Abstract: Techniques for determining device-specific information such as commissioning data, location information, images, and other data descriptive of a process device installed in a plant include obtaining the device-specific information at a local device during the process device's commissioning. Based on this information, the local device determines the relative order of the process device within a process flow, and may determine a process element alignment map indicating the activation order of a plurality of process elements within the flow. A user may modify the map at the local device. The map is transmitted to a process control big data network for use in discovery and learning analytics. The device-specific information and/or the map may be utilized to generate, at the local device, representations/views of the process flow, which may include real-time operational data. A user may zoom in or out on these views for more or less detail.

Abstract: A data modeling studio provides a structured environment for graphically creating and executing models which may be configured for diagnosis, prognosis, analysis, identifying relationships, etc., within a process plant. The data modeling studio includes a configuration engine for generating user interface elements to facilitate graphical construction of a model and a runtime engine for executing data models in, for example, an offline or an on-line environment. The configuration engine includes an interface routine that generates user interface elements, a plurality of templates stored in memory that serve as the building blocks of the model and a model compiler that converts the graphical model into a data format executable by the run-time engine. The run time engine executes the model to produce the desired output and may include a retrieval routine for retrieving data corresponding to the templates from memory and a modeling routine for executing the executable model.

Abstract: A device supporting big data in a process plant includes an interface to a communications network, a cache configured to store data observed by the device, and a multi-processing element processor to cause the data to be cached and transmitted (e.g., streamed) for historization at a unitary, logical centralized data storage area. The data storage area stores multiple types of process control or plant data using a common format. The device time-stamps the cached data, and, in some cases, all data that is generated or created by or received at the device may be cached and/or streamed. The device may be a field device, a controller, an input/output device, a network management device, a user interface device, or a historian device, and the device may be a node of a network supporting big data in the process plant. Multiple devices in the network may support layered or leveled caching of data.

Abstract: A data modeling studio provides a structured environment for graphically creating and executing models which may be configured for diagnosis, prognosis, analysis, identifying relationships, etc., within a process plant. The data modeling studio includes a configuration engine for generating user interface elements to facilitate graphical construction of a model and a runtime engine for executing data models in, for example, an offline or an on-line environment. The configuration engine includes an interface routine that generates user interface elements, a plurality of templates stored in memory that serve as the building blocks of the model and a model compiler that converts the graphical model into a data format executable by the run-time engine. The run time engine executes the model to produce the desired output and may include a retrieval routine for retrieving data corresponding to the templates from memory and a modeling routine for executing the executable model.

Abstract: Techniques for determining device-specific information such as commissioning data, location information, images, and other data descriptive of a process device installed in a plant include obtaining the device-specific information at a local device during the process device's commissioning. Based on this information, the local device determines the relative order of the process device within a process flow, and may determine a process element alignment map indicating the activation order of a plurality of process elements within the flow. A user may modify the map at the local device. The map is transmitted to a process control big data network for use in discovery and learning analytics. The device-specific information and/or the map may be utilized to generate, at the local device, representations/views of the process flow, which may include real-time operational data. A user may zoom in or out on these views for more or less detail.

Abstract: Techniques for determining device-specific information such as commissioning data, location information, images, and other data descriptive of a process device installed in a plant include obtaining the device-specific information at a local device during the process device's commissioning. Based on this information, the local device determines the relative order of the process device within a process flow, and may determine a process element alignment map indicating the activation order of a plurality of process elements within the flow. A user may modify the map at the local device. The map is transmitted to a process control big data network for use in discovery and learning analytics. The device-specific information and/or the map may be utilized to generate, at the local device, representations/views of the process flow, which may include real-time operational data. A user may zoom in or out on these views for more or less detail.

Abstract: A big data network or system for a process control system or plant includes a big data apparatus including a data storage area configured to store, using a common data schema, multiple types of process data and/or plant data (such as configuration and real-time data) that is used in, generated by or received by the process control system, and one or more data receiver computing devices to receive the data from multiple nodes or devices. The data may be cached and time-stamped at the nodes and streamed to the big data apparatus for storage. The process control system big data system provides services and/or data analyzes to automatically or manually discover prescriptive and/or predictive knowledge, and to determine, based on the discovered knowledge, changes and/or additions to the process control system and to the set of services and/or analyzes to optimize the process control system or plant.

