多传感器信息融合的天线促动器故障诊断

促动器作为天线主动面的唯一调整装置，是保障天线反射面精度的关键部件，因此对促动器健康状态的监测至关重要。文中针对单个传感器诊断存在数据维度有限且现有工程数据稀缺的问题，提出了一种连续小波变换与组归一化并行卷积神经网络(Continuous Wavelet Transform-Group Normalization Parallel Convolutional Neural Networks, CWT-GPCNN）的故障诊断方法。首先建立CWT-GPCNN的故障诊断模型，引用组归一化技术加快网络收敛速度并提高诊断精度；然后通过评估超参数对模型性能的影响，确定诊断的最佳模型；最后，采用促动器传动系统实验数据集对所提方法进行验证，实验结果表明所建模型具有较好的泛化能力及多传感器融合的优越性。文中对多传感器融合与单传感器的诊断性能进行了比较，结果证明了多传感器融合诊断的优越性。此外，还对CWT-GPCNN模型与其他3种信息融合模型进行了比较。CWT-GPCNN模型的准确率高达93%，表明它具有良好的诊断性能。

Fault Diagnosis of Antenna Actuator Based on Multi-sensor Information Fusion

The Actuator, as the sole adjustment device for the antenna active surface, is the key part to ensure the precision of antenna reflector. Therefore, monitoring the health status of the actuator is crucial. Aiming at the limitations of single sensor diagnosis due to limited data dimensions and scarce engineering data, a fault diagnosis method based on continuous wavelet transform and group normalization parallel convolutional neural network (CWT-GPCNN) is proposed in this paper. Firstly, an actuator fault diagnosis model integrating CWT-GPCNN is established and the group normalization technology is adopted to quicken net convergence and improve diagnosis accuracy. Then, the optimal model is determined by analyzing the impact of network hyperparameters on model performance. Finally, the proposed method is validated using a dataset from an actuator transmission system experiment. The result demonstrates that this model has good generalization capabilities and superiority of multi-sensor fusion. Comparison between multi-sensor fusion and single sensor diagnosis demonstrates the superior performance of the former. The CWT-GPCNN model is compared with three other information fusion models. The CWT-GPCNN model achieves an accuracy rate of up to 93%, which indicates its excellent diagnosis performance.