电子测量与仪器学报

常家康,吕宁,詹跃东.基于 XGBoost-RFECV 算法和 LSTM 神经网络的 PEMFC 剩余寿命预测[J].电子测量与仪器学报,2022,36(1):126-133

基于 XGBoost-RFECV 算法和 LSTM 神经网络的 PEMFC 剩余寿命预测

Prediction of PEMFC remaining life based on XGBoost-RFECValgorithm and LSTM neural network

DOI：

中文关键词: XGBoost-RFECV 算法 LSTM 神经网络 PEMFC 剩余寿命

英文关键词:XGBoost-RFECV algorithm LSTM neural network proton exchange membrane fuel cell remaining useful life

基金项目:国家自然科学基金（51667012）项目资助

作者	单位
常家康	1. 昆明理工大学信息工程与自动化学院
吕宁	2. 昆明理工大学计算中心
詹跃东	1. 昆明理工大学信息工程与自动化学院

Author	Institution
Chang Jiakang	1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology
Lyu Ning	2. Computer Center, Kunming University of Science and Technology
Zhan Yuedong	1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology

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中文摘要:

针对质子交换膜燃料电池(PEMFC)寿命预测方法中 PEMFC 特征对其寿命的影响程度未知和模型预测精度低的问题, 提出一种基于 XGBoost-RFECV 算法和长短期记忆(LSTM)神经网络的 PEMFC 剩余寿命预测方法。首先通过等间隔采样和 SG 卷积平滑法对 PEMFC 原始数据进行重构和平滑处理,有效提取 PEMFC 退化趋势。然后利用 XGBoost-RFECV 算法计算 PEMFC 不同特征的重要度,并选择平均交叉验证均方误差最小的 10 个 PEMFC 特征组成最优特征子集。最后将最优特征子集输入构建的双层 LSTM 神经网络实现 PEMFC 的剩余寿命预测。实验结果表明,该方法的平均绝对误差和均方根误差分别为 0. 001 9 和 0. 002 5,决定系数 R 2 为 0. 974,与 XGBoost-RNN、XGBoost-LSTM 和 XGBoost-RFECV-RNN 方法相比预测精度更高,能够有效地预测 PEMFC 剩余寿命。

英文摘要:

Aiming at the problem that the influence of PEMFC characteristics on the life prediction method of the proton exchange membrane fuel cell (PEMFC) is unknown and the low prediction accuracy of the model, a PEMFC remaining life prediction method based on XGBoost-RFECV algorithm and LSTM neural network is proposed. First of all, the PEMFC original data is reconstructed and smoothed by equal interval sampling and SG convolution smoothing method, which effectively retains the original data degradation trend. Then the XGBoost-RFECV algorithm is used to calculate the importance of different PEMFC features, and the 10 PEMFC features with the smallest mean square error of average cross-validation are selected to form the optimal feature subset. Finally, the optimal feature subset is input into the constructed two-layer LSTM neural network to realize the remaining life prediction of PEMFC. The experimental results show that the average absolute error and root mean square error of the method are 0. 001 9 and 0. 002 5, respectively, and the coefficient of determination R 2 is 0. 974. Compared with the XGBoost-RNN, XGBoost-LSTM and XGBoost-RFECV-RNN model, the prediction accuracy is higher and it can effectively predict the remaining life of PEMFC.

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