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The invention regards a system reliability or failure predicting apparatus and method that incorporates known information about system component failure into a system model and uses the model with or without other acquired system data to predict the probability of system failure. An embodiment of th

The invention regards a system reliability or failure predicting apparatus and method that incorporates known information about system component failure into a system model and uses the model with or without other acquired system data to predict the probability of system failure. An embodiment of the method includes using probabilistic methods to create a system failure model from the failure models of individual system components, predicting the failure of the system based on the component models and system data, ranking the sensitivity of the system to the system variables, and communicating a failure prediction.

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We claim: 1. A computer-implemented method for predicting failure in a system, the method comprising: receiving data associated with a system, the received data including sensed data indicative of a system response to a specific load on the system while the system is in operation other than undergo

We claim: 1. A computer-implemented method for predicting failure in a system, the method comprising: receiving data associated with a system, the received data including sensed data indicative of a system response to a specific load on the system while the system is in operation other than undergoing a system test; calculating a prediction indicative of a potential failure of said system using a pre-selected probabilistic model and said received data, the probabilistic model selected to calculate said prediction based on at least the specific load; and wherein the probabilistic model utilizes at least one of fast probability methods and simulation techniques. 2. The method of claim 1, wherein said receiving further comprises receiving system information from said system. 3. The method of claim 1, wherein said calculating a prediction further comprises calculating a prediction of a failure of a component of said system. 4. The method of claim 1, wherein said calculating a prediction further comprises calculating a prediction of a failure of multiple systems based on said prediction. 5. The method of claim 1, further comprising comparing said prediction to criteria. 6. The method of claim 1, further comprising communicating the prediction, and wherein at least one of said calculating and communicating steps occurs at a location remote from said system. 7. The method of claim 1, wherein said probabilistic model comprises multiple pre-selected probabilistic models, wherein at least one of the multiple pre-selected probabilistic models is selected to calculate the prediction based on the one or more pre-determined failure modes of the system. 8. The method of claim 1, further comprising ranking variables in said probabilistic model according to said variable's contribution to said prediction. 9. The method of claim 1, applied to predict failure in a material's microstructure. 10. The method of claim 1, wherein said received data further comprises referred data and inferred data and wherein said method further comprises relating said referred data to a first set of variables, relating said sensed data to a second set of variables, and inferring a third set of variables from said sensed data. 11. The method of claim 1, further comprising sending at least some of said received data to a remote location and wherein said calculating said prediction occurs at said remote location. 12. The method of claim 11, further comprising receiving said prediction from said remote location. 13. The method of claim 1, further comprising developing said probabilistic model prior to said calculating said prediction. 14. The method of claim 13, wherein said developing further comprises: identifying at least one failure mechanism of a component of said system from said component's characteristics selected from the group consisting of: material properties, environmental conditions, design characteristics, component loading, and component usage; identifying significant random variables of said at least one failure mechanism; identifying statistical parameters of said significant random variables; and formulating a strategy for probabilistic analysis based on said identifying steps. 15. The method of claim 14, wherein said received data further comprises referred data and inferred data and wherein said developing step further comprises determining which of said significant random variables are related to said referred data, which of said significant random variables are related to said sensed data, and which of said significant random variables are inferred from said sensed data. 16. The method of claim 13, wherein said developing further comprises setting criteria for communicating said prediction. 17. The method of claim 1, wherein said probabilistic model utilizes fast probability methods. 18. The method of claim 17, wherein said fast probability methods are direct fast probability methods selected from the group consisting of: First Order Reliability Methods, Second Order Reliability Methods, Advanced Mean Value methods, and Mean Value methods. 19. The method of claim 17, wherein said fast probability methods are response surface fast probability methods selected from the group consisting of: First Order Reliability Methods, Second Order Reliability Methods, Advanced Mean Value methods, and Mean Value methods. 20. The method of claim 1, wherein said probabilistic model utilizes simulation techniques. 21. The method of claim 20, wherein said simulation techniques are direct methods selected from the group consisting of: Monte Carlo methods and importance sampling methods. 22. The method of claim 20, wherein said simulation techniques are response surface methods selected from the group consisting of: Monte Carlo methods and importance sampling methods. 