.33 innovations primarily related to incremental and technical typologies that add value to products were mapped.In addition,this study provides insightful findings by classifying the value created for the aviation sector into five innovation clusters:(1)aircraft technology,adding value in terms of efficiency and sustainability;(2)innovation in passenger services,creating more personalized services and enhancing the customer experience;(3)innovation in flying,adding value in terms of safety and the security environment;(4)business and operational management,improving procedures and revenue;(5)and general applications,adding value in terms of Aviation 4.o(increases in automation and data exchange,including cyber-physical systems,the Internet of Things(IOT)and cloud computing).
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.
