Remaining useful life estimation for proton exchange membrane fuel cell based on extreme learning machine

Remaining useful life estimation (RUL), as an essential part in prognostics and health management (PHM), has becoming the hot issue and one of the challenging problem with the high requirement on the reliability and safety of the equipment. Extreme learning machine (ELM) is a Single-hidden Layer Feed-forward Neural Networks (SLFNs) learning algorithm which is easy to use. As the new generation of fuel cell, proton exchange membrane fuel cell (PEMFC) is promising in electronic system. In this paper, we study the RUL of the PEMFC using the PEMFC dataset in IEEE PHM 2014 Data Challenge. We analyze the PEMFC degradation trend, at the same time construct the corresponding degradation model utilizing the ELM and realize RUL estimation. Finally, the feasibility and effectiveness of the proposed method are illustrated by a numerical simulation.