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1. 发明申请

WO2012164075A3 METHOD FOR THE COMPUTER-SUPPORTED GENERATION OF A DATA-DRIVEN MODEL OF A TECHNICAL SYSTEM, IN PARTICULAR OF A GAS TURBINE OR WIND TURBINE 审中-公开
标题翻译：方法进行了技术系统的数据驱动模型计算机辅助生成，尤其是燃气涡轮机和风力涡轮
公开(公告)号：WO2012164075A3
公开(公告)日：2013-02-21
申请号：PCT/EP2012060400
申请日：2012-06-01
申请人： SIEMENS AG , DUELL SIEGMUND , HENTSCHEL ALEXANDER , STERZING VOLKMAR , UDLUFT STEFFEN
发明人： DUELL SIEGMUND , HENTSCHEL ALEXANDER , STERZING VOLKMAR , UDLUFT STEFFEN
IPC分类号： G05B17/02
CPC分类号： G06N99/005 , F03D7/046 , F03D17/00 , G05B13/04 , G05B17/02 , G05B23/024 , G06N3/0481 , Y02E10/723
摘要： The invention relates to a method for the computer-supported generation of a data-driven model (NM) of a technical system, in particular of a gas turbine or wind turbine, based on training data. The method according to the invention is characterized in that the data-driven model is preferably learned in regions of training data having a low data density. According to the invention, it is thus ensured that the data-driven model is generated for information-relevant regions of the training data. The data-driven model generated by the method according to the invention is used in a particularly preferred embodiment for calculating a suitable control and/or regulation model or monitoring model for the technical system. By determining optimization criteria, such as low pollutant emissions or low combustion dynamics of a gas turbine, the service life of the technical system in operation can be extended. The data model generated by the method according to the invention can furthermore be determined quickly and using low computing resources, since not all training data is used for learning the data-driven model.
摘要翻译：本发明涉及用于计算机辅助代技术系统的一个数据驱动模型（NM）的，特别是燃气涡轮机或风力涡轮机，基于训练数据的方法。本发明的方法的特征在于，所述数据驱动模型在训练数据，其中低数据密度是本领域优先学习。这确保了对生成的训练数据的信息相关领域的数据驱动模型得到保证。用本发明的方法的数据驱动模型生成的在一个特别优选的实施例的用于为技术系统中的合适的控制和/或调节模型或监视模型的计算。通过设置优化标准，比如低污染物排放和燃气涡轮的低燃烧动力学，从而该技术系统的寿命操作期间被延长。在使用本发明方法的数据模型生成能够快速且具有低的计算资源进一步确定，因为不是所有的训练数据用于研究数据驱动模型。

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