Fisher-rosemount Systems, Inc

Hart Communication Foundation

Fisher Controls International LLC

Dust Networks, Inc.

Fieldbus Foundation

Fieldcomm Group, Inc.

Terrence L Blevins

Mark J Nixon

Wilhelm K Wojsznis

Paul Richard Muston

Ken J Beoughter

Peter Wojsznis

Eric Darrell Rotvold

Daniel Dean Christensen

Gregory K McMillan

Trevor Duncan Schleiss

John Mark Caldwell

Tomas Lennvall

Wallace A Pratt, Jr

Robin S Pramanik

Marion A Keyes, Iv

Dennis L Stevenson

J Michael Lucas

Dirk Thiele

Christopher J Worek

Deji Chen

Ashish Mehta

Michael J Lucas

Ram Ramachandran

Arthur Webb

Yuri Zats

Peter Zornio

Gary Keith Law

Robert Burke Havekost

Stephen Copps Gilbert

Brandon Hieb

Ron Eddie

Vasiliki Tzovla

John A Gudaz

Hiroshi Mori

Mitsugu Tanaka

Robert J Karschnia

Cindy Alsup Scott

Andrew Philip Dove

Neil J Peterson

Bruce Hubert Campney

Kurtis K Jensen

Mitchell Stephen Panther

Alper Turhan Enver

Ling Zhou

John Michael Lucas

Lee Allen Neitzel

Thomas B Kinney

David A Glanzer

Kenneth D Krivoshein

Jack Elias

Christopher John Felts

Chuji Akiyama

S Joe Qin

Yasuo Kumeda

Paul Kenneth Daly

David Richard Gardner

Ron Ottenbacher

Shu Xu

James Beall

William R Hodson

Patricia E Brett

Frank Lynch

Ashok K Gupta

Randolf Reiss

Pete Stoltenow

Manish Misra

Shailesh Kumar

Michael A Boudreau

Richard C Seemann

J Dashene Aren Samson

Todd Maras

Sai Ganesamoorthi

Richard C Seeman

Todd B Brase

Suresh Ganesamoorthi

Tomas P Lennvall

A method of controlling and managing a process control system having a plurality of control loops includes implementing a plurality of control routines to control operation of the plurality of control loops, respectively, wherein the control routines may include at least one non-adaptive control routine. The method then collects operating condition data in connection with the operation of each control loop, and identifies a respective process model for each control loop from the respective operating condition data collected for each control loop. The identification of the respective process models may be automatic as a result of a detected process change or may be on-demand as a result of an injected parameter change. The process models are then analyzed to measure or determine the operation of the process control loops.

Process model identification in a process control system

A method for designing and tuning a PID process controller includes approximating a process as a second order process but in a manner that includes the effects or characteristics introduced by various different devices in the I/O network, and using a lambda tuning method to determine tuning parameters or coefficients for the PID controller. The enhanced controller design and tuning method provides a systematic manner of achieving performance improvement of PID controllers within a process control system and is effective at overcoming challenges arising from signal aliasing, the use of anti-aliasing filtering and the effects of different I/O settings of both traditional and advanced I/O marshalling architectures.

Process controller design with process approximation and lambda tuning

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.

Big data in process control systems

A control technique controls a process in a manner that reduces the number of controller changes provided to a controlled device, and so reduces the power consumption of the controlled device along with the loading of a process control communications network disposed between the controller and the controlled device. This technique is very useful in a control system having wirelessly connected field devices, such as sensors and valves which, in many cases, operate off of battery power. Moreover, the control technique is useful in implementing a control system in which control signals are subject to intermittent, non-synchronized or significantly delayed communications and/or in a control system that receives intermittent, non-synchronized or significantly delayed process variable measurements to be used as feedback signals in the performance of closed-loop control.

Reducing controller updates in a control loop

Techniques for streaming big data in a process plant are disclosed. Generally, these techniques facilitate storage or communication of process control data, including alarms, parameters, events, and the like, in near real-time. Receivers of big data, such as big data historians or devices requesting specific data, are configured via an initial set of metadata, and thereafter receive updated metadata upon requesting it from the transmitting device, such as when the receiving device encounters an identifier in the data, which identifier was not defined in the metadata previously received.

Streaming data for analytics in process control systems

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.

