Equipment health monitoring method and system and engine
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
F01D-021/00
F02D-041/14
F01D-019/00
F03D-007/04
G05B-023/02
F02D-041/22
F02D-041/26
F03D-011/00
출원번호
US-0972896
(2015-12-17)
등록번호
US-9797328
(2017-10-24)
우선권정보
EP-14199397 (2014-12-19)
발명자
/ 주소
Martinez, Alvaro
Sanchez, Luciano
출원인 / 주소
Rolls-Royce Deutschland Ltd & Co KG
대리인 / 주소
Shuttleworth & Ingersoll, PLC
인용정보
피인용 횟수 :
0인용 특허 :
1
초록▼
An Equipment Health Monitoring method for an engine and an Equipment Health Monitoring system for performing the method are provided. At least some of the following units are used: an Engine Simulation Unit, a Possibilistic Drift Computation Unit, a Fuzzy String Generator Unit, an Experience-based S
An Equipment Health Monitoring method for an engine and an Equipment Health Monitoring system for performing the method are provided. At least some of the following units are used: an Engine Simulation Unit, a Possibilistic Drift Computation Unit, a Fuzzy String Generator Unit, an Experience-based String Matching Unit and an Information Fusion and Prognosis Unit.
대표청구항▼
1. An Equipment Health Monitoring method for an engine comprising: a1) providing a plurality of sensors measuring engine parameter values and transmitting data regarding the engine parameter values;a2) providing a health data processing unit, the health data processing unit receiving the transmitted
1. An Equipment Health Monitoring method for an engine comprising: a1) providing a plurality of sensors measuring engine parameter values and transmitting data regarding the engine parameter values;a2) providing a health data processing unit, the health data processing unit receiving the transmitted data from the plurality of sensors;a3) providing a data processing unit comprising a possibilistic drift computation unit, a fuzzy string generator unit, an experience based string matching unit, an information fusion and prognosis unit and an engine simulation unit;a4) operatively coupling the health data processing unit to the data processing unit;a5) providing that the possibilistic drift computation unit automatically allots an upper probability distribution to drift rates of the differences between measured and predicted values generated by the engine simulation unit,b) providing that the fuzzy string generator unit transforms a numerical sequence of upper probabilities of the drift rates generated by the possibilistic drift computation unit into a sequence of quantified terms in a fuzzy term set,c) providing that the experience-based string matching unit compares the string of terms generated by the fuzzy string generator unit with at least one other sequence or portion of sequence of previously obtained fuzzy terms in a database set to determine a degree of similarity therebetween, andd) providing that the information fusion and prognosis unit determines in dependence of the matching patterns or portions of the patterns resulting from the comparisons carried out in the experience-based string matching unit, providing a rate of engine deterioration indicating a current level of deterioration, a rate of deterioration change and a remaining useful life for a given level of deterioration or requirement or otherwise for engine maintenance or a most likely level of deterioration and a likelihood for the requirement or otherwise for engine maintenance of the engine under test;automatically determining a maintenance schedule based on step d). 2. The Equipment Health Monitoring method according to claim 1, wherein data input to the possibilistic drift computation unit is measured with at least one parameter value at one location of the engine during engine operation, the data processing unit recording the at least one measured parameter value. 3. The Equipment Health Monitoring method according to claim 2, wherein the engine simulation unit determines how the at least one measured parameter value compares against a predicted value of a same parameter in a model-based computer simulation of the engine operating at same working conditions or extrapolated to a standardized set of conditions. 4. The Equipment Health Monitoring method according to claim 1, wherein a catalog of terms in use by the fuzzy string generator unit comprises “increased”, “decreased” and “unchanged” or a finer subdivision of at least one of “increased” and “decreased”. 5. The Equipment Health Monitoring method according to claim 1, wherein at least one of the terms produced by the fuzzy string generator unit is used for determining the level of engine and module deterioration. 6. The Equipment Health Monitoring method according to claim 1, and further comprising at least one chosen from using a genetic algorithm to learn sequences of fuzzy terms used by the experience-based string matching unit from historical records of data collected from maintenance operations and inspections of different engines and searching a genetically learned experience knowledge database to find nearest cases to a specific engine and a range of values is determined that bounds a life consumption estimation of the engine. 7. The Equipment Health Monitoring method according to claim 1, wherein the measured engine parameter values comprise at least one chosen from temperature, pressure, speed, vibration, frequency, fuel flow and noise data. 8. The Equipment Health Monitoring method claim 1, wherein the engine is a turbo engine, an aircraft turbo engine, a wind turbine, a static engine, a turbine generator, an engine or generator on a boat, a combustion engine or another system which sustains deterioration over time and parameters are measured. 