US6076048A
Expired / Lapsed
US
⏱️ 0.0 years remaining
System and method for least squares filtering based leak flow estimation/detection using exponentially shaped leak profiles
Assignee: Ultrak Inc
•
Filing: 1997-09-26
•
Granted: 2000-06-13
•
Expires: 2017-09-26
Abstract
Abstract A method and system for detecting and estimating leaks in an industrial boiler whereby the method and system formulate the leak detection problem as a least squares fitting problem, where one or more of the fitted parameters estimate leak flows. The method and system create a representation that incorporates a leak model component, a process model component and a noise model component into the representation. This invention provides a variety of leak dotproduct ij (tCurrent)=exp(-(tCurrent-tPrevious)/Tau ij )* dotproduct ij (tPrevious)+(1-exp(-min(t/Current-tPrevious, maxDt)/ Tau ij ))*x i (tCurrent)*x j (tCurrent).
Inventors
- 👤 John C. Gunther
- 👤 Ke Hong
- 👤 Paul R. Burgmayer
- 👤 Haiwen Chen
- 👤 Virginia E. Durham
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-28 22:48
CPC Classifications
F; F22; F22B; F22B37/00; F22B37/02; F22B37/42; F22B37/421
Claims Analysis
Total Claims: 91
Claim 1
Independent Claim
195 words
A method for detecting and estimating leaks in any conserved flow around an industrial boiler wherein said conserved flow and industrial boiler form a process, said method comprising the steps of: (a) measuring mass flow imbalances in the process to uncover any deviation from the mass flow imbalances being zero defined as variability; (b) partitioning the variability in the measured mass flow imbalances into: 1) a process model component; 2) a leak model component; and 3) a noise component; to form a mathematical representation of a leak in the process; (c) utilizing a family of leak shapes for said leak model component and wherein each of said leak shapes represents a leak that is non-decreasing; (d) applying least squares filtering to said mathematical representation by estimating unknown parameters from said measured mass flow imbalances to generate an estimated leak flow model; (e) estimating statistical distributions of said unknown parameters to determine the statistical significance of said unknown parameters; (f) generating a family of statistics from said family of leak shapes and wherein each of said statistics is optimized to detect a variety of leaks; and (g) combining said statistics to form a significance-testing leak statistic.
Claim 2
Independent Claim
195 words
A method for detecting and estimating leaks in any conserved flow around an industrial boiler wherein said conserved flow and industrial boiler form a process, said method comprising the steps of: (a) measuring mass flow imbalances in the process to uncover any deviation from the mass flow imbalances being zero defined as variability; (b) partitioning the variability in the measured mass flow imbalances into: 1) a process model component; 2) a leak model component; and 3) a noise component; to form a mathematical representation of a leak in the process; (c) utilizing a family of leak shapes for said leak model component and wherein each of said leak shapes represents a leak that is non-decreasing; (d) applying least squares filtering to said mathematical representation by estimating unknown parameters from said measured mass flow imbalances to generate an estimated leak flow model; (e) estimating statistical distributions of said unknown parameters to determine the statistical significance of said unknown parameters; (f) generating a family of statistics from said family of leak shapes and wherein each of said statistics is optimized to detect a variety of leaks; and (g) combining said statistics to form a significance-testing leak statistic.
Claim 3
Dependent Claim
38 words
The method of claim 1 wherein said step of utilizing a family of leak shapes comprises utilizing a family of exponentials, and wherein each of said exponentials has a respective growth rate, for providing a range of statistics.
Claim 4
Dependent Claim
35 words
The method of claim 2 wherein said family of exponentials comprise respective exponential time constants such that the logarithms of said exponential time constants are evenly-spaced to enhance the accuracy of said leak model component.
Claim 5
Dependent Claim
45 words
The claim of claim 2 wherein said step of measuring mass flow imbalances in the process forms collected data and wherein said step of partitioning the variability comprises the application of a pre-whitening transformation to said collected data to account for any serially dependent noise.
Claim 6
Dependent Claim
40 words
The method of claim 4 wherein said optimized statistics are determined by those leak shapes having the highest probability of being associated with the leak and wherein said optimized statistics are defined as maximum likelihood standardized leak flow (MLSLF) estimates.
Claim 7
Dependent Claim
53 words
The method of claim 5 wherein said MLSLFs comprise respective distributions and wherein said step of combining said statistics to form a significance-testing leak statistic comprises determining a statistical distribution of said MLSLFs, said statistical distribution of said MLSLFs defining a standardized maximum likelihood standardized leak flow (SMLSLF) as said significance-testing leak statistic.
