DISTRIBUTED BIG DATA IN A PROCESS CONTROL SYSTEM

• US 20140277604A1
• Filed: 03/14/2014
• Published: 09/18/2014
• Est. Priority Date: 03/14/2013
• Status: Active Grant

• Current Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Original Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Inventors: Nixon, Mark J., Muston, Paul Richard, Blevins, Terrence L., Wojsznis, Wilhelm K.
• Granted Patent: US 10,311,015 B2
• Time in Patent Office: Days
• US Class Current: 700/47
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G05B 19/4185 characterised by the network communication
• CPC Class Codes: G05B 2219/31211 Communicate diagnostic data from intelligent field device controller to central
• CPC Class Codes: G05B 2219/31324 Distributed real time knowledge, database
• CPC Class Codes: G06F 15/173 using an interconnection network, e.g. matrix, shuffle, pyramid, star, snowflake
• CPC Class Codes: G06F 15/17331 Distributed shared memory [DSM], e.g. remote direct memory access [RDMA]
• CPC Class Codes: G06F 2212/2515 being configurable for different purposes, e.g. as cache or non-cache memory
• CPC Class Codes: G06F 9/54 Interprogram communication
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

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.

Claims

1. A process control device for supporting distributed big data in a process plant, the process control device comprising:

a processor;

one or more tangible, non-transitory, computer-readable storage media having stored thereon a set of computer-executable instructions that, when executed by the processor, cause the process control device to operate to control, in real-time, at least a portion of a process executed by the process plant by at least one of;

generating process data used to control the at least the portion of the process, or

operating on received process data to control the at least the portion of the process,

the generated process data and the received process data being process data that is generated from real-time control of the process;

an indication of a type of the process control device, the type corresponding to one of a field device, a controller, or an input/output (I/O) device disposed between and connected to the field device and the controller; and

an embedded big data apparatus that is configured to;

store the generated process data and the received process data;

perform a learning analysis on at least a part of the stored process data;

create learned knowledge based on a result of the learning analysis; and

cause the learned knowledge to be transmitted to another process control device in the process plant.

2. The process control device of claim 1, wherein the stored process data includes multiple types of data, and a set of types of the stored process data includes continuous data, event data, measurement data, batch data, calculated data, and configuration data corresponding to controlling the process executed by the process plant.

3. The process control device of claim 1, wherein the embedded big data apparatus is further configured to determine the learning analysis based on the stored process data, wherein the determination of the learning analysis is at least one of a selection or a derivation of the learning analysis.

4. The process control device of claim 1, wherein the learning analysis includes at least one of a partial least square regression analysis, a random forest, a pattern recognition, a predictive analysis, a correlation analysis, a principle component analysis, data mining, or data discovery.

5. The process control device of claim 1, wherein the embedded big data apparatus is further configured to receive another data analysis algorithm from another big data device and to execute the another data analysis algorithm.

6. The process control device of claim 5, wherein at least one of:

the another big data device is one of another distributed data device or a centralized big data device of the process plant;

or

the another data analysis includes at least one of an R script, a Python script, or a Matlab®

script.

7. The process control device of claim 1, wherein the process control device is further configured to:

modify, based on the learned knowledge, an operation of the process control device to control, in real-time, the process executed by the process plant, and

cause an indication of the modification to be transmitted to the another process control device in conjunction with the learned knowledge.

8. The process control device of claim 7, wherein the modification is an updated process model.

9. The process control device of claim 1, further comprising one or more interfaces connecting the process control device to at least one of a wired communications network or a wireless communications network.

10. The process control device of claim 9, wherein the one or more interfaces include:

a first interface coupled to a first communication network via which the learned knowledge is transmitted to the another process control device, and

a second interface coupled to a second communications network different from the first communications network, the second interface used by the process control device to at least one of transmit the generated process data or receive the received process data.

11. The process control device of claim 1, wherein:

the learned knowledge is first learned knowledge, the learning analysis is a first learning analysis, and the another process control device is a first other process control device; and

the embedded big data apparatus is further configured to;

receive second learned knowledge created by the first other process control device or created by a second other process control device, and

at least one of (i) modify, based on the received second learned knowledge, an operation of the process control device to control, in real-time, the process, or (2) perform a second learning analysis on at least some of the stored process data and the received second learned knowledge.

12. The process control device of claim 1, wherein the learned knowledge includes at least one of additional data that was previously unknown to the process control device, an application, a service, a routine, or a function.

13. A method of supporting distributed big data using a device communicatively coupled to a communications network of a process plant and operating to control a process in real-time in the process plant, the method comprising:

collecting data at the device,

the data including at least one of;

(i) data that is generated by the device, (ii) data that is created by the device, or (iii) data that is received at the device,

the data resulting from the control of the process in real-time, and

a type of the device is included in a set of device types, the set of device types including a field device, a controller, and an input/output (I/O) device;

storing, in an embedded big data apparatus of the device, the collected data;

performing, by the embedded big data apparatus of the device, a learning analysis on at least a portion of the stored data;

generating learned knowledge indicative of a result of the learning analysis; and

modifying, based on the learned knowledge, an operation of the device to control the process in real-time.

