초록 ▼

Power industry boiler tube failures are a major cause of utility forced outages in the United States, with approximately 41,000 tube failures occurring every year at a cost of $5 billion a year. Accordingly, early tube leak detection and isolation is highly desirable. Early detection allows scheduli

Power industry boiler tube failures are a major cause of utility forced outages in the United States, with approximately 41,000 tube failures occurring every year at a cost of $5 billion a year. Accordingly, early tube leak detection and isolation is highly desirable. Early detection allows scheduling of a repair rather than suffering a forced outage, and significantly increases the chance of preventing damage to adjacent tubes. The instant detection scheme starts with identification of boiler tube leak process variables which are divided into universal sensitive variables, local leak sensitive variables, group leak sensitive variables, and subgroup leak sensitive variables, and which may be automatically be obtained using a data driven approach and a leak sensitivity function. One embodiment uses artificial neural networks (ANN) to learn the map between appropriate leak sensitive variables and the leak behavior. The second design philosophy integrates ANNs with approximate reasoning using fuzzy logic and fuzzy sets. In the second design, ANNs are used for learning, while approximate reasoning and inference engines are used for decision making. Advantages include use of already monitored process variables, no additional hardware and/or maintenance requirements, systematic processing does not require an expert system and/or a skilled operator, and the systems are portable and can be easily tailored for use on a variety of different boilers.

대표청구항 ▼

Power industry boiler tube failures are a major cause of utility forced outages in the United States, with approximately 41,000 tube failures occurring every year at a cost of $5 billion a year. Accordingly, early tube leak detection and isolation is highly desirable. Early detection allows scheduli

