become available from an operational system. Similarly, the performance of prediction changes with time that must be tracked and quantified. Another aspect that makes this process different in a PHM context is the time value of a RUL prediction. As a system approaches failure, the time window to take a corrective action gets shorter and consequently the accuracy of predictions becomes more critical for decision making. Finally, randomness and noise in the process, measurements, and prediction models are unavoidable and hence prognostics inevitably involves uncertainty in its estimates. A robust prognostics performance evaluation must incorporate the effects of this uncertainty. Several prognostics performance metrics have evolved with consideration of these issues: Prognostic horizon (PH) quantifies how much in advance an algorithm can predict with a desired accuracy before a failure occurs. A longer prognostic horizon is preferred as more time is then available for a corrective action. α-λ accuracy further tightens the desired accuracy levels using a shrinking cone of desired accuracy as end of life approaches. In order to comply with desired α-λ specifications at all times an algorithm must improve with time to stay within the cone. Relative accuracy quantifies the accuracy relative to the actual time remaining before failure. Convergence quantifies how fast the performance converges for an algorithm as end of life approaches. A visual representation of these metrics can be used to depict prognostic performance over a long time horizon. Uncertainty in prognostics There are many uncertainty parameters that can influence the prediction accuracy. These can be categorized as: Uncertainty in system parameters: this concerns the uncertainty in the values of the physical parameters of the system (resistance, inductance, stiffness, capacitance, etc.). This uncertainty is induced by the environmental and operational conditions where the system evolves. This can be tackled by using adequate methods such interval ones. Uncertainty in nominal system model: this concerns the imprecisions in the mathematical models which is generated to represent the behavior of the system. These imprecisions (or uncertainties) can be the result of a set of assumptions used during the modeling process and which lead to models that don’t fit exactly the real behavior of the system. Uncertainty in system degradation model: the degradation model can be obtained from accelerated life tests which are conducted on different data samples of a component. In practice, the data obtained by accelerated life tests performed under the same operating conditions may have