Systems and methods for monitoring and controlling industrial processes

1. A computer-implemented data processing method for improving prediction and automated, active prevention of paper break on a paper manufacturing line, the method comprising:

receiving, by computing hardware, current thermal imaging data for a portion of a paper web in the paper manufacturing process;

determining, by the computing hardware, force data for the portion of the paper web;

accessing, by the computing hardware, paper profile data for the paper manufacturing line;

processing, by the computing hardware to produce a first data analysis result, current thermal imaging data, the force data, and the paper profile data using a machine-learning model trained with respective historical thermal imaging data, respective historical force data, and respective paper profile data for each respective prior paper breakage event in a set of prior paper breakage events;

generating, by the computing hardware based on the first data analysis result, a prediction as to an occurrence of the paper break on the portion of the paper web;

identifying, by the computing hardware, a preventative action based on the prediction, the preventive action including at least one of:

modifying an operating parameter of at least one machine component used in the paper manufacturing line;

activating a cleaning component for the paper manufacturing line; or

at least temporarily ceasing production of paper on the paper manufacturing line;

facilitating, by the computing hardware, performance of the preventative action;

determining, by the computing hardware, a paper break result indicating whether the paper break occurred subsequent to the performance of the preventative action; and

transmitting, by the computing hardware, the paper break result, the preventative action, the current thermal imaging data, and the force data to an external computing system as additional training data for the machine-learning model.

2. The computer-implemented data processing method of claim 1 , wherein the current thermal imaging data identifies a current moisture profile of the paper web.

3. The computer-implemented data processing method of claim 1 , wherein the preventive action further comprises at least one of activating a siren or generating an alert and transmitting the alert to a computing device.

4. A computer-implemented data processing method for improving prediction and automated, active prevention of paper break on a paper manufacturing line, the method comprising:

receiving, by computing hardware, current thermal imaging data for a portion of a paper web in the paper manufacturing process;

determining, by the computing hardware, force data for the portion of the paper web;

accessing, by the computing hardware, paper profile data for the paper manufacturing line;

processing, by the computing hardware to produce a first data analysis result, current thermal imaging data, the force data, and the paper profile data using a machine-learning model trained with respective historical thermal imaging data, respective historical force data, and respective paper profile data for each respective prior paper breakage event in a set of prior paper breakage events;

generating, by the computing hardware based on the first data analysis result, a prediction as to an occurrence of the paper break on the portion of the paper web;

identifying, by the computing hardware, a preventative action based on the prediction, the preventive action including at least one of:

modifying an operating parameter of at least one machine component used in the paper manufacturing line;

activating a cleaning component for the paper manufacturing line; or

at least temporarily ceasing production of paper on the paper manufacturing line;

facilitating, by the computing hardware, performance of the preventative action;

identifying, by the computing hardware, a component of the paper manufacturing process that is a predicted cause of the occurrence of the paper break;

identifying, by the computing hardware, a manufacturer of the component; and

transmitting, by the computing hardware, data associated with the prediction to a computing system associated with the manufacturer of the component.

5. The computer-implemented data processing method of claim 4 , further comprising:

receiving, by the computing hardware from the computing system associated with the manufacturer, one or more modified operating parameters for the component; and

facilitating, by the computing hardware, implementation of the one or more modified operating parameters for the component.

6. A computer-implemented data processing method for improving prediction and automated, active prevention of paper break on a paper manufacturing line, the method comprising:

receiving, by computing hardware, current thermal imaging data for a portion of a paper web in the paper manufacturing process;

determining, by the computing hardware, force data for the portion of the paper web;

accessing, by the computing hardware, paper profile data for the paper manufacturing line;

processing, by the computing hardware to produce a first data analysis result, current thermal imaging data, the force data, and the paper profile data using a machine-learning model trained with respective historical thermal imaging data, respective historical force data, and respective paper profile data for each respective prior paper breakage event in a set of prior paper breakage events;

generating, by the computing hardware based on the first data analysis result, a prediction as to an occurrence of the paper break on the portion of the paper web;

identifying, by the computing hardware, a preventative action based on the prediction, the preventive action including at least one of:

modifying an operating parameter of at least one machine component used in the paper manufacturing line;

activating a cleaning component for the paper manufacturing line; or

at least temporarily ceasing production of paper on the paper manufacturing line; and

facilitating, by the computing hardware, performance of the preventative action, wherein identifying the preventative action based on the prediction comprises processing the prediction using a second machine-learning model trained with respective preventive action success data for each respective prior paper breakage prediction in a set of prior paper breakage predictions.

