Some life tests result in few or no failures. In such cases, we can, and should, consider using degradation measurements to assess reliability. In real world, product degradation is a stochastic process. Since such degradation is often seen as a monotonous process, literature widely uses Gamma Process to describe and quantify degradation. However, in these publications, scale parameter is considered constant over time and results under this assumption may have big deviation from the actual measurements under nonlinear condition. The purpose of this paper is to improve Gamma Process method to fit a broader class of degradation models. Firstly, we use MLE to estimate the parameters under the timely constant-scale-parameter assumption and analyze why the model does not fit data well. Then we propose an improved model to improve the method and use Monte Carlo simulation to verify the validity of the improved method.
