基于随机森林算法的大功率质子交换膜燃料电池系统故障分类方法

doi:10.13334/j.0258-8013.pcsee.191435

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基于随机森林算法的大功率质子交换膜燃料电池系统故障分类方法

更新时间：2025-07-03

- 基于随机森林算法的大功率质子交换膜燃料电池系统故障分类方法
- Issue 17, Pages: 5591-5599(2020)
- 作者机构：
  
  西南交通大学电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.13334/j.0258-8013.pcsee.191435
  CLC： TM911.4
- Published：2020
- 稿件说明：
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[J]. 2020, (17): 5591-5599.
DOI：

[J]. 2020, (17): 5591-5599. DOI： 10.13334/j.0258-8013.pcsee.191435.

摘要

针对大功率质子交换膜燃料电池(protonexchange membranefuelcell

PEMFC)系统故障分类识别的问题

提出基于随机森林算法的燃料电池系统的故障分类方法。该方法基于Bootstrap法重采样

产生多个样本子集

通过决策树算法中的CART算法构造多个分类器模型

然后通过测试集对每个分类器进行测试得到分类结果

最后投票选出最优的分类结果作为预测类别。通过实例分析

对比支持向量机和K最邻近算法的分类结果

所提方法可有效识别有轨电车实际运行过程中5种故障状态

分类准确率可达94.4%

能明显提高故障分类正确率

对进一步实现大功率PEMFC系统在线故障诊断研究具有重要意义。

Abstract

Considering the fault classification problem of high-power proton exchange membrane fuel cell（PEMFC） systems

a fault classification method based on random forest（RF） algorithm was proposed. This method resampled based on Bootstrap method to generate multiple sample subsets. Multiple classifiers models were constructed by CART algorithm in the decision tree concept. At last

the optimal classification results were selected by voting as prediction categories. Through case analysis

compared with the classification results of support vector machine（SVM） and k-nearest neighbor（KNN）

RF method can effectively identify five fault categories in the actual operation of the hybrid tramway with the classification accuracy up to 94.4%.The result shows that RF method can obviously increases classification accuracy

which has great significance for further realizing the on-line fault diagnosis of high-power PEMFC systems.

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references

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