Failure Prognosis Based on Relevant Measurements Identification and Data-Driven Trend-Modeling: Application to a Fuel Cell System

Processes

Oussama Djedidi¹  Mohand Djeziri¹  Jean-Luc Seguin²  Marc Bendahan²  Samir Benmoussa³ 

[1] CNRS (Centre National de la Recherche Scientifique), LIS (Laboratoire d’Informatique et Systèmes), Aix-Marseille University, Université de Toulon, 13007 Marseille, France;CNRS, IM2NP (Institut Matériaux Microélectronique et Nanosciences de Provence), Aix Marseille Univ, Université de Toulon, 13007 Marseille, France;Laboratoire d’Automatique et de Signaux de Annaba (LASA), Université Badji Mokhtar, Annaba 23000, Algeria;

关键词: remaining useful life;    health index identification;    discrete state model;    trend modeling;    fuel-cell systems;   

DOI  :  10.3390/pr9020328

来源: DOAJ

【 摘 要 】

Fuel cells are key elements in the transition to clean energy thanks to their neutral carbon footprint, as well as their great capacity for the generation of electrical energy by oxidizing hydrogen. However, these cells operate under straining conditions of temperature and humidity that favor degradation processes. Furthermore, the presence of hydrogen—a highly flammable gas—renders the assessment of their degradations and failures crucial to the safety of their use. This paper deals with the combination of physical knowledge and data analysis for the identification of health indices (HIs) that carry information on the degradation process of fuel cells. Then, a failure prognosis method is achieved through the trend modeling of the identified HI using a data-driven and updatable state model. Finally, the remaining useful life is predicted through the calculation of the times of crossing of the predicted HI and the failure threshold. The trend model is updated when the estimation error between the predicted and measured values of the HI surpasses a predefined threshold to guarantee the adaptation of the prediction to changes in the operating conditions of the system. The effectiveness of the proposed approach is demonstrated by evaluating the obtained experimental results with prognosis performance analysis techniques.

【 授权许可】

Unknown