Abstract:

The goal of prognosis on a structure, system or component (SSC) is to predict whether the SSC can perform its function up to the end of its life and in case it cannot, estimate the Time to Failure (TTF), i.e., the lifetime remaining between the present and the instance when it can no longer perform its function. Such prediction on the loss of functionality changes dynamically as time goes by and is typically based on measurements of parameters representative of the SSC state. Uncertainties from two different sources affect the prediction: randomness due to variability inherent in the SSC degradation behavior (aleatory uncertainty) and imprecision due to incomplete knowledge and information on the SSC failure mechanisms (epistemic uncertainty). Such uncertainties must be adequately represented and propagated in order for the prognostic results to have operational significance, e.g., in terms of maintenance and renovation decisions. This work addresses the problem of predicting the reliability and TTF of a SSC, as measurements of parameters representative of its state become available in time. The representation and propagation of the uncertainties associated to the prediction are done alternatively by a pure probabilistic method and an hybrid Monte Carlo and possibilistic method. A case study is considered, regarding a component which is randomly degrading in time according to a stochastic fatigue crack growth model of literature; the maximum level of degradation beyond which failure occurs is affected by epistemic uncertainty.
Received on February 11, 2009 and revised on April 11, 2010
References: 29