Invention Grant
US09466032B2 Method for the computer-supported generation of a data-driven model of a technical system, in particular of a gas turbine or wind turbine
有权
计算机支持生成技术系统,特别是燃气轮机或风力涡轮机的数据驱动模型的方法
- Patent Title: Method for the computer-supported generation of a data-driven model of a technical system, in particular of a gas turbine or wind turbine
- Patent Title (中): 计算机支持生成技术系统,特别是燃气轮机或风力涡轮机的数据驱动模型的方法
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Application No.: US14123401Application Date: 2012-06-01
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Publication No.: US09466032B2Publication Date: 2016-10-11
- Inventor: Siegmund Düll , Alexander Hentschel , Volkmar Sterzing , Steffen Udluft
- Applicant: Siegmund Düll , Alexander Hentschel , Volkmar Sterzing , Steffen Udluft
- Applicant Address: DE
- Assignee: SIEMENS AKTIENGESELLSCHAFT
- Current Assignee: SIEMENS AKTIENGESELLSCHAFT
- Current Assignee Address: DE
- Agency: Ostrolenk Faber LLP
- Priority: DE102011076936 20110603
- International Application: PCT/EP2012/060400 WO 20120601
- International Announcement: WO2012/164075 WO 20121206
- Main IPC: G06F19/00
- IPC: G06F19/00 ; G06N99/00 ; G05B17/02 ; G05B23/02 ; G05B13/04 ; G06N3/04

Abstract:
A method for the computer-supported generation of a data-driven model of a technical system, in particular of a gas turbine or wind turbine, based on training data is disclosed. 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 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.
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