IPC분류정보
국가/구분 |
United States(US) Patent
등록
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국제특허분류(IPC7판) |
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출원번호 |
UP-0669696
(2007-01-31)
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등록번호 |
US-7827006
(2010-11-22)
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발명자
/ 주소 |
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출원인 / 주소 |
- Fisher-Rosemount Systems, Inc.
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대리인 / 주소 |
Marshall, Gerstein & Borun LLP
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인용정보 |
피인용 횟수 :
1 인용 특허 :
160 |
초록
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Detection of one or more abnormal situations is performed using various statistical measures, such as a mean, a median, a standard deviation, etc. of one or more process parameters or variable measurements made by statistical process monitoring blocks within a plant. This detection may include deter
Detection of one or more abnormal situations is performed using various statistical measures, such as a mean, a median, a standard deviation, etc. of one or more process parameters or variable measurements made by statistical process monitoring blocks within a plant. This detection may include determination of the health and performance of one or more heat exchangers in the plant, and in particular, detection of a fouling condition of the one or more heat exchangers. Among the statistical measures, the detection may include calculation of an overall thermal resistance of the heat exchanger, which may be indicative under certain circumstances of heat exchanger performance and in particularly degradation of heat exchanger performance as a result of heat exchanger fouling.
대표청구항
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What is claimed is: 1. A method of detecting an abnormal situation associated with a heat exchanger, comprising: receiving measured data pertaining to a process parameter sensed by at least one sensor device associated with the heat exchanger; determining a baseline value of an operating characteri
What is claimed is: 1. A method of detecting an abnormal situation associated with a heat exchanger, comprising: receiving measured data pertaining to a process parameter sensed by at least one sensor device associated with the heat exchanger; determining a baseline value of an operating characteristic of the heat exchanger from the measured data, wherein the baseline value corresponds to healthy operation of the heat exchanger; determining one or more statistical measures associated with the operating characteristic; and using the baseline value of the operating characteristic and the one or more statistical measures associated with the operating characteristic to detect the abnormal situation within the heat exchanger; wherein the operating characteristic is a thermal resistance 1/UA of the heat exchanger, wherein U is an average heat transfer coefficient per unit surface area and A is a surface are of the heat transfer. 2. The method of claim 1, wherein the abnormal situation comprises heat exchanger fouling. 3. The method of claim 1, wherein the process parameter relates to at least one of a first fluid inlet temperature, a first fluid outlet temperature, a first fluid flow rate, a second fluid inlet temperature, a second fluid outlet temperature or a second fluid flow rate. 4. The method of claim 1, wherein the process parameter is one of a group of process parameters consisting of: a control demand of a first fluid control valve CD(h), a control demand of a second fluid control valve CD(c), a temperature setpoint of a master cascade control loop SP(t), a setpoint for the first fluid flow rate control SP(h), a differential pressure across the heat exchanger for first fluid side DP(h), a valve position for first fluid control valve VP(h), a valve position for second fluid control valve VP(c) and a setpoint for a second fluid flow rate SP(c). 5. The method of claim 1, further including processing the measured data to produce processed data and wherein determining the one or more statistical measures associated with the operating characteristic includes determining the one or more statistical measures using the processed data. 6. The method of claim 1, wherein using the baseline value of the operating characteristic and the one or more statistical measures associated with the operating characteristic to detect the abnormal situation within the heat exchanger includes comparing the baseline value of the operating characteristic to the one or more statistical measures associated with the operating characteristic to detect the abnormal situation. 7. The method of claim 6, wherein comparing the baseline value of the operating characteristic to the one or more statistical measures associated with the operating characteristic to detect the abnormal situation within the heat exchanger comprises evaluating a relative deviation of each of the one or more statistical measures associated with the operating characteristic from the baseline value of the operating characteristic. 8. The method of claim 1, wherein using the baseline value of the operating characteristic and the one or more statistical measures associated with the operating characteristic to detect the abnormal situation within the heat exchanger comprises detecting a rate of change of the operating characteristic. 9. The method of claim 1, wherein determining the one or more statistical measures associated with the operating characteristic comprises providing a statistical process monitoring (SPM) block associated with a process device, the SPM block being configured to monitor the operating characteristic and to provide the one or more statistical measures. 