Data-driven Prognostics of Proton Exchange Membrane Fuel Cell Stack with constraint based Summation-Wavelet Extreme Learning Machine.
Data-driven Prognostics of Proton Exchange Membrane Fuel Cell Stack with constraint based Summation-Wavelet Extreme Learning Machine.
复制标题
DOI：
--
复制
发表时间：
2015-02
期刊：
影响因子：
--
通讯作者：
Kamran Javed;R. Gouriveau;N. Zerhouni;D. Hissel
中科院分区：
文献类型：
--
作者：
Kamran Javed;R. Gouriveau;N. Zerhouni;D. Hissel
燃料电池（FC）的老化是一个不可避免的过程，但管理运行条件并及时进行维护或控制可以延长其使用寿命。更准确地说，FC 的预后是当今关注的主要领域。本文提出了一种使用基于约束的求和小波极限学习机 (SW-ELM) 进行质子交换膜燃料电池 (PEMFC) 堆栈预测的数据驱动方法。该提议旨在提高老化 PEMFC 堆栈数据驱动预测的稳健性和适用性，并利用有限数据估计 RUL。所提出的方法应用于 2014 年 PHM 挑战赛中 PEMFC 堆栈的运行至故障数据，该堆栈的寿命为 1155 小时。该方法的性能被判断为遇到简约问题。结果表明，基于约束的 SW-ELM 在有限的学习数据下具有适应性，并且适合频繁间隔的 PEMFC 堆栈预测。
Aging of a fuel cell (FC) is an unavoidable process, nevertheless managing operating conditions and performing timely maintenance or control can prolong its life span. More precisely, the prognostics of FC is major area of focus nowadays. This paper presents a data-driven approach for prognostics of Proton Exchange Membrane Fuel Cell (PEMFC) stack using constraint based Summation-Wavelet Extreme Learning Machine (SW-ELM). The proposition aims at improving the robustness and the applicability of data-driven prognostics of aging PEMFC stack and estimating the RUL with limited data. The proposed method is applied to run-to-failure data of PEMFC stack from PHM challenge 2014, which had the life span of 1155 hours. Performances of the approach are judged to encounter parsimony problems. Results show the adaptability of constraint based SW-ELM with limited learning data and its suitability for prognostics of PEMFC stack at frequent intervals.