Enhancing Convolutional Neural Network Deep Learning for Remaining Useful Life Estimation in Smart Factory Applications

摘要

Estimating the remaining useful life (RUL) of machines or components is essential for prognostics and health management (PHM) in smart factories. This paper enhances the convolutional neural network (CNN) deep learning for RUL estimation in smart factory applications. The enhanced CNN deep learning is applied to NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) data set to estimate the RUL of aero-propulsion engines. It is shown to have better performance than other related methods.