System and method for least squares filtering based leak flow estimation/detection using exponentially shaped leak profiles

• US 6,076,048 A
• Filed: 09/26/1997
• Issued: 06/13/2000
• Est. Priority Date: 09/26/1997
• Status: Expired due to Term

• Current Assignee: BetzDearborn Inc. (Veolia Environnement SA)
• Original Assignee: BetzDearborn Inc. (Veolia Environnement SA)
• Inventors: Durham, Virginia E., Hong, Ke, Burgmayer, Paul R., Gunther, John C., Chen, Haiwen
• Primary Examiner(s): WACHSMAN, HAL D
• Application Number:
  US08/938,191
• Time in Patent Office: 991 Days
• Field of Search: 702/51, 702/23, 702/31, 702/32, 702/45, 702/50, 702/55, 702/179-181, 702/190, 702/182-184, 702/189, 702/191, 702/194, 702/195, 702/197, 702/FOR 127 , 702/FOR 128, 702/FOR 164, 702/FOR 166, 364/528.01, 364/16, 364/528.17, 364/18, 395/500.27, 395/3, 395/500.31, 73/40.5 R, 73/40, 73/41.4, 73/40.7, 73/45.1, 73/45.2, 73/49.1, 73/861.02, 73/861.03, 73/6, 73/861.356, 73/195, 73/196, 165/70, 250/356.1, 250/302, 250/551, 250/557, 250/558, 250/386, 436/50, 436/55, 436/56, 422/62, 377/21, 706/906, 706/907, 706/914, 706/915, 706/920, 700/266, 700/274, 700/281, 700/282, 700/285, 700/299, 700/300
• US Class Current: 702/51
• CPC Class Codes: F22B 37/421 Arrangements for detecting leaks

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).

Claims

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. The method of claim 6 further comprising the step of empirically estimating non-modeled variation in said optimized statistics that form said MLSLFs.

8. The method of claim 6 further comprising the step of analytically estimating non-modeled variation in said optimized statistics that form said MLSLFs.

9. 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.

10. 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.

11. The method of claim 4 wherein said pre-whitening transformation comprises an auto regressive integrated moving average (ARIMA) model.

12. The method of claim 4 wherein said application of said pre-whitening transformation is conducted dynamically on-line as data is collected.

13. 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.

14. 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.

15. The method of claim 14 wherein said pre-whitening transformation comprises an auto regressive integrated moving average (ARIMA) model.

16. 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.

17. 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.

18. 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.

19. 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.

20. 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.

21. 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.

22. The method of claim 1 wherein said mathematical representation comprises a vector relationship having the form,

23. 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,

24. The method of claim 22 wherein v_i is expressed as a product of a measured function and at least one exponential multiplier.

25. The method of claim 24 wherein said measured function comprises a function that has been fed through an exponentially-weighted moving average.

26. The method of claim 25 wherein said measured function comprises a function that has been smoothed by an exponentially-weighted moving average.

27. The method of claim 24 wherein said measured function comprises a function that has been differentiated with respect to time.

28. 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.

29. 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.

30. The method of claim 29 wherein said step of more efficiently updating said least squares filtering comprises the following relationship:

space="preserve" listing-type="equation">dotproduct.sub.ij (t0)=0(this initializes the dot product),

space="preserve" listing-type="equation">dotproduct.sub.ij (tCurrent)=exp(-(tCurrent-tPrevious)/Tau.sub.ij)*

space="preserve" listing-type="equation">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.

31. The method of claim 1 wherein said step of partitioning the variability comprises process model parameterization that is conducted dynamically on-line.

32. The method of claim 1 wherein said step of partitioning the variability comprises process model parameterization that is conducted statically off-line.

33. The method claim 1 wherein said step of partitioning the variability comprises leak model parameterization that is conducted dynamically on-line.

34. The method of claim 1 wherein said step of partitioning the variability comprises leak model parameterization that is conducted statically off-line.

35. The method of claim 1 wherein said process accounts for concentration changes due to steaming rate changes.

36. The method of claim 35 wherein the industrial boiler includes a drum and wherein said process model component includes drum level process variables.

37. The method of claim 35 wherein the process includes flow meters and wherein said process model component includes flow meter miscalibrations.

38. The method of claim 1 wherein said step of estimating statistical distributions comprises computing exponentially-weighted standard deviations of said unknown parameters.

39. 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.

40. 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.

41. The method of claim 1 wherein said step of partitioning the variability comprises fitting parameters of said mathematical representation off-line.

42. 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.

43. The method of claim 42 further comprising the step of combining said statistics to from a significance-testing leak statistic.

44. 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.

45. 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.

46. 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.

47. 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.

48. The system of claim 47 wherein said pre-whitening transforming means comprises an auto regressive integrated moving average (ARIMA) model.

49. 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.

50. 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.

51. 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.

52. The system of claim 49 wherein said pre-whitening transforming means comprises an auto regressive integrated moving average (ARIMA) model.

53. 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.

54. 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.

55. 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.

56. 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.

57. 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.

58. The system of claim 57 wherein said pre-whitening transformation means operates dynamically on-line as data is collected.

59. The system of claim 57 wherein said pre-whitening transformation means operates statically off-line using a user-selected portion of historical data.

60. 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.

61. 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.

62. The system of claim 44 wherein said mathematical representation comprises a vector relationship having the form,

63. The system of claim 62 wherein said means for applying least squares filtering determine those values of a_i such that,

64. The system of claim 62 wherein v_i is expressed as a product of a measured function and at least one exponential multiplier.

65. The system of claim 64 wherein said measured function comprises a function that has been fed through an exponentially-weighted moving average.

66. The system of claim 64 wherein said measured function comprises a function that has been smoothed by an exponentially-weighed moving average.

67. The system of claim 64 wherein said measured function comprises a function that has been differentiated with respect to time.

68. 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.

69. 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.

70. The system of claim 69 wherein said means for more efficiently updating said least squares filtering utilizes the following relationship:

space="preserve" listing-type="equation">dotproduct.sub.ij (t0)=0(this initializes the dot product),

space="preserve" listing-type="equation">dotproduct.sub.ij (tCurrent)=exp(-(tCurrent-tPrevious)/Tau.sub.ij)*

space="preserve" listing-type="equation">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.

71. The system of claim 44 wherein said modeling means parameterizes said process model component dynamically on-line.

72. The system of claim 44 wherein said modeling means parameterizes said process model component statically off-line.

73. The system of claim 44 wherein said modeling means parameterizes said leak model component dynamically on-line.

74. The system of claim 44 wherein said modeling means parameterizes said leak model component statically off-line.

75. The system of claim 44 wherein said means for estimating statistical distributions comprises computing exponentially-weighted standard deviations of said unknown parameters.

76. The system of claim 44 wherein said modeling means further comprises means for accounting for concentration changes due to steaming rate changes.

77. The system of claim 76 wherein the industrial boiler comprises a drum and wherein said process model component includes drum level process variables.

78. The system of claim 76 wherein the process includes flow meters and wherein said process model component includes flow meter miscalibrations.

79. The system of claim 44 further comprising means for determining all chemical flows into and out of the industrial boiler.

80. 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.

81. 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.

82. The system of claim 44 further comprising means for determining all water mass balance flows into and out of the industrial boiler.

83. 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.

84. The system of claim 44 wherein said modeling means determines said mathematical representation off-line.

85. 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.

86. 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.

87. The system of claim 86 wherein said generating means combines said statistics to form a single significance-testing leak statistic.