Claim 8
Dependent Claim
22 words
The method of claim 6 further comprising the step of empirically estimating non-modeled variation in said optimized statistics that form said MLSLFs.
Claim 9
Dependent Claim
22 words
The method of claim 6 further comprising the step of analytically estimating non-modeled variation in said optimized statistics that form said MLSLFs.
Claim 10
Dependent Claim
25 words
The method of claim 6 wherein said statistical distribution is approximated by a normal distribution by determining a standard deviation and average of said MLSLFs.
Claim 11
Dependent Claim
29 words
The method of claim 6 wherein said step of determining a statistical distribution of said MLSLFs comprises continuously selecting the best approximating shape as the leak evolves over time.
Claim 12
Dependent Claim
18 words
The method of claim 4 wherein said pre-whitening transformation comprises an auto regressive integrated moving average (ARIMA) model.
Claim 13
Dependent Claim
20 words
The method of claim 4 wherein said application of said pre-whitening transformation is conducted dynamically on-line as data is collected.
Claim 14
Dependent Claim
23 words
The method of claim 4 wherein said application of said pre-whitening transformation is conducted statically off-line using a user-selected portion of historical data.
Claim 15
Dependent Claim
45 words
The method of claim 1 wherein said step of measuring mass flow imbalances in the process forms collected data and wherein said step of partitioning the variability comprises the application of a pre-whitening transformation to said collected data to account for any serially dependent noise.
Claim 16
Dependent Claim
18 words
The method of claim 14 wherein said pre-whitening transformation comprises an auto regressive integrated moving average (ARIMA) model.
Claim 17
Dependent Claim
40 words
The method of claim 1 wherein said optimized statistics are determined by those leak shapes having the highest probability of being associated with the leak and wherein said optimized statistics are defined as maximum likelihood standardized leak flow (MLSLF) estimates.
Claim 18
Dependent Claim
53 words
The method of claim 16, wherein said MLSLFs comprise respective distributions and wherein said step of combining said statistics to form a significance-testing leak statistic comprises determining a statistical distribution of said MLSLFs, said statistical distribution of said MLSLFs defining a standardized maximum likelihood standardized leak flow (SMLSLF) as said significance-testing leak statistic.
Claim 19
Dependent Claim
28 words
The method of claim 17 wherein said statistical distribution of said MLSLFs is approximated by a normal distribution by determining a standard deviation and average of said MLSLFs.
Claim 20
Dependent Claim
29 words
The method of claim 17 wherein said step of determining a statistical distribution of said MLSLFs comprises continuously selecting the best approximating shape as the leak evolves over time.
Claim 21
Dependent Claim
58 words
The method of claim 16 further wherein said step of measuring mass flow imbalances in the process forms collected data and wherein the method further comprises the steps of: (a) applying a pre-whitening transformation to said collected data to account for any serially-dependent noise; and (b) empirically estimating non-modeled variation in said optimized statistics that form said MLSLFs.
Claim 22
Dependent Claim
58 words
The method of claim 16 further wherein said step of measuring mass flow imbalances in the process forms collected data and wherein the method further comprises the steps of: (a) applying a pre-whitening transformation to said collected data to account for any serially-dependent noise; and (b) analytically estimating non-modeled variation in said optimized statistics that form said MLSLFs.
Claim 23
Dependent Claim
77 words
The method of claim 1 wherein said mathematical representation comprises a vector relationship having the form, Σv.sub.i =Σa.sub.i *v.sub.i +residuals, wherein v i is a response vector, a i *v i is a fitted vector where a i is a value to be fitted, and said residuals is a vector defined as the difference between said response vector and the sum of said fitted vectors and where i represents an index for a plurality of response vectors.
Claim 24
Dependent Claim
38 words
The method of claim 22 wherein said step of applying least squares filtering said mathematical representation by estimating unknown parameters comprises the step of determining those values of a i such that, Minimize: |residuals|.sup.2 =|Σv.sub.i -Σa.sub.i *v.sub.i |.sup.2.
Claim 25
Dependent Claim
23 words
The method of claim 22 wherein v i is expressed as a product of a measured function and at least one exponential multiplier.
Claim 26
Dependent Claim
21 words
The method of claim 24 wherein said measured function comprises a function that has been fed through an exponentially-weighted moving average.
Claim 27
Dependent Claim
21 words
The method of claim 25 wherein said measured function comprises a function that has been smoothed by an exponentially-weighted moving average.
