Distributed big data in a process control system

Patent metadata

Field	Value
Publication number	US-10311015-B2
Application number	US-201414212493-A
Country	US
Kind code	B2
Filing date	Mar 14, 2014
Priority date	Mar 14, 2013
Publication date	Jun 4, 2019
Grant date	Jun 4, 2019

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 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.

First claim

What is claimed:

1. A first process control device for supporting distributed big data in a process plant, the first process control device being a field device that performs a physical function to control at least a pre-determined portion of a process in the process plant, and the field device including:
(i) a device processor;
(ii) one or more device tangible, non-transitory, computer-readable storage media having stored thereon a set of computer-executable instructions that, when executed by the device processor, cause the field device to at least one of:
(i) perform the physical function, generate process data corresponding to the performed physical function, and transmit, via a communication network within the process plant, the generated process data to a second process control device operating to control the process in real-time, or
(ii) receive process data from the second process control device via the communication network within the process plant, and perform the physical function based on the received process data;

(iii) a first interface via which the field device is connected to an input/output (I/O) device, the I/O device disposed between the field device and a controller and communicatively connecting the field device to the communication network, the generated process data transmitted via the first interface, and the received process data received via the first interface; and
(iv) an embedded big data apparatus having embedded data storage and embedded computer-executable instructions stored on one or more embedded tangible, non-transitory, computer-readable storage media that, when executed by one or more processors, cause the embedded big data apparatus to:
locally store, in the embedded big data storage, the generated process data and the received process data;
perform a learning analysis on at least a part of the locally stored process data;
create learned knowledge based on a result of the learning analysis, the learned knowledge including at least one of a time relationship or a causal relationship between process variables having values included in the locally stored process data;
locally store the created learned knowledge in the embedded big data storage of the embedded big data apparatus included in the field device; and
cause at least a portion of the locally stored learned knowledge to be distributed, via a second interface included in the field device and communicatively connecting the field device with a process control big data network different than the communication network, to the second process control device in the process plant to thereby modify, based upon the distributed learned knowledge, an operation of the second process control device, the second process control device including a respective embedded big data apparatus and operating in real-time to control the process.

2. The first process control device of claim 1 , wherein the locally stored process data includes multiple types of data, and a set of types of the locally 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 first process control device of claim 1 , wherein the embedded computer-executable instructions of the embedded big data apparatus are further executable to cause the embedded big data apparatus to determine the learning analysis based on the locally 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 first 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 first process control device of claim 1 , wherein the learning analysis is a first data analysis algorithm, and the embedded computer-executable instructions of the embedded big data apparatus are further executable to cause the embedded big data apparatus to receive another data analysis algorithm from another big data device via the process control big data network and to execute the another data analysis algorithm.

6. The first 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 algorithm includes at least one of an R script, a Python script, or a Matlab® script.

7. The first process control device of claim 1 , wherein the first process control device is further configured to:
modify, based on the learned knowledge, an operation of the first 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 second process control device or to a third process control device in conjunction with the learned knowledge.

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

9. The first process control device of claim 1 , wherein the communication network comprises at least one of a wired communications network or a wireless communications network.

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

11. The first process control device of claim 1 , wherein:
the learned knowledge is first learned knowledge and the learning analysis is a first learning analysis; and
the embedded computer-executable instructions of the embedded big data apparatus are further executable to cause the embedded big data apparatus to:
receive second learned knowledge created by the second process control device or by a third process control device, and
at least one of (i) modify, based on the received second learned knowledge, an operation of the first process control device to control, in real-time, the process, or (2) perform a second learning analysis on at least some of the locally stored process data and the received second learned knowledge.

12. A method of supporting distributed big data using a field device that is (i) communicatively coupled to a first communications network of a process plant, (ii) operating, based on control signals delivered via the first communications network, to control a process in real-time in the process plant, and (iii) configured to perform a physical function to control at least a pre-determined portion of the process in real-time, the method comprising, at the field device:
at least one of:
receiving, at the field device, data via the first communications network and using the received data to perform the physical function to control at least the pre-determined portion of the process in real-time, or
generating, by the field device, data as a result of performing the physical function and transmitting the generated data via the first communications network to control at least the pre-determined portion of the process in real-time;

collecting data at the field device, the data including at least one of: (i) the data that is generated by the field device as a result of performing the physical function, (ii) data that is created by the field device, or (iii) the data that is received at the field device and used by the field device to perform the physical function;
locally storing, in an embedded big data appara

Assignees

• Fisher Rosemount Systems Inc

Inventors

• Nixon Mark J
• Muston Paul Richard
• Blevins Terrence L
• Wojsznis Wilhelm K

Classifications

• G05B2219/31324

  Distributed real time knowledge, database · CPC title

• G05B2219/31211

  Communicate diagnostic data from intelligent field device controller to central · CPC title

• G05B19/41885

  characterised by modeling, simulation of the manufacturing system · CPC title

• G05B19/4188

  characterised by CIM planning or realisation · CPC title

• G05B19/41855 Primary

  by local area network [LAN], network structure · CPC title