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    Systems and methods for remote monitoring, security, diagnostics, and prognostics
    11.
    发明授权
    Systems and methods for remote monitoring, security, diagnostics, and prognostics 有权
    用于远程监控，安全，诊断和预测的系统和方法

    公开(公告)号：US09245116B2

    公开(公告)日：2016-01-26

    申请号：US13848354

    申请日：2013-03-21

    Applicant: General Electric Company

    Inventor： Scott Charles Evans ,  Richard Brownell Arthur ,  Bouchra Bouqata ,  Piyush Mishra ,  Weizhong Yan ,  Anil Varma

    IPC: G06F21/55

    CPC classification number: G06F21/55

    Abstract: A system includes a physical analysis module, a cyber analysis module, and a determination module. The physical analysis module is configured to obtain physical diagnostic information, and to determine physical analysis information using the physical diagnostic information. The cyber analysis module is configured to obtain cyber security data of the functional system, and to determine cyber analysis information using the cyber security data. The determination module is configured to obtain the physical analysis information and the cyber analysis information, and to determine a state of the functional system using the physical analysis information and the cyber analysis information. The state determined corresponds to at least one of physical condition or cyber security threat. The determination module is also configured to identify if the state corresponds to one or more of a non-malicious condition or a malicious condition.

    Abstract translation: 系统包括物理分析模块，网络分析模块和确定模块。 物理分析模块被配置为获得物理诊断信息，并且使用物理诊断信息来确定物理分析信息。 网络分析模块被配置为获取功能系统的网络安全数据，并使用网络安全数据来确定网络分析信息。 确定模块被配置为获得物理分析信息和网络分析信息，并且使用物理分析信息和网络分析信息来确定功能系统的状态。 所确定的状态对应于身体状况或网络安全威胁中的至少一种。 确定模块还被配置为识别该状态是否对应于非恶意条件或恶意条件中的一个或多个。

    SYSTEM AND METHOD FOR FAULT DETECTION IN AN ELECTRICAL DEVICE
    12.
    发明申请
    SYSTEM AND METHOD FOR FAULT DETECTION IN AN ELECTRICAL DEVICE 有权
    用于电气设备中故障检测的系统和方法

    公开(公告)号：US20150167661A1

    公开(公告)日：2015-06-18

    申请号：US14105819

    申请日：2013-12-13

    Applicant: General Electric Company

    Inventor： Dustin Ross Garvey ,  Scott Charles Evans ,  Bing Hu

    IPC: F04B51/00 ,  E21B43/12

    CPC classification number: F04B51/00 ,  E21B43/128 ,  F04B47/06 ,  F04B49/065 ,  F04B2201/0802 ,  F04B2203/0201 ,  F04B2203/0202 ,  F04B2205/05

    Abstract: A method for fault detection includes selecting a measured parameter from a subsurface electrical device and obtaining a plurality of samples for the measured parameter. The method also includes removing at least one invalid sample from the plurality of samples to generate a remaining number of samples. The method further includes computing a diagnostic parameter based on the remaining number of samples, if the remaining number of samples is greater than a predefined threshold number and terminating the method otherwise. The method also includes obtaining a rule from a plurality of rules stored in a database, based on the diagnostic parameter. The rule is indicative of a standard operating condition of the subsurface electrical device. The method further includes evaluating whether the determined diagnostic parameter satisfies the obtained rule, to generate an output and determining a measured operating condition of the subsurface electrical device based on the output.

    Abstract translation: 一种用于故障检测的方法包括从地下电气设备中选择测量的参数并获得所测量的参数的多个样本。 该方法还包括从多个样本中去除至少一个无效样本以产生剩余数量的样本。 该方法还包括如果剩余的采样数量大于预定义的阈值数量并且否则终止该方法，则基于剩余样本数量来计算诊断参数。 该方法还包括基于诊断参数从存储在数据库中的多个规则获得规则。 该规则表示地下电气设备的标准操作条件。 该方法还包括基于输出来评估确定的诊断参数是否满足所获得的规则，以产生输出并确定地下电气设备的测量操作条件。

    SYSTEMS AND METHODS FOR DATA LOSS PREVENTION
    13.
    发明申请
    SYSTEMS AND METHODS FOR DATA LOSS PREVENTION 审中-公开
    数据丢失防范的系统和方法

    公开(公告)号：US20150020207A1

    公开(公告)日：2015-01-15

    申请号：US13942318

    申请日：2013-07-15

    Applicant: General Electric Company

    Inventor： Shiva Prasad Kasiviswanathan ,  Lei Wu ,  Daniel Edward Marthaler ,  Scott Charles Evans ,  Varian Paul Powles ,  Philip Paul Beauchamp

    IPC: G06F21/60

    CPC classification number: G06F21/60 ,  G06F21/554

    Abstract: One method for developing a data loss prevention model includes receiving, at a processing device, an event record corresponding to an operation performed on a computing device. The event record includes an event type and event data. The method also includes transforming, using the processing device, the event type to an event number corresponding to the event type. The method includes transforming, using the processing device, the event data to a numerical representation of the event data. The method includes associating an indication of whether the event type and the event data correspond to a data loss event with the event number and the numerical representation. The method also includes determining the data loss prevention model using the indication, the event number, and the numerical representation.