23. A computer-implemented method for predicting failure in a system, the method comprising: receiving data associated with a system, the received data including sensed data indicative of a system response to a specific load on the system while the system is in operation other than undergoing a system test; calculating a prediction indicative of a potential failure of said system using a pre-selected probabilistic model and said received data, the probabilistic model selected to calculate said prediction based on at least the specific load, wherein the data indicative of a system response to a specific load comprises a bend angle. 24. The method of claim 23, further comprising using the bend angle and the probabilistic model to generate a response surface. 25. A computer-implemented method for predicting failure in a system, the method comprising: receiving data associated with a system, the received data including sensed data indicative of a system response to a specific load on the system while the system is in operation other than undergoing a system test; calculating a prediction indicative of a potential failure of said system using a pre-selected probabilistic model and said received data, the probabilistic model selected to calculate said prediction based on at least the specific load, wherein the probabilistic model is selected based on at least one failure mechanism including a failure mechanism described by an equation having at least a capacity section and a demand section. 26. The method of claim 25, further comprising communicating the prediction. 27. An apparatus for monitoring a system, said apparatus comprising: sensors for acquiring sensed data indicative of a current physical state of a particular system; and one or more data processing systems including a first computer comprising: a processor; and a memory comprising: instructions for receiving data including said acquired data; instructions for determining a current operation status of said particular system using a probabilistic model to determine the current operation status based on a probable response of the particular system to one or more external parameters at a current time, and further using said acquired data; and wherein the probabilistic model utilizes at least one of fast probability methods and simulation techniques. 28. The apparatus of claim 27, wherein said instructions for determining the current operation status further comprise instructions for determining a probable response of at least one component of said system to the one or more external parameters at the current time. 29. The apparatus of claim 27, the data processing system further comprising: a second computer, said second computer comprising: a processor; and a memory, said memory comprising: instructions for receiving said current operation status; and instructions for communicating said current operation status; and a second communication device for communicating said current operation status. 30. The apparatus of claim 27, further including a communication device, and wherein said communication device is configured to generate a warning signal. 31. The apparatus of claim 27, said apparatus further comprising a sending device for sending at least some of said received data to a location remote from said system. 32. The apparatus of claim 31, wherein said first computer is located remote from said system. 33. The apparatus of claim 27, further comprising instructions for comparing said current operation status to criteria. 34. The apparatus of claim 27, wherein said probabilistic model comprises multiple models. 35. The apparatus of claim 27, wherein said probabilistic model comprises variables ranked according to said variables' contribution to said current operation status. 36. The apparatus of claim 27, applied to predict failure in a material's microstructure. 37. The apparatus of claim 27, wherein said received data further comprises referred data and inferred data and wherein said apparatus further comprises instructions for: relating said referred data to a first set of variables; relating said acquired data to a second set of variables; and inferring a third set of variables from said acquired data. 38. The apparatus of claim 27, wherein said probabilistic model utilizes fast probability methods. 39. The apparatus of claim 38, wherein said fast probability methods are direct fast probability methods selected from the group consisting of: First Order Reliability Methods, Second Order Reliability Methods, Advanced Mean Value methods, and Mean Value methods. 40. The apparatus of claim 38, wherein said fast probability methods are response surface fast probability methods selected from the group consisting of: First Order Reliability Methods, Second Order Reliability Methods, Advanced Mean Value methods, and Mean Value methods. 41. The apparatus of claim 27, wherein said probabilistic model utilizes simulation techniques. 42. The apparatus of claim 41, wherein said simulation techniques are direct methods selected from the group consisting of: Monte Carlo methods; and importance sampling methods. 43. The apparatus of claim 41, wherein said simulation techniques are response surface methods selected from the group consisting of: Monte Carlo methods; and importance sampling methods. 44. The apparatus of claim 27, wherein said instructions for creating further comprise instructions for creating a prediction of a failure of multiple systems based on said prediction. 45. The apparatus of claim 27, said probabilistic model is pre-selected based on at least one failure mechanism of a component of said system. 46. The apparatus of claim 45, wherein said at least one failure mechanism relates to a material microstructure. 47. The apparatus of claim 27, further comprising: instructions for communicating said current operation status; and a communication device for communicating said current operation status. 