Private Reversible Aging-Aware Fuel Cell Prognostic: A Federated Multitask Learning Approach

Fuel cell prognostic has been considered an efficient way for the health management of fuel cell system and contribute to its lifespan. Nevertheless, accurate fuel cell prognostic is a challenge task because the lifespan of fuel cell is influenced by the operating conditions and there could be reversible aging due to the electrochemical reaction nature of fuel cell. In addition, conventional data-driven prognostic method trains a prediction model for each working condition without leveraging the common aging features for various conditions and thus may yield a less generalized model. What's more, training a promising data-driven prognostic model require large amount of data, which is hard to be collected by one institution due to the high cost and long-term degradation nature of fuel cell, whereas collecting aging data from different entities face the challenges of privacy concern. To address the above-mentioned issue, this paper proposes a private reversible aging-aware method. To address the reversible aging issue, a hint function based method is proposed to add indication terms in the input feature to help the model better deal with the reversible aging. After that, a multitask learning approach is considered to enable the institution to learn shared representations of the aging data, where the multitask learning is reformulated by federated learning to protect the data privacy of each institution. Experiment on the PHM2014 data challenge prove the effectiveness and robustness of the proposed method. © 2023 IEEE.