Data analytic services for distributed industrial performance monitoring

• US 10,678,225 B2
• Filed: 09/23/2016
• Issued: 06/09/2020
• Est. Priority Date: 03/04/2013
• Status: Active Grant

• Current Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Original Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Inventors: Kidd, Joshua Brian, Nixon, Mark John, Enver, Alper Turhan, Bell, Noel Howard, Muston, Paul R.
• Primary Examiner(s): Lee, Thomas C
• Assistant Examiner(s): Collins, Gary
• Application Number:
  US15/274,519
• Publication Number:
  US 20170102693A1
• Time in Patent Office: 1,355 Days
• Field of Search: None
• CPC Class Codes: G05B 19/41865 characterised by job scheduling, process planning, material flow
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 2219/31455 Monitor process status
• CPC Class Codes: G05B 2219/32128 Gui graphical user interface
• CPC Class Codes: G05B 2219/32339 Object oriented modeling, design, analysis, implementation, simulation language
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

A data analytics service for performing data analytics functions within a distributed process control environment is provided. The service may provide a user interface for creating a first diagram, representing a data model. The first diagram may be configured, compiled, and evaluated using off-line (i.e., historical) data from a distributed process control system, which may include data stored in distributed data engines (DDEs). Following evaluation, the first diagram may be automatically converted into a second diagram that is bound to on-line (i.e., real-time) data sources within the process control environment, which may then be compiled and executed to generate performance or predictive analytics data for the process. The diagrams may comprise a plurality of configurable function blocks or modules, connected logically via wires conveying outputs or inputs of the blocks or modules.

Claims

1. A system for performing real-time analytics in a process control environment, the system comprising:

a plurality of process control devices operating in a process plant;

a controller communicatively coupled to the plurality of process control devices via a control network;

a big data storage node comprising a tangible, non-transitory storage medium storing data of the operation of the process control environment;

a processor, communicatively coupled to the big data storage node;

a program memory, communicatively coupled to the processor, and comprising a tangible, non-transitory storage medium storing instructions that, when executed by the processor, cause the processor to;

display a graphical user interface comprising an editing canvas;

receive a selection of a plurality of graphical programming elements for placement onto the editing canvas to create a first diagram representing first programming instructions operable to cause the processor to access data stored on the big data storage node as an offline data source and to manipulate the data stored on the big data storage node;

compile the first programming instructions represented by the first diagram;

execute the compiled first programming instructions represented by the first diagram to create a resultant first output;

create a predictive model based on the resultant first output;

automatically generate a second diagram corresponding to the first diagram from the first diagram by replacing the offline data source with a live data source providing live data being generated in the process control environment, wherein the live data source is indicated by previously stored configuration data, the second diagram representing second programming instructions operable to cause the processor to bind to the live data source and to evaluate data received from the live data source using at least the predictive model created based on the first output;

compile the second programming instructions represented by the second diagram; and

execute the compiled second programming instructions represented by the second diagram to predict an aspect of the operation of the process control environment and to cause results of analysis of the live data generated in the process control environment to be used as an input to a process control algorithm performing control functions in the process control environment.

2. The system according to claim 1, further comprising a web server comprising the processor and the program memory, wherein the instructions cause the processor to display the graphical user interface as a web application on a workstation.

3. The system according to claim 1, further comprising a library of graphical programming elements, each of the graphical programming elements comprising programming instructions operable to cause the processor to perform a particular manipulation of the data stored on the big data storage node.

4. The system according to claim 1, wherein creating the first diagram comprises placing on the editing canvas of the graphical user interface a plurality of graphical programming elements, each of the plurality of graphical programming elements selected from a library of graphical programming elements, and each representing respective programming instructions, and

wherein various ones of the graphical programming elements represent programming instructions operable to cause the processor to do one or more of:

loading data, filtering loaded data, selecting columns from loaded data, scaling loaded data, normalizing loaded data, discovering an anomaly in loaded data, performing a temporal offset of the data, and performing a dimensionality reduction on the data.

5. The system according to claim 1, further comprising a library of graphical programming elements, each of the graphical programming elements comprising programming instructions operable to cause the processor to perform a particular manipulation of the data stored on the big data storage node, and

wherein the library of graphical programming elements includes a data loading element, the data loading element representing programming instructions operable to cause the processor to load data from the big data storage node, and

wherein the data loading element is configurable to specify at least one of:

the data to be loaded or a location of the data on the big data storage node.

6. The system according to claim 1, further comprising a library of graphical programming elements, each of the graphical programming elements comprising programming instructions operable to cause the processor to perform a particular manipulation of the data stored on the big data storage node, and

wherein the library of graphical programming elements includes a column selection element, the column selection element representing programming instructions operable to cause the processor to create a column selection element output comprising only data in a selected one or more columns of a plurality of columns in loaded data, and

wherein the column selection element is configurable to select the one or more columns from the plurality of columns.