48. An apparatus for monitoring a system, said apparatus comprising: sensors for acquiring sensed data indicative of a current physical state of a particular system; and one or more data processing systems including a first computer comprising: a processor; and a memory comprising: instructions for receiving data including said acquired data; instructions for determining a current operation status of said particular system using a probabilistic model to determine the current operation status based on a probable response of the particular system to one or more external parameters at a current time, and further using said acquired data, wherein said instructions for determining a probable response of said at least one component of said system to the one or more external parameters at the current time comprises instructions for performing finite element analysis using at least a component configuration and data indicative of the one or more external parameters at the current time. 49. The apparatus of claim 48, wherein the one or more data processing systems further comprise instructions for determining a future operation status of said particular system using the probabilistic model. 50. The apparatus of claim 48, further comprising: instructions for communicating said current operation status; and a communication device for communicating said current operation status. 51. An apparatus for monitoring a system, said apparatus comprising: sensors for acquiring sensed data indicative of a current physical state of a particular system; and one or more data processing systems including a first computer comprising: a processor; and a memory comprising: instructions for receiving data including said acquired data; instructions for determining a current operation status of said particular system using a probabilistic model to determine the current operation status based on a probable response of the particular system to one or more external parameters at a current time, and further using said acquired data, wherein the probabilistic model selected based on at least one failure mechanism including a failure mechanism is described by an equation including a capacity section and a demand section. 52. The apparatus of claim 51, further comprising: instructions for communicating said current operation status; and a communication device for communicating said current operation status. 53. A computer program product for predicting failure of a system for use in conjunction with a computer system, said computer program product comprising: a computer readable storage medium and a computer program mechanism embedded therein, said computer program mechanism comprising: instructions for receiving data including sensed data indicative of a current physical state; instructions for determining a failure probability of said system using a probabilistic model and said data, the probabilistic model to determine the failure probability based on modeling a response of the system to at least one force; and wherein the probabilistic model utilizes at least one of fast probability methods and simulation techniques. 54. The computer program product of claim 53, wherein the instructions for determining the failure probability of the system further comprise instructions for determining a probable response of at least one component of said system to the at least one force. 55. The computer program product of claim 54, further comprising: instructions for determining a future failure probability of said system using the probabilistic model. 56. The computer program product of claim 53, wherein said instructions for determining a failure probability further comprise instructions for determining a failure probability of multiple systems based on said sensed data indicative of the current physical state. 57. The computer program product of claim 53, said probabilistic model is pre-selected based on at least one pre-determined failure mechanism of a component of said system. 58. The computer program product of claim 53, wherein said at least one pre-determined failure mechanism relates to a material microstructure. 59. The computer program product of claim 53, further comprising instructions for comparing said failure probability to criteria. 60. The computer program product of claim 53 wherein said probabilistic model comprises multiple probabilistic models. 61. The computer program product of claim 53, further comprising ranking variables in said probabilistic model according to said variables' contribution to said failure probability. 62. The computer program product of claim 53, applied to predict failure in a material's microstructure. 63. The computer program product of claim 53, wherein said received data further comprises: referred data; and inferred data and wherein said apparatus further comprises: instructions for relating said referred data to a first set of variables; instructions for relating said sensed data to a second set of variables; and instructions for inferring a third set of variables from said sensed data. 64. The computer program product of claim 53, wherein said probabilistic model utilizes fast probability methods. 65. The computer program product of claim 64, wherein said fast probability methods are direct fast probability methods selected from the group consisting of: First Order Reliability Methods, Second Order Reliability Methods, Advanced Mean Value methods, and Mean Value methods. 66. The computer program product of claim 64, wherein said fast probability methods are response surface fast probability methods selected from the group consisting of: First Order Reliability Methods, Second Order Reliability Methods, Advanced Mean Value methods, and Mean Value methods. 67. The computer program product of claim 53, wherein said probabilistic model utilizes simulation techniques. 68. The computer program product of claim 67, wherein said simulation techniques are direct methods selected from the group consisting of: Monte Carlo methods, and importance sampling methods. 69. The computer program product of claim 67, wherein said simulation techniques are response surface methods selected from the group consisting of: Monte Carlo methods, and importance sampling methods. 