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公开(公告)号：US20190058715A1
公开(公告)日：2019-02-21
申请号：US15681827
申请日：2017-08-21
Applicant: General Electric Company
Inventor： Masoud ABBASZADEH , Lalit Keshav MESTHA , Weizhong YAN
IPC: H04L29/06
Abstract: According to some embodiments, a plurality of monitoring nodes may each generate a series of current monitoring node values over time that represent a current operation of the industrial asset. A node classifier computer, coupled to the plurality of monitoring nodes, may receive the series of current monitoring node values and generate a set of current feature vectors. The node classifier computer may also access at least one multi-class classifier model having at least one decision boundary. The at least one multi-class classifier model may be executed and the system may transmit a classification result based on the set of current feature vectors and the at least one decision boundary. The classification result may indicate, for example, whether a monitoring node status is normal, attacked, or faulty.
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公开(公告)号：US20180330083A1
公开(公告)日：2018-11-15
申请号：US15594779
申请日：2017-05-15
Applicant: General Electric Company
Inventor： Masoud ABBASZADEH , Lalit Keshav MESTHA
CPC classification number: G06F21/552 , G06N5/04
Abstract: The example embodiments are directed to a system and method for forecasting anomalies in feature detection. In one example, the method includes storing feature behavior information of at least one monitoring node of an asset, including a normalcy boundary identifying normal feature behavior and abnormal feature behavior for the at least one monitoring node in feature space, receiving input signals from the at least one monitoring node of the asset and transforming the input signals into feature values in the feature space, wherein the feature values are located within the normalcy boundary, forecasting that a future feature value corresponding to a future input signal from the at least one monitoring node is going to be positioned outside the normalcy boundary based on the feature values within the normalcy boundary, and outputting information concerning the forecasted future feature value being outside the normalcy boundary for display.
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公开(公告)号：US20250028288A1
公开(公告)日：2025-01-23
申请号：US18353486
申请日：2023-07-17
Applicant: General Electric Company
Inventor： Hema K. ACHANTA , Masoud ABBASZADEH
IPC: G05B13/02
Abstract: A training data store may contain training data associated with monitoring node values during normal operation of an industrial asset and simulated abnormal data. An offline model tuning platform accesses the training data from normal operation of the industrial asset and the simulated abnormal data in the training data store. Based on the training data from normal operation of the industrial asset, the simulated abnormal data, an abnormal operating condition, and a constrained optimization solution, controller tuning parameters are created for at least one tuned data-driven adaptive controller such that an operating condition of the industrial asset will move from the abnormal operating condition to a normal operation condition through a stable trajectory. An online monitoring platform receives a stream of current monitoring node values and, when the abnormal operating condition is detected, utilizes the controller tuning parameters to implement the at least one tuned data-driven adaptive controller.
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公开(公告)号：US20240419154A1
公开(公告)日：2024-12-19
申请号：US18336491
申请日：2023-06-16
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Masoud ABBASZADEH , Johan Michael REIMANN , Hema K ACHANTA
IPC: G05B19/418
Abstract: In some embodiments, a system node data store may contain historical system node data associated with normal operation of an industrial asset, and a plurality of artificial intelligence model construction platforms may receive historical system node data. Each platform may then automatically construct a data-driven, dynamic artificial intelligence model associated with the industrial asset based on received system node data. The plurality of artificial intelligence models are interconnected and simultaneously trained to create a digital twin of the industrial asset. A synthetic disturbance platform may inject at least one synthetic disturbance into the plurality of artificial intelligence models to create, for each of a plurality of monitoring nodes, a series of synthetic disturbance monitoring node values over time that represent simulated abnormal operation of the industrial asset.
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公开(公告)号：US20220357729A1
公开(公告)日：2022-11-10
申请号：US17239054
申请日：2021-04-23
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Rui XU , Weizhong YAN , Masoud ABBASZADEH , Matthew Christian NIELSEN
Abstract: An industrial asset may have monitoring nodes that generate current monitoring node values representing a current operation of the industrial asset. An abnormality detection computer may detect when a monitoring node is currently being attacked or experiencing a fault based on a current feature vector, calculated in accordance with current monitoring node values, and a detection model that includes a decision boundary. A model updater (e.g., a continuous learning model updater) may determine an update time-frame (e.g., short-term, mid-term, long-term, etc.) associated with the system based on trigger occurrence detection (e.g., associated with a time-based trigger, a performance-based trigger, an event-based trigger, etc.). The model updater may then update the detection model in accordance with the determined update time-frame (and, in some embodiments, continuous learning).
