IPC분류정보
국가/구분 |
United States(US) Patent
등록
|
국제특허분류(IPC7판) |
|
출원번호 |
US-0950927
(2007-12-05)
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등록번호 |
US-8090559
(2012-01-03)
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발명자
/ 주소 |
- Parthasarathy, Girija
- Mylaraswamy, Dinkar
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출원인 / 주소 |
- Honeywell International Inc.
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대리인 / 주소 |
Ingrassia Fisher & Lorenz, P.C.
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인용정보 |
피인용 횟수 :
17 인용 특허 :
4 |
초록
▼
A method for performing diagnostics for an engine comprises the steps of identifying an engine component as potentially being related to operational data of an engine, calculating a deviation from a thermodynamic model, and comparing the deviation with root cause deviation measures. The deviation re
A method for performing diagnostics for an engine comprises the steps of identifying an engine component as potentially being related to operational data of an engine, calculating a deviation from a thermodynamic model, and comparing the deviation with root cause deviation measures. The deviation relates the engine component to an adjustment to the thermodynamic model with respect to a variable of the thermodynamic model, based at least in part on the operational data. Each root cause deviation measure relates one of a plurality of potential root causes to the thermodynamic model with respect to the variable of the thermodynamic model.
대표청구항
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1. A method for performing diagnostics for an engine using operational data for the engine, the operational data pertaining to a plurality of performance measures of the engine, the method comprising the steps of: determining a symptom of the engine from the operational data, the symptom pertaining
1. A method for performing diagnostics for an engine using operational data for the engine, the operational data pertaining to a plurality of performance measures of the engine, the method comprising the steps of: determining a symptom of the engine from the operational data, the symptom pertaining to a measure of health of the engine that is unexpected, undesirable, or both;identifying an engine component as potentially being related to the symptom using the operational data;calculating a component deviation from a first model, wherein the component deviation relates the engine component to an adjustment to the first model with respect to a variable of the first model, the adjustment believed to be potentially caused by the engine component, based at least in part on the operational data;determining a plurality of root cause deviation measures offline via a second model using simulated operational data representing various different possible conditions for the engine;comparing the component deviation with the plurality of root cause deviation measures, each root cause deviation measure relating one of a plurality of potential root causes for the symptom to the first model with respect to the variable of the first model, thereby generating a plurality of comparisons; andselecting one of the plurality of potential root causes as a likely root cause of the symptom based on the plurality of comparisons. 2. The method of claim 1, wherein: the first model comprises a thermodynamic model; andthe second model comprises an aerodynamic model. 3. The method of claim 2, wherein the component deviation and the plurality of root cause deviation measures comprise scalars, each scalar comprising a multiplicative or additive deviation to the thermodynamic model. 4. The method of claim 2, further comprising the steps of: calculating a second component deviation from the thermodynamic model, wherein the second component deviation relates the engine component to a second adjustment to the thermodynamic model with respect to a second variable of the thermodynamic model, based at least in part on the operational data; andcomparing the second component deviation with a plurality of root cause second deviation measures, each root cause second deviation measure relating one of the plurality of potential root causes to the thermodynamic model with respect to the second variable of the thermodynamic model. 5. The method of claim 4, selecting one of the plurality of potential root causes comprises the step of selecting one of the plurality of potential root causes as the likely root cause of the symptom, based at least in part on a similarity of a root cause deviation measure and a root cause second deviation measure of the likely root cause to the component deviation and the second component deviation, respectively. 6. The method of claim 4, wherein the variable or the second variable, or both, of the thermodynamic model comprise a measure of efficiency, airflow, or pressure ratio relating to the engine component. 7. The method of claim 4, further comprising the steps of: identifying an additional engine component as potentially being related to the operational data;calculating an additional component deviation from the thermodynamic model, wherein the additional component deviation relates the additional engine component to an additional adjustment to the thermodynamic model with respect to the variable or the second variable, or both, of the thermodynamic model, based at least in part on the operational data; andcomparing the additional component deviation with a plurality of additional root cause deviation measures, each additional root cause deviation measure relating one of a plurality of additional potential root causes to the thermodynamic model with respect to the variable or the second variable, or both, of the thermodynamic model. 