Gamma process with recursive MLE for wear PDF prediction in precognitive maintenance under aperiodic monitoring

Effective degradation prediction under aperiodic monitoring can assist to save inspection costs and schedule maintenance for the realistic manufacturing systems. In this paper, an extended time-/condition-based framework will be proposed to predict the Probability Density Function (PDF) of unobservable degradation under aperiodic condition monitoring. Furthering our earlier work of indirect degradation estimation via a mixed time-/condition-based approach, a stage-based Gamma process is designed and implemented for the prediction of degradation PDF where a recursive Maximum Likelihood Estimation (MLE) algorithm deduced from the conventional MLE algorithm is able to update the modeling parameters based on each aperiodic monitoring interval. The effectiveness of our extended framework is tested on the tool wear experiments in an industrial high speed computer numerical control milling machine, which can achieve an acceptable prediction performance with the average error of predicted bounds less than 15.0% as well as the average accuracy more than 94.3%. (C) 2015 Elsevier Ltd. All rights reserved.