A Machine Learning-Driven Wireless System for Structural Health Monitoring
This addresses proactive maintenance and safety for aircraft operators and manufacturers, but it is incremental as it integrates existing technologies like sensors and ML models into a new application.
The paper tackles structural health monitoring for carbon fiber reinforced polymer structures in aerospace by developing a wireless system with a deep neural network model, achieving a mean absolute error of 0.14 for predicting mechanical properties and data transmission latency under one second.
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.