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Patent Agency Ranking
16.发明申请公开(公告)号：US20190342318A1
公开(公告)日：2019-11-07
申请号：US16511463
申请日：2019-07-15
Applicant: General Electric Company
Inventor： Daniel Francis HOLZHAUER , Cody Joe BUSHEY , Lalit Keshav MESTHA , Masoud ABBASZADEH , Justin Varkey JOHN
Abstract: According to some embodiments, streams of monitoring node signal values may be received over time that represent a current operation of an industrial asset control system. A current operating mode of the industrial asset control system may be received and used to determine a current operating mode group from a set of potential operating mode groups. For each stream of monitoring node signal values, a current monitoring node feature vector may be determined. Based on the current operating mode group, an appropriate decision boundary may be selected for each monitoring node, the appropriate decision boundary separating a normal state from an abnormal state for that monitoring node in the current operating mode. Each generated current monitoring node feature vector may be compared with the selected corresponding appropriate decision boundary, and a threat alert signal may be automatically transmitted based on results of said comparisons.
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公开(公告)号：US20190056722A1
公开(公告)日：2019-02-21
申请号：US15681974
申请日：2017-08-21
Applicant: General Electric Company
Inventor： Masoud ABBASZADEH , Lalit Keshav MESTHA , Cody Joe BUSHEY
IPC: G05B19/418 , G06F21/60 , G06F21/56
Abstract: In some embodiments, a system model construction platform may receive, from a system node data store, system node data associated with an industrial asset. The system model construction platform may automatically construct a data-driven, dynamic system model for the industrial asset based on the received system node data. A synthetic attack platform may then inject at least one synthetic attack into the data-driven, dynamic system model to create, for each of a plurality of monitoring nodes, a series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. The synthetic attack platform may store, in a synthetic attack space data source, the series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. This information may then be used, for example, along with normal operational data to construct a threat detection model for the industrial asset.
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公开(公告)号：US20180255091A1
公开(公告)日：2018-09-06
申请号：US15454144
申请日：2017-03-09
Applicant: General Electric Company
Inventor： Lalit Keshav MESTHA , Olugbenga ANUBI , Masoud ABBASZADEH
IPC: H04L29/06
Abstract: The example embodiments are directed to a system and method for neutralizing abnormal signals in a cyber-physical system. In one example, the method includes receiving input signals comprising time series data associated with an asset and transforming the input signals into feature values in a feature space, detecting one or more abnormal feature values in the feature space based on a predetermined normalcy boundary associated with the asset, and determining an estimated true value for each abnormal feature value, and performing an inverse transform of each estimated true value to generate neutralized signals comprising time series data and outputting the neutralized signals.
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19.发明申请公开(公告)号：US20180191758A1
公开(公告)日：2018-07-05
申请号：US15397062
申请日：2017-01-03
Applicant: General Electric Company
Inventor： Masoud ABBASZADEH , Cody Joe BUSHEY , Lalit Keshav MESTHA , Daniel Francis HOLZHAUER
Abstract: According to some embodiments, a threat detection model creation computer may receive a series of monitoring node values (representing normal and/or threatened operation of the industrial asset control system) and generate a set of normal feature vectors. The threat detection model creation computer may identify a first cluster and a second cluster in the set of feature vectors. The threat detection model creation computer may then automatically determine a plurality of cluster-based decision boundaries for a threat detection model. For example, a first potential cluster-based decision boundary for the threat detection model may be automatically calculated based on the first cluster in the set of feature vectors. Similarly, the threat detection model creation computer may also automatically calculate a second potential cluster-based decision boundary for the threat detection model based on the second cluster in the set of feature vectors.
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公开(公告)号：US20230093713A1
公开(公告)日：2023-03-23
申请号：US17479370
申请日：2021-09-20
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Georgios BOUTSELIS , Masoud ABBASZADEH
IPC: G06F21/57 , G06F16/2457
Abstract: The present application describes techniques for node selection and ranking for, e.g., attack detection and localization in cyber-physical systems, without relying on digital twins, computer models of assets, or operational domain expertise. The described techniques include obtaining an input dataset of values for a plurality of nodes (e.g., sensors, actuators, controllers, software nodes) of industrial assets, computing a plurality of principal components (PCs) for the input dataset according to variance of values for each node, computing a set of common weighted PCs based on the plurality of PCs according to variance of each PC, and ranking each node based on the node's contribution to the set of common weighted PCs.
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