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1.发明申请Systems and Methods for Controlling Power Generation Plant Operations via a Human-Machine Interface 审中-公开通过人机界面控制发电厂运行的系统和方法公开(公告)号：US20160328133A1
公开(公告)日：2016-11-10
申请号：US14920464
申请日：2015-10-22
Applicant: General Electric Company
Inventor： So Young Kim , Nagesh Laxminarayana Kurella , Lester D. Childs , James Vanwormer , Jonathan Mann
IPC: G06F3/0484 , G05B19/042 , G06F3/0482
Abstract: Embodiments of the present disclosure relate to systems and methods for controlling power generation plant operations via a human-machine interface. In one embodiment, a method can provide: receiving a user selection of a power generation plant component in a first navigation menu of a human-machine interface (HMI); based at least in part on the user selection, generating a second navigation menu of the HMI; receiving a subsequent user selection of a subcomponent in the second navigation menu of the HMI, wherein the subcomponent is associated with the power generation plant component; generating a control region for the HMI, the control region operable to manipulate the subcomponent; receiving a user input for the control region; and based at least in part on the user input, facilitating manipulation of one or more operations of the subcomponent.
Abstract translation: 本公开的实施例涉及用于经由人机界面来控制发电厂运行的系统和方法。 在一个实施例中，一种方法可以提供：在人机接口（HMI）的第一导航菜单中接收用户对发电设备部件的选择; 至少部分地基于用户选择，生成HMI的第二导航菜单; 在所述HMI的第二导航菜单中接收对子组件的后续用户选择，其中所述子组件与所述发电设备组件相关联; 生成用于所述HMI的控制区域，所述控制区域可操作以操纵所述子部件; 接收用于所述控制区域的用户输入; 并且至少部分地基于用户输入，便于操纵子组件的一个或多个操作。
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公开(公告)号：US10060358B2
公开(公告)日：2018-08-28
申请号：US15132782
申请日：2016-04-19
Applicant: General Electric Company
Inventor： Awadesh Kumar Tiwari , Aditi Koppikar , Jerome Lefin , Nimmy Paulose , James Vanwormer
CPC classification number: F02C9/18 , F01D17/105 , F04D27/001 , F04D27/0215 , F04D27/023 , F05D2260/80 , F05D2260/81 , F05D2270/44 , F05D2270/709 , F05D2270/71
Abstract: In one example embodiment, a gas turbine system includes a gas turbine, a compressor bleed valve, various sensors, and a valve health assessment system. The compressor bleed valve bleeds a portion of air received by the compressor during certain operating modes. The valve health assessment system includes a processor that uses a neural network model to generate a first failure mode predictor of the compressor bleed valve by processing operational data provided by the various sensors. The processor also generates a second failure mode predictor of the compressor bleed valve by using empirical data and a valve transfer function. The processor further generates a third failure mode predictor of the compressor bleed valve by using predictive data and a prediction model. The processor then applies a holistic procedure to the three failure mode predictors to derive a probability of occurrence of a failure in the compressor bleed valve.
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3.发明授权公开(公告)号：US10572130B2
公开(公告)日：2020-02-25
申请号：US14920464
申请日：2015-10-22
Applicant: General Electric Company
Inventor： So Young Kim , Nagesh Laxminarayana Kurella , Lester D. Childs , James Vanwormer , Jonathan Mann
IPC: G06F3/0482 , G06F3/0484 , G05B19/042
Abstract: Embodiments of the present disclosure relate to systems and methods for controlling power generation plant operations via a human-machine interface. In one embodiment, a method can provide: receiving a user selection of a power generation plant component in a first navigation menu of a human-machine interface (HMI); based at least in part on the user selection, generating a second navigation menu of the HMI; receiving a subsequent user selection of a subcomponent in the second navigation menu of the HMI, wherein the subcomponent is associated with the power generation plant component; generating a control region for the HMI, the control region operable to manipulate the subcomponent; receiving a user input for the control region; and based at least in part on the user input, facilitating manipulation of one or more operations of the subcomponent.
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公开(公告)号：US20170298836A1
公开(公告)日：2017-10-19
申请号：US15132782
申请日：2016-04-19
Applicant: General Electric Company
Inventor： Awadesh Kumar Tiwari , Aditi Koppikar , Jerome Lefin , Nimmy Paulose , James Vanwormer
CPC classification number: F02C9/18 , F01D17/105 , F04D27/001 , F04D27/0215 , F04D27/023 , F05D2260/80 , F05D2260/81 , F05D2270/44 , F05D2270/709 , F05D2270/71
Abstract: In one example embodiment, a gas turbine system includes a gas turbine, a compressor bleed valve, various sensors, and a valve health assessment system. The compressor bleed valve bleeds a portion of air received by the compressor during certain operating modes. The valve health assessment system includes a processor that uses a neural network model to generate a first failure mode predictor of the compressor bleed valve by processing operational data provided by the various sensors. The processor also generates a second failure mode predictor of the compressor bleed valve by using empirical data and a valve transfer function. The processor further generates a third failure mode predictor of the compressor bleed valve by using predictive data and a prediction model. The processor then applies a holistic procedure to the three failure mode predictors to derive a probability of occurrence of a failure in the compressor bleed valve.
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