7. A computer-implemented data processing method for improving prediction and automated, active prevention of paper break on a paper manufacturing line, the method comprising:

receiving, by computing hardware, current thermal imaging data for a portion of a paper web in the paper manufacturing process;

determining, by the computing hardware, force data for the portion of the paper web;

accessing, by the computing hardware, paper profile data for the paper manufacturing line;

processing, by the computing hardware to produce a first data analysis result, current thermal imaging data, the force data, and the paper profile data using a machine-learning model trained with respective historical thermal imaging data, respective historical force data, and respective paper profile data for each respective prior paper breakage event in a set of prior paper breakage events;

generating, by the computing hardware based on the first data analysis result, a prediction as to an occurrence of the paper break on the portion of the paper web;

identifying, by the computing hardware, a preventative action based on the prediction, the preventive action including at least one of:

modifying an operating parameter of at least one machine component used in the paper manufacturing line;

activating a cleaning component for the paper manufacturing line; or

at least temporarily ceasing production of paper on the paper manufacturing line; and

facilitating, by the computing hardware, performance of the preventative action, wherein:

the respective historical thermal imaging data indicates paper weak spot properties for each respective prior paper breakage event in the set of prior paper breakage events;

the respective historical force data indicates paper web process frequency data or paper web amplitude data for each respective paper web during each respective prior paper breakage event in a set of prior paper breakage events; and

the machine learning model comprises an artificial neural network using the paper weak spot properties for each respective prior paper breakage and the paper web process frequency data or paper web amplitude data a set of inputs to establish a set of causation relationships between the set of inputs and the set of prior paper breakage events.

8. A system comprising:

a non-transitory computer-readable medium storing instructions; and

a processing device communicatively coupled to the non-transitory computer-readable medium, wherein the processing device is configured to execute the instructions and thereby perform operations comprising:

determining moisture data for a portion of a paper web in a paper manufacturing line;

determining force data for the portion of the paper web;

accessing paper profile data for the paper manufacturing line;

accessing historical paper breakage event data;

processing the moisture data, the force data, the paper profile data, and the historical paper breakage event data using at least one of a rules-based model or a machine-learning model to generate a prediction of an occurrence of a paper break on the portion of the paper web; and

responsive to the prediction of the occurrence of the paper break, facilitating performance of a preventative action, wherein the preventative action comprises at least one of:

modifying an operating parameter of at least one machine component used in the paper manufacturing line;

activating a cleaning component for the paper manufacturing line;

activating a siren;

generating an alert and transmitting the alert to a computing device; or

at least temporarily ceasing production of paper on the paper manufacturing line, wherein the operations further comprise processing the prediction and historical preventative action success data using at least one of a rules-based model or a machine-learning model to select the preventative action.

9. A system comprising:

a non-transitory computer-readable medium storing instructions; and

a processing device communicatively coupled to the non-transitory computer-readable medium, wherein the processing device is configured to execute the instructions and thereby perform operations comprising:

determining moisture data for a portion of a paper web in a paper manufacturing line;

determining force data for the portion of the paper web;

accessing paper profile data for the paper manufacturing line;

accessing historical paper breakage event data;

processing the moisture data, the force data, the paper profile data, and the historical paper breakage event data using at least one of a rules-based model or a machine-learning model to generate a prediction of an occurrence of a paper break on the portion of the paper web;

responsive to the prediction of the occurrence of the paper break, facilitating performance of a preventative action, wherein the preventative action comprises at least one of:

modifying an operating parameter of at least one machine component used in the paper manufacturing line;

activating a cleaning component for the paper manufacturing line;

activating a siren;

generating an alert and transmitting the alert to a computing device; or

at least temporarily ceasing production of paper on the paper manufacturing line;

identifying a component on the paper manufacturing line identified by the prediction;

identifying a manufacturer of the component; and

transmitting data associated with the prediction to a computing system associated with the manufacturer of the component.

10. The system of claim 8 , wherein the operations further comprise:

receiving, from the computing system associated with the manufacturer, one or more modified operating parameters for the component; and

facilitating implementation of the one or more modified operating parameters for the component on the paper manufacturing line.

