Systems and methods for learning data patterns predictive of an outcome
System and methods for learning data patterns predictive of an outcome are described. An example system may include a plurality of input sensors communicatively coupled to a controller; a data collection circuit structured to collect output data from the plurality of input sensors; and a machine learning data analysis circuit structured to receive the output data, learn received output data patterns indicative of an outcome, and learn a preferred input data collection band among a plurality of available input data collection bands. The machine learning data analysis circuit may be structured to learn received output data patterns by being seeded with a model based on industry-specific feedback. The outcome may be at least one of: a reaction rate, a production volume, or a required maintenance.
1. A system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a controller;
a data collection circuit structured to collect output data from the plurality of input sensors; and
a machine learning data analysis circuit structured to receive the output data, learn received output data patterns indicative of a type of an industrial machine outcome, and learn a preferred subset of the plurality of input sensors,
wherein:
the machine learning data analysis circuit is structured to learn the received output data patterns by being seeded with a model, wherein the seeding is based on industry-specific feedback, and
the type of the industrial machine outcome is at least one of: a reaction rate of a machine of the industrial environment, a production volume of a machine of the industrial environment, or a required maintenance of a machine of the industrial environment.
2. The system of claim 1 , wherein the model is at least one of a physical model, an operational model, or a system model.
3. The system of claim 1 , wherein an outcome of the type of industrial machine outcome is at least one of an outcome of a process, an outcome of a calculation, an outcome of an event, or an outcome of an activity.
4. The system of claim 1 , wherein the industry-specific feedback comprises at least one feedback value that is at least one of: a utilization measure of a machine of the industrial environment, an efficiency measure of a machine of the industrial environment, a measure of success in prediction or anticipation of states of a machine of the industrial environment, a measure of success in avoidance of faults of a machine of the industrial environment, a measure of success in mitigation of faults of a machine of the industrial environment, or a productivity measure of a machine of the industrial environment.
5. The system of claim 1 , wherein the industry-specific feedback comprises at least one of a power efficiency measure of a machine of the industrial environment or a financial efficiency measure of a machine of the industrial environment.
6. The system of claim 1 , wherein the machine learning data analysis circuit is further structured to learn received output data patterns based on the type of industrial machine outcome.
7. The system of claim 1 , wherein the controller keeps or modifies at least one of an operational parameter or equipment of the industrial environment.
8. The system of claim 1 , wherein the controller, based on at least one of: the learned received output data patterns, or an outcome of the type of industrial machine outcome, performs at least one of: removing under-utilized equipment, or re-tasking under-utilized equipment.
9. The system of claim 1 , wherein the machine learning data analysis circuit is structured to learn received output data patterns indicative of one of progress or alignment with at least one of goals or guidelines.
10. The system of claim 1 , wherein the machine learning data analysis circuit is further structured to learn received output data patterns indicating at least one of:
an unknown variable; or
a preferred input among available inputs.
11. The system of claim 1 , wherein the industry-specific feedback comprises at least one of: an amount of power generated by a machine about which the plurality of input sensors provide information during operation of the machine; a measure of an output of an assembly line about which the plurality of input sensors provide information; a failure rate of units of product produced by a machine about which the plurality of input sensors provide information; or a fault rate of a machine about which a plurality of input sensors provide information.
12. The system of claim 1 , wherein the industry-specific feedback comprises a power utilization efficiency of a machine about which the plurality of input sensors provides information, wherein the machine comprises at least one of: a turbine, a transformer, a generator, a compressor, a machine that stores energy, or at least one power train component.
13. The system of claim 12 , wherein the industry-specific feedback comprises at least one of: a rate of extraction of a material by the machine; a rate of production of a gas by the machine; a rate of production of a hydrocarbon product by the machine; or a rate of production of a chemical product by the machine.
14. The system of claim 1 , wherein the industry-specific feedback comprises at least one feedback value that is at least one of a yield measure of a machine of the industrial environment or a profit measure of a machine of the industrial environment.
15. A method for data collection in an industrial environment, the method comprising:
receiving output data from a large number of sensors in the industrial environment;
seeding a machine learning circuit with a model, wherein the seeding is based on performance measures; and
learning received output data patterns indicative of:
a type of an industrial machine outcome from the received output data; and
a preferred subset of the large number of sensors; and
adjusting, based on at least one of the learned output data patterns or the type of industrial machine outcome, at least one of a weighting of the model or a number of data points collected from the large number of sensors.
16. The method of claim 15 , further comprising changing, based on at least one of: the learned received output data patterns, or the type of industrial machine outcome, at least one of: a data storage technique for the output data, a data presentation mode, or a data presentation manner.
17. The method of claim 15 , further comprising filtering the output data using at least one of: a low pass filter, a high pass filter, or a band pass filter.
18. The method of claim 15 , wherein the performance measures include at least one of: a return on investment, a profit, or a revenue.
19. A system for data collection in an industrial environment, comprising:
a data collection circuit structured to collect output data from a plurality of input sensors;
a machine learning data analysis circuit structured to receive the output data and learn received output data patterns indicative of one of progress or alignment with at least one of industry-specific goals or guidelines; and
a controller structured to keep or modify at least one of operational parameters or equipment of the industrial environment based, at least in part, on the output data patterns indicative of the progress or the alignment with the at least one of the industry-specific goals or guidelines,
wherein:
the machine learning data analysis circuit is seeded with a model based on historic output data patterns and wherein the seeding is based on industry-specific associated states, and
the industry-specific goals or guidelines is at least one of: a reaction rate of a machine of the industrial environment, a production volume of a machine of the industrial environment, or required maintenance of a machine of the industrial environment.
20. The system of claim 19 , wherein the controller adjusts, based on at least one of: the learned received output data patterns, or the indicated progress or alignment with the at least one of the industry-specific goals or guidelines, at least one of a weighting of the machine learning data analysis circuit or a number of data points collected from the plurality of input sensors.
21. The system of claim 19 , wherein the at least one industry-specific goal or guideline comprises at least one of: a specified output production rate of a machine of the industrial environment, a specified generation rate of a machine of the industrial environment, an operational efficiency of a machine of the industrial environment, an operational failure rate of a machine of the industrial environment, a financial efficiency goal of a machine of the industrial environment, a financial profit goal of a machine of the industrial environment, a power efficiency of a machine of the industrial environment, or a resource utilization of a machine of the industrial environment.