8. A method for generating a root cause deviation for a component of an engine for use in performing diagnostics for the engine, the method comprising the steps of: identifying a plurality of root causes of a symptom of the engine, the symptom pertaining to a measure of health of the engine that is unexpected, undesirable, or both, each root cause comprising a condition of the component;simulating conditions of the component for each of the plurality of root causes, to thereby generate simulated data;developing a mapping between each potential root cause and a first variable related to engine performance, using the simulated data and a first model;determining values of a second variable via a second model using simulated operational data representing various different possible conditions for the engine; andrelating the mapping to the second variable, to thereby determine a root cause deviation measure for the component. 9. The method of claim 8, wherein: the first model comprises an aerodynamic model; andthe second model comprises a thermodynamic model. 10. The method of claim 9, further comprising the steps of: selecting an additional component of the engine;identifying a plurality of additional root causes of the symptom, each additional root cause comprising a condition of the additional component;simulating conditions of the additional component for each of the plurality of additional root causes, to thereby generate additional simulated data;developing an additional mapping between each potential additional root cause and a first additional variable related to engine performance, using the additional simulated data and the aerodynamic model; andrelating the additional mapping to a second additional variable of the thermodynamic model, to thereby determine an additional root cause deviation measure for the additional component. 11. The method of claim 10, wherein the root cause deviation measure and the additional root cause deviation measure comprise scalars, each scalar comprising a multiplicative or additive deviation to the thermodynamic model. 12. A program product for performing diagnostics for an engine using operational data for the engine, the operational data pertaining to a plurality of performance measures of the engine, the program product comprising: (a) a program configured to at least facilitate: determining a symptom of the engine from the operational data, the symptom pertaining to a measure of health of the engine that is unexpected, undesirable, or both;identifying an engine component as potentially being related to the symptom using the operational data;calculating a component deviation from a thermodynamic model, wherein the component deviation relates the engine component to an adjustment to the thermodynamic model with respect to a variable of the thermodynamic model, the adjustment believed to be potentially caused by the engine component, based at least in part on the operational data;determining a plurality of root cause deviation measures offline via an aerodynamic model using simulated operational data representing various different possible conditions for the engine;comparing the component deviation with the plurality of root cause deviation measures, each root cause deviation measure relating one of a plurality of potential root causes for the symptom to the thermodynamic model with respect to the variable of the thermodynamic model, thereby generating a plurality of comparisons; andselecting one of the plurality of potential root causes as a likely root cause of the symptom based on the plurality of comparisons; and(b) a non-transitory computer readable storage medium bearing the program and containing computer instructions for causing a computer processor to perform the program. 13. The program product of claim 12, wherein the program is further configured to at least facilitate: calculating a second component deviation from the thermodynamic model, wherein the second component deviation relates the engine component to a second adjustment to the thermodynamic model with respect to a second variable of the thermodynamic model, based at least in part on the operational data; andcomparing the second component deviation with a plurality of root cause second deviation measures, each root cause second deviation measure relating one of the plurality of potential root causes to the thermodynamic model with respect to the second variable of the thermodynamic model. 14. The program product of claim 13, wherein the program is further configured to at least facilitate selecting one of the plurality of potential root causes as a likely root cause, based at least in part on a similarity of a root cause deviation measure and a root cause second deviation measure of the likely root cause to the component deviation and the second component deviation, respectively. 15. The program product of claim 13, wherein the program is further configured to at least facilitate: identifying an additional engine component as potentially being related to the operational data;calculating an additional component deviation from the thermodynamic model, wherein the additional component deviation relates the additional engine component to an additional adjustment to the thermodynamic model with respect to the variable, the second variable, or both, of the thermodynamic model, based at least in part on the operational data;comparing the additional component deviation with a plurality of additional root cause deviation measures, each additional root cause deviation measure relating one of a plurality of additional potential root causes to the thermodynamic model with respect to the variable or the second variable, or both, of the thermodynamic model. 