Claim 28
Dependent Claim
20 words
The method of claim 24 wherein said measured function comprises a function that has been differentiated with respect to time.
Claim 29
Dependent Claim
29 words
The method of claim 24 further comprising the step of lagging one measured function with respect to another measured function for proper synchronization of said measured mass flow imbalances.
Claim 30
Dependent Claim
50 words
The method of claim 22 further comprising the step of more efficiently updating said least squares filtering by utilizing the dot product of two response vectors, defined by the integral of a product of said two response vectors, wherein said dot product can be approximated as an exponentially weighted sum.
Claim 31
Dependent Claim
129 words
The method of claim 29 wherein said step of more efficiently updating said least squares filtering comprises the following relationship: dotproduct.sub.ij (t0)=0(this initializes the dot product), dotproduct.sub.ij (tCurrent)=exp(-(tCurrent-tPrevious)/Tau.sub.ij)* dotproduct.sub.ij (tPrevious)+(1-exp(-min(tCurrent-tPrevious, maxDt)/Tau.sub.ij))*x.sub.i)(tCurrent)*x.sub.j (tCurrent), and wherein tCurrent and tPrevious are the times of the current and previous measurements, maxDt is the longest time between samples before data is declared to be missing, and Tau ij =(Tau i *Tau j )/(Tau i +Tau j ), where Tau i and Tau j are the tau's associated with the exponential weight of vectors v i and v j formed from measured sequences x i (t) and x j (t), and wherein j represents another index for said plurality of response vectors such that all possible combinations of response vectors are accounted for, including i=j.
Claim 32
Dependent Claim
21 words
The method of claim 1 wherein said step of partitioning the variability comprises process model parameterization that is conducted dynamically on-line.
Claim 33
Dependent Claim
21 words
The method of claim 1 wherein said step of partitioning the variability comprises process model parameterization that is conducted statically off-line.
Claim 34
Dependent Claim
20 words
The method claim 1 wherein said step of partitioning the variability comprises leak model parameterization that is conducted dynamically on-line.
Claim 35
Dependent Claim
21 words
The method of claim 1 wherein said step of partitioning the variability comprises leak model parameterization that is conducted statically off-line.
Claim 36
Dependent Claim
17 words
The method of claim 1 wherein said process accounts for concentration changes due to steaming rate changes.
Claim 37
Dependent Claim
23 words
The method of claim 35 wherein the industrial boiler includes a drum and wherein said process model component includes drum level process variables.
Claim 38
Dependent Claim
21 words
The method of claim 35 wherein the process includes flow meters and wherein said process model component includes flow meter miscalibrations.
Claim 39
Dependent Claim
21 words
The method of claim 1 wherein said step of estimating statistical distributions comprises computing exponentially-weighted standard deviations of said unknown parameters.
Claim 40
Dependent Claim
31 words
The method of claim 1 wherein said process includes chemical mass flow and wherein said process model component accounts for all chemical mass flows into and out of the industrial boiler.
Claim 41
Dependent Claim
31 words
The method of claim 1 wherein said process includes water mass flow and wherein said process model component accounts for all water mass flows into and out of the industrial boiler.
Claim 42
Dependent Claim
20 words
The method of claim 1 wherein said step of partitioning the variability comprises fitting parameters of said mathematical representation off-line.
Claim 43
Independent Claim
174 words
A method for detecting and estimating leaks in any conserved flow around an industrial boiler wherein said conserved flow and industrial boiler form a process, said method comprising the steps of: (a) measuring mass flow imbalances in the process to uncover any deviation from the mass flow imbalances being zero defined as variability; (b) partitioning the variability in the measured mass flow imbalances into: 1) a process model component; 2) a leak model component; and 3) a noise component; to form a mathematical representation of a leak in the process; (c) utilizing at least one leak shape for said leak model component and wherein said at least one leak shape represents a leak that is non-decreasing; (d) applying least squares filtering to said mathematical representation by estimating unknown parameters from said measured mass flow imbalances to generate an estimated leak flow model; (e) estimating statistical distributions of said unknown parameters to determine the statistical significance of said unknown parameters; and (f) generating statistics from said at least one leak shape to detect a leak.
