A Machine Learning-Driven Wireless System for Structural Health Monitoring

TL;DR

This paper introduces a wireless structural health monitoring system using embedded CNT sensors and deep learning to predict mechanical properties and potential failures in aerospace CFRP structures, enabling real-time, scalable maintenance solutions.

Contribution

The work presents a novel integrated wireless SHM system with embedded sensors and a deep neural network for accurate, real-time structural assessment in aerospace applications.

Findings

Mean absolute error of 0.14 in property prediction

Wireless data transmission latency under one second

System demonstrates potential for real-time aerospace monitoring

Abstract

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.