Effective prediction of unobservable degradation can assist to schedule preventive maintenance and reduce unexpected downtime for realistic industrial systems. In this paper, an extended time-/condition-based framework is proposed for the Probability Density Function (PDF) prediction of unobservable industrial wear. Furthering our earlier work of unobservable degradation estimation, a stage-based Gamma process is developed to predict the degradation PDF where the modeling parameters are updated by a recursive Maximum Likelihood Estimation (MLE) algorithm derived from the conventional MLE. The effectiveness of our extended framework is tested on an industry experiment of a high speed computer numerical control milling machine, and it achieved the predicted bounds with an average error of 12.1% as well as average accuracy of 96.9%.