Claim 44
Independent Claim
174 words
A method for detecting and estimating leaks in any conserved flow around an industrial boiler wherein said conserved flow and industrial boiler form a process, said method comprising the steps of: (a) measuring mass flow imbalances in the process to uncover any deviation from the mass flow imbalances being zero defined as variability; (b) partitioning the variability in the measured mass flow imbalances into: 1) a process model component; 2) a leak model component; and 3) a noise component; to form a mathematical representation of a leak in the process; (c) utilizing at least one leak shape for said leak model component and wherein said at least one leak shape represents a leak that is non-decreasing; (d) applying least squares filtering to said mathematical representation by estimating unknown parameters from said measured mass flow imbalances to generate an estimated leak flow model; (e) estimating statistical distributions of said unknown parameters to determine the statistical significance of said unknown parameters; and (f) generating statistics from said at least one leak shape to detect a leak.
Claim 45
Dependent Claim
19 words
The method of claim 42 further comprising the step of combining said statistics to from a significance-testing leak statistic.
Claim 46
Independent Claim
189 words
A system for detecting and estimating leaks in any conserved flow around an industrial boiler wherein said conserved flow and industrial boiler form a process, said system comprising: (a) means for measuring mass flow imbalances in the process; (b) means for modeling a mathematical representation of a leak in the process, said mathematical representation comprising a process model component, a leak model component and a noise component, said modeling means utilizing a family of leak shapes for said leak model component and wherein each of said leak shapes represents a leak that is non-decreasing; (c) means for applying least squares filtering to said mathematical representation by estimating unknown parameters from said mass flow imbalances in order to generate an estimated leak flow model; (d) means for estimating statistical distributions of said unknown parameters to determine statistical significance of said unknown parameters; (e) means for generating a family of statistics from said family of leak shapes, wherein each of said statistics is optimized to detect a variety of leaks; and (f) wherein said means for generating a family of statistics combines said statistics to form a single significance-testing leak statistic.
Claim 47
Independent Claim
189 words
A system for detecting and estimating leaks in any conserved flow around an industrial boiler wherein said conserved flow and industrial boiler form a process, said system comprising: (a) means for measuring mass flow imbalances in the process; (b) means for modeling a mathematical representation of a leak in the process, said mathematical representation comprising a process model component, a leak model component and a noise component, said modeling means utilizing a family of leak shapes for said leak model component and wherein each of said leak shapes represents a leak that is non-decreasing; (c) means for applying least squares filtering to said mathematical representation by estimating unknown parameters from said mass flow imbalances in order to generate an estimated leak flow model; (d) means for estimating statistical distributions of said unknown parameters to determine statistical significance of said unknown parameters; (e) means for generating a family of statistics from said family of leak shapes, wherein each of said statistics is optimized to detect a variety of leaks; and (f) wherein said means for generating a family of statistics combines said statistics to form a single significance-testing leak statistic.
Claim 48
Dependent Claim
30 words
The system of claim 44 wherein said modeling means uses a family of exponentials having respective growth rates for said family of leak shapes to provide a range of statistics.
Claim 49
Dependent Claim
35 words
The system of claim 45 wherein said family of exponentials comprise respective exponential time constants such that the logarithms of said exponential time constants are evenly-spaced to enhance the accuracy of said leak model component.
Claim 50
Dependent Claim
40 words
The system of claim 45 wherein said measured mass flow imbalances form collected data and wherein said system further comprises pre-whitening transforming means, said collected data being fed into said pre-whitening transforming means to account for any serially dependent noise.
Claim 51
Dependent Claim
19 words
The system of claim 47 wherein said pre-whitening transforming means comprises an auto regressive integrated moving average (ARIMA) model.
Claim 52
Dependent Claim
40 words
The system of claim 44 wherein said measured mass flow imbalances form collected data and wherein said system further comprises pre-whitening transforming means, said collected data being fed into said pre-whitening transforming means to account for any serially dependent noise.
Claim 53
Dependent Claim
51 words
The system of claim 49 wherein said means for generating a family of statistics generates said optimized statistics based on those leaks shapes having the highest probability of being associated with the leak and wherein said optimized statistics are maximum likelihood standardized leak flow (MLSLF) estimates, said MLSLFs having respective distributions.
Claim 54
Dependent Claim
37 words
The system of claim 50 wherein said means for generating a family of statistics determines a statistical distribution of said MLSLFs to form a standardized maximum likelihood standardized leak flow (SMLSLF) statistic as said significance-testing leak statistic.
Claim 55
Dependent Claim
19 words
The system of claim 49 wherein said pre-whitening transforming means comprises an auto regressive integrated moving average (ARIMA) model.
