SAE及其航空航天标准

简要介绍了SAE的发展历史及其合作伙伴等情况,重点介绍了SAE航空航天标准类型、标准组织机构及标准制定过程.     

基于堆栈自编码网络的铣刀磨损特征提取方法

针对传统切削数据人工提取的特征主观性和盲目性强、特征提取过程耗时且提取质量难以保证等问题,提出一种基于堆栈自编码网络(SAE)的切削信号数据特征提取方法,构建了由3个自动编码器(AE)组成的SAE网络。前一个AE无监督训练后得到隐藏层特征,作为下一个AE的输入,最后整体利用反向传播算法进行有监督微调,从而得到更优的特征表达。从基于SAE的数据重构性能分析和加工信号特征主成分分析2个层面,对切削信号特征提取的优劣进行评估。实例验证说明,相比于传统手工提取特征的方法,所提方法在压缩信号的特征提取方面表现出明显的优势,进一步说明了SAE特征提取的有效性。     

Best practices for model-based and simulation-aided engineering of power transmission and motion control systems

This paper deals with the modelling and simulation of aircraft systems, in particular for power transmission and control. It is intended to review, propose and disseminate best practices for making model-based/simulation-aided engineering more efficient at any phase of the system life cycle. The proposals are aimed at creating value, not only by increasing the performance of the product under study but also by shortening the time to market, capitalizing knowledge, mitigating risks and facilitating concurrent engineering. The needs associated with the engineering activities are firstly identified to define a set of requirements for the models. Then, these requirements are used to drive the considerations leading to model development, focusing in particular on the process, modelled physical effects, modelling level, model architecting and concurrent engineering. The third part deals with the model implementation, giving special consideration to the different types of models, causalities, parameterization, implementation and verification. Each part is illustrated by examples related to safety critical actuators.     

SAE AS4893《通用开放式结构(GOA)框架》评析

介绍和分析了SAE AS4893《通用开放式结构(GOA)框架》的编制背景、主要内容和应用原则;并与目前流行的开放式结构ASAAC、ISO OSI及POSIX进行了比较;最后分析了我国制定相应标准的必要性和可行性,并提出了相关标准化工作的建议。     

涡轮叶片早期裂纹的三维叶尖间隙EEMD能量熵融合诊断方法

为了解决航空发动机涡轮叶片早期裂纹故障信号微弱、难以识别的问题,提出一种基于三维叶尖间隙集成经验模态分解（EEMD）能量熵融合的涡轮叶片早期裂纹诊断方法。采集涡轮叶片三维叶尖间隙信息,利用EEMD分别对三维叶尖间隙各维信号进行处理,得到相应的固有模态函数(IMF),以此计算每一维信号分量EEMD能量熵,构建能表征叶片裂纹状态的不同EEMD能量熵高维矢量集。建立多个堆叠自动编码器(SAE)分别对各高维矢量集进行特征学习并提取所学习的深层特征表达。利用支持向量机算法(SVM)和遗传算法(GA)融合各维深层特征以综合不同维度信息进而充分判定叶片裂纹状态。通过涡轮叶片裂纹诊断试验,结果表明:所提方法能有效提高叶片早期裂纹诊断精度,其平均准确率达到98.415%,标准差仅为0.697%,具有很好的稳定性、泛化性和自适应性。