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公开(公告)号：US11343266B2
公开(公告)日：2022-05-24
申请号：US16436093
申请日：2019-06-10
Applicant: General Electric Company
Inventor： Masoud Abbaszadeh , Hema K. Achanta , Mustafa Tekin Dokucu , Matthew Nielsen , Justin Varkey John
Abstract: Methods and systems for self-certifying secure operation of a cyber-physical system having a plurality of monitoring nodes. In an embodiment, an artificial intelligence (AI) watchdog computer platform obtains, using the output of a local features extraction process of time series data of a plurality of monitoring nodes of a cyber-physical system and a global features extraction process, global features extraction data. The AI watchdog computer platform then obtains reduced dimensional data, generates an updated decision boundary, compares the updated decision boundary to a certification manifold, determines based on the comparison that the updated decision boundary is certified, and determines, based on an anomaly detection process, whether the cyber-physical system is behaving normally or abnormally.
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公开(公告)号：US11070584B1
公开(公告)日：2021-07-20
申请号：US16734499
申请日：2020-01-06
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Honggang Wang , Masoud Abbaszadeh , Mustafa Tekin Dokucu
IPC: H04L29/06 , G05B19/406
Abstract: A procedure for neutralizing an attack on a control system of an industrial asset includes detecting an anomaly in a first sensor node associated with a first unit operating in a first operational mode, and receiving time series data associated with the first sensor node. A subset of the time series data is provided to each of a plurality of virtual sensor models A first virtual sensor model is selected from among a plurality of virtual sensor models based upon the subset of the time series data received by each of the plurality of virtual sensor models. A first confidence level of the first virtual sensor is determined. Responsive to determining that the first confidence level is below a first confidence level threshold, the first unit is transferred to a second operational mode using sensor readings associated with a second sensor node of a second unit of the industrial asset.
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公开(公告)号：US10809683B2
公开(公告)日：2020-10-20
申请号：US15794769
申请日：2017-10-26
Applicant: General Electric Company
Inventor： Chaitanya Ashok Baone , Nan Duan , Anup Menon , Mustafa Tekin Dokucu
IPC: G06F30/20 , G05B17/02 , H02J13/00 , G06F30/367 , H02J3/24
Abstract: A power system model parameter conditioning tool including a server control processor in communication with phasor measurement unit monitored data records of multiple disturbance events, a model calibration unit providing event screening, power system model simulation, and simultaneous tuning of model parameters. The model calibration performing a simulation using default model parameters, the processor comparing the simulation results to the monitored data. If the prediction is within threshold, then terminating conditioning; else performing parameter identifiability analysis to determine differing effects of various model parameters on power system model accuracy, selecting a parameter set causing a degradation in power system model prediction, and updating the default model parameters corresponding to members of the parameter set with values selected to reduce the degradation. A method and a non-transitory computer readable medium are also disclosed.
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公开(公告)号：US10358983B2
公开(公告)日：2019-07-23
申请号：US15132963
申请日：2016-04-19
Applicant: General Electric Company
Inventor： Frederick William Block , Scott William Szepek , William Forrester Seely , Mustafa Tekin Dokucu , John Joseph Raffensperger
IPC: G05B13/04 , F02C9/00 , G05B19/4065
Abstract: A system includes a model-based control system configured to receive data relating to parameters of a machinery via a plurality of sensors coupled to the machinery and select one or more models configured to generate a desired parameter of the machinery based on a determined relationship between the parameters and the desired parameter. The one or more models represent a performance of a device of the machinery. The model-based control system is configured to generate the desired parameter using the data and the one or more models control a plurality of actuators coupled to the machinery based on the desired parameter. Further, the model-based control system is configured to empirically tune the one or more models based on the data, the one or more parameters, and the desired parameter, compare the empirical tuning to a baseline tuning, and determine an operational state of the device based on the comparison.
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公开(公告)号：US20190146000A1
公开(公告)日：2019-05-16
申请号：US15809747
申请日：2017-11-10
Applicant: General Electric Company
Inventor： William Edwin Hurst , Mei Gao , Vivek Gandhi , Donald Horn , Gregory Jon Chiaramonte , Katherine Tharp Nowicki , Steven Richard Levin , Michael William Bailey , Ronald Plybon , Mustafa Tekin Dokucu , Aditya Kumar
Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to perform prognostic health monitoring of a turbine engine. An example apparatus includes a health quantifier calculator to execute a computer-generated model to generate first sensor data of a turbine engine, the first sensor data based on simulating a sensor monitoring the turbine engine using asset monitoring information, a parameter tracker to execute a tracking filter using the first sensor data and second sensor data to generate third sensor data corresponding to the turbine engine, the second sensor data based on obtaining sensor data from a sensor monitoring the turbine engine, the third sensor data based on comparing the first sensor data to the second sensor data, the health quantifier calculator to execute the computer-generated model using the third sensor data to generate an asset health quantifier of the turbine engine; and a report generator to generate a report including the asset health quantifier and a workscope recommendation based on the asset health quantifier when the asset health quantifier satisfies a threshold.
