公开(公告)号：US10954812B2

公开(公告)日：2021-03-23

申请号：US14905641

申请日：2015-12-11

Applicant: General Electric Company ,  William Forrester Seely ,  Xu Fu ,  Mustafa Tekin Dokucu ,  Ronghui Zhou

Inventor： William Forrester Seely ,  Xu Fu ,  Mustafa Tekin Dokucu ,  Ronghui Zhou

IPC: F01D21/14 ,  F04D27/02 ,  F04D29/66 ,  F01D25/06 ,  G01H1/00 ,  F01D21/04 ,  F02C9/00 ,  F01D21/00 ,  G01H13/00 ,  G05B13/04

Abstract: A flutter control system for a turbine includes a processor. The processor is configured to detect blade flutter of a turbine. The blade flutter indicates that blades of the turbine are in a deflected position different from a nominal operating position. The processor is configured to control operational parameters of the turbine that reduce or eliminate the blade flutter to improve the reliability and efficiency of the turbine.

公开(公告)号：US20160365736A1

公开(公告)日：2016-12-15

申请号：US14738870

申请日：2015-06-13

Applicant: General Electric Company

Inventor： Frederick William Block ,  Mustafa Tekin Dokucu

IPC: H02J3/46 ,  G05B13/04

CPC classification number: H02J3/46 ,  G05B13/0265 ,  G05B13/042 ,  G05B17/02 ,  H02J2003/007 ,  Y02E60/76 ,  Y04S40/22

Abstract: A method includes selecting a first desired parameter of a machinery configured to produce power, a first surrogate parameter related to the desired parameter, and a first model configured to generate the desired parameter based on a first relationship between the first surrogate parameter and the first desired parameter. The method also includes receiving data related to the first surrogate parameter from a plurality of sensors coupled to the machinery and generating the first desired parameter using the data and the first model. Further, the method includes deriving a first set of empirical data relating the first surrogate parameter to the desired parameter and adjusting the first model based on the data, the first surrogate parameter, and the first set of empirical data, wherein the adjustment to the first model occurs in real-time.

Abstract translation: 一种方法包括选择配置成产生电力的机械的第一所需参数，与期望参数相关的第一替代参数，以及第一模型，其被配置为基于第一代理参数和第一期望参数之间的第一关系产生期望参数 参数。 该方法还包括从耦合到机器的多个传感器接收与第一替代参数有关的数据，并使用数据和第一模型生成第一期望参数。 此外，该方法包括将与第一代理参数相关的第一组经验数据导出到期望参数，并且基于数据，第一替代参数和第一组经验数据来调整第一模型，其中调整到第一 模型发生在实时。

公开(公告)号：US20150362923A1

公开(公告)日：2015-12-17

申请号：US14305063

申请日：2014-06-16

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Eric Richard Westervelt ,  Benjamin Paul Breig ,  Mustafa Tekin Dokucu ,  Neil Richard Garrigan ,  William Dwight Gerstler ,  Javier Armando Parrilla

IPC: G05D7/06 ,  G05B13/02 ,  G05B15/02

CPC classification number: G05B13/026 ,  G05B15/02 ,  G05D23/1917

Abstract: A control system for an adaptive-power thermal management system of an aircraft having at least one adaptive cycle gas turbine engine includes a real time optimization solver that utilizes a plurality of models of systems to be controlled, the plurality of models each being defined by algorithms configured to predict changes to each system caused by current changes in input to each system. The real time optimization solver is configured to solve an open-loop optimal control problem on-line at each of a plurality of sampling times, to provide a series of optimal control actions as a solution to the open-loop optimal control problem. The real time optimization solver implements a first control action in a sequence of control actions and at a next sampling time the open-loop optimal control problem is re-posed and re-solved.

Abstract translation: 用于具有至少一个自适应循环燃气涡轮发动机的飞机的自适应功率热管理系统的控制系统包括利用要控制的多个系统模型的实时优化求解器，所述多个模型各自由算法 配置为预测由每个系统的输入的当前变化引起的每个系统的变化。 实时优化求解器被配置为在多个采样时间中的每一个在线解决开环最优控制问题，以提供作为开环最优控制问题的解决方案的一系列最佳控制动作。 实时优化求解器在控制动作序列中实现第一控制动作，并且在下一个采样时间，重新提出并解决了开环最优控制问题。

公开(公告)号：US12180939B2

公开(公告)日：2024-12-31

申请号：US17698040

申请日：2022-03-18

Applicant: General Electric Company

Inventor： Kalpesh Singal ,  Mustafa Tekin Dokucu ,  Fernando Javier D'Amato ,  Georgios Boutselis

