Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load

TL;DR

This paper presents a data-driven method for predicting the lifespan of proton exchange membrane fuel cells under dynamic loads, utilizing time-frequency analysis and symbolic recurrent neural networks to improve accuracy and reduce computational costs.

Contribution

It introduces a novel prognostics approach combining Hilbert-Huang transform and symbolic-based gated recurrent units for better prediction of fuel cell degradation.

Findings

Provides a competitive prognostics horizon compared to state-of-the-art methods.

Achieves higher prediction accuracy with lower computational cost.

Demonstrates consistent and generalizable performance under different failure thresholds.

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.