Initial evaluation of an active/passive structural neural system for health monitoring of composite materials

Abstract

Structural health monitoring is an underlying technology that can help to ensure safe operation and provide cost effective maintenance of advanced composite structures. While several general methods of health monitoring have evolved in recent years, there is still the goal of reducing the overall cost of applying health monitoring to large structures. Data acquisition hardware typically consumes most of the investment in a structural monitoring system. On a conventional system based on acoustic emission monitoring, a separate high sampling rate data acquisition channel is needed for each sensor to convert analog signals to digital signals to locate damage. Other methods of damage detection are likewise complicated, and require many sensors and actuators, auxiliary signal processing, and data storage instrumentation. This paper proposes a structural neural system that uses firing of sensor neurons to reduce the number of data acquisition channels needed for damage detection. The neural system can perform passive acoustic emission sensing or active wave propagation monitoring. A prototype structural neural system with four sensor inputs was built and tested, and experimental results are presented in the paper. One signal output from the structural neural system is used to predict the location of damage. A second signal provides the time domain response of the sensors. Therefore, passive and active health monitoring can be performed using two channels of data acquisition. The structural neural system significantly reduces the data acquisition hardware required for health monitoring, and combines some of the advantages that exist individually for passive and active health monitoring.