7. The system according to claim 1, further comprising a library of graphical programming elements, each of the graphical programming elements comprising programming instructions operable to cause the processor to perform a particular manipulation of the data stored on the big data storage node, and

wherein the library of graphical programming elements comprises a query element, the query element representing programming instructions operable to cause the processor to create a query element output comprising only data returned in response to a query written in a first query language,

wherein the query element is configurable to select at least one of:

query parameters or a tangible storage medium on which the data to be queried is stored.

8. The system according to claim 7, wherein the query is converted during execution of the programming instructions represented by the query element to a second query language.

9. The system according to claim 1, further comprising a library of graphical programming elements, each of the graphical programming elements comprising programming instructions operable to cause the processor to perform a particular manipulation of the data stored on the big data storage node, and

wherein each of the graphical programming elements comprises one or more of:

at least one input or at least one output, and

wherein inputs and outputs of the graphical programming elements may be connected to one another by one or more wires operable to convey data from outputs to inputs.

10. The system according to claim 9, wherein each of the one or more wires is operable to convey data in the form of a single value, an array, a matrix, or a set of programming instructions.

11. The system according to claim 1, wherein automatically generating the second diagram from the first diagram comprises automatically generating the second diagram from the first diagram in response to a single user input, the single user input comprising a key press, a touch on a touch screen, or a click with a pointing device.

12. The system according to claim 1, wherein the instructions stored in the program memory are operable to cause the processor to compile a sub-set of the first programming instructions and execute the compiled subset of the first programming instructions to achieve an element-wise execution of a plurality of graphical programming elements on the editing canvas of the graphical user interface.

13. The system according to claim 1, wherein each of the graphical programming elements, when placed on the editing canvas, includes a status indicator.

14. The system according to claim 13, wherein the status indicator indicates a status of the graphical programming element as unexecuted, currently executing, or execution complete.

15. The system according to claim 1, wherein the instructions stored in the program memory that are operable to cause the processor to bind to the live data source comprise instructions causing the processor to bind to a live data source corresponding to the data stored on the big data storage node.

16. The system according to claim 1, wherein the instructions stored in the program memory that are operable to cause the processor to bind to the live data source comprise instructions causing the processor to bind to a live data source comprising a streamed source of data corresponding to the data stored on the big data storage node.

17. The system according to claim 1, further comprising a job management module and a job execution module, and

wherein the instructions that cause the processor to execute the compiled first programming instructions represented by the first diagram comprise instructions that cause the processor to download the compiled first programming instructions to the job management module, which job management module transmits one or more jobs corresponding to the compiled first programming instructions to a job execution module.

18. The system according to claim 1, wherein the instructions that cause the processor to execute the compiled second programming instructions represented by the second diagram comprise:

instructions that cause the processor to download the compiled second programming instructions to a job management module and to cause one or more jobs corresponding to the compiled second programming instructions to be transmitted to the processor.

19. A method for performing real-time analytics in a process control environment, the method comprising:

creating a first diagram on a graphical user interface, the first diagram representing first programming instructions operable to cause a processor to access data generated and stored previously by the process control environment as an offline data source and to manipulate the data generated and stored previously by the process control environment;

compiling the first programming instructions represented by the first diagram;

executing the compiled first programming instructions represented by the first diagram to create a resultant first output;

evaluating the resultant first output to determine a predictive value of a model used to create the resultant first output;

automatically generating a second diagram corresponding to the first diagram from the first diagram by replacing the offline data source with a live data source providing live data being generated in the process control environment, wherein the live data source is indicated by previously stored configuration data, the second diagram representing second programming instructions operable to cause the processor to bind to the live data source and to evaluate data received from the live data source using at least the model used to create the resultant first output;

compiling the second programming instructions represented by the second diagram; and

executing the compiled second programming instructions represented by the second diagram to predict an aspect of the operation of the process control environment and to cause results of analysis of the live data generated in the process control environment to be used as an input to a process control algorithm performing control functions in the process control environment.

20. The method according to claim 19, wherein creating the first diagram comprises placing on an editing canvas of the graphical user interface a plurality of blocks, each of the plurality of blocks representing respective programming instructions, the respective programming instructions operable to cause the processor to do one or more of:

loading data, filtering loaded data, selecting columns from loaded data, scaling loaded data, normalizing loaded data, discovering an anomaly in loaded data, performing a temporal offset of the data, and performing a dimensionality reduction on the data.

21. The method according to claim 19, wherein creating the first diagram comprises placing on an editing canvas a data loading block, the data loading block representing programming instructions operable to cause the processor to load data from a tangible storage medium on which the data is stored,

wherein the data loading block is configurable to specify at least one of:

the data to be loaded or the tangible storage medium on which the data is stored.

