초록 ▼

A remotely configurable locator system includes a detection device to locate concealed underground conductors. The detection device is configured to connect to a communications network via a graphical user interface. Also included is a database management tool configured to connect to the communicat

A remotely configurable locator system includes a detection device to locate concealed underground conductors. The detection device is configured to connect to a communications network via a graphical user interface. Also included is a database management tool configured to connect to the communications network and communicate with the detection device via the communications network. A database is included and is adapted to store data related to the detection device wherein the data is configured to be accessed by the database management tool. Finally, the graphical user interface is configured to permit a user to remotely perform one of updating, analyzing, and diagnosing the detection device based upon the accessed stored data.

대표청구항 ▼

A remotely configurable locator system includes a detection device to locate concealed underground conductors. The detection device is configured to connect to a communications network via a graphical user interface. Also included is a database management tool configured to connect to the communicat

A remotely configurable locator system includes a detection device to locate concealed underground conductors. The detection device is configured to connect to a communications network via a graphical user interface. Also included is a database management tool configured to connect to the communications network and communicate with the detection device via the communications network. A database is included and is adapted to store data related to the detection device wherein the data is configured to be accessed by the database management tool. Finally, the graphical user interface is configured to permit a user to remotely perform one of updating, analyzing, and diagnosing the detection device based upon the accessed stored data. rix maximizing a distance between two of said plurality of health classifications;determining a covariance matrix for each of said health classifications using said at least one condition indicator and said observed values; anddetermining said current quantity in accordance with said covariance matrices, said transformation matrix and said observed values.6. The method of claim 5, wherein, for two of said plurality of health classifications, said current quantity, h(X), may be represented as:h (X)=½YT&Lgr;−1Y−((−K−1L)T)Y+(−½LTK−1L−½ln|K|−ln P2/P1)wherein, X represents said at least one conditional indicator forming a vector of individual conditional indicator values;Y represents &Lgr;−½&PHgr;TX;&Lgr; represents (I−K−1)−1, diagonal matrix of eigenvalues of X using a characteristic equation of &Sgr;&PHgr;=&PHgr;&Lgr;, &PHgr;T&PHgr;=identity matrix;&PHgr; represents an nxn matrix of eigenvectors, &PHgr; . . . &PHgr;n, of X using said characteristic equation;I represents AT&Sgr;1A;K represents AT&Sgr;2A;L represents AT(M2−M1);P2 represents an aposteriori probability of a first of said two health classifications given X;P1 represents an aposteriori probability of a second of said two health classifications given X;A represents said transformation matrix maximizing a distance between distributions of said two health classifications represented as &Lgr;−½&PHgr;T;&Sgr;1 represents a covariance matrix of said first health classification having a vector of expected values M1; and&Sgr;2 represents a covariance matrix of said second health classification having a vector of expected values M2.7. A method for determining a health status of a component comprising: selecting a plurality of condition indicators having a value and each having a corresponding weighting factor, and at least one threshold value defining at least two health classifications;determining a contribution to a health indicator for each of said condition indicators, wherein said determining further comprises, for each of said plurality of indicators:determining which of said at least two classifications said value of said each condition indicator belongs; anddetermining said contribution to said health indicator by said each condition indicator in accordance with a selected one of said at least two classifications and said weighted factor; anddetermining said health indicator according to all contributions by each of said condition indicator values.8. The method of claim 7, wherein first, second and third classifications are associated with each of said condition indicators formed by two threshold values, and the method further comprises:determining that said contribution to said health indicator for a first of said condition indicators is zero if said value of said condition indicator is in said first classification;determining that said contribution to said health indicator is a first multiple of said weighting factor if said value of said condition indicator is in said second classification; anddetermining that said contribution to said health indicator is a second multiple of said weighting factor if said value of said condition indicator is in said third classification.9. The method of claim 8, where the two threshold values are alarm level and warning level.10. A computer program product executed on a processor for determining a health indicator associated with a component, comprising machine executable code for determining a plurality of health classifications;determining at least one condition indicator quantifying a characteristic of said component;determining a probability associated with each of said health classifications, said probability being an estimation that said component is of a particular health classification given said at least one condition indicator; anddetermining, for a given set of observed values, which of said plurality of health classifications is associated with said component using said probabilities associated with said health classifications,wherein machine executable code for determining at least one condition indicator, determining a probability, and determining which of said health classifications is associated with said component, are performed upon two of said plurality of health classifications, and the computer program product further includes machine executable code, wherein, if a determination is made that a first of said health classifications is not associated with said component, for repeatedly determining at least one condition indicator, repeatedly determining a probability, and repeatedly determining which of said health classifications is associated with said component using two other health classifications.11. The computer program product of claim 10, further comprising machine executable code for:storing previously observed and calculated data associated with said component; andusing a portion of said previously observed and calculated data to determine said probability associated with each health classification.12. The computer program product of claim 10, further comprising machine executable code for:obtaining data estimates using models of said component; andusing said data estimates to determine said probability associated with said each health classification.13. A computer program product executed on a processor for determining a health indicator associated with a component, comprising machine executable code for determining a plurality of health classifications;determining at least one condition indicator quantifying a characteristic of said component;determining a probability associated with each of said health classifications, said probability being an estimation that said component is of a particular health classification given said at least one condition indicator;determining, for a given set of observed values, which of said plurality of health classifications is associated with said component using said probabilities associated with said health classifications,determining a threshold using a ratio of a portion of said probabilities;calculating a current quantity associated with said component using said observed values; andcomparing said current quantity to said ratio to determine which of said health classifications is associated with said component.14. The computer program product of claim 13, wherein said machine executable code for calculating said current quantity further comprises machine executable code for:determining a transformation matrix maximizing a distance between two of said plurality of health classifications;determining a covariance matrix for each of said health classifications using said at least one condition indicator and said observed values; anddetermining said current quantity in accordance with said covariance matrices, said transformation matrix and said observed values.15. The computer program product of claim 14, wherein, for two of said plurality of health classifications, said current quantity, h(X), may be represented as:h (X)=½YT&Lgr;−1Y−((−K−1L)T)Y+(−½LTK−1L−½ln|K|−ln P2/P1)wherein, X represents said at least one conditional