2013/12/25 by Raïssa Onanena, Onanena, Raïssa, Faïcel Chamroukhi +9
Computer Science · Engineering · #Advanced Control Systems Optimization #Applications (stat.AP) #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.1312.7003
openalex publication_date 2013/12/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper describes a pattern recognition approach aiming to estimate fuel cell duration time from electrochemical impedance spectroscopy measurements. It consists in first extracting features from both real and imaginary parts of the impedance spectrum. A parametric model is considered in the case of the real part, whereas regression model with latent variables is used in the latter case. Then, a linear regression model using different subsets of extracted features is used fo r the estimation of fuel cell time duration. The performances of the proposed approach are evaluated on experimental data set to show its feasibility. This could lead to interesting perspectives for predictive maintenance policy of fuel cell.