System and method for dynamic multi-objective optimization of machine selection, integration and utilization

• US 7,797,062 B2
• Filed: 09/30/2003
• Issued: 09/14/2010
• Est. Priority Date: 08/10/2001
• Status: Expired due to Term

• Current Assignee: Rockwell Automation Technologies Incorporated (Rockwell Automation, Inc.)
• Original Assignee: Rockwell Automation Technologies Incorporated (Rockwell Automation, Inc.)
• Inventors: Discenzo, Frederick M., Pai, Ramdas M., Schaller, Gerald Peter, Roote, Michael Scott, Novak, Richard James, Jensen, David Lee, Ference, John Crandall, Williams, Bennet Ray
• Primary Examiner(s): Rodriguez, Paul L
• Assistant Examiner(s): ORTIZ RODRIGUEZ, CARLOS R
• Application Number:
  US10/674,966
• Publication Number:
  US 20040267395A1
• Time in Patent Office: 2,541 Days
• Field of Search: 700108-109, 700/111, 700174-180, 702/182, 702/184
• US Class Current: 700/28
• CPC Class Codes: G05B 13/024 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G05B 2219/32015 Optimize, process management, optimize production line
• CPC Class Codes: G05B 2219/32017 Adapt real process as function of changing simulation model, changing for better results
• CPC Class Codes: G05B 23/0294 Optimizing process, e.g. process efficiency, product quality
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 69/40 for recovering from a failure of a protocol instance or entity, e.g. service redundancy protocols, protocol state redundancy or protocol service redirection management of faults, events, alarms or notifications in data switching networks H04L41/06
• CPC Class Codes: Y02P 90/82 Energy audits or management systems therefor

Abstract

The invention provides control systems and methodologies for controlling a process having one or more motorized pumps and associated motor drives, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like. More particularly, the subject invention provides for employing machine diagnostic and/or prognostic information in connection with optimizing an overall business operation over a time horizon.

Claims

1. A system that optimizes industrial business operations, comprising:

a processor;

a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions configured to implement the industrial business operations including;

a component that receives data relating to a plurality of machines that are part of the industrial business operations;

a correlation engine that analyzes the data and correlates efficiency information for at least two of the plurality of machines to derive correlated system efficiency information;

an optimization component that employs the correlated system efficiency information to select an operating point within an allowable range of operation about a system setpoint and controls the industrial business operations in part according to the operating point, the operating point selected to optimize a total energy utilization of the plurality of machines based on the correlated system efficiency information; and

a prognostics engine that generates an inference of a future state of a subset of operations for the plurality of machines based in part on the received data;

wherein at least a subset of the plurality of machines comprise respective prognostic components that share state information with one another; and

wherein the prognostic engine and the prognostic components collaborate to control an operating rate of at least a subset of the plurality of machines.

2. The system of claim 1, the prognostic engine comprising a classifier.

3. The system of claim 2, the classifier performs a probabilistic analysis in connection with the inference.

4. The system of claim 1, wherein the state information comprises state information relating to at least one of historical, current, or predicted operating states of the plurality of machines.

5. The system of claim 4, at least one of the prognostic components performs prognoses for a cluster of machines.

6. The system of claim 1, at least a subset of the plurality of machines are respectively represented by intelligent agents that collaborate to determine at least one control modification of the plurality of machines that optimizes the total energy utilization for the plurality of machines.

7. The system of claim 6, wherein the intelligent agents share energy utilization information, and control modes are assigned to the respective intelligent agents based on a comparison of the energy utilization information from the respective intelligent agents.

8. The system of claim 1, at least a subset of the plurality of machines are physically located remotely from one another.

9. The system of claim 1, wherein the plurality of machines comprise at least a motorized pump system having an electric motor operatively coupled with a pump, and a motor drive providing electrical power to the motor, and the correlation engine correlates efficiency information for at least two of the pump, the motor, or the motor drive.

