公开(公告)号：US10452845B2

公开(公告)日：2019-10-22

申请号：US15453544

申请日：2017-03-08

Applicant: General Electric Company

Inventor： Lalit Keshav Mestha ,  Santosh Sambamoorthy Veda ,  Masoud Abbaszadeh ,  Chaitanya Ashok Baone ,  Weizhong Yan ,  Saikat Ray Majumder ,  Sumit Bose ,  Annartia Giani ,  Olugbenga Anubi

IPC: G06F21/55 ,  G05B23/02

Abstract: According to some embodiments, a plurality of heterogeneous data source nodes may each generate a series of current data source node values over time that represent a current operation of an electric power grid. A real-time threat detection computer, coupled to the plurality of heterogeneous data source nodes, may receive the series of current data source node values and generate a set of current feature vectors. The threat detection computer may then access an abnormal state detection model having at least one decision boundary created offline using at least one of normal and abnormal feature vectors. The abnormal state detection model may be executed, and a threat alert signal may be transmitted if appropriate based on the set of current feature vectors and the at least one decision boundary.

公开(公告)号：US09552567B2

公开(公告)日：2017-01-24

申请号：US13728378

申请日：2012-12-27

Applicant: General Electric Company

Inventor： Naresh Sundaram Iyer ,  Anil Varma ,  James Kenneth Aragones ,  Weizhong Yan ,  Piero Patrone Bonissone ,  Feng Xue

IPC: G06N99/00 ,  G06Q10/08 ,  G06Q10/06

CPC classification number: G06N5/04 ,  G06N99/005 ,  G06Q10/0631 ,  G06Q10/087 ,  G07C5/008

Abstract: A method for determining fleet conditions and operational management thereof, performed by a central system includes receiving fleet data from at least one distributed data repository. The fleet data is substantially representative of information associated with a fleet of physical assets. The method also includes processing the received fleet data for the fleet using at least one process of a plurality of processes. The plurality of processes assess the received fleet data into processed fleet data. The method additionally includes determining a fleet condition status using the processed fleet data and the at least one process of the plurality of processes. The method further includes generating a fleet response. The fleet response is substantially representative of a next operational step for the fleet of physical assets. The method also includes transmitting the fleet response to at least one of a plurality of fleet response recipients.

Abstract translation: 由中央系统执行的用于确定车队状况和其操作管理的方法包括从至少一个分布式数据存储库接收车队数据。 船队数据实质上代表与实体资产船队相关联的信息。 该方法还包括使用多个过程的至少一个过程处理车队接收的车队数据。 多个过程将接收到的车队数据评估为处理后的车队数据。 该方法另外包括使用处理的车队数据和多个过程中的至少一个过程来确定车队状况状态。 该方法还包括产生车队响应。 舰队反应实质上代表了有形资产队伍的下一个操作步骤。 该方法还包括将车队响应发送到多个车队响应接收者中的至少一个。

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。

公开(公告)号：US12099571B2

公开(公告)日：2024-09-24

申请号：US15984896

申请日：2018-05-21

Applicant: General Electric Company

Inventor： Weizhong Yan ,  Lalit Keshav Mestha ,  Daniel Francis Holzhauer

IPC: G06F18/213 ,  G05B13/02 ,  G05B23/02 ,  G06N3/008

CPC classification number: G06F18/213 ,  G05B13/027 ,  G05B23/024 ,  G05B23/0254 ,  G06F2218/08 ,  G06N3/008

Abstract: Heterogeneous monitoring nodes may each generate a series of monitoring node values over time associated with operation of an industrial asset. An offline abnormal state detection model creation computer may receive the series of monitoring node values and perform a feature extraction process using a multi-modal, multi-disciplinary framework to generate an initial set of feature vectors. Then feature dimensionality reduction is performed to generate a selected feature vector subset. The model creation computer may derive digital models through a data-driven machine learning modeling method, based on input/output variables identified by domain experts or by learning from the data. The system may then automatically generate domain level features based on a difference between sensor measurements and digital model output. A decision boundary may then be automatically calculated and output for an abnormal state detection model based on the selected feature vector subset and the plurality of derived generated domain level features.

公开(公告)号：US11415975B2

公开(公告)日：2022-08-16

申请号：US16564283

申请日：2019-09-09

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Hao Huang ,  Feng Xue ,  Weizhong Yan

IPC: G05B23/00 ,  G05B23/02 ,  G06F17/16 ,  G06N3/08 ,  G06N3/04 ,  G05B19/406

Abstract: According to embodiments, a system, method and non-transitory computer-readable medium are provided to receive time series data associated with one or more sensors values of a piece of machinery at a first time period, perform a non-linear transformation on the time-series data to produce one or more nonlinear temporal embedding outputs, and projecting each of the nonlinear temporal embedding outputs to a different dimension space to identify at least one causal relationship in the nonlinear temporal embedding outputs. The nonlinear embeddings are further projected to the original dimension space to produce one or more causality learning outputs. Nonlinear dimensional reduction is performed on the one or more causality learning outputs to produce reduced dimension causality learning outputs. The learning outputs are mapped to one or more predicted outputs which include a prediction of one or more of the sensor values at a second time period.

