Quan Rui, Wu Fan, Wang Chengji, Tan Baohua, Chang Yufang
Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology Wuhan 430068 China
Agricultural Mechanical Engineering Research and Design Institute, Hubei University of Technology Wuhan 430068 China.
RSC Adv. 2020 Jan 31;10(9):5163-5172. doi: 10.1039/c9ra09884d. eCollection 2020 Jan 29.
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 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.
空气供应子系统的安全性、可靠性和稳定性仍然是燃料电池商业应用面临的问题；因此，发动机故障诊断和容错控制对于保护燃料电池堆至关重要。在本研究中，提出了一种基于人工神经网络（ANN）的故障诊断和容错控制方法。基于50kW级燃料电池发动机试验台当前传感器的其他相关传感器信号，采用Levenberg-Marquardt（LM）算法训练离线ANN修正模型。根据其他相关传感器，通过ANN识别模型预测输出电流，并与采样的电流传感器信号进行比较。一旦差值超过给定阈值，立即检测出电流传感器中的故障，并用ANN识别模型的预测输出值替换电流传感器的无效信号。最后，将重构后的电流传感器信号发送回燃料电池控制器单元（FCU），以调节空气流量和空气压缩机的转速。实验结果表明，当阈值为15A时，电流传感器中的典型故障可在0.5s内被诊断和区分。电流传感器的无效信号可在0.1s内重构。这确保了空气压缩机正常运行，避免了氧气不足。所提出的方法可以保护燃料电池堆，提高燃料电池发动机中空气供应子系统的容错性能，有望应用于各种燃料电池发动机和多传感器系统的故障诊断和容错控制。
