| 纪泽源,许晓凡,孙伟,胡云涛,高远飞,邵夏静.融合多传感器的飞行器电缆网状态在线监测系统[J].电子测量与仪器学报,2024,38(3):77-85 |
| 融合多传感器的飞行器电缆网状态在线监测系统 |
| Multi-sensor monitoring system and health statusclassification for air-craft cable networks |
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| DOI： |
| 中文关键词: 电缆网 状态监测 传感器 神经网络 通信协议 |
| 英文关键词:cable network state monitoring sensors neural networks communication protocols |
| 基金项目: |
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| Author | Institution |
| Ji Zeyuan | Beijing Electro-mechanical Engineering Institute, Beijing 100074, China |
| Xu Xiaofan | Beijing Electro-mechanical Engineering Institute, Beijing 100074, China |
| Sun Wei | Beijing Electro-mechanical Engineering Institute, Beijing 100074, China |
| Hu Yuntao | Beijing Electro-mechanical Engineering Institute, Beijing 100074, China |
| Gao Yuanfei | Beijing Electro-mechanical Engineering Institute, Beijing 100074, China |
| Shao Xiajing | Beijing Electro-mechanical Engineering Institute, Beijing 100074, China |
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| 全文下载次数: 2281 |
| 中文摘要: |
| 飞行器电缆网承担着电气、信号和数据传输等关键职能。在飞行器超声速飞行过程中电缆网面临诸如高温、振动、电流过载和低气压等挑战，对飞行器电气系统的安全性和可靠性产生影响。本研究设计了电缆网多传感器监测系统和基于多传感器融合的电缆网健康状态监测算法。在监测系统中实现了电压、电流、温度、加速度和气压等数据采集、存储以及无线传输功能。算法在预处理阶段通过归一化的方式，综合考虑了高温、振动、电流过载和低气压等稳态和瞬态值对电缆网健康状态的影响，算法健康状态分类部分设计了多层分类网络对电缆网状态进行分类，在实际实验数据集与仿真数据集中，本文多层分类网络相比于SVM分类网络正确率平均提升6.4%,虚警率平均降低了77.2%；本文的多传感器监测算法相比于单通道监测算法，正确率有显著提升，对比实验结果验证了本文算法在电缆网健康状态分类任务中的有效性。实验结果表明，电缆网多传感器监测系统可以有效监测并识别飞行器电缆网的健康状态，为飞行器电气系统运行提供了有力保障。 |
| 英文摘要: |
| Aircraft cable networks play a crucial role in electrical, signal, and data transmission functions. During supersonic flight, aircraft cable networks face challenges such as high temperatures, vibrations, current overload, and low pressure, which affect the safety and reliability of the aircraft electrical system. This study designs a multi-sensor monitoring system for cable networks and a cable network health monitoring algorithm based on multi-sensor fusion. The monitoring system achieves functions including data collection, storage, and wireless transmission of voltage, current, temperature, acceleration, and pressure. In the preprocessing stage, the algorithm comprehensively considers the effects of steady-state and transient values such as high temperatures, vibrations, current overload, and low pressure on the health status of cable networks through normalization. For the health status classification part, a multi-layer classification network is designed to classify the cable network states. In both practical experimental datasets and simulated datasets, the multi-layer classification network in this study achieves an average increase in accuracy of 6.4% and a decrease in false alarm rate of 77.2% compared to the SVM classification network. Compared to single-channel monitoring algorithms, the multi-sensor monitoring algorithm in this study significantly improves accuracy. Experimental results validate the effectiveness of the algorithm in cable network health status classification tasks. The results indicate that the multi-sensor monitoring system for cable networks can effectively monitor and identify the health status of aircraft cable networks, providing strong assurance for the operation of aircraft electrical systems. |
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