9. The Equipment Health Monitoring method according to claim 8, and further comprising using as engine parameter values at least one chosen from pressure at an entry of the turbo engine, pressure at an entry of a low pressure compressor, exit pressure of a high pressure compressor, exit pressure of a low pressure turbine, ambient or atmospheric temperature outside the engine, temperature at the entry of the low pressure compressor, high pressure delivery temperature, turbine gas temperature, temperature at an entry to a high pressure turbine, low pressure shaft speed, high pressure shaft speed, and fuel flow. 10. The Equipment Health Monitoring method according to claim 1, wherein the engine parameter values are measured at at least one chosen from a stable cruise condition, after take-off and after climb. 11. The Equipment Health Monitoring method according to claim 1, wherein a prognostic report is generated in dependence of the experience-based matching unit results in an automatic notification of at least one chosen from a level of maintenance, level of deterioration, time until a certain level of deterioration and cost assessment. 12. The Equipment Health Monitoring method according to claim 1, wherein a temporal sequence of at least one of the measured engine parameter values is used by at least one chosen from the engine simulation unit, the possibilistic drift computation unit, the fuzzy string generator unit, the experienced-based string matching unit and the information fusion and prognosis unit. 13. The Equipment Health Monitoring method according to claim 1, and further comprising at least one chosen from using genetic learning algorithms to process EHM data of a large sample of engines to generate a knowledge database of time specific sequences associated to specific levels or types of deterioration, matching each chain in a genetically pruned database to a chain computed for the engine under test and identifying closest sample engines or combining RULs of nearest engines in the database to carry out a prognostic assessment of engines based on a most likely or similar level of deterioration rate of change, based on a service knowledge database of engines previously assessed in a same form. 14. An Equipment Health Monitoring system, comprising: a plurality of sensors measuring engine parameter values and transmitting data regarding the engine parameter values;a health data processing unit, the health data processing unit receiving the transmitted data from the plurality of sensors;a data processing unit comprising a possibilistic drift computation unit, a fuzzy string generator unit, an experience based string matching unit and an information fusion and prognosis unit;wherein the health data processing unit is coupled to the data processing unit;an engine simulation unit configured to determine how at least one measured engine parameter value compares against at least one predicted value of a same parameter in a model-based computer simulation of the engine, and assigns at least one quantified fuzzy term to an upper probability of drift of the difference between the measured engine parameter value and the predicted value, andwherein the possibilistic drift computation unit is configured to automatically allot an upper probability distribution to drift rates of the differences between measured and predicted values generated by the engine simulation unit,wherein the fuzzy string generator unit is configured to transform a numerical sequence of upper probabilities of the drift rates generated by the possibilistic drift computation unit into a sequence of quantified terms in a fuzzy term set,wherein the experience-based string matching unit is configured to compare the string of terms generated by the fuzzy string generator unit with at least one other sequence or portion of sequence of previously obtained fuzzy terms in a database set to determine a degree of similarity therebetween,wherein the information fusion and prognosis unit determines in dependence of the matching patterns or portions of the patterns resulting from the comparisons carried out in the experience-based string matching unit, providing a rate of engine deterioration indicating a current level of deterioration, a rate of deterioration change and a remaining useful life for a given level of deterioration or requirement or otherwise for engine maintenance or a most likely level of deterioration and a likelihood for the requirement or otherwise for engine maintenance of the engine under test,wherein the experience knowledge database string matching unit is configured to compare the string of terms generated by the fuzzy string generator unit with information stored in a database populated with a genetic learning algorithm and in dependence of values resulting from the comparison to the signal generated by an information fusion and prognosis unit indicating the associated status monitored, which may be the current level of engine deterioration, a level of required maintenance, or the rate of deterioration change of the engine under test;the data processing unit configured to automatically determining a maintenance schedule based on output from the experience knowledge database string matching unit. 15. The Equipment Health Monitoring method according to claim 1, and further comprising providing an engine electronic control which includes the health data processing unit. 16. An aircraft turbo engine, interacting with the Equipment Health Monitoring system according to claim 14. 17. The Equipment Health Monitoring system according to claim 14, and further comprising an engine electronic control which includes the health data processing unit.
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이 특허에 인용된 특허 (1)
Feeney, Mark Edward; Leslie, Keith John; Syed, Yusuf Razi; Hartropp, Simon John, Method of monitoring gas turbine engine operation.
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