초록 ▼

A system (830) and method (800) for diagnosing a malfunctioning machine. A fault event is selected (806) together with sequential operating parameter data (808) from a selectively focused time interval about the fault event for evaluation of a machine (810). The selectively focused time interval may

A system (830) and method (800) for diagnosing a malfunctioning machine. A fault event is selected (806) together with sequential operating parameter data (808) from a selectively focused time interval about the fault event for evaluation of a machine (810). The selectively focused time interval may include data occurring just before, just after, or spanning the fault event. Characterizing information such as slope, rate of change, and absolute sign of the data may be derived (809) from the operating parameter data over the selectively focused time interval and used in the diagnosis. The fault event and operating parameter data may be compared to existing cases in a case database (834). A set of rules (817) or candidate anomalies (841) may be executed over the operating parameter data to further improve the accuracy of the diagnosis.

대표청구항 ▼

1. A computer implemented method for diagnosing a malfunction of a machine, the method comprising:receiving sequential operating parameter data from a machine; receiving a fault indication from the machine; selecting sequential operating parameter data from a selectively focused time interval about

1. A computer implemented method for diagnosing a malfunction of a machine, the method comprising:receiving sequential operating parameter data from a machine; receiving a fault indication from the machine; selecting sequential operating parameter data from a selectively focused time interval about the fault indication; developing characterizing information from the sequential operaating paramerer data over the selectively focused time interval; and using the characterizing information and the fault indication to diagnose a malfunction of the machine. 2. The method of claim 1, further comprising:using the selected sequential operating parameter data and the fault indication to construct a new case; and comparing the new case to known cases in a case database to diagnose a malfunction of the machine. 3. The method of claim 1, further comprising applying the selected sequential operating parameter data and the fault indication to a rule base to diagnose a malfunction of the machine.4. The method of claim 1, further comprising selecting the sequential operating parameter data from a time interval sequentially prior to the fault indication.5. The method of claim 1, further comprising selecting the sequential operating parameter data from a time interval sequentially after the fault indication.6. The method of claim 1, further comprising:developing rate of change information from the selected sequential operating parameter data; and using the rate of change information and the fault indication to diagnose a malfunction of the machine. 7. The method of claim 1, further comprising:developing absolute sign information from the selected sequential operating parameter data; and using the absolute sign information and the fault indication to diagnose a malfunction of the machine. 8. The method of claim 1, further comprising:developing direction of change information from the selected sequential operating parameter data; and using the direction of change information and the fault indication to diagnose a malfunction of the machine. 9. The method of claim 1, further comprising:developing slope information from the selected sequential operating parameter data; and using the slope information and the fault indication to diagnose a malfunction of the machine. 10. The method of claim 1, further comprising developing the characterizing information to include one of the group of rate of change information, absolute sign information, direction of change information, slope information, derivative information, regression analysis information, high-pass filter information an low-pass filter information.11. A computer implemented method of dignosing a malfunction of a mobile vehicle, the method comprising:recording sequential operating parameter data from the vehicle; receiving a fault indication from the vehicle; selecting sequential operating parameter data from a selectively focused time interval about the fault indication; developing characterizing information from the selected sequential operating parameter data over the selectively focused time interval; and using characterizing information and the fault indication to diagnose a malfunction of the vehicle. 12. The method of claim 11, further comprising:using the selected sequential operating parameter data and the fault indication to construct a new case; and comparing the new case to known cases in a case database to diagnose a malfunction of the vehicle. 13. The method of claim 11, further comprising applying the selected sequential operating parameter data and the fault indication to a rule base to diagnose a malfunction of the vehicle.14. The method of claim 11, further comprising:developing rate of change information from the selected sequential operating parameter data; and using the rate of change information and the fault indication to diagnose a malfunction of the vehicle. 15. The method of claim 11, further comprising:developing absolute sign information from the selected sequential operating parameter data; and using the absolute sign information and the fault indication to diagnose a malfunction of the vehicle. 16. The method of claim 11, further comprising:developing direction of change information from the selected sequential operating parameter data; and using the direction of change information and the fault indication to diagnose a malfunction of the vehicle. 17. The method of claim 11, further comprising:developing slope information from the selected sequential operating parameter data; and using the slope information and the fault indication to diagnose a malfunction of the vehicle. 18. The method of claim 11, further comprising developing the characterizing information to include one of the group of rate of change information, absolute sign information, direction of change information, slope information, derivative information, regression analysis information, high-pass filter information and low-pass filter information.19. The method of claim 11, wherein the selectively focused time interval consists of a time period just before the fault indication.20. The method of claim 11, wherein the selectively focused time interval consists of a time period just after the fault indication.21. The method of claim 11, further comprising applying a filter to ignore predetermined fault indications.22. The method of claim 11, further comprising using a regression analysis to develop the characterizing information.23. An apparatus for diagnosing a malfunction of a machine, the apparatus comprising:an operating parameter database containing sequential operating parameter data from a machine; a fault log database containing fault log data from the machine; a processor connected to the operating parameter database and the fault log database; programed instructions executable by the processor to select a fault event; programmed instructions executable by the processor to select sequential operating; parameter data from a selectively focused time interval about the fault event; programmed instructions executable by the processor to develop characterizing information from the selected sequential operating parameter data over the selectively focused time interval; and programmed instructions executable by the processor to use the characterizing information and the fault event to diagnose a malfunction of the machine.