Method for estimating and reducing uncertainties in process measurements
First Claim
1. A method of controlling an m variable multivariate system or process, characterized by:
- (a) obtaining (220) historical reference data comprising numerical measurements of said m variables, said measurements collectively encompassing a plurality of variations within one or more operational states of said system or process;
(b) modeling (222) said historical reference data to produce modeled values, modeling uncertainties and measuring uncertainties of said reference data set measurements;
(c) deriving (224–
230) a final model of said historical reference data by sequentially repeating said modeling until successively derived sums of all of said measurement uncertainties are approximately equal and successively derived sums of all of said modeling uncertainties are approximately equal;
(d) deriving (232) a set of modeling parameters characteristic of said final model; and
,(e) combining said measuring uncertainties with a new data set (200) comprising numerical measurements of said m variables, said new data set measurements collectively encompassing said plurality of variations within said one or more operational states of said system or process to control (218) continued operation of said multivariate system or process.
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Abstract
A reference matrix contains valid measurements characterizing operation of a multivariate process (220). Modeling parameters of the reference matrix are derived (222–232). The final model parameters, balanced with respect to measuring and modeling uncertainties (232), are applied to model (204) a new set of measurements (200). If the new set has no faults (206) then all modeled values and modeling uncertainties (208) can be used to control the process (218). If the new set has only one fault (210) ten the modeled value and modeling uncertainty of the faulted measurement plus the measured values and measuring uncertainties of the unfaulted measurements (212) can be used to control the process (218) while repair procedures are implemented for the identified fault (216). If the new set has more than one fault (214) then the process (218) should be shut down, and repair procedures should be implemented (216) for all identified faults.
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Citations
30 Claims
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1. A method of controlling an m variable multivariate system or process, characterized by:
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(a) obtaining (220) historical reference data comprising numerical measurements of said m variables, said measurements collectively encompassing a plurality of variations within one or more operational states of said system or process; (b) modeling (222) said historical reference data to produce modeled values, modeling uncertainties and measuring uncertainties of said reference data set measurements; (c) deriving (224–
230) a final model of said historical reference data by sequentially repeating said modeling until successively derived sums of all of said measurement uncertainties are approximately equal and successively derived sums of all of said modeling uncertainties are approximately equal;(d) deriving (232) a set of modeling parameters characteristic of said final model; and
,(e) combining said measuring uncertainties with a new data set (200) comprising numerical measurements of said m variables, said new data set measurements collectively encompassing said plurality of variations within said one or more operational states of said system or process to control (218) continued operation of said multivariate system or process. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A method of modeling m variable multivariate system or process, said method characterized by:
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(a) forming (10) an m row by n column matrix X having; (i) n column vectors Xj respectively comprising numerical measurements of said m variables, said measurements collectively encompassing a plurality of variations within one or more operational states of said system or process, each one of said column vectors having elements xij where i=1 to m and j=1 to n; (ii) m row vectors iX, each said row vector iX comprising elements xij having a range ranxi and an average avexi; and
,(b) deriving (12) a similarity xij#xik=max(0, 1, −
|xij−
xik|/wi) between a jth example of an ith one of said measurements and a kth example of said ith one of said measurements, where wi is a weighting factor having an initial value equal to ranxi. - View Dependent Claims (26, 27, 28, 29, 30)
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Specification