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 diagnostic tester evaluates at least one inputted test signal corresponding to test data relating to at least one predetermined parameter of a system being tested, to produce first and second candidate signals corresponding respectively to first and second possible diagnoses of the condition of the system respectively having the first and second highest levels of certainty of being valid, and first and second certainty signals corresponding respectively to values of the first and second highest levels of certainty. The diagnostic tester further determines the sufficiency of the testing that has taken place responsive to the first and second certainty signals, and produces an output signal indicative of whether sufficient test data has been evaluated to declare a diagnosis. Preferably, an uncertainty signal corresponding to a measure of the uncertainty that the evaluated at least one test signal can be validly evaluated is also produced and used to produce the output signal.