In this paper, the influence of the integration of several sensors in composite structures is investigated. The plates and the structures in simple shapes, composed of laminated and sandwich materials, are considered. The mechanical behaviour, the acoustics activity and the location of damage sources in various structures with and without piezoelectric implant are compared. The analysis of results allowed a better identification of the influence of the impact of piezoelectric implant on the mechanical behaviour of different structures under different loads. Then, the analysis and the observation of Acoustic Emission (AE) signals led to the identification of the main acoustic signatures of different damage modes dominant in each type of composite materials (laminates and sandwich). Viewpoint comparison between integrated and non-integrated structures, acoustic activity is more significant in the case of integrated material. The location of the sources of damage has shown that acoustic events occurred far from the positions of integrated sensors.

Use of piezoelectric as acoustic emission sensor for in situ monitoring of composite structures

A prototype structural neural system (SNS) is tested for the first time and damage detection results are presented in this study. The SNS is a passive online structural health monitoring (SHM) system that mimics the synaptic parallel computation networks present in the human biological neural system. Piezoelectric ceramic sensors and analog electronics are used to form neurons that measure strain waves generated by damage. The sensing of strain waves is similar to the proven nondestructive evaluation (NDE) technique of acoustic emission (AE) monitoring. Fatigue testing of a composite specimen on a four-point bending fiXture is performed, and the SNS is used to monitor the specimen for damage in real time. The prototype SNS used four sensors as inputs, but the number of inputs can be in the tens or hundreds depending on the type of SNS processor used. This is an area of continuing development. The SNS has two channels of signal output that are digitized and processed in a computer. The first output channel...

https://journals.sagepub.com/doi/pdf/10.1177/1475921707081971

A Structural Neural System for Real-time Health Monitoring of Composite Materials

With the discovery in plants of the proteinaceous forisome crystalloid (Knoblauch, et al. 2003), a novel, non-living, ATP-independent biological material became available to the designer of smart materials for advanced actuating and sensing. The in vitro studies of Knoblauch, et al. show that forisomes (2-4 micron wide and 10-40 micron long) can be repeatedly stimulated to contract and expand anisotropically by shifting either the ambient pH or the ambient calcium ion concentration. Because of their unique abilities to develop and reverse strains greater than 20% in time periods less than one second, forisomes have the potential to outperform current smart materials as advanced, biomimetic, multi-functional, smart sensors or actuators. Probing forisome material properties is an immediate need to lay the foundation for synthesizing forisome-based smart materials for health monitoring of structural integrity in civil infrastructure and for aerospace hardware. Microfluidics is a growing, vibrant technology with increasingly diverse applications. Here, we use microfluidics to study the surface interaction between forisome and substrate and the conformational dynamics of forisomes within a confined geometry to lay the foundation for forisome-based smart materials synthesis in controlled and repeatable environment.

Forisome Based Biomimetic Smart Materials

A distributed real time health monitoring system is described for monitoring of acoustic emission signals from different regions of a structure such as aircraft or spacecraft structures. The health monitoring system has its analysis and prognosis intelligence distributed out to the local regions being monitored and therefore does not require extensive cabling systems to carry the high bandwidth information characteristic of acoustic emission.

Distributed mode system for real time acoustic emission monitoring

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.

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

This paper discusses the modeling of acoustic emissions in plate structures and their sensing by embedded or surface bonded piezoelectric sensor arrays. Three different modeling efforts for acoustic emission (AE) wave generation and propagation are discussed briefly along with their advantages and disadvantages. Continuous sensors placed at right angles on a plate are being discussed as a new approach to measure and locate the source of acoustic waves. Evolutionary novel signal processing algorithms and bio-inspired distributed sensor array systems are used on large structures and integrated aerospace vehicles for AE source localization and preliminary results are presented. These systems allow for a great reduction in the amount of data that needs to be processed and also reduce the chances of false alarms from ambient noises. It is envisioned that these biomimetic sensor arrays and signal processing techniques will be useful for both wireless and wired sensor arrays for real time health monitoring of large integrated aerospace vehicles and earth fixed civil structures. The sensor array architectures can also be used with other types of sensors and for other applications.

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Concepts and Development of Bio-Inspired Distributed Embedded Wired/Wireless Sensor Array Architectures for Acoustic Wave Sensing in Integrated Aerospace Vehicles

Derke J. Hughes

Papers

Concepts and Development of Bio-Inspired Distributed Embedded Wired/Wireless Sensor Array Architectures for Acoustic Wave Sensing in Integrated Aerospace Vehicles