SYSTEM AND METHOD FOR DYNAMIC MULTI-OBJECTIVE OPTIMIZATION OF MACHINE SELECTION, INTEGRATION AND UTILIZATION

• US 20100306001A1
• Filed: 08/13/2010
• Published: 12/02/2010
• Est. Priority Date: 08/10/2001
• Status: Active Grant

• Current Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Original Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Inventors: Ference, John Crandall P.E., Pai, Ramdas M., Roote, Michael Scott, Williams, Bennet Ray, Schaller, Gerald Peter, Jensen, David Lee, Discenzo, Frederick M., Novak, Richard James
• Granted Patent: US 8,126,574 B2
• Time in Patent Office: Days
• US Class Current: 705/7
• 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 facilitates optimization of business operations, comprising:

a component configured to capture data relating to a current state of at least one process that is part of the business operations;

a prognostics engine configured to infer, at predetermined time intervals, a future state of a subset of the business operations based in part on the data and to determine, at the predetermined time intervals, a preferred future state of the subset of the business operations given a specified one or more business objectives; and

an optimization component configured to select, at the predetermined time intervals, a value for at least one control variable associated with at the at least one process, the value selected to drive the subset of the business operations from the current state to the preferred future state.

2. The system of claim 1, wherein the one or more business objectives comprise optimization of at least one of energy usage of the business operations, energy cost incurred by the business operation, revenue generated by the business operations, product throughput, machine life cycle cost, or machine longevity.

3. The system of claim 2, wherein the optimization component is configured to select the value for the at least one control variable that optimizes at least two of the one or more business objectives based in part on a determined correlation between the at least two of the one or more business objectives.

4. The system of claim 1, wherein the current state, the future state, and the predicted future state relate to at least one of production throughput, product quality, possibility of equipment failure, machine temperature, part order arrival, or material stock quality.

5. The system of claim 1, wherein the prognostics engine and the optimization engine iteratively infer the future state and select the value for the at least one control variable over multiple time intervals to facilitate convergence of the subset of the business operations on the preferred future state.

6. The system of claim 1, wherein the optimization component is configured to select the value for the at least one control variable based additionally on data relating to factors that are extrinsic to the at least one process.

7. The system of claim 6, wherein the factors include at least one of real-time energy costs, availability of replacement components, or costs of replacement components.

8. The system of claim 6, wherein the factors include environmental data collected for the environment in which the at least one process is operating.

9. The system of claim 1, further comprising a correlation engine that analyzes the data and correlates efficiency information for at least two machines comprising the at least one process to derive correlated system efficiency information, wherein the optimization component selects the value for the at least one control variable based in part on the correlated efficiency information.

10. The system of claim 1, wherein the prognostics engine infers the future state based in part on analysis of historical data associated with prior operation of the at least one process.

11. A method for optimizing one or more aspects of business operations, comprising:

specifying one or more business objectives;

capturing data relating to a present state of at least one process that is part of the business operations; and

iteratively updating at least one control variable associated with the at least one process, the updating comprising;

inferring a future state of a subset of the business operations based in part on the data;

determining a preferred future state of the subset of the business operations given the one or more business objectives;

computing an updated value for the at least one control variable, the updated value calculated to drive the subset of the business operations from the present state to the preferred future state; and

setting the at least one control variable to the updated value.

12. The method of claim 11, wherein the specifying the one or more business objectives comprises specifying that at least one of the following is to be optimized:

energy usage of the business operations, energy cost incurred by the business operations, revenue generated by the business operations, product throughput, machine life cycle cost, or machine longevity.

13. The method of claim 12, wherein the computing the updated value comprises computing an updated value that optimizes at least two of the one or more business objectives.

14. The method of claim 11, wherein inferring the future state comprises inferring at least one of production throughput, product quality, probability of equipment failure, machine temperature, part order arrival, or material stock quality.

15. The method of claim 11, wherein the computing the updated value comprises computing the updated value further based on data relating to extrinsic factors with respect to the at least one process.

16. The method of claim 15, wherein the computing the updated value further based on data relating to extrinsic factors comprises computing the updated value based on at least one of real-time or predicted energy costs, availability of replacement components, or costs of replacement components.

17. The method of claim 11, further comprising:

correlating efficiency information for at least two machines comprising the at least one process to yield correlated system efficiency information; and

computing the updated value based additionally on the correlated efficiency information.

18. The method of claim 11, wherein the inferring the future state comprises inferring the future state based in part on analysis of trend data associate with prior operation of the at least one process.

19. A system that controls a process within a business operation in a manner that optimizes a given business objective, comprising:

means for collecting operational data from at least one process within a business operation;

means for inferring a future state of a subset of the business operation using the operational data;

means for determining a preferred future state of the subset of the business operation that substantially optimizes a specified business objective;

means for calculating a value for at least one variable controlling the at least one process that is predicted to drive the subset of the business operation to the preferred future state.

20. The system of claim 19, further comprising:

means for correlating efficiency information for at least two machines comprising the at least one process to yield correlated system efficiency information; and

means for calculating the value based additionally on the correlated efficiency information.