Abstract: Techniques for determining device-specific information such as commissioning data, location information, images, and other data descriptive of a process device installed in a plant include obtaining the device-specific information at a local device during the process device's commissioning. Based on this information, the local device determines the relative order of the process device within a process flow, and may determine a process element alignment map indicating the activation order of a plurality of process elements within the flow. A user may modify the map at the local device. The map is transmitted to a process control big data network for use in discovery and learning analytics. The device-specific information and/or the map may be utilized to generate, at the local device, representations/views of the process flow, which may include real-time operational data. A user may zoom in or out on these views for more or less detail.

Abstract: A data modeling studio provides a structured environment for graphically creating and executing models which may be configured for diagnosis, prognosis, analysis, identifying relationships, etc., within a process plant. The data modeling studio includes a configuration engine for generating user interface elements to facilitate graphical construction of a model and a runtime engine for executing data models in, for example, an offline or an on-line environment. The configuration engine includes an interface routine that generates user interface elements, a plurality of templates stored in memory that serve as the building blocks of the model and a model compiler that converts the graphical model into a data format executable by the run-time engine. The run time engine executes the model to produce the desired output and may include a retrieval routine for retrieving data corresponding to the templates from memory and a modeling routine for executing the executable model.

Abstract: A device supporting big data in a process plant includes an interface to a communications network, a cache configured to store data observed by the device, and a multi-processing element processor to cause the data to be cached and transmitted (e.g., streamed) for historization at a unitary, logical centralized data storage area. The data storage area stores multiple types of process control or plant data using a common format. The device time-stamps the cached data, and, in some cases, all data that is generated or created by or received at the device may be cached and/or streamed. The device may be a field device, a controller, an input/output device, a network management device, a user interface device, or a historian device, and the device may be a node of a network supporting big data in the process plant. Multiple devices in the network may support layered or leveled caching of data.

Abstract: A data modeling studio provides a structured environment for graphically creating and executing models which may be configured for diagnosis, prognosis, analysis, identifying relationships, etc., within a process plant. The data modeling studio includes a configuration engine for generating user interface elements to facilitate graphical construction of a model and a runtime engine for executing data models in, for example, an offline or an on-line environment. The configuration engine includes an interface routine that generates user interface elements, a plurality of templates stored in memory that serve as the building blocks of the model and a model compiler that converts the graphical model into a data format executable by the run-time engine. The run time engine executes the model to produce the desired output and may include a retrieval routine for retrieving data corresponding to the templates from memory and a modeling routine for executing the executable model.

Abstract: A big data network or system for a process control system or plant includes a big data apparatus including a data storage area configured to store, using a common data schema, multiple types of process data and/or plant data (such as configuration and real-time data) that is used in, generated by or received by the process control system, and one or more data receiver computing devices to receive the data from multiple nodes or devices. The data may be cached and time-stamped at the nodes and streamed to the big data apparatus for storage. The process control system big data system provides services and/or data analyses to automatically or manually discover prescriptive and/or predictive knowledge, and to determine, based on the discovered knowledge, changes and/or additions to the process control system and to the set of services and/or analyses to optimize the process control system or plant.

Abstract: A method or apparatus automatically configures a control module for synchronous execution on a Fieldbus segment of a process control network by determining if all of the critical function blocks of a particular control module can be assigned to FOUNDATION® Fieldbus field devices or to the I/O device associated with a particular Fieldbus segment. If so, the method or apparatus automatically assigns the function blocks of the control module, which would otherwise be scheduled to execute in a controller, to the I/O device for the Fieldbus segment. This technique enables all of the critical or necessary function blocks within the control module to execute in one macrocycle of the Fieldbus segment, thereby executing synchronously on a Fieldbus segment.

Abstract: A versatile controller that can be used as either a stand-alone controller in a relatively small process plant or as one of numerous controllers in a distributed process control system depending on the needs of the process plant includes a processor adapted to be programmed to execute one or more programming routines and a memory, such as a non-volatile memory, coupled to the processor and adapted to store the one or more programming routines to be executed on the processor. The versatile controller also includes a plurality of field device input/output ports communicatively connected to the processor, a configuration communication port connected to the processor and to the memory to enable the controller to be configured with the programming routines and a second communication port which enables a user interface to be intermittently connected to the controller to view information stored within the controller memory.