Power industry boiler tube failures are a major cause of utility forced outages in the United States, with approximately 41,000 tube failures occurring every year at a cost of $5 billion a year. Accordingly, early tube leak detection and isolation is highly desirable. Early detection allows scheduling of a repair rather than suffering a forced outage, and significantly increases the chance of preventing damage to adjacent tubes. The instant detection scheme starts with identification of boiler tube leak process variables which are divided into universal sensitive variables, local leak sensitive variables, group leak sensitive variables, and subgroup leak sensitive variables, and which may be automatically be obtained using a data driven approach and a leak sensitivity function. One embodiment uses artificial neural networks (ANN) to learn the map between appropriate leak sensitive variables and the leak behavior. The second design philosophy integrates ANNs with approximate reasoning using fuzzy logic and fuzzy sets. In the second design, ANNs are used for learning, while approximate reasoning and inference engines are used for decision making. Advantages include use of already monitored process variables, no additional hardware and/or maintenance requirements, systematic processing does not require an expert system and/or a skilled operator, and the systems are portable and can be easily tailored for use on a variety of different boilers. on-and-storage device, said identification data including an operator ID and a fishing vessel ID; and e) coupling said electronic data-collection-and-storage device to said central data repository by said telephone link and transmitting said operator catch-data to said central data repository, said operator catch-data including said identification data and all said fish-catch data including a time interval and said latitude/longitude specification associated with each instance of said fish-catch data acquired by said electronic data-collection-and-storage device during said single fishing trip. 6. The method described in claim 5, wherein, before coupling said electronic datallection-and-storage device to said central data repository, said method further comprises the step of: removing said electronic data-collection-and-storage device from said fishing vessel following said single fishing trip. 7. The method described in claim 5 wherein fish-sale data for said single fishing trip is includable in said operator catch-data, said method also comprising the step of entering said fish sale data into said electronic data-collection-and-storage device. 8. The method described in claim 5, also comprising the step of connecting said electronic data-collection-and-storage device to a power source. 9. The method described in claim 5, wherein transmitting said operator catch-data includes transmission of data through said telephone link, and wherein said coupling said device to said central data repository includes a step of linking said electronic data-collection-and-storage device by said telephone link to said central data repository and wherein said transmitting said operator catch-data to said central data repository includes transmitting said data via said telephone link to said central data repository. 10. The method described in claim 5 also comprising the steps of: (f) encrypting operator catch-data during said transmitting to said central data repository; (g) storing encrypted operator catch-data in an operator file in said central data repository; and (h) restricting access to said operator file to an operator whose operator ID is said operator ID included in said operator catch-data. 11. The method described in claim 10 also comprising causing said central data repository to generate a fisherman report based on said encrypted operator catch-data. 12. The method described in claim 11, wherein said fisherman report can comport with an information form required by a regulatory agency. 13. The method described in claim 12, wherein said particular fishing industry is a lobster fishing industry, said fishing vessel is a lobster-fishing vessel, said fishing location is a lobster trap location, said fishing trip is a lobster-fishing trip, and said fish-catch data includes lobster-catch data. 14. The method described in claim 10 also comprising the steps of: (l) creating an aggregate fishery file in said central data repository; and (m) generating fisheries management reports from said aggregate fishery file. 15. The method described in claim 14 further comprising the steps of: (n) creating anonymous catch-data by removing said identification data and said sale data from said encrypted operator catch-data; (o) accumulating said anonymous catch-data of each said operator in said aggregate fishery file; (p) entering fishery-related data from regulatory or research sources in said aggregate fishery file; and (q) generating fisheries management reports based on said anonymous catch-data and fishery-related data from said aggregate fishery file. 16. The method described in claim 5 wherein said telephone link includes a first telephone link and a second telephone link, said method further comprising the steps of: (i) coupling said electronic data-collection-and-storage device to a receiving station by said first telephone link; (j) coupling said receiving station to said central data repository by said second telephone link; and (k) transmitting said operator catch-data from said device to said receiving station and subsequently transmitting said operator catch-data from said receiving station to said central data repository. 17. The method described in claim 1, wherein includable in said fish-catch data are data on species, legal size, undersize, illegal species, release back into water, and data on water temperature, water depth, water salinity, water currents, tide phase, and/or weather conditions. 18. The method described in claim 17, wherein said fish-catch data is lobster-catch data and the step of entering into said electronic data-collection-and-storage device fish-catch data further includes the steps of: (r) entering a count in a catch category or in one of a plurality of non-retained categories for each lobster found in a set of traps hauled up at a lobster trap location, said plurality of non-retained categories including an oversize category, an undersize category, and a V-notched female category; and (s) releasing back into water each said lobster found to belong in one of said non-retained categories. 19. The method described in claim 18, wherein said V-notched female category includes V-notched female lobsters and egg-bearing female lobsters. 20. The method described in claim 1, also comprising the step of connecting said electronic data-collection-and-storage electronic data-collection-and-storage device to a power source. 21. The method described in claim 1, also comprising entering a local time-offset from Universal Time. 22. A method for collecting and managing lobster-catch data using a navigational system for determining a longitude/latitude specification of a location, an electronic data-collection-and-storage device connectable to said navigational system, a telephone link, and a central data repository, wherein said electronic date-collection-and-storage device is constructed so as to facilitate entry of information of specific interest to a lobster fishing industry, said method comprising the steps of: a) upon an initial power-up of said device, storing identification data in said electronic data-collection-and-storage device, said identification data including an operator ID and a lobster vessel ID; b) at each of a plurality of lobster-trap locations in a single lobster-fishing trip, placing said electronic data-collection-and-storage device at each said lobster trap location and coupling said electronic data-collection-and-storage device to said navigational system so as to automatically acquire and electronically store a time, a date, and said latitude/longitude specification associated with each said lobster trap location; c) entering lobster-catch data into said electronic data-collection-and-storage device at each said first lobster trap location by entering a lobster count in a catch category or in one of a plurality of non-retained categories for each lobster found in a set of traps hauled up at said lobster trap location, said plurality of non-retained categories including an oversize category, and undersize category, and a V-notched female category, said V-notched female category including said count of V-notched female lobsters and egg-bearing lobsters; d) releasing back into water each said lobster found to belong in one of said non-retained categories, e) entering into said electronic data-collection-and-storage device lobster-sale data from a sale of lobster-catch from said single lobster-fishing trip; f) coupling said electronic datallection-and-storage device to said central data repository via said telephone link and transmitting via said telephone link encrypted operator catch data to said central data repository, said encrypted operator catch-data including said identification data and said lobster catch-data including said time, said date, and said latitude/longitude specification associated with each lobster trap location in said single lobster-fishing trip; g) stori ng said encrypted operator catch-data in an operator file in said central data repository and restricting access to said operator file to an operator whose operator ID is said operator ID included in said encrypted operator catch-data; h) causing said central data repository to generate a fisherman report based on said encrypted operator catch-data; i) creating an aggregate fishery file in said central data repository; j) accumulate said encrypted operator catch-data in said aggregate fishery file k) and l) generating resources management reports from said aggregate fishery file. 23. The method as described in claim 22 wherein said navigational system for determining said longitude/latitude specification is a global positioning system (GPS). 24. The method as described in claim 22 wherein said navigational system for determining said longitude/latitude specification is a LORAN radio navigation system. 25. The method as described in claim 22 further comprising the step of creating anonymous catch-data by removing said identification data and said lobster-sale data from said encrypted operator catch-data and wherein said catch-data accumulated in said aggregate fishery file is anonymous catch-data. 26. The method as described in claim 22, wherein said telephone link includes a first telephone link and a second telephone link, said method further comprising the steps of: (m) coupling said electronic data-collection-and-storage device to a receiving station by said first telephone link; (n) transmitting said, encrypted operator catch-data to said receiving station via said first, telephone link; (o) coupling said receiving station to said central data repository by said second telephone link; and (p) transmitting said encrypted operator catch-data from said receiving station to said central data repository via said second telephone means. e current picture. Preferably, no further evaluations are performed if it is determined that the error value for the first motion vector candidate is below a prescribed motion estimation termination threshold value. The method is executed by software that operates a software-implemented state machine. The software preferably includes source code that defines a search sequence, and a function that builds the state machine in a prescribed memory space.