10. A method of detecting an abnormal situation in a heat exchanger, comprising: providing a plurality of statistical process monitoring (SPM) blocks associated with the heat exchanger, each SPM block receiving measurements of a process parameter associated with the heat exchanger and determining a statistical measure of the process parameter from the process parameter measurements to provide a plurality of statistical measures; providing a baseline value for each of the statistical measures; determining a difference between each statistical measure and its associated baseline value; and detecting the existence of the abnormal situation within the heat exchanger based on the differences between the statistical measures of the process parameter and the respective baseline values; wherein the process parameter is one of a group of process parameters consisting of: a control demand of a first fluid control valve CD(h), a control demand of a second fluid control valve CD(c), a temperature setpoint of a master cascade control loop SP(t), a setpoint for the first fluid flow rate control SP(h), a differential pressure across the heat exchanger for first fluid side DP(h), a valve position for first fluid control valve VP(h), a valve position for second fluid control valve VP(c) and a setpoint for a second fluid flow rate SP(c). 11. The method of claim 10, further comprising providing a root cause diagnostic (RCD) table and organizing the statistical measures within the RCD table. 12. The method of claim 10, wherein the abnormal situation comprises heat exchanger fouling. 13. The method of claim 10, wherein the process parameter relates to an overall thermal resistance 1/UA of the heat exchanger, wherein U is an average heat transfer coefficient per unit surface area and A is a surface area of heat transfer. 14. The method of claim 10, wherein detecting the existence of the abnormal situation within the heat exchanger is further based on at least one of a first fluid inlet temperature, a first fluid outlet temperature, a first fluid flow rate, a second fluid inlet temperature, a second fluid outlet temperature or a second fluid flow rate. 15. The method of claim 10, wherein the baseline values comprise at least one of a learned mean value, a learned standard deviation value, a multiple of a learned mean value, a multiple of a learned standard deviation value, or a linear combination of a learned mean value and a learned standard deviation value. 16. A method of detecting an abnormal situation associated with a heat exchanger, comprising: receiving measured data using a statistical process monitoring (SPM) block associated with a process device, the measured data pertaining to a process parameter sensed by at least one sensor device associated with the heat exchanger; determining, within the SPM block, one or more statistical measures associated with the process parameter using the measured data; and using the one or more statistical measures associated with the process parameter to detect the abnormal situation within the heat exchanger; wherein the process parameter is one of a group of process parameters consisting of: a control demand of a first fluid control valve CD(h), a control demand of a second fluid control valve CD(c), a temperature setpoint of a master cascade control loop SP(t), a setpoint for the first fluid flow rate control SP(h), a differential pressure across the heat exchanger for first fluid side DP(h), a valve position for first fluid control valve VP(h), a valve position for second fluid control valve VP(c) and a setpoint for a second fluid flow rate SP(c). 17. The method of claim 16, further comprising organizing the measured data in accordance with a root cause diagnostic fault table, and wherein using the one or more statistical measures associated with the process parameter to detect the abnormal situation within the heat exchanger comprises evaluating a relative deviation of each of the one or more statistical measures associated with the process parameter from a baseline value. 18. The method of claim 17, wherein the baseline value is determined as a statistical measure of a first set of the measured data, and wherein the one or more statistical measures associated with the process parameter are determined from a second set of the measured data. 19. The method of claim 16, wherein using the one or more statistical measures associated with the process parameter to detect the abnormal situation within the heat exchanger comprises evaluating the process parameter relative to a setpoint value. 20. A method of detecting an abnormal situation associated with a heat exchanger, comprising: receiving measured data pertaining to a process parameter sensed by at least one sensor device associated with the heat exchanger; determining a baseline value of a first statistical measure of the process parameter; determining a further statistical measure of the process parameter from the measured data; and detecting the abnormal situation within the heat exchanger by comparing the baseline value of the first statistical measure of the process parameter to the further statistical measure of the process parameter; wherein the process parameter is one of a group of process parameters consisting of: a control demand of a first fluid control valve CD(h), a control demand of a second fluid control valve CD(c), a temperature setpoint of a master cascade control loop SP(t), a setpoint for the first fluid flow rate control SP(h), a differential pressure across the heat exchanger for first fluid side DP(h), a valve position for first fluid control valve VP(h), a valve position for second fluid control valve VP(c) and a setpoint for a second fluid flow rate SP(c). 21. The method of claim 20, wherein determining the baseline value of the first statistical measure of the process parameter includes determining the baseline value as a statistical measure of a first set of the measured data, and wherein determining the further statistical measure of the process parameter from the measured data includes determining the further statistical measure of the process parameter from a second set of the measured data. 22. The method of claim 20, wherein determining the baseline value of the first statistical measure of the process parameter includes using a predetermined value of the process parameter as the baseline value of the first statistical measure of the process parameter. 23. The method of claim 16, wherein the process parameter relates to an overall thermal resistance 1/UA of the heat exchanger, wherein U is an average heat transfer coefficient per unit surface area and A is a surface area of heat transfer. 24. The method of claim 20, wherein the process parameter relates to an overall thermal resistance 1/UA of the heat exchanger, wherein U is an average heat transfer coefficient per unit surface area and A is a surface area of heat transfer.
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