Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load
Abstract
Data-centric prognostics is beneficial to improve the reliability and safety of proton exchange membrane fuel cell (PEMFC). For the prognostics of PEMFC operating under dynamic load, the challenges come from extracting degradation features, improving prediction accuracy, expanding the prognostics horizon, and reducing computational cost. To address these issues, this work proposes a data-driven PEMFC prognostics approach, in which Hilbert-Huang transform is used to extract health indicator in dynamic operating conditions and symbolic-based gated recurrent unit model is used to enhance the accuracy of life prediction. Comparing with other state-of-the-art methods, the proposed data-driven prognostics approach provides a competitive prognostics horizon with lower computational cost. The prognostics performance shows consistency and generalizability under different failure threshold settings.
Cite
@article{arxiv.2302.10771,
title = {Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load},
author = {Chu Wang and Manfeng Dou and Zhongliang Li and Rachid Outbib and Dongdong Zhao and Jian Zuo and Yuanlin Wang and Bin Liang and Peng Wang},
journal= {arXiv preprint arXiv:2302.10771},
year = {2023}
}