    Abstract translation: 用于开发数据丢失预防模型的一种方法包括在处理设备处接收与在计算设备上执行的操作相对应的事件记录。 事件记录包括事件类型和事件数据。 该方法还包括使用处理装置将事件类型转换为与事件类型对应的事件号。 该方法包括使用处理装置将事件数据转换为事件数据的数字表示。 该方法包括将事件类型和事件数据是否对应于具有事件号和数字表示的数据丢失事件的指示。 该方法还包括使用指示，事件编号和数字表示来确定数据丢失预防模型。

    Systems and methods for continuous machine learning based control of wind turbines
    14.
    发明授权
    Systems and methods for continuous machine learning based control of wind turbines 有权

    公开(公告)号：US11060504B1

    公开(公告)日：2021-07-13

    申请号：US16784798

    申请日：2020-02-07

    Applicant: General Electric Company

    Inventor： Nurali Virani ,  Scott Charles Evans ,  Samuel Davoust ,  Samuel Bryan Shartzer ,  Dhiraj Arora

    IPC: F03D7/04 ,  F03D7/02 ,  F03D17/00 ,  G06N3/08 ,  G06N20/00

    Abstract: A control system is disclosed. The control system includes a wind turbine, at least one sensor configured to detect at least one property of the wind turbine to generate measurement data, and a controller communicatively coupled to the wind turbine and the at least one sensor. The controller includes at least one processor in communication with at least one memory device. The at least one processor is configured to control, during a training phase, the wind turbine according to at least one test parameter, receive, from the at least one sensor, during the training phase, first measurement data, generate, based on the at least one test parameter and the received first measurement data, a control model, receive, during an operating phase, second measurement data from the at least one sensor, and update the control model continuously based on the second measurement data.

    Machine-Learning Model-Based Analytic for Monitoring Wind Farm Power Performance
    15.
    发明申请
    Machine-Learning Model-Based Analytic for Monitoring Wind Farm Power Performance 有权

    公开(公告)号：US20210102525A1

    公开(公告)日：2021-04-08

    申请号：US16590580

    申请日：2019-10-02

    Applicant: General Electric Company

    Inventor： Scott Charles Evans ,  Arunvenkataraman Subramanian ,  Gerald Bowden Wise ,  Frank William Ripple, JR. ,  John Edmund LaFleche

    IPC: F03D7/04 ,  G06N20/10 ,  G06F11/07 ,  G06Q50/06

    Abstract: A method for controlling a wind turbine includes detecting a plurality of analytic outputs relating to power performance of the wind turbine from a plurality of different analytics. The method also includes analyzing the plurality of analytic outputs relating to power performance of the wind turbine. Further, the method includes generating at least one computer-based model of the power performance of the wind turbine using at least a portion of the analyzed plurality of analytic outputs. Moreover, the method includes training the computer-based model(s) of the power performance of the wind turbine using annotated analytic outputs relating to the power performance of the wind turbine. In addition, the method includes estimating a power magnitude of the wind turbine using the machine-learned computer-based model(s). As such, the method includes implementing a control action when the power magnitude of the wind turbine is outside of a selected range.

    METHOD FOR CONTROLLING OPERATION OF A WIND TURBINE
    18.
    发明申请
    METHOD FOR CONTROLLING OPERATION OF A WIND TURBINE 审中-公开

    公开(公告)号：US20200056589A1

    公开(公告)日：2020-02-20

    申请号：US16105076

    申请日：2018-08-20

    Applicant: General Electric Company

    Inventor： Scott Charles Evans ,  Sara Simonne Louisa Delport ,  Samuel Davoust ,  Nurali Virani ,  Samuel Bryan Shartzer

    IPC: F03D7/04 ,  F03D7/02

    Abstract: A method for controlling operation of a wind turbine includes collecting training data, training a machine learning model, obtaining recent data, and applying the machine learning model the recent data to output a reference power or reference power differential corresponding to the recent data. The machine learning model is then applied to the recent data to output at least one of estimated power or estimated power differential corresponding to values of the pitch setpoints and the tip speed ratio setpoints which differ from the recent data. A turbine setting is determined by comparing the estimated power or estimated power differential to the reference power or reference power differential, and then applying the turbine setting to the wind turbine if the estimated power or estimated power differential is greater than or equal to a threshold amount above the reference power or reference power differential.

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