IPC: F03D7/04 ,  F03D7/02

Abstract: A method for providing backup control for a supervisory controller of at least one wind turbine includes observing, via a learning-based backup controller of the at least one wind turbine, at least one operating parameter of the supervisory controller under normal operation. The method also includes learning, via the learning-based backup controller, one or more control actions of the at least one wind turbine based on the operating parameter(s). Further, the method includes receiving, via the learning-based backup controller, an indication that the supervisory controller is unavailable to continue the normal operation. Upon receipt of the indication, the method includes controlling, via the learning-based backup controller, the wind turbine(s) using the learned one or more control actions until the supervisory controller becomes available again. Moreover, the control action(s) defines a delta that one or more setpoints of the wind turbine(s) should be adjusted by to achieve a desired outcome.

公开(公告)号：US12141052B2

公开(公告)日：2024-11-12

申请号：US18321545

申请日：2023-05-22

Applicant: General Electric Company

Inventor： Hema K Achanta ,  Masoud Abbaszadeh ,  Weizhong Yan ,  Mustafa Tekin Dokucu

IPC: G06F11/36 ,  G06F11/263 ,  G06F18/213 ,  G06F18/214 ,  G06F18/24 ,  G06N3/08 ,  G06V10/82 ,  H04L9/40 ,  H04L41/16 ,  H04L43/12 ,  H04W12/128 ,  G06N3/04

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided to protect a cyber-physical system having a plurality of monitoring nodes comprising: a normal space data source storing, for each of the plurality of monitoring nodes, a series of normal monitoring node values over time that represent normal operation of the cyber-physical system; a situational awareness module including an abnormal data generation platform, wherein the abnormal data generation platform is operative to generate abnormal data to represent abnormal operation of the cyber-physical system using values in the normal space data source and a generative model; a memory for storing program instructions; and a situational awareness processor, coupled to the memory, and in communication with the situational awareness module and operative to execute the program instructions to: receive a data signal, wherein the received data signal is an aggregation of data signals received from one or more of the plurality of monitoring nodes, wherein the data signal includes at least one real-time stream of data source signal values that represent a current operation of the cyber-physical system; determine, via a trained classifier, whether the received data signal is a normal signal or an abnormal signal, wherein the trained classifier is trained with the generated abnormal data and normal data; localize an origin of an anomaly when it is determined the received data signal is the abnormal signal; receive the determination and localization at a resilient estimator module; execute the resilient estimator module to generate a state estimation for the cyber-physical system. Numerous other aspects are provided.

公开(公告)号：US11729190B2

公开(公告)日：2023-08-15

申请号：US16666807

申请日：2019-10-29

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Subhrajit Roychowdhury ,  Masoud Abbaszadeh ,  Mustafa Tekin Dokucu

IPC: G06F21/00 ,  H04L9/40 ,  G06F18/214 ,  G06F18/21

CPC classification number: H04L63/1425 ,  G06F18/214 ,  G06F18/2185 ,  H04L63/1416 ,  H04L63/1433 ,  H04L63/1458 ,  H04L63/1466

Abstract: An industrial asset may have monitoring nodes that generate current monitoring node values. A dynamic, resilient estimator may split a temporal monitoring node space into normal and one or more abnormal subspaces associated with different kinds of attack vectors. According to some embodiments, a neutralization model is constructed and trained for each attack vector using supervised learning and the associated abnormal subspace. In other embodiments, a single model is created using out-of-range values for abnormal monitoring nodes. Responsive to an indication of a particular abnormal monitoring node or nodes, the system may automatically invoke the appropriate neutralization model to determine estimated values of the particular abnormal monitoring node or nodes (e.g., by selecting the correct model or using out-of-range values). The series of current monitoring node values from the abnormal monitoring node or nodes may then be replaced with the estimated values.

公开(公告)号：US11693763B2

公开(公告)日：2023-07-04

申请号：US16525807

申请日：2019-07-30

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Hema K Achanta ,  Masoud Abbaszadeh ,  Weizhong Yan ,  Mustafa Tekin Dokucu

IPC: G06F11/36 ,  H04L9/40 ,  H04L41/16 ,  G06N3/08 ,  G06F18/24 ,  G06F18/214 ,  H04L43/12 ,  G06V10/82 ,  G06F11/263 ,  H04W12/128 ,  G06N3/04

CPC classification number: G06F11/3684 ,  G06F11/263 ,  G06F18/214 ,  G06F18/24 ,  G06N3/08 ,  G06V10/82 ,  H04L41/16 ,  H04L43/12 ,  H04L63/1441 ,  H04W12/128 ,  G06N3/0418

