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公开(公告)号：US20200160208A1
公开(公告)日：2020-05-21
申请号：US16191993
申请日：2018-11-15
Applicant: General Electric Company
Inventor： Huan TAN , Colin PARRIS , Xiao BIAN , Shaopeng LIU , Kiersten RALSTON , Guiju SONG , Dayu HUANG
Abstract: The example embodiments are directed to a system and method for sharing machine learning model parameters among edge devices in a clustered group of edge devices sensing data about an industrial asset. In one example, the method may include one or more of storing unique parameters of a machine learning (ML) model associated with an industrial asset which are unique with respect to unique parameters of other edge systems in the group of edge systems, receiving common parameter information from the group of edge systems which is shared among the group of edge systems, generating updated parameter values for an ML model based on a combination of the unique parameters and the received common parameter information, and executing the updated ML model based on incoming data from the industrial asset to generate predictive information about the industrial asset.
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公开(公告)号：US20200160227A1
公开(公告)日：2020-05-21
申请号：US16191879
申请日：2018-11-15
Applicant: General Electric Company
Inventor： Shaopeng LIU , Colin PARRIS , Xiao BIAN , Huan TAN , Kiersten RALSTON , Guiju SONG , Dayu HUANG
IPC: G06N99/00
Abstract: The example embodiments are directed to a system for triggering a model update for an edge device in an IIoT network. In one example, the method may include one or more of receiving data of an operation performed by an industrial asset, the received data comprising input for a machine learning (ML) model associated with the industrial asset, determining that the received data comprises a change in data pattern with respect to a training data set which was used to previously train the ML model, storing the received data comprising the change in data pattern in a new data set, and in response to the new data set reaching a minimum threshold size, at least one of updating the ML model based on the new data set and transmitting a request to update the ML model based on the new data set.
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Patent Agency Ranking
公开(公告)号：US20200167652A1
公开(公告)日：2020-05-28
申请号：US16199877
申请日：2018-11-26
Applicant: General Electric Company
Inventor： Dayu HUANG , Colin PARRIS , Xiao BIAN , Huan TAN , Kiersten RALSTON , Shaopeng LIU , Guiju SONG
Abstract: The example embodiments are directed to a system and method for cold start deployment of an ML model for an edge system associated with an industrial asset. In one example, the method may include one or more of storing an incremental ML model comprising a plurality increments which sequentially increase a complexity of a predictive function of the incremental ML model, receiving performance information from an edge system that processes incoming data of an industrial asset using a current increment of the incremental ML model, dynamically determining to modify the current increment of the incremental ML model used by the edge system with a next increment of the incremental ML model having increased complexity based on the received performance information, and transmitting the next increment of the incremental ML model to the edge system.
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公开(公告)号：US20200167202A1
公开(公告)日：2020-05-28
申请号：US16199713
申请日：2018-11-26
Applicant: General Electric Company
Inventor： Dayu HUANG , Colin PARRIS , Xiao BIAN , Huan TAN , Kiersten RALSTON , Shaopeng LIU , Guiju SONG
IPC: G06F9/50 , H04L12/24 , G06N20/10 , G05B13/02 , G05B19/418
Abstract: The example embodiments are directed to a system and method for cold start deployment of an ML model for an edge system associated with an industrial asset. In one example, the method may include one or more of storing machine learning (ML) models and local edge information where the ML models are already deployed, receiving, via a network, meta information of an edge system associated with an industrial asset in response to a cold start of the edge system, dynamically determining an optimum ML model for the cold start of the edge system from among the already deployed ML models based on the received meta information and the local edge information, and transmitting the determined optimum ML model to the edge system.
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公开(公告)号：US20200159195A1
公开(公告)日：2020-05-21
申请号：US16192968
申请日：2018-11-16
Applicant: General Electric Company
Inventor： Xiao BIAN , Colin PARRIS , Dayu HUANG , Huan TAN , Kiersten RALSTON , Shaopeng LIU , Guiju SONG
IPC: G05B19/418 , G06N99/00
Abstract: The example embodiments are directed to a system and method for optimizing data the is transmitted from an edge device to a central server such as the cloud platform. In one example, the method may include one or more of receiving incoming data which is associated with an industrial asset positioned at an edge of an Internet of Things (IoT) network, transforming the incoming data into a pattern of data points within a feature space based on a machine learning model configured to detect patterns within the data, selecting a subset of data points from the pattern based on a distance between data points in the pattern of data points with respect to a previous pattern of data points in a previous dataset associated with the industrial asset, and transmitting the selected subset of data points to a central platform via the IoT network.
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公开(公告)号：US20230082184A1
公开(公告)日：2023-03-16
申请号：US17493993
申请日：2021-10-05
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Honggang WANG , Gang ZHENG , Shaopeng LIU , Manu PARASHAR , Mohammadreza Maddipour FARROKHIFARD , Aravindhan VADIVEL
Abstract: Provided is a system and method for detecting source(s) of oscillation on a power grid. In one example, the method may include receiving measurements from one or more sensors on a power grid, the measurements including data of an oscillation within the power grid, determining, via execution of one or more machine learning model, a candidate set of power system components disposed on the power grid that are candidates for being the source(s) of the oscillation, identifying, via execution of an optimization model, a component from among the candidate set of power system components which is the source (e.g., location, controller type, and/or asset type) of the oscillation, and displaying, via a user interface, information about the identified component.
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公开(公告)号：US20200160207A1
公开(公告)日：2020-05-21
申请号：US16191826
申请日：2018-11-15
Applicant: General Electric Company
Inventor： Guiju SONG , Colin PARRIS , Xiao BIAN , Huan TAN , Kiersten RALSTON , Shaopeng LIU , Dayu HUANG
Abstract: The example embodiments are directed to a system and methods for determining to update a machine learning model based on model degradation. In one example, the method may include one or more of receiving data acquired at an edge of an Internet of things (IoT) network from an industrial asset, executing a machine learning model with the received data as input to generate a predictive output associated with the industrial asset, determining that a performance of the machine learning model on the edge has degraded based on the generated predictive output of the machine learning model, and transmitting information about the degraded performance of the machine learning model to a central server within the IoT network.
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