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Data-driven fault diagnosis for PEMFC systems of hybrid tram based on deep learning
International Journal of Hydrogen Energy ( IF 7.2 ) Pub Date : 2020-04-03 , DOI: 10.1016/j.ijhydene.2020.03.035
Xuexia Zhang , Jingzhe Zhou , Weirong Chen

The running state of the hybrid tram and the service life of fuel cell stacks are related to the fault diagnosis strategy of the proton exchange membrane fuel cell (PEMFC) system. In order to accurately detect various fault types, a novel method is proposed to classify the different health states, which is composed of simulated annealing genetic algorithm fuzzy c-means clustering (SAGAFCM) and deep belief network (DBN) combined with synthetic minority over-sampling technique (SMOTE). Operation data generated by the tram are clustered by SAGAFCM algorithm, and valid data are selected as fault diagnosis samples which include the training sample and the test sample. However, the fault samples are usually unbalanced data. To reduce the influence of unbalanced data on the fault diagnosis accuracy, SMOTE is employed to form a new training sample by supplementing the data of the small sample. Then DBN is trained by the new training sample to obtain the fault diagnosis model. In this paper, the proposed method can well distinguish the four health states, which are high deionized water inlet temperature fault, hydrogen leakage fault, low air pressure fault and the normal state, with an accuracy of 99.97% for the training sample and 100% for the test sample.



中文翻译:

基于深度学习的混合有轨电车PEMFC系统数据驱动故障诊断

混合有轨电车的运行状态和燃料电池堆的使用寿命与质子交换膜燃料电池(PEMFC)系统的故障诊断策略有关。为了准确地检测各种故障类型,提出了一种新的方法来对不同的健康状态进行分类,该方法由模拟退火遗传算法模糊c均值聚类(SAGAFCM)和深度置信网络(DBN)结合综合少数族裔组成。采样技术(SMOTE)。电车产生的运行数据通过SAGAFCM算法进行聚类,并选择有效数据作为故障诊断样本,包括训练样本和测试样本。但是,故障样本通常是不平衡数据。为了减少不平衡数据对故障诊断准确性的影响,通过补充小样本的数据,SMOTE用于形成新的训练样本。然后,通过新的训练样本对DBN进行训练,以获得故障诊断模型。本文提出的方法能够很好地区分高去离子水入口温度故障,氢泄漏故障,低气压故障和正常状态这四个健康状态,训练样本的准确度为99.97%,准确度为100%用于测试样品。

更新日期:2020-04-03
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