11. The system of claim 8 , wherein:

the preventative action comprises activating the cleaning component for the paper manufacturing line;

activating the cleaning component comprises washing one or more felt components on the paper manufacturing line; and

activating the cleaning component occurs automatically in response to the prediction of the occurrence of the paper break.

12. A system comprising:

a non-transitory computer-readable medium storing instructions; and

a processing device communicatively coupled to the non-transitory computer-readable medium, wherein the processing device is configured to execute the instructions and thereby perform operations comprising:

determining moisture data for a portion of a paper web in a paper manufacturing line;

determining force data for the portion of the paper web;

accessing paper profile data for the paper manufacturing line;

accessing historical paper breakage event data;

processing the moisture data, the force data, the paper profile data, and the historical paper breakage event data using at least one of a rules-based model or a machine-learning model to generate a prediction of an occurrence of a paper break on the portion of the paper web;

responsive to the prediction of the occurrence of the paper break, facilitating performance of a preventative action, wherein the preventative action comprises at least one of:

modifying an operating parameter of at least one machine component used in the paper manufacturing line;

activating a cleaning component for the paper manufacturing line;

activating a siren;

generating an alert and transmitting the alert to a computing device; or

at least temporarily ceasing production of paper on the paper manufacturing line, wherein the operations further comprise:

determining whether the prediction of the occurrence of the paper break was derived from the force data or the moisture data or both; and

selecting the preventative action based on whether the prediction of the occurrence of the paper break was derived from the force data or the moisture data or both.

13. The system of claim 8 , wherein determining the moisture data comprises deriving the moisture data from one or more thermal images of the paper web.

14. A non-transitory computer-readable medium having program code that is stored thereon, the program code executable by one or more processing devices for performing operations comprising:

receiving image data for a portion of a paper web in a paper manufacturing line;

determining force data for the portion of the paper web;

accessing paper profile data for the paper manufacturing line;

accessing historical paper breakage event data, the historical paper breakage event data being derived from a set of historical paper breakage events for a plurality of paper manufacturing lines, wherein the plurality of paper manufacturing lines:

each have at least one common piece of machinery as the paper manufacturing line; and

produce paper that shares the paper profile data for the paper manufacturing line;

processing the image data, the force data, the paper profile data, and the historical paper breakage event data using at least one of a rules-based model, a machine-learning model, or an artificial neural network to generate an output comprising a prediction of an occurrence of a paper break on the portion of the paper web; and

responsive to the prediction of the occurrence of the paper break, facilitating performance of a preventative action, wherein the preventative action comprises at least one of:

modifying an operating parameter of at least one machine component used in the paper manufacturing line;

activating a cleaning component for the paper manufacturing line;

activating an alarm;

generating an alert and transmitting the alert to a computing device; or

at least temporarily ceasing production of paper on the paper manufacturing line.

15. The non-transitory computer-readable medium of claim 14 , wherein processing the image data, the force data, the paper profile data, and the historical paper breakage event data using at least one of the rules-based model, the machine-learning model, or the artificial neural network to generate the prediction of the occurrence of the paper break on the portion of the paper web comprises providing the historical paper breakage event data as a set of inputs to the artificial neural network to establish a causation relationship between the set of inputs and the set of historical paper breakage events, the historical paper breakage event data comprising at least one of paper moisture spot property data during each breakage event in the set of historical paper breakage events, paper web process frequency data during each breakage event in the set of historical paper breakage events, or paper web amplitude data during each breakage event in the set of historical paper breakage events.

16. The non-transitory computer-readable medium of claim 15 , wherein:

the rules-based model, the machine-learning model, or the artificial neural network provide predictive breakage outputs that are specific to the at least one common piece of machinery when used to produce paper that shares the paper profile data; and

the operations further comprise providing the image data and force data as training data to the rules-based model, the machine-learning model, or the artificial neural network.

17. The non-transitory computer-readable medium of claim 14 , wherein the operations further comprise:

identifying a component on the paper manufacturing line identified by the prediction;

identifying a manufacturer of the component;

transmitting data associated with the prediction to a computing system associated with the manufacturer of the component;

receiving, from the computing system associated with the manufacturer, one or more modified operating parameters for the component; and

facilitating implementation of the one or more modified operating parameters for the component on the paper manufacturing line.

18. The non-transitory computer-readable medium of claim 14 , wherein the operations further comprise:

accessing historical preventative action success data; and

processing the prediction and the historical preventative action success data using at least one of the rules-based model, the machine-learning model, or the artificial neural network to select the preventative action.