Claim 56
Dependent Claim
48 words
The system of claim 44 wherein said means for generating a family of statistics generates said optimized statistics based on those leak shapes having the highest probability of being associated with the leak and wherein said optimized statistics are defined as maximum likelihood standardized leak flow (MLSLF) estimates.
Claim 57
Dependent Claim
37 words
The system of claim 53 wherein said means for generating a family of statistics determines a statistical distribution of said MLSLFs to form a standardized maximum likelihood standardized leak flow (SMLSLF) statistic as said significance-testing leak statistic.
Claim 58
Dependent Claim
25 words
The system of claim 54 wherein said statistical distribution is approximated by a normal distribution by determining a standard deviation and average of said MLSLFs.
Claim 59
Dependent Claim
26 words
The system of claim 54 wherein said means for generating a family of statistics continuously selects the best approximating shape as the leak evolves over time.
Claim 60
Dependent Claim
59 words
The system of claim 53 wherein the measured mass flow imbalances forms collected data and wherein said system further comprises pre-whitening transformation means for processing said collected data to account for any serially dependent noise and wherein said means for estimating statistical distributions of said unknown parameters empirically estimates non-modeled variation in said optimized statistics that form said MLSLFs.
Claim 61
Dependent Claim
17 words
The system of claim 57 wherein said pre-whitening transformation means operates dynamically on-line as data is collected.
Claim 62
Dependent Claim
20 words
The system of claim 57 wherein said pre-whitening transformation means operates statically off-line using a user-selected portion of historical data.
Claim 63
Dependent Claim
59 words
The system of claim 53 wherein the measured mass flow imbalances forms collected data and wherein said system further comprise pre-whitening transformation means for processing said collected data to account for any serially dependent noise and wherein said means for estimating statistical distributions of said unknown parameters analytically estimates non-modeled variation in said optimized statistics that form said MLSLFs.
Claim 64
Dependent Claim
26 words
The system of claim 51 wherein said means for generating a family of statistics continuously selects the best approximating shape as the leak evolves over time.
Claim 65
Dependent Claim
77 words
The system of claim 44 wherein said mathematical representation comprises a vector relationship having the form, Σv.sub.i =Σa.sub.i *v.sub.i +residuals, wherein v i is a response vector, a i *v i is a fitted vector where a i is a value to be fitted, and said residuals is a vector defined as the difference between said response vector and the sum of said fitted vectors and where i represents an index for a plurality of response vectors.
Claim 66
Dependent Claim
27 words
The system of claim 62 wherein said means for applying least squares filtering determine those values of a i such that, Minimize: |residuals|.sup.2 =|Σv.sub.i -Σa.sub.i *v.sub.i |.sup.2.
Claim 67
Dependent Claim
23 words
The system of claim 62 wherein v i is expressed as a product of a measured function and at least one exponential multiplier.
Claim 68
Dependent Claim
21 words
The system of claim 64 wherein said measured function comprises a function that has been fed through an exponentially-weighted moving average.
Claim 69
Dependent Claim
21 words
The system of claim 64 wherein said measured function comprises a function that has been smoothed by an exponentially-weighed moving average.
Claim 70
Dependent Claim
20 words
The system of claim 64 wherein said measured function comprises a function that has been differentiated with respect to time.
Claim 71
Dependent Claim
28 words
The system of claim 64 further comprising means for lagging one measured function with respect to another measured function for proper synchronization of said measured mass flow imbalances.
Claim 72
Dependent Claim
57 words
The system of claim 62 wherein said means for applying least squares filtering further comprises means for more efficiently updating said least squares filtering by utilizing the dot product of two response vectors, defined by the integral of a product of said two response vectors, wherein said dot product can be approximated as an exponentially weighted sum.
Claim 73
Dependent Claim
129 words
The system of claim 69 wherein said means for more efficiently updating said least squares filtering utilizes the following relationship: dotproduct.sub.ij (t0)=0(this initializes the dot product), dotproduct.sub.ij (tCurrent)=exp(-(tCurrent-tPrevious)/Tau.sub.ij)* dotproduct.sub.ij (tPrevious)+(1-exp(-min(tCurrent-tPrevious, maxDt)/Tau.sub.ij))*x.sub.i)(tCurrent)*x.sub.j (tCurrent), and wherein tCurrent and tPrevious are the times of the current and previous measurements, maxDt is the longest time between samples before data is declared to be missing, and Tau ij =(Tau i *Tau j )/(Tau i +Tau j ), where Tau i and Tau j are the tau's associated with the exponential weight of vectors v i and v j formed from measured sequences x i (t) and x j (t), and wherein j represents another index for said plurality of response vectors such that all possible combinations of response vectors are accounted for including i=j.