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided to protect a cyber-physical system having a plurality of monitoring nodes comprising: a normal space data source storing, for each of the plurality of monitoring nodes, a series of normal monitoring node values over time that represent normal operation of the cyber-physical system; a situational awareness module including an abnormal data generation platform, wherein the abnormal data generation platform is operative to generate abnormal data to represent abnormal operation of the cyber-physical system using values in the normal space data source and a generative model; a memory for storing program instructions; and a situational awareness processor, coupled to the memory, and in communication with the situational awareness module and operative to execute the program instructions to: receive a data signal, wherein the received data signal is an aggregation of data signals received from one or more of the plurality of monitoring nodes, wherein the data signal includes at least one real-time stream of data source signal values that represent a current operation of the cyber-physical system; determine, via a trained classifier, whether the received data signal is a normal signal or an abnormal signal, wherein the trained classifier is trained with the generated abnormal data and normal data; localize an origin of an anomaly when it is determined the received data signal is the abnormal signal; receive the determination and localization at a resilient estimator module; execute the resilient estimator module to generate a state estimation for the cyber-physical system. Numerous other aspects are provided.

公开(公告)号：US20220334540A1

公开(公告)日：2022-10-20

申请号：US17229934

申请日：2021-04-14

Applicant: General Electric Company

Inventor： Fernando Javier D'Amato ,  Mustafa Tekin Dokucu ,  Hema Kumari Achanta, III ,  Kalpesh Singal ,  Masoud Abbaszadeh ,  Yuh-Shyang Wang ,  Karla Kvaternik ,  Souransu Nandi ,  Georgios Boutselis

IPC: G05B15/02 ,  G06F21/55 ,  G06F9/455 ,  G06N20/00

Abstract: Systems and methods are provided for the control of an industrial asset, such as a power generating asset. Accordingly, a cyber-attack model predicts a plurality of operational impacts on the industrial asset resulting from a plurality of potential cyber-attacks. The cyber-attack model also predicts a corresponding plurality of potential mitigation responses. In operation, a cyber-attack impacting at least one component of the industrial asset is detected via the cyber-attack neutralization module and a protected operational impact of the cyber-attack is identified based on the cyber-attack model. The cyber-attack neutralization module selects at least one mitigation response of the plurality of mitigation responses based on the predicted operational impact and an operating state of the industrial asset is altered based on the selected mitigation response.

公开(公告)号：US20210182385A1

公开(公告)日：2021-06-17

申请号：US16710051

申请日：2019-12-11

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Subhrajit Roychowdhury ,  Masoud Abbaszadeh ,  Mustafa Tekin Dokucu

IPC: G06F21/55 ,  G06N3/08 ,  G06N3/04 ,  G05B13/04 ,  G05B13/02

Abstract: An industrial asset may have monitoring nodes (e.g., sensor or actuator nodes) that generate current monitoring node values. An abnormality detection and localization computer may receive the series of current monitoring node values and output an indication of at least one abnormal monitoring node that is currently being attacked or experiencing a fault. An actor-critic platform may tune a dynamic, resilient state estimator for a sensor node and output tuning parameters for a controller that improve operation of the industrial asset during the current attack or fault. The actor-critic platform may include, for example, a dynamic, resilient state estimator, an actor model, and a critic model. According to some embodiments, a value function of the critic model is updated for each action of the actor model and each action of the actor model is evaluated by the critic model to update a policy of the actor-critic platform.

公开(公告)号：US20180370827A1

公开(公告)日：2018-12-27

申请号：US16103886

申请日：2018-08-14

Applicant: General Electric Company

Inventor： Aditya Kumar ,  Anthony John Murray ,  Ruijie Shi ,  Zhaoyang Wan ,  Mustafa Tekin Dokucu ,  Vijaysai Prasad

IPC: C02F3/00 ,  G01N33/18 ,  C02F1/66 ,  C02F3/28 ,  C02F3/30 ,  C02F3/12

Abstract: A method of operating a waste water treatment plant (WWTP) having at least one of an aerobic digester (AD) and a membrane bioreactor (MBR) is described. The method of operating AD is comprised of monitoring and controlling AD in real-time using an online extended Kalman filter (EKF) having a online dynamic model of AD. The EKF uses real-time AD measured data, and online dynamic model of AD to update adapted model parameters and estimate model based inferred variables for AD, which are used for AD control by AD control system having supervisory and low-level control layers. The method of operating MBR is similar to that of AD. The supervisory control ensures the WWTP satisfying the effluent quality requirement while minimize the operation cost. A WWTP having at least one of an AD or MBR is disclosed. The method of operating a WWTP can be implemented using a computer.