14. The method of claim 13, wherein collecting the data at the device comprises at least one of:

collecting all data that is generated by the device, collecting all data that is created by the device, or collecting all data that is received at the device.

15. The method of claim 13, wherein collecting the data at the device comprises at least one of:

collecting data that is generated by the device at a rate of generation, collecting all data that is created by the device at a rate of creation, or collecting all data that is received at the device at a rate of reception.

16. The method of claim 13, wherein collecting the data at the device comprises collecting, at the device, at least one type of data included in a set of types of data that includes continuous data, event data, measurement data, batch data, calculated data, and configuration data.

17. The method of claim 13, wherein:

the device is a first device, the learned knowledge is first learned knowledge, and the operation of the device is a first operation; and

the method further comprises;

receiving, at the first device, second learned knowledge generated by a second device;

performing, by the embedded big data apparatus of the first device, a further learning analysis on the second learned knowledge and at least some of the stored data;

generating, by the embedded big data apparatus of the first device, further learned knowledge indicative of a result of the further learning analysis; and

modifying the first operation or a second operation of the device to control the process in real-time based on further learned knowledge.

18. The method of claim 17, wherein receiving the second learned knowledge generated by the second device comprises receiving the second learned knowledge generated by another device that is one of:

a field device, a controller, an I/O device, a user interface device, a gateway device, an access point, a routing device, a historian device, or a network management device.

19. The method of claim 13, further comprising causing the learned knowledge to be transmitted to another device, wherein a device type of the another device is included in the set of device types.

20. The method of claim 13, wherein generating the learned knowledge comprises generating at least one of:

additional data that was previously unknown to the device, a new or modified application, a new or modified function, a new or modified routine, or a new or modified service.

21. The method of claim 13, wherein performing the learning analysis comprises performing at least one of a machine learning analysis, a predictive analysis, data mining, or data discovery.

22. A system for supporting distributed big data in a process plant comprising:

a communications network having a plurality of nodes, at least one of which is a process control device operating, in real-time, to control a process executing in the process plant, and each of the plurality of nodes is configured to;

collect data generated in real-time resulting from control of the process executing in the process plant;

locally store the collected data at a respective embedded big data apparatus included in the each of the plurality of nodes; and

perform, by the respective embedded big data apparatus included in the each of the plurality of nodes, a respective learning analysis on at least a portion of the locally stored data, and

wherein a first node included in the plurality of nodes is further configured to cause learned knowledge indicative of a result of a performance of the respective learning analysis to be transmitted to a second node included in the plurality of nodes for use in the respective learning analysis of the second node or in another respective learning analysis of the second node.

23. The system of claim 22, wherein:

the process control device is a controller configured to receive a set of inputs, determine, based on the set of inputs, a value of an output, and cause the output to be transmitted to a field device to control the process executed by the process plant; and

the field device is configured to perform a physical function based on the output of the controller to control the process executed by the process plant.

24. The system of claim 23, wherein the communications network is a first communications network, and wherein the controller is configured to at least one of:

receive at least one input of the set of inputs at an interface to a second communications network, or

cause the output to be transmitted to the field device via the interface to the second communications network.

25. The system of claim 22, wherein the learned knowledge includes at least one of an application, a function, a service, or a routine.

26. The system of claim 22, wherein the result of the performance of the respective learning analysis includes a prediction based on properties of the at least the portion of the locally stored data.

27. The system of claim 22, wherein the result of the performance of the respective learning analysis includes additional data that was previously unknown to the first node.

28. The system of claim 22, wherein the learned knowledge is first learned knowledge, and wherein the second node is configured to:

receive the first learned knowledge from the first node;

perform, by a respective embedded big data apparatus included in the second node, the respective learning analysis of the second node or another respective learning analysis of the second node on the first learned knowledge and at least a portion of locally collected and stored data at the second node; and

at least one of;

store, at the respective embedded big data apparatus of the second node, second learned knowledge indicative of a result the performance of the respective learning analysis of the second node or the another respective learning analysis of the second node;

modify an operation of the second node to control the process based on the second learned knowledge;

or

cause the second learned knowledge to be transmitted to a third node of the plurality of nodes.

29. The system of claim 28, wherein the third node is configured to:

receive the second learned knowledge from the second node;

perform, by a respective embedded big data apparatus of the third node, the respective learning analysis of the third node or another respective learning analysis of the third node on the second learned knowledge and at least a portion of locally collected and stored data at the third node; and

at least one of;

store, at the respective embedded big data apparatus of the third node, third learned knowledge indicative of a result the performance of the respective learning analysis of the third node or the another respective learning analysis of the third node;

modify an operation of the third node to control the process based on the third learned knowledge;

or

cause the third learned knowledge to be transmitted to a fourth node of the plurality of nodes.

30. The system of claim 22, wherein the plurality of nodes includes at least two devices from a set of devices including:

a controller configured to receive a set of inputs, determine, based on the set of inputs, a value of an output, and cause the output to be transmitted to a first field device to control the process executed by the process plant, the first field device being configured to perform a physical function based on the output of the controller to control the process;

the first field device or a second field device;

an input/output (I/O) device having a interface to the controller and an interface to at least one of the first field device or the second field device;

a user interface device;

a gateway device;

an access point;

a routing device;

a historian device; and

a network management device.