16. A program product for generating a root cause deviation for a component of an engine for use in performing diagnostics for the engine, the program product comprising: (a) a program configured to at least facilitate:identifying a plurality of root causes of a symptom of the engine, the symptom pertaining to a measure of health of the engine that is unexpected, undesirable, or both, each root cause comprising a condition of the component;simulating conditions of the component for each of the plurality of root cases, to thereby generate simulated data;developing a mapping between each potential root cause and a first variable related to engine performance, using the simulated data and an aerodynamic model;determining values pertaining to a second variable of a thermodynamic model using simulated operational data representing various different possible conditions for the engine via an aerodynamic model; andrelating the mapping to second variable, to thereby determine a root cause deviation measure for the component; and(b) a non-transitory computer readable storage medium bearing the program and containing computer instructions for causing a computer processor to perform the program. 17. The program product of claim 16, wherein the program is further configured to at least facilitate: selecting an additional component of the engine;identifying a plurality of additional root causes of the symptom, each additional root cause comprising a condition of the additional component;simulating conditions of the additional component for each of the plurality of additional root causes, to thereby generate additional simulated data;developing an additional mapping between each potential additional root cause and a first additional variable related to engine performance, using the additional simulated data and the aerodynamic model; andrelating the additional mapping to a second additional variable of the thermodynamic model, to thereby determine an additional root cause deviation measure for the additional component;wherein the root cause deviation measure and the additional root cause deviation measure comprise scalars, each scalar comprising a multiplicative or additive deviation to the thermodynamic model. 18. A health maintenance system for an engine, the health maintenance system comprising: a sensing device configured to sense operational data for the engine, the operational data pertaining to a plurality of performance measures of the engine; anda processor configured to at least facilitate: determining a symptom of the engine from the operational data, the symptom pertaining to a measure of health of the engine that is unexpected, undesirable, or both;identifying an engine component as potentially being related to the symptom using the operational data;calculating a component deviation from a thermodynamic model, wherein the component deviation relates the engine component to an adjustment to the thermodynamic model with respect to a variable of the thermodynamic model, the adjustment believed to be potentially caused by the engine component, based at least in part on the operational data;determining a plurality of root cause deviation measures offline via an aerodynamic model using simulated operational data representing various different possible conditions for the engine;comparing the component deviation with the plurality of root cause deviation measures, each root cause deviation measure relating one of the plurality of potential root causes for the symptom to the thermodynamic model with respect to the variable of the thermodynamic model, thereby generating a plurality of comparisons; andselecting one of the plurality of potential root causes as a likely root cause of the symptom based on the plurality of comparisons. 19. The health maintenance system of claim 18, further comprising: a memory configured to store the plurality of root cause deviation measures. 20. The health maintenance system of claim 18, wherein the processor is further configured to at least facilitate: calculating a second component deviation from the thermodynamic model, wherein the second component deviation relates the engine component to a second adjustment to the thermodynamic model with respect to a second variable of the thermodynamic model, based at least in part on the operational data; andcomparing the second component deviation with a plurality of root cause second deviation measures, each root cause second deviation measure relating one of the plurality of potential root causes to the thermodynamic model with respect to the second variable of the thermodynamic model. 21. The health maintenance system of claim 20, wherein the processor is further configured to at least facilitate selecting one of the plurality of potential root causes as a likely root cause, based at least in part on a similarity of a root cause deviation measure and a root cause second deviation measure of the likely root cause to the component deviation and the second component deviation, respectively. 22. The health maintenance system of claim 21, wherein the processor is further configured to at least facilitate quantifying an estimated severity of the likely root cause, based at least in part on a magnitude of the root cause deviation measure or the root cause second deviation measure, or both, of the likely root cause.
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