Commissioning field devices in a process control system supported by big data

A technique for controlling a process using slow or non-periodically received process variable measurements enables more robust controller responses to setpoint changes and disturbance changes even when the process variable measurement feedback signals are reviewed at a rate on the order of the rate associated with the response time of the process dynamic or variable being controlled. The control technique implements iterations of a control routine to generate a control signal using a reset or rate contribution component that, in some sense, defines an expected process response to the control signal. When a new measurement of the process variable is unavailable to the controller, the reset or rate contribution component is maintained at zero or at some other previous level when generating the control signal. However, the reset contribution component is iteratively recalculated during each controller execution cycle, even when no new process variable measurement has been received, so that the output of the reset contribution component incorporates expected process changes that occur as a result of a setpoint or a feed-forward change that impacts the process input or control signal between the times that actual process variable measurement values are received at the controller. This technique makes the controller more robust when producing control signals in the presence of setpoint or feed-forward changes received between the times at which non-periodic process variable measurements are received at the controller and makes the controller operate better when the process variable feedback time interval is greater, equal to or on the order of the process response time.

Velocity based control in a non-periodically updated controller

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 analyzes 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.

Distributed big data in a process control system

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.

Data modeling studio

Techniques for automatically or autonomously performing signal processing-based learning in a process plant are disclosed. Generally, said techniques automatically or autonomously perform signal processing on a real-time signal that is generated based on the process plant controlling a process. Typically, the signal corresponds to a parameter value that varies over time, and the signal is processed as it is generated in real-time during on-line plant operations. Results of the signal processing may indicate characteristics of the signal, and one or more analytics functions may determine the sources of the characteristics, which may include a process element or device, a piece of equipment, and/or an asset of the process plant that is upstream, within the process, of the source of the signal. An autonomous signal processor may be integrated with or included in a process control device and/or a big data node of the process plant.

Automatic signal processing-based learning in a process plant

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.

Collecting and delivering data to a big data machine in a process control system

A data pipeline is used as a fundamental processing element for implementing techniques that automatically or autonomously perform signal processing-based learning in a process plant or monitoring system. Each data pipeline includes a set of communicatively interconnected data processing blocks that perform processing on one or more sources of data in a predetermined order to, for example, clean the data, filter the data, select data for further processing, perform supervised or unsupervised learning on the data, etc. The individual processing blocks or modules within a data pipeline may be stored and executed at different devices in a plant network to perform distributed data processing. Moreover, each data pipeline can be integrated into one or more higher level analytic modules that perform higher level analytics, such as quality prediction, fault detection, etc. on the processed data. The use of data pipelines within a plant network enables data collected within a plant control or monitoring system to be processed automatically and used in various higher level analytic modules within the plant during ongoing operation of the plant.

Data pipeline for process control system analytics

A process modeling technique uses a single statistical model, such as a PLS, PRC, MLR, etc. model, developed from historical data for a typical process and uses this model to perform quality prediction or fault detection for various different process states of a process. The modeling technique determines means (and possibly standard deviations) of process parameters for each of a set of product grades, throughputs, etc., compares on-line process parameter measurements to these means and uses these comparisons in a single process model to perform quality prediction or fault detection across the various states of the process. Because only the means and standard deviations of the process parameters of the process model are updated, a single process model can be used to perform quality prediction or fault detection while the process is operating in any of the defined process stages or states. Moreover, the sensitivity (robustness) of the process model may be manually or automatically adjusted for each process parameter to tune or adapt the model over time.

Inferential process modeling, quality prediction and fault detection using multi-stage data segregation

A multiple-input/multiple-output control routine in the form of a model predictive control (MPC) routine operates with wireless or other sensors that provide non-periodic, intermittent or otherwise delayed process variable measurement signals at an effective rate that is slower than the MPC controller scan or execution rate. The wireless MPC routine operates normally even when the measurement scan period for the controlled process variables is significantly larger than the operational scan period of the MPC controller routine, while providing control signals that enable control of the process in a robust and acceptable manner. During operation, the MPC routine uses an internal process model to simulate one or more measured process parameter values without performing model bias correction during the scan periods at which no new process parameter measurements are transmitted to the controller. When a new measurement for a particular process variable is available at the controller, the model prediction and simulated parameter values are updated with model bias correction based on the new measurement value, according to traditional MPC techniques.

Model predictive control using wireless process signals

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Round Rock, TX, United States of America

Terrence L Blevins