堆栈(抽象数据类型)
计算机科学
非线性系统
可靠性(半导体)
理论(学习稳定性)
人工神经网络
商业化
质子交换膜燃料电池
计算理论
功率(物理)
人工智能
燃料电池
机器学习
算法
工程类
物理
量子力学
化学工程
程序设计语言
法学
政治学
作者
Zhixue Zheng,Simon Morando,Marie‐Cécile Pera,Daniel Hissel,Laurent Larger,Romain Martinenghi,Antonio Baylon Fuentes
标识
DOI:10.1016/j.ijhydene.2016.11.043
摘要
Features such as low greenhouse-gas emission, high energy efficiency and operating stability make fuel cell (FC) an attractive power source for a wide variety of applications. Nevertheless, to achieve its commercialization, durability and reliability remain big challenges. This work aims at developing an efficient data-driven fault detection and identification methodology through the use of a recently proposed brain-inspired computational paradigm, Reservoir Computing (RC). The considered “Reservoir” is made of a particular class of complex dynamics emulating a virtual neural network, and modeled by a nonlinear delay equation. This original and experimentally compatible approach indeed demonstrated recently excellent performances on complex nonlinear problems such as classification and prediction tasks. In this work, a first attempt is made to introduce the RC method into the field of FC diagnosis. Targeted fault types include CO poisoning, low air flow rate, defective cooling and natural degradation. Experimental results show the simplicity and efficiency of RC method to discriminate the abovementioned health states. Moreover, the influence of four key RC parameters and also of the learning database is investigated in order to explore the possibility of further facilitating and generalizing the RC approach.
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