AI extensions and intelligent model validation for an industrial digital twin

    公开(公告)号:US11900277B2

    公开(公告)日:2024-02-13

    申请号:US17744980

    申请日:2022-05-16

    CPC classification number: G06N5/048 G06N20/10

    Abstract: Industrial smart data tags conforming to structured data types serve as the basis for creating a digital twin of an industrial asset. The digital twin can comprise an automation model and a mechanical model or other type of non-automation model, both of which reference the smart tags in connection with digitally modeling the industrial asset. The structured data topology offered by the smart tags allows the digital twin to be readily interfaced with artificial intelligence (AI) systems. AI analysis can leverage the smart tags to discover new relationships between key performance indicators and other variables of the asset and encode these relationships in the smart tags themselves. These enhanced smart tags can also be leveraged to perform AI-based validation the digital twin. Additional contextualization provided by the enhanced smart tags can simplify AI analysis and assist in quickly converging on desired analytic results.

    Industrial automation information contextualization method and system

    公开(公告)号:US11144042B2

    公开(公告)日:2021-10-12

    申请号:US16030257

    申请日:2018-07-09

    Abstract: An industrial data presentation system leverages structured data types defined on industrial devices to generate and deliver meaningful presentations of industrial data. Industrial devices are configured to support structured data types referred to as basic information data types (BIDTs) comprising a finite set of structured information data types, including a rate data type, a state data type, an odometer data type, and an event data type. The BIDTs can be referenced by both automation models of an industrial asset and non-automation models of the asset, allowing data points of both types of models to be easily linked using a common data source nomenclature.

    Smart gateway platform for industrial internet of things

    公开(公告)号:US11086298B2

    公开(公告)日:2021-08-10

    申请号:US16384106

    申请日:2019-04-15

    Abstract: A smart gateway platform leverages pre-defined industrial expertise to identify limited subsets of available industrial data deemed relevant to a desired business objective, and to collect and model this relevant data to apply useful constraints on subsequent artificial intelligence or machine learning analytics applied to the data. This approach can reduce the data space to which AI analytics are applied, and assist data analytic systems to more quickly derive valuable insights and business outcomes. In some embodiments, the smart gateway platform can operate within the context of a multi-level industrial analytic system, feeding pre-modeled data to one or more AI or machine learning systems executing on one or more different levels of an industrial enterprise.

    Runtime process diagnostics
    7.
    发明授权
    Runtime process diagnostics 有权
    运行时过程诊断

    公开(公告)号:US09135000B2

    公开(公告)日:2015-09-15

    申请号:US13670166

    申请日:2012-11-06

    CPC classification number: G06F8/71 G06F8/36 G06Q10/06 G06Q10/0633

    Abstract: Content management includes populating a library with modular objects and metadata associated with the modular objects. In response to a query, the library can be searched based in part on the metadata. The query can relate to implementation of an industrial process. One or more modular objects in the library can be identified as satisfying the query. A result of the query can be output and the output can include the identified modular objects and the respective metadata associated with the identified modular objects. The metadata can be anything known about the object that might not be accessible at runtime control.

    Abstract translation: 内容管理包括使用与模块化对象相关联的模块化对象和元数据填充库。 响应于查询,可以部分地基于元数据来搜索库。 该查询可以涉及工业过程的实现。 库中的一个或多个模块化对象可以被识别为满足查询。 可以输出查询的结果,并且输出可以包括所识别的模块化对象和与所识别的模块对象相关联的相应元数据。 元数据可以是关于在运行时控制可能无法访问的对象的任何已知的。

    Method and apparatus for online simulation of complex motion systems

    公开(公告)号:US11086281B2

    公开(公告)日:2021-08-10

    申请号:US15812565

    申请日:2017-11-14

    Abstract: A system and method for online simulation of a controlled machine or process utilizes a simplified model of the system dynamics and may be used with hardware in the loop to evaluate performance of the controlled system or with software in the loop to perform commissioning of the control program prior to completion of the mechanical installation. The simplified model includes dominant order dynamics of the controlled system such as the inertia of the system and a damping factor. Further, the online simulation is scheduled to execute at an update rate slower than the update rate of the control loops within the motor drive. The simplified model and reduced update rate reduce the computational burden on the processor such that the simulation may be performed either on the industrial controller or on the motor drive.

    AI EXTENSIONS AND INTELLIGENT MODEL VALIDATION FOR AN INDUSTRIAL DIGITAL TWIN

    公开(公告)号:US20200265329A1

    公开(公告)日:2020-08-20

    申请号:US16276108

    申请日:2019-02-14

    Abstract: Industrial smart data tags conforming to structured data types serve as the basis for creating a digital twin of an industrial asset. The digital twin can comprise an automation model and a mechanical model or other type of non-automation model, both of which reference the smart tags in connection with digitally modeling the industrial asset. The structured data topology offered by the smart tags allows the digital twin to be readily interfaced with artificial intelligence (AI) systems. AI analysis can leverage the smart tags to discover new relationships between key performance indicators and other variables of the asset and encode these relationships in the smart tags themselves. These enhanced smart tags can also be leveraged to perform AI-based validation the digital twin. Additional contextualization provided by the enhanced smart tags can simplify AI analysis and assist in quickly converging on desired analytic results.

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