10. A method that optimizes assets in an industrial automation system, comprising:

employing a processor executing computer executable instructions stored on a computer-readable storage medium to implement the following acts;

receiving and analyzing in real-time data relating to diagnoses and prognoses of operational aspects of a subset of machines that are part of the industrial automation system;

modeling and representing interactions of the subset of machines;

modifying asset utilization in the industrial automation system as a function of the analyzed diagnostic and prognostic machine data;

correlating efficiency information for at least two machines within the subset of machines to derive correlated system efficiency information;

selecting an operating point within an allowable range of operation about a system setpoint, the operating point selected to optimize a total cost of energy utilization of the subset of machines according to the correlated system efficiency information;

controlling at least one machine within the subset of machines according to the operating point;

predicting a failure of at least one identified component of the subset of machines based on the correlated system efficiency information;

automatically ordering a replacement for the identified component via the Internet in response to the predicting;

selecting, in response to predicting, a new operating point calculated to maintain continued operation of the identified component until at least an anticipated arrival time of the replacement component; and

controlling at least one machine within the subset of machines according to the new operating point.

11. The method of claim 10, further comprising:

monitoring current energy costs; and

selecting the operating setpoint based at least in part on the current energy costs.

12. The method of claim 10, further comprising employing an options based analysis in connection with asset management.

13. The method of claim 10, wherein controlling the at least one machine within the subset of machines according to the operating point comprises providing a motor speed signal to a motor drive according to the operating point.

14. The method of claim 10, further comprising obtaining at least one of the efficiency information, the allowable range, or the system setpoint user via manual input.

15. The method of claim 10, further comprising obtaining at least one of the efficiency information, the allowable range, or the system setpoint from a host computer via a network.

16. The method of claim 15, wherein the at least one of the efficiency information, the allowable range, or the system setpoint is obtained via wireless communications.

17. The method of claim 10, further comprising obtaining at least a portion of one of the efficiency information, the allowable range, or the setpoint from prior operation of the industrial automation system.

18. The method of claim 10, wherein the selecting the operating point comprises measuring at least one process variable from a sensor associated with the industrial automation system.

19. The method of claim 10, wherein the selecting the operating point comprises:

correlating component performance information associated with at least two components in the industrial automation system in order to derive correlated system performance information; and

selecting the operating point within the allowable range of operation according to the correlated system performance information.

20. The method of claim 19, wherein the controlling at least one machine within the subset of machines according to the operating point comprises providing the operating point to a controller associated with the industrial automation system.

21. The method of claim 19, further comprising obtaining at least one of the performance information, the allowable range, or the system setpoint from a host computer.

22. The method of claim 21, further comprising obtaining at least one of the performance information, the allowable range, or the system setpoint via a network.

23. The method of claim 22, further comprising obtaining at least one of the performance information, the allowable range, or the system setpoint via wireless communications.

24. The method of claim 19, further comprising obtaining at least a portion of one of the performance information, the allowable range, or the setpoint from prior operation of the industrial automation system.

25. The method of claim 19, wherein the correlating the component performance information comprises correlating at least one of life cycle cost information, efficiency information, life expectancy information, safety information, emissions information, operational cost information, MTBF information, noise information, and or vibration information.

26. The method of claim 19, wherein the correlating the component performance information comprises correlating efficiency information related to at least two of a motor, a pump, or a motor drive to derive cost information related to the industrial automation system operational cost per unit of fluid pumped through the pump.

27. The method of claim 26, further comprising selecting the operating point to further optimize a cost of operation associated with at least one of the motor, the pump, or the motor drive based on the correlated system efficiency information.

