Prognostics methodologies determine the health state of a system and predict the end of life and remaining useful life. This information enables operators to take maintenance related decisions, thus effectively streamlining operational and mission-level activities. Prognostics testbeds help in the prototyping, development, and maturation of prognostic technologies. In this work, we present a prognostics testbed for pneumatic valves. Pneumatic valves are critical components in many industrial
processes, and the testbed will be used to showcase how remaining life prediction works in the context of cryogenic refueling operations. The testbed allows for the injection of time-varying leaks
with specified damage progression profiles in order to emulate common valve faults. In addition, the testbed contains a battery used to power some components, allowing the study of the effects of battery degradation on the operation of the valves. Prognostic algorithms will utilize sensor data collected from the different transducers in order to estimate component health and make life predictions, based on mathematical models describing the underlying physics of component degradation and employing a Bayesian filtering algorithm for state-parameter estimation from
which life predictions are made.