The prediction of Exhaust Gas Temperature Margin(EGTM)after washing aeroengines can provide a theoretical basis for airlines not only to evaluate the energy-saving effect and emission reduction,but also to formulate r...The prediction of Exhaust Gas Temperature Margin(EGTM)after washing aeroengines can provide a theoretical basis for airlines not only to evaluate the energy-saving effect and emission reduction,but also to formulate reasonable maintenance plans.However,the EGTM encounters step changes after washing aeroengines,while,in the traditional models,a persistence tendency exists between the prediction results and the previous data,resulting in low accuracy in prediction.In order to solve the problem,this paper develops a step parameters prediction model based on Transfer Process Neural Networks(TPNN).Especially,“step parameters”represent the parameters that can reflect EGTM step changes.They are analyzed in this study,and thus the model concentrates on the prediction of step changes rather than the extension of data trends.Transfer learning is used to handle the problem that few cleaning records result in few step changes for model learning.In comparison with Long Short-Term Memory(LSTM)and Kernel Extreme Learning Machine(KELM)models,the effectiveness of the proposed method is verified on CFM56-5B engine data.展开更多
介绍了STEP-NC的概念、数据模型及其结构特点,然后通过对比MLP(Machining Line Planner)和STEP-NC数控程序对特征和操作的不同定义方法,分析了在MLP中特征及加工工艺与STEP-NC的对应关系,探讨了在MLP中实现输出STEP-NC格式的零件加工程...介绍了STEP-NC的概念、数据模型及其结构特点,然后通过对比MLP(Machining Line Planner)和STEP-NC数控程序对特征和操作的不同定义方法,分析了在MLP中特征及加工工艺与STEP-NC的对应关系,探讨了在MLP中实现输出STEP-NC格式的零件加工程序的方法。展开更多
数据库实现是当前STEP数据模型实现数据交换的较优方法。研究STEP数据模型在关系数据库SQL Server上的映射,介绍数据字典的建立,详细阐述EXPRESS数据模式向SQL Server 2000转换的规则和方法,并给出一个具体的应用实例。结果表明,在关系...数据库实现是当前STEP数据模型实现数据交换的较优方法。研究STEP数据模型在关系数据库SQL Server上的映射,介绍数据字典的建立,详细阐述EXPRESS数据模式向SQL Server 2000转换的规则和方法,并给出一个具体的应用实例。结果表明,在关系数据库SQL Server上实现STEP数据模型的映射是可行的。展开更多
基金supported by the National Natural Science Foundation of China(No.1733201)。
文摘The prediction of Exhaust Gas Temperature Margin(EGTM)after washing aeroengines can provide a theoretical basis for airlines not only to evaluate the energy-saving effect and emission reduction,but also to formulate reasonable maintenance plans.However,the EGTM encounters step changes after washing aeroengines,while,in the traditional models,a persistence tendency exists between the prediction results and the previous data,resulting in low accuracy in prediction.In order to solve the problem,this paper develops a step parameters prediction model based on Transfer Process Neural Networks(TPNN).Especially,“step parameters”represent the parameters that can reflect EGTM step changes.They are analyzed in this study,and thus the model concentrates on the prediction of step changes rather than the extension of data trends.Transfer learning is used to handle the problem that few cleaning records result in few step changes for model learning.In comparison with Long Short-Term Memory(LSTM)and Kernel Extreme Learning Machine(KELM)models,the effectiveness of the proposed method is verified on CFM56-5B engine data.
文摘数据库实现是当前STEP数据模型实现数据交换的较优方法。研究STEP数据模型在关系数据库SQL Server上的映射,介绍数据字典的建立,详细阐述EXPRESS数据模式向SQL Server 2000转换的规则和方法,并给出一个具体的应用实例。结果表明,在关系数据库SQL Server上实现STEP数据模型的映射是可行的。
