Patent application number | Description | Published |
20100318934 | METHODS AND APPARATUS TO PREDICT PROCESS QUALITY IN A PROCESS CONTROL SYSTEM - Example methods and apparatus to predict process quality in a process control system are disclosed. A disclosed example method includes receiving process control information relating to a process at a first time including a first value associated with a first measured variable and a second value associated with a second measured variable, determining if a variation based on the received process control information associated with the process exceeds a threshold, if the variation exceeds the threshold, calculating a first contribution value based on a contribution of the first measured variable to the variation and a second contribution value based on a contribution of the second measured variable to the variation, determining at least one corrective action based on the first contribution value, the second contribution value, the first value, or the second value, and calculating a predicted process quality based on the at least one corrective action at a time after the first time. | 12-16-2010 |
20140250153 | BIG DATA IN PROCESS CONTROL SYSTEMS - 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. | 09-04-2014 |
20140277604 | DISTRIBUTED BIG DATA IN A PROCESS CONTROL SYSTEM - A distributed big data device in a process plant includes an embedded big data appliance configured to locally stream and store, as big data, data that is generated, received, or observed by the device, and to perform one or more learning analyses on at least a portion of the stored data. The embedded big data appliance generates or creates learned knowledge based on a result of the learning analysis, which the device may use to modify its operation to control a process in real-time in the process plant, and/or which the device may transmit to other devices in the process plant. The distributed big data device may be a field device, a controller, an input/output device, or other process plant device, and may utilize learned knowledge created by other devices when performing its learning analysis. | 09-18-2014 |
20140277656 | COLLECTING AND DELIVERING DATA TO A BIG DATA MACHINE IN A PROCESS CONTROL SYSTEM - 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. | 09-18-2014 |
20140278312 | DATA MODELING STUDIO - 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. | 09-18-2014 |
20140280678 | COLLECTING AND DELIVERING DATA TO A BIG DATA MACHINE IN A PROCESS CONTROL SYSTEM - 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. | 09-18-2014 |
20140282227 | DATA MODELING STUDIO - 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. | 09-18-2014 |
20150220080 | Managing Big Data In Process Control Systems - A big data network or system for a process control system or plant includes a data storage device configured to receive process control data from control system devices and store the process control data. The big data network or system identifies various parameters or attributes from the process control data, and creates and uses rowkeys to store the parameters according to various combinations, such as combinations using timestamps. The big data network or system may also store certain aggregate data analyses associated with time periods specified by the timestamps. Accordingly, the big data network or system efficiently stores real-time data having measurements within a database schema, and users or administrators can leverage the aggregate data to analyze certain data associated with certain time periods. | 08-06-2015 |