Claim 74
Dependent Claim
16 words
The system of claim 44 wherein said modeling means parameterizes said process model component dynamically on-line.
Claim 75
Dependent Claim
16 words
The system of claim 44 wherein said modeling means parameterizes said process model component statically off-line.
Claim 76
Dependent Claim
16 words
The system of claim 44 wherein said modeling means parameterizes said leak model component dynamically on-line.
Claim 77
Dependent Claim
16 words
The system of claim 44 wherein said modeling means parameterizes said leak model component statically off-line.
Claim 78
Dependent Claim
21 words
The system of claim 44 wherein said means for estimating statistical distributions comprises computing exponentially-weighted standard deviations of said unknown parameters.
Claim 79
Dependent Claim
22 words
The system of claim 44 wherein said modeling means further comprises means for accounting for concentration changes due to steaming rate changes.
Claim 80
Dependent Claim
23 words
The system of claim 76 wherein the industrial boiler comprises a drum and wherein said process model component includes drum level process variables.
Claim 81
Dependent Claim
21 words
The system of claim 76 wherein the process includes flow meters and wherein said process model component includes flow meter miscalibrations.
Claim 82
Dependent Claim
20 words
The system of claim 44 further comprising means for determining all chemical flows into and out of the industrial boiler.
Claim 83
Dependent Claim
55 words
The system of claim 79 wherein the industrial boiler comprises a non-volatile chemical mass input flow into a feedwater into the boiler fluid and wherein said industrial boiler further comprises a blowdown flow and wherein said means for measuring mass flow imbalances comprises means for measuring the non-volatile chemical mass flow in the blowdown flow.
Claim 84
Dependent Claim
48 words
The system of claim 80 wherein said feedwater comprises a chemical pump and a chemical feed pump controller, said chemical feed pump controller coupled to said means for applying least squares filtering for providing non-volatile chemical feed mass flow rate to said means for applying least squares filtering.
Claim 85
Dependent Claim
22 words
The system of claim 44 further comprising means for determining all water mass balance flows into and out of the industrial boiler.
Claim 86
Dependent Claim
43 words
The system of claim 82 wherein the industrial boiler comprises a boiler fluid, a steam flow and a blowdown flow and wherein said means for measuring mass flow imbalances comprises blowdown flow measuring means, steam flow measuring means and feedwater flow measuring means.
Claim 87
Dependent Claim
14 words
The system of claim 44 wherein said modeling means determines said mathematical representation off-line.
Claim 88
Dependent Claim
35 words
The system of claim 44 wherein said modeling means, said means for applying least squares filtering, said means for estimating statistical distributions and said means for generating a family of statistics reside in a computer.
Claim 89
Independent Claim
159 words
A system for detecting and estimating leaks in any conserved flow around an industrial boiler wherein said conserved flow and industrial boiler form a process, said system comprising: (a) means for measuring mass flow imbalances in the process; (b) means for modeling a mathematical representation of a leak in the process, said mathematical representation comprising a process model component, a leak model component and a noise component, said modeling means utilizing at least one leak shape for said leak model component and wherein said at least one leak shape represents a leak that is non-decreasing; (c) means for applying least squares filtering to said mathematical representation by estimating unknown parameters from said mass flow imbalances in order to generate an estimated leak flow model; (d) means for estimating statistical distributions of said unknown parameters to determine statistical significance of said unknown parameters; and (e) means for generating statistics from said at least one leak shape to detect a leak.
Claim 90
Independent Claim
159 words
A system for detecting and estimating leaks in any conserved flow around an industrial boiler wherein said conserved flow and industrial boiler form a process, said system comprising: (a) means for measuring mass flow imbalances in the process; (b) means for modeling a mathematical representation of a leak in the process, said mathematical representation comprising a process model component, a leak model component and a noise component, said modeling means utilizing at least one leak shape for said leak model component and wherein said at least one leak shape represents a leak that is non-decreasing; (c) means for applying least squares filtering to said mathematical representation by estimating unknown parameters from said mass flow imbalances in order to generate an estimated leak flow model; (d) means for estimating statistical distributions of said unknown parameters to determine statistical significance of said unknown parameters; and (e) means for generating statistics from said at least one leak shape to detect a leak.
Claim 91
Dependent Claim
19 words
The system of claim 86 wherein said generating means combines said statistics to form a single significance-testing leak statistic.
References Cited (Backward Citations)
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