Abstract: A method is provided for managing availability of runtime asset data used by client applications hosted on workstations of an industrial system, the method including distributing runtime asset data about assets of the industrial system received at the plurality of workstations among runtime asset data caches associated with the client applications. At least a portion of locally stored runtime asset data stored on a local runtime asset data cache is replicated and stored remotely on the runtime asset data cache associated with another client application. The locally stored runtime asset data is periodically evaluated to determine if it is up-to-date, and in response to determining it is not up-to-date, the locally stored runtime asset data is updated by requesting and retrieving a replicated and updated version of the locally stored runtime asset data from the runtime asset data cache remotely storing the replicated version.

Abstract: Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.

Abstract: Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.

Abstract: Systems and methods for controlling industrial an industrial plant comprise: inputting an engineering diagram for a unit of the industrial plant, the engineering diagram including symbols representing assets of the industrial plant; extracting one or more assets from the engineering diagram using machine learning to recognize the one or more assets, the one or more assets including equipment, instruments, connectors, and lines, the lines relating the equipment, instruments, and connectors to one another; determining one or more relationships between the equipment, instruments, connectors, and lines to one another using machine learning to recognize the one or more relationships; and creating a flow graph from the equipment, instruments, connectors, and lines and the relationships between the equipment, instruments, connectors, and lines.

Abstract: A method is provided for managing availability of runtime asset data used by client applications hosted on workstations of an industrial system, the method including distributing runtime asset data about assets of the industrial system received at the plurality of workstations among runtime asset data caches associated with the client applications. At least a portion of locally stored runtime asset data stored on a local runtime asset data cache is replicated and stored remotely on the runtime asset data cache associated with another client application. The locally stored runtime asset data is periodically evaluated to determine if it is up-to-date, and in response to determining it is not up-to-date, the locally stored runtime asset data is updated by requesting and retrieving a replicated and updated version of the locally stored runtime asset data from the runtime asset data cache remotely storing the replicated version.

Abstract: Automated evaluation and extraction of information from piping and instrumentation diagrams (P&IDs). Aspects of the systems and methods utilize machine learning and image processing techniques to extract relevant information, such as tag names, tag numbers, and symbols, and their positions, from P&IDs. Further aspects feed errors back to a machine learning system to update its learning and improve operation of the systems and methods.

Abstract: Automated evaluation and extraction of information from piping and instrumentation diagrams (P&IDs). Aspects of the systems and methods utilize machine learning and image processing techniques to extract relevant information, such as tag names, tag numbers, and symbols, and their positions, from P&IDs. Further aspects feed errors back to a machine learning system to update its learning and improve operation of the systems and methods.

Abstract: Automated evaluation and extraction of information from piping and instrumentation diagrams (P&IDs). Aspects of the systems and methods utilize machine learning and image processing techniques to extract relevant information, such as tag names, tag numbers, and symbols, and their positions, from P&IDs. Further aspects feed errors back to a machine learning system to update its learning and improve operation of the systems and methods.

Abstract: Automated hardware device engineering and design by representing hardware engineering requirements via a wiring typical schema. An engineering workbench application executing on a computing device utilizes a wiring typical schema representing generic and project-specific requirements to automate hardware device engineering and design tasks in real time.

Abstract: Automated hardware device engineering and design by representing hardware engineering requirements via a wiring typical schema. An engineering workbench application executing on a computing device utilizes a wiring typical schema representing generic and project-specific requirements to automate hardware device engineering and design tasks in real time.

Abstract: A method of automatic manufacturability evaluation of plastic models comprises generation of a likely pulling direction, recognition of common features on plastic parts, and then applying manufacturability rules The manufacturability rules can be specified and customized through user specified rule parameters and depend upon the geometric parameters of the recognized features. A system comprises a user interface for selection and customization of DFX (Design for ‘X’) rules for evaluation of a design. The system includes a user interface integrated with a CAD system for receiving the CAD data and displaying the results to the user. Geometry analysis engines are integrated into the system, for extracting the various features and corresponding parameters required as input to the manufacturability rules. The system further involves extensible interfaces for rules and analysis engines which allows users to write their own customized rules and engines and integrate these into the CAD based DFX evaluation system.

Abstract: A method of automatic manufacturability evaluation of plastic models comprises generation of a likely pulling direction, recognition of common features on plastic parts, and then applying manufacturability rules The manufacturability rules can be specified and customized through user specified rule parameters and depend upon the geometric parameters of the recognized features. A system comprises a user interface for selection and customization of DFX (Design for ‘X’) rules for evaluation of a design. The system includes a user interface integrated with a CAD system for receiving the CAD data and displaying the results to the user. Geometry analysis engines are integrated into the system, for extracting the various features and corresponding parameters required as input to the manufacturability rules. The system further involves extensible interfaces for rules and analysis engines which allows users to write their own customized rules and engines and integrate these into the CAD based DFX evaluation system.

Abstract: A method of automatic manufacturability evaluation of plastic models comprises generation of a likely pulling direction, recognition of common features on plastic parts, and then applying manufacturability rules The manufacturability rules can be specified and customized through user specified rule parameters and depend upon the geometric parameters of the recognized features. A system comprises a user interface for selection and customization of DFX (Design for ‘X’) rules for evaluation of a design. The system includes a user interface integrated with a CAD system for receiving the CAD data and displaying the results to the user. Geometry analysis engines are integrated into the system, for extracting the various features and corresponding parameters required as input to the manufacturability rules. The system further involves extensible interfaces for rules and analysis engines which allows users to write their own customized rules and engines and integrate these into the CAD based DFX evaluation system.