22. The method according to claim 19, wherein creating the first diagram comprises placing on an editing canvas a column selection block, the column selection block representing programming instructions operable to cause the processor to create a column block output comprising only data in a selected one or more columns of a plurality of columns in loaded data,

wherein the column selection block is configurable to select the one or more columns from the plurality of columns.

23. The method according to claim 19, wherein creating the first diagram comprises placing on an editing canvas a query block, the query block representing programming instructions operable to cause the processor to create a query output comprising only data returned in response to a query written in a first query language,

wherein the query block is configurable to select at least one of:

query parameters or a tangible storage media on which the data to be queried is stored.

24. The method according to claim 23, wherein the query is converted during execution of the programming instructions represented by the query block to a second query language.

25. The method according to claim 19, wherein creating the first diagram comprises placing on an editing canvas of the graphical user interface a plurality of blocks, each of the plurality of blocks representing respective programming instructions, the respective programming instructions operable to cause the processor to do one or more of:

loading data, filtering loaded data, selecting columns from loaded data, scaling loaded data, normalizing loaded data, discovering an anomaly in loaded data, performing a temporal offset of the data, and performing a dimensionality reduction on the data,

wherein each of the plurality of blocks comprises one or more of;

at least one input or at least one output, and

wherein inputs and outputs of the plurality of blocks may be connected to one another by one or more wires operable to convey data from outputs to inputs.

26. The method according to claim 25, wherein each of the one or more wires is operable to convey data in the form a single value, an array, a matrix, or a set of programming instructions.

27. The method according to claim 19, wherein automatically generating the second diagram from the first diagram comprises automatically generating the second diagram from the first diagram in response to a single user input, the single user input comprising a key press, a touch on a touch screen, or a click with a pointing device.

28. The method according to claim 19, wherein causing the processor to manipulate data generated and stored previously by the process control environment comprises causing the processor to manipulate data stored on one or more big data nodes in the process control environment.

29. The method according to claim 19, wherein compiling the first programming instructions and executing the compiled first programming instructions comprises compiling a sub-set of the first programming instructions and executing the compiled subset of the first programming instructions to achieve a block-wise execution of a plurality of blocks on an editing canvas of the graphical user interface.

30. The method according to claim 19, wherein creating the first diagram comprises placing on an editing canvas of the graphical user interface a plurality of blocks, and

wherein each of the plurality of blocks includes a status indicator.

31. The method according to claim 30, wherein the status indicator indicates a status of the block as unexecuted, currently executing, or execution complete.

32. The method according to claim 19, wherein causing the processor to bind to the live data source comprises causing the processor to bind to a live data source corresponding to the data generated and stored previously by the process control environment.

33. The method according to claim 19, wherein causing the processor to bind to the live data source comprises causing the processor to bind to a live data source comprising a streamed source of data corresponding to the data generated and stored previously by the process control environment.

34. The method according to claim 19, wherein executing the compiled first programming instructions represented by the first diagram comprises:

downloading the compiled first programming instructions to a job manager and transmitting one or more jobs corresponding to the compiled first programming instructions to the processor.

35. The method according to claim 19, wherein executing the compiled second programming instructions represented by the second diagram comprises:

downloading the compiled second programming instructions to a job manager and transmitting one or more jobs corresponding to the compiled second programming instructions to the processor.

36. An analytics service product for performing data analytics in a process control environment, the analytics service product comprising:

a library of block definitions, the library stored on a tangible, non-transitory medium, each block definition comprising (i) a target algorithm for performing an action with respect to data in the process control environment, and (ii) one or more block properties;

a set of machine-readable instructions, stored on the tangible, non-transitory medium, the set of machine-readable instructions operable, when executed by a processor, to;

(1) present a canvas to a user via a display;

(2) present the library of block definitions to the user;

(3) receive one or more selections of one or more corresponding block definitions;

(4) place one or more blocks corresponding to the one or more block definitions on the canvas to create a module comprising the one or more blocks;

(5) facilitate configuration of one or more properties for any of (i) the one or more blocks, (ii) the module, or (iii) the one or more blocks and the module; and

(6) cause evaluation of (i) one or more of the blocks or (ii) the module;

an execution service operating on a processor, the execution service configured to;

(1) receive a request to evaluate the one or more blocks or the module;

(2) compile instructions corresponding to the one or more blocks or the module;

(3) create one or more job processes to execute the compiled instructions and cause the one or more job processes to execute the compiled instructions; and

(4) receive results of the one or more job processes;

a diagram transformer operable to transform a first diagram on the canvas representing an offline module associated with an offline data source into a second diagram on the canvas representing an online module associated with an online data source to cause results of analysis of real-time continuous data generated in the process control environment to be used as an input to a process control algorithm performing control functions in the process control environment; and

a communication network coupling one or more processors executing the one or more job processes to one or more distributed data engines of a plurality of distributed data engines in the process control environment, each distributed data engine comprising a tangible, non-transitory storage medium storing data generated in the process control environment.

