Currently,experimental research on variable stiffness design mainly focuses on laminates.To ensure adaptability in practical application,it is imperative to conduct a systematic study on stiffened variable stiffness s...Currently,experimental research on variable stiffness design mainly focuses on laminates.To ensure adaptability in practical application,it is imperative to conduct a systematic study on stiffened variable stiffness structures,including design,manufacture,experiment,and simulation.Based on the minimum curvature radius and process schemes,two types of T-stiffened panels were designed and manufactured.Uniaxial compression tests have been carried out and the results indicate that the buckling load of variable stiffness specimens is increased by 26.0%,while the failure load is decreased by 19.6%.The influence mechanism of variable stiffness design on the buckling and failure behavior of T-stiffened panels was explicated by numerical analysis.The primary reason for the reduced strength is the significantly increased load bearing ratio of stiffeners.As experimental investigations of stiffened variable stiffness structures are very rare,this study can be considered a reference for future work.展开更多
In practical mechanical fault detection and diagnosis,it is difficult and expensive to collect enough large-scale supervised data to train deep networks.Transfer learning can reuse the knowledge obtained from the sour...In practical mechanical fault detection and diagnosis,it is difficult and expensive to collect enough large-scale supervised data to train deep networks.Transfer learning can reuse the knowledge obtained from the source task to improve the performance of the target task,which performs well on small data and reduces the demand for high computation power.However,the detection performance is significantly reduced by the direct transfer due to the domain difference.Domain adaptation(DA)can transfer the distribution information from the source domain to the target domain and solve a series of problems caused by the distribution difference of data.In this survey,we review various current DA strategies combined with deep learning(DL)and analyze the principles,advantages,and disadvantages of each method.We also summarize the application of DA combined with DL in the field of fault diagnosis.This paper provides a summary of the research results and proposes future work based on analysis of the key technologies.展开更多
基金Supported by the National Natural Science Foundation of China(No.11902124).
文摘Currently,experimental research on variable stiffness design mainly focuses on laminates.To ensure adaptability in practical application,it is imperative to conduct a systematic study on stiffened variable stiffness structures,including design,manufacture,experiment,and simulation.Based on the minimum curvature radius and process schemes,two types of T-stiffened panels were designed and manufactured.Uniaxial compression tests have been carried out and the results indicate that the buckling load of variable stiffness specimens is increased by 26.0%,while the failure load is decreased by 19.6%.The influence mechanism of variable stiffness design on the buckling and failure behavior of T-stiffened panels was explicated by numerical analysis.The primary reason for the reduced strength is the significantly increased load bearing ratio of stiffeners.As experimental investigations of stiffened variable stiffness structures are very rare,this study can be considered a reference for future work.
基金supported by the National Natural Science Foundation of China(Grant Nos.52175096,51775243,11902124),the fellowship of China Postdoctoral Science Foundation(Grant No.2021T140279)111 Project(Grant No.B18027).
文摘In practical mechanical fault detection and diagnosis,it is difficult and expensive to collect enough large-scale supervised data to train deep networks.Transfer learning can reuse the knowledge obtained from the source task to improve the performance of the target task,which performs well on small data and reduces the demand for high computation power.However,the detection performance is significantly reduced by the direct transfer due to the domain difference.Domain adaptation(DA)can transfer the distribution information from the source domain to the target domain and solve a series of problems caused by the distribution difference of data.In this survey,we review various current DA strategies combined with deep learning(DL)and analyze the principles,advantages,and disadvantages of each method.We also summarize the application of DA combined with DL in the field of fault diagnosis.This paper provides a summary of the research results and proposes future work based on analysis of the key technologies.