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Patent Agency Ranking
公开(公告)号：US10006330B2
公开(公告)日：2018-06-26
申请号：US14526223
申请日：2014-10-28
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Sidharth Abrol , Rajeeva Kumar , Mustafa Tekin Dokucu
CPC classification number: F01N3/208 , B01D53/8625 , B01D53/8696 , B01D2251/2062 , F01N2560/00 , F01N2610/02 , F01N2900/14 , F23J2219/10 , F23N2041/20 , G05B15/02 , Y02T10/24 , Y02T50/677
Abstract: A system includes an emissions control system. The emissions control system includes a processor programmed to receive one or more selective catalytic reduction (SCR) operating conditions for an SCR system. The SCR system is included in an aftertreatment system for an exhaust stream. The processor is also programmed to receive one or more gas turbine operating conditions for a gas turbine engine. The gas turbine engine is configured to direct the exhaust stream into the aftertreatment system. The processor is further programmed to derive a NH3 flow to the SCR system based on an SCR model and the one or more SCR operating conditions, to derive a NO/NOx ratio, and to derive a fuel split for the gas turbine engine based on the NH3 flow, the NO/NOx ratio, or a combination thereof.
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公开(公告)号：US12278490B2
公开(公告)日：2025-04-15
申请号：US17288617
申请日：2018-11-05
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Anup Menon , Chaitanya Ashok Baone , Honggang Wang , Mustafa Tekin Dokucu
IPC: H02J3/00
Abstract: A dynamic simulation engine, having system parameters, may be provided for a component of an electrical power system (e.g., a generator, wind turbine, etc.). A model parameter tuning engine may receive, from a measurement data store, measurement data measured by an electrical power system measurement unit (e.g., a phasor measurement unit or digital fault recorder measuring a disturbance event). The model parameter tuning engine may then pre-condition the measurement data and set-up an optimization problem based on a result of the pre-conditioning. The system parameters of the dynamic simulation engine may be determined by solving the optimization problem with an iterative method until at least one convergence criteria is met. According to some embodiments, solving the optimization problem includes a Jacobian approximation that does not call the dynamic simulation engine if an improvement of residual meets a pre-defined criteria.
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公开(公告)号：US20230385186A1
公开(公告)日：2023-11-30
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 , H04L9/40 , H04L41/16 , G06N3/08 , G06F18/24 , G06F18/214 , H04L43/12 , G06V10/82 , G06F11/263 , H04W12/128
CPC classification number: G06F11/3684 , H04L63/1441 , H04L41/16 , G06N3/08 , G06F18/24 , G06F18/214 , H04L43/12 , G06V10/82 , G06F11/263 , 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.
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公开(公告)号：US20230296078A1
公开(公告)日：2023-09-21
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
CPC classification number: F03D7/046 , F03D7/0264 , F03D7/045 , F05B2270/1031 , F05B2270/1074 , F05B2270/328 , F05B2270/335 , F05B2270/709
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.
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公开(公告)号：US11487598B2
公开(公告)日：2022-11-01
申请号：US16574493
申请日：2019-09-18
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Masoud Abbaszadeh , Mustafa Tekin Dokucu , Justin Varkey John
Abstract: An industrial asset may have a plurality of monitoring nodes, each monitoring node generating a series of monitoring node values over time representing current operation of the industrial asset. An abnormality detection computer may determine that an abnormal monitoring node is currently being attacked or experiencing a fault. An autonomous, resilient estimator may continuously execute an adaptive learning process to create or update virtual sensor models for that monitoring node. Responsive to an indication that a monitoring node is currently being attacked or experiencing a fault, a level of neutralization may be automatically determined. The autonomous, resilient estimator may then be dynamically reconfigured to estimate a series of virtual node values based on information from normal monitoring nodes, appropriate virtual sensor models, and the determined level of neutralization. The series of monitoring node values from the abnormal monitoring node or nodes may then be replaced with the virtual node values.
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