公开(公告)号：US20210173462A1

公开(公告)日：2021-06-10

申请号：US16707399

申请日：2019-12-09

Applicant: General Electric Company

Inventor： Weizhong Yan ,  Honggang Wang

IPC: G06F1/28 ,  G06F16/28 ,  G06N20/00

Abstract: A computing device for detecting and identifying power system events is provided. The computing device includes at least one processor in communication with at least one memory device. The at least one processor is programmed to store a database including a plurality of categorized events. Each categorized event of the plurality of categorized events is associated with an event category. The at least one processor is also programmed to receive sensor data from a plurality of sensors monitoring a power grid, identify one or more features contained in the sensor data, compare the one or more features to the plurality of categorized events, and determine an event category based on the comparison.

公开(公告)号：US10928811B2

公开(公告)日：2021-02-23

申请号：US15793590

申请日：2017-10-25

Applicant: General Electric Company

Inventor： Fuxiao Xin ,  Larry Swanson ,  Rui Xu ,  Morgan Salter ,  Achalesh Pandey ,  Ramu Chandra ,  Weizhong Yan

IPC: G05B23/02 ,  F03D80/50 ,  G06N3/02 ,  G05B19/042 ,  G06Q50/06

Abstract: According to some embodiments, a system and method are provided to model a sparse data asset. The system comprises a processor and a non-transitory computer-readable medium comprising instructions that when executed by the processor perform a method to model a sparse data asset. Relevant data and operational data associated with the newly operational are received. A transfer model based on the relevant data and the received operational data. An input into the transfer model is received and a predication based on data associated with the received operational data and the relevant data is output.

公开(公告)号：US20200293032A1

公开(公告)日：2020-09-17

申请号：US16562570

申请日：2019-09-06

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Honggang Wang ,  Weizhong Yan ,  Lijun He ,  Liwei Hao

IPC: G05B23/02 ,  G06N20/00 ,  G06N5/04 ,  H02J13/00

Abstract: embodiments are directed to a system, method, and article for monitoring a power substation asset. During an offline analysis mode, training data may be acquired and processing, and one or more classifiers may be generated for an online anomaly detection and localization mode. During the online anomaly detection and localization mode, power system related data may be received from field devices, a state of a substation system and of the power substation asset component and an unclassified state of one or instances may be generated based on the one or more classifiers. An alert may be generated to indicate the state of the substation system and of the power substation asset.

公开(公告)号：US10594712B2

公开(公告)日：2020-03-17

申请号：US15484282

申请日：2017-04-11

Applicant: General Electric Company

Inventor： Lalit Keshav Mestha ,  Justin Varkey John ,  Weizhong Yan ,  David Joseph Hartman

IPC: G06F21/00 ,  H04L29/06 ,  G06N20/00 ,  G06N20/10 ,  G06N3/04 ,  G06N3/08 ,  G06N7/00

Abstract: A threat detection model creation computer receives normal monitoring node values and abnormal monitoring node values. At least some received monitoring node values may be processed with a deep learning model to determine parameters of the deep learning model (e.g., a weight matrix and affine terms). The parameters of the deep learning model and received monitoring node values may then be used to compute feature vectors. The feature vectors may be spatial along a plurality of monitoring nodes. At least one decision boundary for a threat detection model may be automatically calculated based on the computed feature vectors, and the system may output the decision boundary separating a normal state from an abnormal state for that monitoring node. The decision boundary may also be obtained by combining feature vectors from multiple nodes. The decision boundary may then be used to detect normal and abnormal operation of an industrial asset.

公开(公告)号：US20140189703A1

公开(公告)日：2014-07-03

申请号：US13730450

申请日：2012-12-28

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Mark Richard Gilder ,  Gerald Bowden Wise ,  Weizhong Yan ,  Umang Gopdalhai Brahmakshatriya

IPC: G06F9/50

CPC classification number: G06F9/50 ,  G06F9/5027 ,  G06F2209/5011

Abstract: A system for distributed computing includes a job scheduler module configured to identify a job request including request requirements and comprising one or more individual jobs. The system also includes a resource module configured to determine an execution set of computing resources from a pool of computing resources based on the request requirements. Each computing resource of the pool of computing resources has an application programming interface. The pool of computing resources comprises public cloud computing resources and internal computing resources. The system further includes a plurality of interface modules, where each interface module is configured to facilitate communication with the computing resources using the associated application programming interface. The system also includes an executor module configured to identify the appropriate interface module based on facilitating communication with the execution computing resource and transmit jobs for execution to the execution computing resource using the interface modules.

Abstract translation: 一种用于分布式计算的系统包括作业调度器模块，其配置为识别包括请求要求并包括一个或多个单独作业的作业请求。 该系统还包括资源模块，该资源模块被配置为基于请求要求从计算资源池确定计算资源的执行集。 计算资源池的每个计算资源都有一个应用程序编程接口。 计算资源池包括公共云计算资源和内部计算资源。 该系统还包括多个接口模块，其中每个接口模块被配置为便于使用相关联的应用编程接口与计算资源进行通信。 该系统还包括执行器模块，该执行器模块被配置为基于促进与执行计算资源的通信来识别适当的接口模块，并且使用接口模块将执行的作业发送到执行计算资源。