用机器学习实现碳纤维结构的无线实时健康监测
A Machine Learning-Driven Wireless System for Structural Health Monitoring
- 嵌入碳纳米管传感器，无线采集数据并上传至云端
- 深度神经网络预测力学性能，测试误差仅0.14（MAE）
- 系统延迟低于1秒，适合航空等实时监控场景
本文提出一种集成机器学习模型的无线系统，用于碳纤维增强聚合物（CFRP）结构的健康监测，主要面向航空航天应用。系统通过嵌入在CFRP试样中的碳纳米管（CNT）压阻传感器采集数据，并无线传输至中心服务器进行处理。采用深度神经网络（DNN）模型预测材料力学性能，可扩展用于结构失效预警，支持主动维护并提升安全性。系统采用模块化设计，可嵌入数字孪生框架，为航空公司和制造商带来显著优势。该模型在测试数据上的平均绝对误差（MAE）为0.14。在局域网环境下，整个系统的数据传输延迟小于1秒，凸显其在航空航天及其他行业实时监测中的潜力。然而，传感器在极端环境下的可靠性以及处理多源数据流所需的先进机器学习模型仍是未来研究重点。
原文摘要 · Abstract (English)
The paper presents a wireless system integrated with a machine learning (ML) model for structural health monitoring (SHM) of carbon fiber reinforced polymer (CFRP) structures, primarily targeting aerospace applications. The system collects data via carbon nanotube (CNT) piezoresistive sensors embedded within CFRP coupons, wirelessly transmitting these data to a central server for processing. A deep neural network (DNN) model predicts mechanical properties and can be extended to forecast structural failures, facilitating proactive maintenance and enhancing safety. The modular design supports scalability and can be embedded within digital twin frameworks, offering significant benefits to aircraft operators and manufacturers. The system utilizes an ML model with a mean absolute error (MAE) of 0.14 on test data for forecasting mechanical properties. Data transmission latency throughout the entire system is less than one second in a LAN setup, highlighting its potential for real-time monitoring applications in aerospace and other industries. However, while the system shows promise, challenges such as sensor reliability under extreme environmental conditions and the need for advanced ML models to handle diverse data streams have been identified as areas for future research.
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