37. The analytics service product according to claim 36, wherein the library of block definitions includes at least one block definition defining a block that loads data from one of the distributed data engines.

38. The analytics service product according to claim 36, wherein the library of block definitions includes at least one block definition defining a block that performs a data analysis function on data from one of the distributed data engines.

39. The analytics service product according to claim 36, wherein the set of machine-readable instructions, stored on the tangible, non-transitory medium, is stored on a web server, and wherein the instructions are transmitted to a web client and executed by a processor associated with the web client to present the canvas to the user.

40. The analytics service product according to claim 36, wherein the analytics service is operable to execute one or more blocks or modules in each of an offline mode and an online mode.

41. The analytics service product according to claim 40, wherein, in the offline mode, the one or more job processes execute the compiled instructions to perform analysis of data stored in the one or more distributed data engines.

42. The analytics service product according to claim 40, wherein, in the online mode, the one or more job processes execute the compiled instructions to perform analysis of real-time continuous data generated in the process control environment.

43. The analytics service product according to claim 40, wherein a module represented on the canvas by the first diagram, and configured in the offline mode to perform analysis of historized data in the process control environment, has a corresponding module represented on the canvas by the second diagram, and configured to be evaluated in the online mode to perform analysis of the real-time continuous data generated in the process control environment.

44. The analytics service product according to claim 36, wherein transforming the first diagram into the second diagram comprises replacing at least one historized data source specified in the first diagram with at least one source of live data being generated in the process control environment.

45. The analytics service product according to claim 36, wherein transforming the first diagram into the second diagram comprises adding a block that, when evaluated, will cause the results of the analysis of the real-time continuous data generated in the process control environment to be (i) displayed to the user and/or (ii) historized in one of the distributed data engines.

46. The analytics service product according to claim 36, wherein transforming the first diagram into the second diagram comprises implementing in the second diagram a block that outputs a predicted value of the process control environment, the block that outputs a predicted value of the process control environment corresponding to a model developed in the first diagram.

47. The analytics service product according to claim 36, wherein each of the block definitions corresponds to a configuration file.

48. The analytics service product according to claim 47, wherein the module corresponds to a module configuration file that includes the content of a plurality of block configuration files corresponding to the blocks of which the module is comprised.

49. The analytics service product according to claim 48, wherein the configuration files are JSON files.

50. The analytics service product according to claim 47, wherein receiving a request to evaluate the one or more blocks or the module comprises receiving a configuration file comprising at least an algorithm and a parameter of the algorithm.

51. The analytics service product according to claim 36, wherein receiving a request to evaluate the one or more blocks or the module comprises receiving a configuration file specifying a target environment in which to perform the evaluation.

52. The analytics service product according to claim 51, further comprising one or more compilers configured to receive the configuration file specifying the target environment in which to perform the evaluation, and produce a compiled output executable by a processor in the specified target environment.

53. The analytics service product according to claim 36, wherein each of the one or more job processes may be executable by a processor in a different specified target environment.

54. The analytics service product according to claim 36, further comprising, for each of one or more block definitions, a corresponding data plugin, each of the data plugins comprising a set of computer-executable instructions that conforms to an interface that can be called to perform a specific task.

55. The analytics service product according to claim 54, wherein each of the data plugins includes logic specific to a computing environment in which the analytics service is operating or logic specific to the process control environment for which analytics services are being performed.

56. The analytics service product according to claim 54, wherein each data plugin is called when metadata specifying the plugin is encountered in a configuration file corresponding to a block or a module.

57. The analytics service product according to claim 36, wherein facilitating configuration of one or more properties comprises:

invoking a data plugin to retrieve information specific to the process control environment in which the analytics service is operating.

58. The analytics service product according to claim 36, wherein facilitating configuration of one or more properties comprises:

receiving a command to display the properties for a block or module of the one or more blocks or the module;

for each of the one or more properties;

retrieving metadata for the property;

checking for plugin information in the metadata for the property;

if plugin information is present for the property;

discovering and loading a plugin specified by the metadata for the property; and

invoking the plugin specified by the metadata for the property to get required property values;

otherwise, if plugin information is not present for the property, finding a default value in the metadata for the property;

finding a UI control type in the metadata for the property, the UI control type indicating the manner in which to display the property;

adding the required property values or the default values to a property dialog;

determining if there is an additional property for the block or module; and

if there are no additional properties for the block or module, displaying the property dialog.