28. A system that optimizes assets in an industrial automation system, comprising:

processing means for processing data from a memory having stored therein computer-executable instructions configured to implement the industrial business operations including;

means for receiving and analyzing in real-time data relating to prognoses of operational aspects of a plurality of machines that are part of the industrial automation system;

means for modeling and representing interactions between the plurality of machines;

means for regulating a subset of the plurality of machines as a function of analyzed diagnostic and prognostic machine data;

means for correlating efficiency information for at least two of the plurality of machines in order to derive correlated system efficiency information;

means for selecting an operating point within an allowable range of operation about a system setpoint according to the correlated system efficiency information, the operating point selected to optimize a total energy utilization of the industrial automation system;

means for controlling at least one of the plurality of machines according to the operating point;

means for predicting a failure of at least one identified component of the subset of the plurality of machines based on the correlated system efficiency information;

means for automatically ordering a replacement for the identified component via the Internet in response to predicting the failure;

means for selecting, in response to predicting the failure, a new operating point calculated to maintain continued operation of the identified component until at least an anticipated arrival time of the replacement component; and

controlling at least one of the plurality of machines according to the new operating point.

29. The system of claim 28 further comprising means for inferring future states of the subset of the plurality of machines.

30. The system of claim 28 further comprising means for inferring future states of a subset of business components.

31. An industrial automation control method, comprising:

employing a processor executing computer executable instructions stored on a computer-readable storage medium to implement the following acts;

receiving data relating to a plurality of machines that are part of an industrial business operations;

analyzing the data and correlating efficiency information for at least two of the plurality of the plurality of machines to derive correlated system efficiency information;

selecting an operating point within an allowable range of operation about a system setpoint based on the correlated system efficiency information, the operating point selected to optimize a total energy utilization of the plurality of machines given the correlated system efficiency information;

controlling the industrial business operations in accordance with the operating point;

employing a prognostics engine to infer a future state of a subset of operations for the plurality of machines based in part on the received data;

executing prognostic components on at least a subset of the plurality of machines, the prognostic components sharing state information with one another; and

controlling an operating rate of at least a subset of the plurality of machines based on a collaboration between the prognostic engine and the prognostic components.

32. A computer-implemented method for ordering parts and optimizing assets in an industrial automation system comprising:

employing a processor executing computer executable instructions stored on a computer readable storage medium to implement the following acts;

correlating component performance information for at least two devices operating within the industrial automation system;

deriving correlated component efficiency information based on the correlating;

inferring a failure period for at least one device operating within the industrial automation system machine based on the correlated component efficiency information;

automatically ordering a replacement component for the at least one device prior to an inferred failure period;

selecting, in response to the inferring, an operating point calculated to maintain continued operation of the at least one device until at least an anticipated arrival time of the replacement component and to optimize energy utilization by the at least one device;

controlling at least one machine in the industrial automation system according to the operating point;

detecting an updated anticipated arrival time for the replacement component;

selecting, in response to the detecting, a new operating point calculated to maintain continued operation of at least one part until at least the updated anticipated arrival time; and

controlling the at least one machine in the industrial automation system according to the new operating point.

33. The method of claim 32 further comprising employing an options based scheme in connection with machine management.

34. The method of claim 32 further comprising employing an options based scheme in connection with decision support.

35. The method of claim 32 further comprising employing an options based scheme in connection with asset optimization.

36. A system that facilitates optimizing industrial business operations, comprising:

a processor;

a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions configured to implement the industrial business operations including;

a component that receives data relating to a state of a subset of machines that are part of the industrial business operations and correlates efficiency information for at least two machines within the subset of machines to derive correlated system efficiency information;

a prognostics engine that infers a future state of at least a subset of the industrial business operations based in part on the received data; and

an optimization component that selects an operating point within an allowable range of operation about a system set point based on the correlated system efficiency information and controls at least one machine within the subset of machines according to the operating point, the operating point selected to optimize a total energy utilization of the industrial business operations;

wherein the subset of machines comprise respective prognostic components that share state information with one another; and

wherein the prognostic engine and the prognostic components collaborate to control an operating rate of at least one machine within the subset of machines.

37. The system of claim 36, the prognostic engine infers at least one future business condition.

38. The system of claim 37, the at least one future business condition comprising at least one of future raw materials or future product demand.