IPC분류정보
국가/구분 |
United States(US) Patent
등록
|
국제특허분류(IPC7판) |
|
출원번호 |
US-0856304
(2010-08-13)
|
등록번호 |
US-8126574
(2012-02-28)
|
발명자
/ 주소 |
- Discenzo, Frederick M.
- Pai, Ramdas M.
- Schaller, Gerald Peter
- Roote, Michael Scott
- Novak, Richard James
- Jensen, David Lee
- Ference, John Crandall
- Williams, Bennet Ray
|
출원인 / 주소 |
- Rockwell Automation Technologies, Inc.
|
대리인 / 주소 |
|
인용정보 |
피인용 횟수 :
29 인용 특허 :
44 |
초록
▼
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, emi
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.
대표청구항
▼
1. A system that facilitates optimization of business operation, comprising: a processor;a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions configured to implement the system, including: a first component configured to capture state d
1. A system that facilitates optimization of business operation, comprising: a processor;a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions configured to implement the system, including: a first component configured to capture state data relating to a current state of a set of machines associated with at least one process that is part of the business operation;a second component configured to capture second state data of the at least one process;a correlation engine configured to correlate at least the first state data, the second state data, two or more specified business objectives, and efficiency information for at least one machine of the set of machines to derive correlated information; anda prognostics engine configured to infer, at predetermined time intervals, a future state of a subset of the business operation based in part on the correlated information, and to determine, at the predetermined time intervals, an alternate future state of the subset of the business operation predicted to achieve the two or more specified business objectives; andan optimization component configured to select, at the predetermined time intervals, a value for at least one control variable associated with at least one of the set of machines, wherein the value is selected to drive the subset of the business operation from the current state to the alternate future state,wherein the two or more specified business objectives include at least two of decreased energy usage of the business operation, decreased energy cost incurred by the business operation, increased revenue generated by the business operation, increased product throughput, decreased machine life cycle cost, increased machine longevity, reduced machine maintenance cost, or reduced overhead. 2. The system of claim 1, wherein the current state, the future state, and the alternate 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. 3. The system of claim 1, wherein the prognostics engine and the optimization component are configured to 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 operation on the alternate future state. 4. 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 extrinsic data relating to one or more factors that are extrinsic to the at least one process. 5. The system of claim 4, wherein the one or more factors include at least one of a real-time energy cost, an availability of a replacement component, or a costs of the replacement component. 6. The system of claim 4, wherein the one or more factors include environmental data collected from an environment in which the at set of machines operate. 7. The system of claim 1, wherein the prognostics engine is configured to infer the future state based in part on analysis of historical data associated with prior operation of the at least one process. 8. A method for controlling one or more aspects of a business operation, comprising: employing a processor executing computer-executable instructions stored on a computer-readable storage medium to implement acts, including specifying two or more business objectives, including specifying two or more of the following: reducing energy usage of the business operation, reducing energy cost incurred by the business operation, increasing revenue generated by the business operation, increasing product throughput, reducing machine life cycle cost, increasing machine longevity, reducing overhead, or reducing machine repair cost;capturing first state data relating to a present state of one or more machines that control at least one process that is part of the business operation;capturing second state data relating to the at least one process;correlating the first state data, the second state data, the two or more business objectives, and efficiency information for at least one machine of the one or more machines to yield correlated information;anditeratively updating at least one control variable associated with the one or more machines, the updating comprising: inferring a future state of the one or more machines based in part on the first state data;determining an alternate future state of the one or more machines that achieves the two or more business objectives based on the correlated information;computing an updated value for the at least one control variable, wherein the updated value is calculated to drive the one or more machines from the present state to the alternate future state; andsetting the at least one control variable to the updated value. 9. The method of claim 8, wherein the 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. 10. The method of claim 8, wherein the computing the updated value comprises computing the updated value further based on extrinsic data relating to one or more extrinsic factors with respect to the at least one process. 11. The method of claim 10, wherein the computing the updated value further based on the extrinsic data comprises computing the updated value based on at least one of a real-time or predicted energy cost, an availability of a replacement component, or a cost of the replacement component. 12. The method of claim 8, wherein the inferring the future state comprises inferring the future state based in part on an analysis of trend data associated with prior operation of the one or more machines. 13. The method of claim 8, wherein the updating includes iteratively performing the inferring, the determining, the computing, and the setting over multiple time intervals to facilitate convergence of the one or more machines toward the alternate future state. 14. The method of claim 8, wherein the determining the alternate future state includes: generating a suite of possible operating conditions for the one or more machines;mapping the suit of possible operating scenarios against the present state of the one or more machines; andselecting the alternate future state from the suit of possible operating conditions based at least in part on the mapping. 15. A computer-readable medium having stored thereon computer-executable instructions that, in response to execution, cause a computer to perform operations including: collecting operational data from a set of machines and at least one process associated with the set of machines within a business operation;correlating the operational data, two or more specified business objectives, and efficiency information for at least one machine of the one or more machines to yield correlated information;inferring a future state of the at least one process using the operational data;determining an alternative future state of the at least one process that moves at least one process closer, relative to the future state, to two or more specified business objectives, wherein the determining is based on the correlated information, wherein the two or more specified business objectives include two or more of reducing energy usage of the business operation, reducing an energy cost incurred by the business operation, increasing revenue generated by the business operation, increasing product throughput, reducing machine life cycle cost, increasing machine longevity, decreasing overhead, or decreasing machine repair cost; andcalculating, based on the correlated information, a value for at least one variable controlling the at least one process predicted to drive the at least one process to the alternative future state.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.