70. The computer program product of claim 53, further comprising: a second computer program product, said second computer program product comprising: a second computer readable storage medium and a second computer program mechanism embedded therein, said second computer program mechanism comprising: instructions for receiving said failure probability; and instructions for communicating said failure probability. 71. A computer program product for predicting failure of a system for use in conjunction with a computer system, said computer program product comprising: a computer readable storage medium and a computer program mechanism embedded therein, said computer program mechanism comprising: instructions for receiving data including sensed data indicative of a current physical state; instructions for determining a failure probability of said system using a probabilistic model and said data, the probabilistic model to determine the failure probability based on modeling a response of the system to at least one force, wherein said instructions for determining the probable response of at least one component of the system to the at least one force comprise instructions for performing finite element analysis using at least a component configuration and data indicative of the at least one force. 72. A computer program product for predicting failure of a system for use in conjunction with a computer system, said computer program product comprising: a computer readable storage medium and a computer program mechanism embedded therein, said computer program mechanism comprising: instructions for receiving data including sensed data indicative of a current physical state; instructions for determining a failure probability of said system using a probabilistic model and said data, the probabilistic model to determine the failure probability based on modeling a response of the system to at least one force, wherein the probabilistic model is selected based on at least one pre-determined failure mechanism including a mechanism described by an equation having at least a capacity section and a demand section. 73. A computer-implemented method for predicting failure in a system, the method comprising: receiving data associated with the system while the system is in operation other than undergoing system test; during system operation, ascertaining a probability of failure for each of a plurality of pre-determined failure mechanisms using a physics based first probabilistic failure model, wherein said probability of failure for each of said failure mechanisms is based at least partially on said received data and said pre-determined failure mechanisms; predicting a probability of failure for the system using a physics based second probabilistic failure model, wherein said probability of failure for the system is at least partially based on said probability of failure of said failure mechanisms; and communicating the probability of failure of the system. 74. The method of claim 73, further comprising, before said ascertaining, determining one or more suitable physics based probabilistic failure models for each failure mechanism. 75. The method of claim 73, wherein said failure mechanisms are selected from the group consisting of: cracking, delamination, shearing, bending, and tension fracture. 76. The method of claim 73, wherein said failure mechanisms are selected from the group consisting of: material properties, environmental conditions, design characteristics, component loading, and component usage. 77. The method of claim 73, wherein said probability of failure for each of said failure mechanisms is further based on variability of physical parameters of said system. 78. The method of claim 73, wherein said probability of failure for each of said failure mechanisms is further based on a variability of directly sensed variables, a variability of referred variables, and a variability of inferred variables. 79. A computer implemented method for predicting failure in a system, comprising: determining failure mechanisms for a system; receiving data associated with the system while the system is in operation other than undergoing system test; selecting at least one suitable physics based probabilistic failure model for each failure mechanism; ascertaining a probability of failure for each of said failure mechanisms using a selected physics based first probabilistic failure model, wherein said probability of failure for each of said failure mechanisms is based at least partially on said received data, said failure mechanisms, and variability of physical parameters of said system; predicting a probability of failure for the system using a selected physics based second probabilistic failure model, wherein said probability of failure for the system is at least partially based on said probability of failure for each of said failure mechanisms; and communicating said probability of failure for the system. 80. The method of claim 79, wherein said variability of physical parameters comprises a variability of directly sensed variables, a variability of referred variables, and a variability of inferred determined variables. 81. The method of claim 79, further comprising, before said communicating, determining a confidence of said probability of failure of said system based on historical failure data. 82. A computer-implemented method for predicting failure in a system, the method comprising: receiving data associated with a system, the received data including sensed data indicative of a system response to a specific load on the system while the system is in operation other than undergoing a system test; calculating a prediction indicative of a potential failure of said system using a pre-selected probabilistic model and said received data, the probabilistic model selected to calculate said prediction based on at least the specific load, wherein calculating the prediction comprises determining a probable response of at least one component of said system to one or more external parameters by performing finite element analysis using at least a component configuration and data indicative of the one or more external parameters. 83. The computer-implemented method of claim 82, further comprising communicating the prediction.