Insight driven programming tags in an industrial automation environment
Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to optimize a target variable in an industrial automation environment. In some examples, a design application generates a control program configured and selects a program tag that represents a target variable in an industrial process. A processing application identifies a set of available program tags that represent independent variables in the industrial process and determines correlations between ones of the independent variables and the target variable. The processing application selects available program tags that represent independent variables correlated with the target variable and generates a recommendation that indicates the selected available program tags. The design application modifies the control program using the selected available program tags to optimize the target variable. The design application transfers the control program for implementation by the programmable logic controller.
1. A system to train a machine learning model for implementation in an industrial automation environment, the system comprising:
a memory that stores executable components; and
a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:
a processing component configured to identify program tags that represent process variables in an industrial process, ingest feature vectors that represent the program tags, utilize machine learning algorithms to process the feature vectors and determine correlations between the program tags, and generate a correlation matrix that indicates the correlations between the program tags based on the utilization of the machine learning algorithms;
a design component configured to generate a control program configured for implementation by a programmable logic controller and select a program tag in the control program that represents a target variable in the industrial process, wherein the target variable comprises one of the process variables;
the processing component configured to access the correlation matrix, select ones of the program tags that represent independent process variables that are correlated with the selected program tag that represents the target variable in the industrial process based on the correlations indicated by the correlation matrix, and generate a recommendation that indicates the ones of the program tags; and
the design component configured to modify the control program using the ones of the program tags to optimize the target variable based on the correlations between the independent process variables represented by the ones of the program tags and the target variable represented by the selected program tag and transfer the control program for implementation by the programmable logic controller.
2. The system of claim 1 wherein the processing component is further configured to utilize the machine learning algorithms to perform statistical analysis on the feature vectors and determine the correlations between the program tags based on the statistical analysis.
3. The system of claim 1 wherein the processing component is further configured to utilize the machine learning algorithms to determine a historical relationship between the feature vectors based on a previous association between the program tags and determine the correlations between the program tags based on the historical relationship.
4. The system of claim 1 wherein the processing component is further configured to utilize the machine learning algorithms to perform statistical analysis on the feature vectors, determine a historical relationship between the feature vectors based on a previous association between the program tags, and determine the correlations between the program tags based on the statistical analysis and the historical relationship.
5. The system of claim 1 wherein the recommendation that indicates the ones of the program tags comprises a machine learning output that indicates the ones of the program tags.
6. The system of claim 1 wherein the industrial process comprises one or more of a manufacturing process, a chemical production process, or a food processing process.
7. The system of claim 1 wherein the control program comprises a ladder logic diagram configured for implementation by the programmable logic controller.
8. A non-transitory computer-readable medium storing instructions to perform variable optimization in an industrial automation environment, wherein the instructions, in response to execution by a processor, cause the processor to drive a system to perform operations comprising:
identifying program tags that represent process variables in an industrial process, ingesting feature vectors that represent the program tags, utilizing machine learning algorithms to process the feature vectors and determine correlations between the program tags, and generate a correlation matrix that indicates the correlations between the program tags based on the utilization of the machine learning algorithms;
generating a control program configured for implementation by a programmable logic controller and selecting a program tag in the control program that represents a target variable in the industrial process, wherein target variable comprises one of the process variables;
accessing the correlation matrix, selecting ones of the program tags that represent independent process variables that are correlated with the selected program tag that represents the target variable in the industrial process based on the correlations indicated by the correlation matrix, and generating a recommendation that indicates the ones of the program tags; and
modifying the control program using the ones of the program tags to optimize the target variable based on the correlations between the independent process variables represented by the ones of the program tags and the target variable represented by the selected program tag and transferring the control program for implementation by the programmable logic controller.
9. The non-transitory computer-readable medium of claim 8 , wherein the operations further comprise utilizing the machine learning algorithms to perform statistical analysis on the feature vectors and determining the correlations between the program tags based on the statistical analysis.
10. The non-transitory computer-readable medium of claim 8 , wherein the operations further comprise utilizing the machine learning algorithms to determine a historical relationship between the feature vectors based on a previous association between the program tags and determining the correlations between the program tags based on the historical relationship.
11. The non-transitory computer-readable medium of claim 8 , wherein the operations further comprise utilizing the machine learning algorithms to perform statistical analysis on the feature vectors and determine a historical relationship between the feature vectors based on a previous association between the program tags and determining the correlations between the program tags based on the statistical analysis and the historical relationship.
12. The non-transitory computer-readable medium of claim 8 , wherein generating the recommendation that indicates the ones of the program tags comprises generating a machine learning output that indicates the ones of the program tags.
13. The non-transitory computer-readable medium of claim 8 , wherein the industrial process.
14. The non-transitory computer-readable medium of claim 8 wherein the control program comprises a ladder logic diagram configured for implementation by the programmable logic controller.
15. A method to perform variable optimization in an industrial automation environment, the method comprising:
identifying program tags that represent process variables in an industrial process, ingesting feature vectors that represent the program tags, utilizing machine learning algorithms to process the feature vectors and determine correlations between the program tags, and generate a correlation matrix that indicates the correlations between the program tags based on the utilization of the machine learning algorithms;
generating a control program configured for implementation by a programmable logic controller and selecting a program tag in the control program that represents a target variable in the industrial process, wherein the target variable comprises one of the process variables;
accessing the correlation matrix, selecting ones of the program tags that represent independent process variables that are correlated with the selected program tag that represents the target variable in the industrial process based on the correlations indicated by the correlation matrix, and generating a recommendation that indicates the ones of the program tags; and
modifying the control program using the ones of the program tags to optimize the target variable based on the correlations between the independent process variables represented by the ones of the program tags and the target variable represented by the selected program tag and transferring the control program for implementation by the programmable logic controller.
16. The method of claim 15 further comprising utilizing the machine learning algorithms to perform statistical analysis on the feature vectors and determining the correlations between the program tags based on the statistical analysis.
17. The method of claim 15 further comprising utilizing the machine learning algorithms to determine a historical relationship between the feature vectors based on a previous association between the program tags and determining the correlations between the program tags based on the historical relationship.
18. The method of claim 15 further comprising utilizing the machine learning algorithms to perform statistical analysis on the feature vectors and determine a historical relationship between the feature vectors based on a previous association between the program tags and determining the correlations between the program tags based on the statistical analysis and the historical relationship.
19. The method of claim 15 wherein generating the recommendation that indicates the ones of the program tags comprises generating a machine learning output that indicates the ones of the program tags.
20. The method of claim 15 wherein the industrial process comprises one or more of a manufacturing process, a chemical production process, or a food processing process.