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

IPC: G05B23/02 ,  G06N3/04 ,  G06N3/08

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).

公开(公告)号：US20190121336A1

公开(公告)日：2019-04-25

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

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：US20180136617A1

公开(公告)日：2018-05-17

申请号：US15806999

申请日：2017-11-08

Applicant: General Electric Company

Inventor： Rui XU ,  Yunwen XU ,  Weizhong YAN

IPC: G05B13/02

CPC classification number: G05B13/0265 ,  G05B13/027 ,  G05B17/02

Abstract: A method of continuously modeling industrial asset performance includes an initial model build block creating a first model based on a combination of an industrial asset historical data, configuration data and training data, filtering at least one of the historical data, configuration data, and training data, and a continuous learning block predicting performance of one or more members of an ensemble of models by evaluating a result of the one or more ensemble members to a predetermined threshold. A model application block pushing a selected model ensemble member to a performance diagnostic center, selecting the member based on comparing model ensemble members to a fielded modeling algorithm. A system and computer-readable medium are disclosed.

公开(公告)号：US20160147816A1

公开(公告)日：2016-05-26

申请号：US14550405

申请日：2014-11-21

Applicant: General Electric Company

Inventor： Jerrold Allen Cline ,  Kete Long ,  Rui XU ,  Zhanpan Zhang

IPC: G06F17/30 ,  G06Q10/06

CPC classification number: G06F16/2365 ,  G06Q10/067

Abstract: According to some embodiments, a system includes a communication device operative to communicate with a user to receive a data set including a plurality of samples at a clustering module; a clustering module to receive the data set, store the data set, and calculate one or more clusters of samples using a clustering strategy; an optimization module to receive and store the one or more clusters of samples from the clustering module and generate one or more samples from the one or more clusters of samples using an optimization strategy; a memory for storing program instructions; at least one sample selection platform processor, coupled to the memory, and in communication with the clustering module and the optimization module and operative to execute program instructions to: calculate one or more clusters of samples based on the clustering strategy by executing the clustering module; analyze the data associated with the one or more clusters received from the clustering module using the optimization strategy associated with the optimization module to automatically select one or more samples from the one or more clusters; and provide one or more samples generated by the optimization module for replication in a validation model. Numerous other aspects are provided.

Abstract translation: 根据一些实施例，系统包括通信设备，其可操作以与用户通信以在聚类模块处接收包括多个样本的数据集; 聚类模块，用于接收数据集，存储数据集，并使用聚类策略计算一个或多个样本簇; 优化模块，用于从所述聚类模块接收和存储所述一个或多个样本簇，并使用优化策略从所述一个或多个样本簇生成一个或多个样本; 用于存储程序指令的存储器; 耦合到所述存储器并与所述聚类模块和所述优化模块通信的至少一个样本选择平台处理器，并且可操作以执行程序指令以：通过执行所述聚类模块，基于所述聚类策略来计算一个或多个样本簇; 使用与所述优化模块相关联的优化策略来分析与所述聚类模块接收的所述一个或多个聚类相关联的数据，以自动从所述一个或多个聚类中选择一个或多个样本; 并且在验证模型中提供由优化模块生成的用于复制的一个或多个样本。 提供了许多其他方面。