Entry-Exit Water Perception and Damage Monitoring of Rotors for Cross-Media Vehicle
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摘要: 跨介质航行器旋翼在介质转换过程中, 因介质物理参数突变面临复杂瞬时应变问题, 长期交变载荷易引发桨叶微裂纹萌生、扩展甚至断裂, 严重威胁航行器运行可靠性与任务连续性。为解决旋翼出入水状态感知及损伤监测核心难题, 文中构建“信号采集-处理-特征提取-模型训练-状态感知”的完整技术体系: 实验装置通过导轨控制旋翼驱动结构出入水, 利用光纤光栅解调仪实时采集旋翼出入水全过程信号, 选取奇异点剔除法识别并剔除异常数据点, 完整保留信号入水、水中及出水特征, 通过图像转换保留时间序列的时间戳与幅度信息, 增强时间相关性特征并消除冗余, 而后将特征信号和转换图像用于模型训练。结果表明, 训练模型实现了跨介质航行器旋翼出入水状态的精准识别与实时损伤预警, 为航行器航线调整提供决策支持, 有效提升其智能感知与突防能力, 同时推动光学监测技术在跨介质航行器领域的深入应用。Abstract: In the process of medium conversion, the rotor of cross-medium vehicle faces complex instantaneous strain problems due to the sudden change of medium physical parameters. Long-term alternating load can easily lead to the initiation, propagation and even fracture of blade microcracks, which seriously threatens the operational reliability and task continuity of the vehicle. In order to solve the core problems of rotor inlet and outlet water state perception and damage monitoring, this paper constructs a complete technical system of ' signal acquisition-processing-feature extraction-model training-state perception ': The experimental device controls the inlet and outlet water of the rotor drive structure through the guide rail, and uses the fiber grating demodulator to collect the whole process signal of the rotor inlet and outlet water in real time. The singular point elimination method is selected to identify and eliminate the abnormal data points, and the characteristics of the signal entering water, water and outlet water are completely retained. The timestamp and amplitude information of the time series are retained by image conversion, and the time correlation characteristics are enhanced and the redundancy is eliminated. Then the feature signal and the converted image are used for model training. The results show that the training model realizes the accurate identification and real-time damage warning of the rotor inlet and outlet water state of the cross-media vehicle, provides decision support for the route adjustment of the vehicle, effectively improves its intelligent perception and penetration ability, and promotes the in-depth application of optical monitoring technology in the field of new weapon equipment and national defense.
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图 4 突变点检测法处理非定常扰动
Figure 4. The mutation point detection method deals with unsteady disturbances
图 5 奇异值剔除法处理非定常扰动
Figure 5. Singular value elimination method to deal with unsteady disturbance
图 7 奇异值剔除法处理非定常扰动结果
Figure 7. The singular value elimination method is used to deal with the unsteady disturbance results
图 10 无损伤旋翼特征信号二维图像转换
Figure 10. Two-dimensional image conversion of non-destructive rotor characteristic signal
图 11 损伤旋翼特征信号二维图像转换
Figure 11. Two-dimensional image conversion of damage rotor characteristic signal
图 14 ResNet-18模型下旋翼训练/验证损失曲线和准确率曲线
Figure 14. Rotor training/verification loss curve and accuracy curve under ResNet-18 model
图 15 VGG16模型下旋翼训练/验证损失曲线和准确率曲线
Figure 15. Rotor training/verification loss curve and accuracy curve under VGG16 model
图 16 290 r/min转速下无损伤旋翼监测结果及数据
Figure 16. Monitoring results and data of non-destructive rotor at 290 r/min speed
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