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Fault diagnosis in a current sensor and its application to fault-tolerant control for an air supply subsystem of a 50 kW-Grade fuel cell engine

机译：电流传感器故障诊断及其在50千瓦级燃料电池发动机供气子系统的容错控制容错

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摘要

The safety, reliability and stability of air supply subsystems are still problems for the commercial applications of fuel cells; therefore, engine fault diagnosis and fault-tolerant control are essential to protect the fuel cell stack. In this study, a fault diagnosis and fault-tolerant control method based on artificial neural networks (ANNs) has been proposed. The offline ANN modification model was trained with a Levenberg-Marquardt (LM) algorithm based on other sensors' signals relevant to the current sensor of a 50 kW-grade fuel cell engine test bench. The output current was predicted via the ANN identification model according to other relevant sensors and compared with the sampled current sensor signal. The faults in the current sensor were detected immediately once the difference exceeded the given threshold value, and the invalid signals of the current sensor were substituted with the predictive output value of the ANN identification model. Finally, the reconstructed current sensor signals were sent back to a fuel cell controller unit (FCU) to adjust the air flow and rotate speeds of the air compressor. Experimental results show that the typical faults in the current sensor can be diagnosed and distinguished within 0.5 s when the threshold value is 15 A. The invalid signal of current sensor can be reconstructed within 0.1 s. Which ensures that the air compressor operate normally and avoids oxygen starvation. The proposed method can protect the fuel cell stack and enhance the fault-tolerant performance of air supply subsystem used in the fuel cell engine, and it is promising to be utilized in the fault diagnosis and fault-tolerant control of various fuel cell engines and multiple sensor systems.
机译：的安全性，可靠性和供气子系统的稳定性是用于燃料电池的商业应用仍然存在问题;因此，发动机的故障诊断和容错控制是必不可少的，以保护燃料电池堆。在这项研究中，基于人工神经网络（人工神经网络）的故障诊断与容错控制方法已经被提出。脱机ANN修改模型用基于相关的一个50千瓦级燃料电池发动机试验台的所述电流传感器的其他传感器的信号的列文伯格 - 马夸尔特（LM）算法来训练。输出电流根据其他相关的传感器经由ANN的识别模型预测，并与所采样的电流传感器信号进行比较。一旦差超过所述给定阈值的电流传感器的故障被立即检测到，并且该电流传感器的无效信号所取代与ANN的识别模型的预测输出值。最后，重建的当前传感器信号被送回到燃料电池控制器单元（FCU），以调节所述空气压缩机的空气流量和旋转速度。实验结果表明，在电流传感器的典型故障可以被诊断和在0.5秒内，当阈值是15 A.电流传感器的无效信号可以0.1秒内被重建区分。这确保空气压缩机正常运转，并且避免了氧饥饿。所提出的方法可以保护燃料电池堆和提高燃料电池发动机用空气供给子系统的容错性能，并且它是有前途的故障诊断中利用和容错各种燃料电池发动机和多个控制传感器系统。

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  • 来源
    《RSC Advances》 |2020年第9期|共10页
  • 作者

    Quan Rui; Wu Fan; Wang Chengji; Tan Baohua; Chang Yufang;

  • 作者单位

    Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan 430068 Peoples R China;

    Hubei Univ Technol Agr Mech Engn Res &

    Design Inst Wuhan 430068 Peoples R China;

    Hubei Univ Technol Agr Mech Engn Res &

    Design Inst Wuhan 430068 Peoples R China;

    Hubei Univ Technol Sch Sci Wuhan 430068 Peoples R China;

    Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan 430068 Peoples R China;

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  • 正文语种 eng
  • 中图分类 化学;
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