为提升飞轮的可靠性, 本文对飞轮故障诊断技术进行了研究。通过对基于数学解析模型与基于智能计算的故障诊断方法的对比研究, 提出了一种基于神经网络的混合故障诊断方法。该方法首先使用数学解析模型与原系统输出的差值作为一级残差; 而后利用该一级残差以及系统可测状态对神经网络进行训练; 然后使用混合模型输出的二级残差对系统故障进行检测; 最后以飞轮注入母线电压以及电枢电流故障对该方法进行验证: 在存在母线电压故障工况下混合模型避免了解析模型电流估计的发散问题, 与单神经网络模型相比最大跟踪误差降低了44%。在存在电流故障时, 不同的转速工况下与两种单模型相比混合模型的最大跟踪误差降低了90%, 跟踪方差减小了10倍以上。混合方法可以有效解决由于解析模型存在建模误差引起的故障诊断不够准确的问题以及由于缺乏训练数据所引起的单神经网络模型不能适应新工况的故障诊断问题。

In order to improve flywheel reliability, flywheel fault diagnosis technology was studied. A hybrid fault diagnosis method based on a neural network was proposed, which compares the mathematical analysis model with the flywheel fault diagnosis based on intelligent computing. In this method, the difference between the mathematical model and the original system output was used as the first-order residual. Then, the first-order residual and the system measurements were used to train the neural network. Finally, the second-order residual of the mixed model output was used to detect the system fault. This method was validated using the flywheel injection bus voltage and armature current faults. Under the bus voltage fault working conditions, the hybrid model avoided the divergence problem of current estimation because of the analytical model, which reduced the maximum tracking error by 44% compared with a single neural network model. Under the current fault working conditions, the maximum tracking error of the hybrid model was reduced by 90% and the tracking variance was reduced by more than 10 times under different speed conditions compared with two single neural network models. These results illustrate that the hybrid method can effectively solve the problem of inaccurate fault diagnosis due to the existence of modeling errors in the analytical model, as well as the problem of a single neural network model being unable to adapt to fault diagnosis corresponding to new working conditions because of the lack of training data.