Forecasting the remaining useful life of proton exchange membrane fuel cells by utilizing nonlinear autoregressive exogenous networks enhanced by genetic algorithms

IF 5.4 Q2 CHEMISTRY, PHYSICAL
Yitong Shen , Mohamad Alzayed , Hicham Chaoui
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引用次数: 0

Abstract

The Proton Exchange Membrane Fuel Cell (PEMFC), known for its efficient energy conversion, minimal electrolyte leakage, and low operating temperature, shows great potential as a clean energy source. However, its lifespan is limited due to degradation during normal operation, which, if uncontrolled, can result in dangerous failures such as explosions. Hence, accurately estimating the remaining useful life (RUL) is vital. In this research, a combined prediction method using genetic algorithms (GA) and nonlinear autoregressive neural networks (NARX) with external inputs is proposed. The method's performance was trained and validated using the 2014 IEEE PHM Data Challenge dataset, and it was compared to two commonly used artificial neural network algorithms: GA-based backpropagation neural network (GA-BPNN) and GA-based time delay neural network (GA-TDNN). The findings demonstrate that the proposed approach surpasses the other two artificial neural network algorithms in terms of prediction accuracy. Although GA is known for its computational requirement, optimization is performed offline. Once optimal neural network (NN) hyper-parameters are determined, the optimized NN is used online for RUL prediction.

利用遗传算法增强的非线性自回归外源网络预测质子交换膜燃料电池的剩余使用寿命
质子交换膜燃料电池(PEMFC)以其高效的能量转换、最小的电解质泄漏和低的工作温度而闻名,作为一种清洁能源显示出巨大的潜力。然而,它的寿命是有限的,因为在正常操作过程中的退化,如果不加控制,可能导致危险的故障,如爆炸。因此,准确估计剩余使用寿命(RUL)是至关重要的。提出了一种基于遗传算法(GA)和非线性自回归神经网络(NARX)的组合预测方法。使用2014 IEEE PHM数据挑战数据集对该方法的性能进行了训练和验证,并将其与两种常用的人工神经网络算法:基于遗传算法的反向传播神经网络(GA-BPNN)和基于遗传算法的时延神经网络(GA-TDNN)进行了比较。研究结果表明,该方法在预测精度方面优于其他两种人工神经网络算法。虽然遗传算法以其计算需求而闻名,但优化是离线执行的。一旦确定了最优神经网络超参数,就将优化后的神经网络用于RUL预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.10
自引率
0.00%
发文量
18
审稿时长
64 days
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