Abstract: A method for prognostic modeling includes obtaining probability values for possible health states of a system or component part using one or more data driven models and one or more physics failure models. A probabilistic network is built using a plurality of observed and latent variables. The probable outcomes from the one or more physics of failure models and the one or more data driven models are combined to create an integrated model for failure prognosis. A health state of the system or system component is predicted using the integrated model and the probabilistic network.

Abstract: A Transistor Resonant Characteristic Sensor (TReCS) includes a sensing element positioned along electronic equipment so that the sensing element is electromagnetically coupled to the electronic equipment. The sensing element includes a coil. The sensing element is configured to detect magnetic oscillations associated with a characteristic signal generated by the electronic equipment. The TReCS sensor further includes an evaluation circuit connected to the sensing element for monitoring health state of the electronic equipment. The evaluation circuit includes one or more processing elements configured to diagnose health state of the electronic equipment based on extracted baseband information associated with the characteristic signal.

Abstract: A Transistor Resonant Characteristic Sensor (TReCS) includes a sensing element positioned along electronic equipment so that the sensing element is electromagnetically coupled to the electronic equipment. The sensing element includes a coil. The sensing element is configured to detect magnetic oscillations associated with a characteristic signal generated by the electronic equipment. The TReCS sensor further includes an evaluation circuit connected to the sensing element for monitoring health state of the electronic equipment. The evaluation circuit includes one or more processing elements configured to diagnose health state of the electronic equipment based on extracted baseband information associated with the characteristic signal.

Abstract: A method for prognostic modeling includes obtaining probability values for possible health states of a system or component part using one or more data driven models and one or more physics of failure models. A probabilistic network is built using a plurality of observed and latent variables. The probable outcomes from the one or more physics of failure models and the one or more data driven models are combined to create an integrated model for failure prognosis. A health state of the system or system component is predicted using possible health states using the integrated model and the probabilistic network.

Abstract: A measuring system, method, and/or computer program that realizes physics-based relations between sensor readings to monitor for degradations and predict failures of power systems is provided. For example, the supply and return of fuel in a power system is provided by a fuel injection system. The fuel injection system responds to changes in loads and system conditions, such as to compensate for fuel loss due to fuel combustion, by continuously and instantaneously changing the fuel flow. This behavior by the fuel injection system creates characteristic signatures. These characteristic signatures are instantaneously detected and utilized by the measuring system, method, and/or computer program to detect healthy, degrading, failing, and failed mechanical conditions so as to monitor for degradations and predict failures of the power system.

Abstract: A method for monitoring a health state of electronic equipment is provided and includes capturing data of an operation of the electronic equipment over a sampling range of predefined length, storing captured data in a storage unit and characterizing a behavior of the operation of the electronic equipment based on an analysis of stored captured data in an event a predefined time has expired or the storage unit has reached maximum capacity.

Abstract: Disclosed are systems and methods for prognostic health management (PHM) of electronic systems. Such systems and methods present challenges traditionally viewed as either insurmountable or otherwise not worth the cost of pursuit. The systems and methods are directed to the health monitoring and failure prediction of electronic systems, including the diagnostic methods employed to assess current health state and prognostic methods for the prediction of electronic system failures and remaining useful life. The disclosed methodologies include three techniques: (1) use of existing electronic systems data (circuit as a sensor); (2) use of available external measurements as condition indicators and degradation assessor; and (3) performance assessment metrics derived from available external measurements.

Abstract: Disclosed are systems and methods for prognostic health management (PHM) of electronic systems. Such systems and methods present challenges traditionally viewed as either insurmountable or otherwise not worth the cost of pursuit. The systems and methods are directed to the health monitoring and failure prediction of electronic systems, including the diagnostic methods employed to assess current health state and prognostic methods for the prediction of electronic system failures and remaining useful life. The disclosed methodologies include three techniques: (1) use of existing electronic systems data (circuit as a sensor); (2) use of available external measurements as condition indicators and degradation assessor; and (3) performance assessment metrics derived from available external measurements.

Abstract: Disclosed are systems and methods for prognostic health management (PHM) of electronic systems. Such systems and methods present challenges traditionally viewed as either insurmountable or otherwise not worth the cost of pursuit. The systems and methods are directed to the health monitoring and failure prediction of electronic systems, including the diagnostic methods employed to assess current health state and prognostic methods for the prediction of electronic system failures and remaining useful life. The disclosed methodologies include three techniques: (1) use of existing electronic systems data (circuit as a sensor); (2) use of available external measurements as condition indicators and degradation assessor; and (3) performance assessment metrics derived from available external measurements.

Abstract: Disclosed are systems and methods for prognostic health management (PHM) of electronic systems. Such systems and methods present challenges traditionally viewed as either insurmountable or otherwise not worth the cost of pursuit. The systems and methods are directed to the health monitoring and failure prediction of electronic systems, including the diagnostic methods employed to assess current health state and prognostic methods for the prediction of electronic system failures and remaining useful life. The disclosed methodologies include three techniques: (1) use of existing electronic systems data (circuit as a sensor); (2) use of available external measurements as condition indicators and degradation assessor; and (3) performance assessment metrics derived from available external measurements.