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

Center for Quality Engineering and Failure Prevention, Department of Mechanical Engineering, CAT Building, RM 327, 2137 N.Sheridan Road, Evanston, IL 60208, Goutham.Kirikera{at}northwestern.edu

Vishal Shinde

195 Clarksville Rd, Physical Acoustics Corporation, Princeton Jn, NewJersey, 08550

Mark J. Schulz

Smart Structures Bio-Nanotechnology Laboratory (SSBNL), 408B Rhodes Hall Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH USA, 45221

Anindya Ghoshal

United Technology Research Center, 411, Silver Lane, MS 129-73 East Hartford, CT 06108, USA

Mannur J. Sundaresan

Intelligent Structures and Mechanisms Laboratory, Department of Mechanical Engineering, North Carolina A&T state University, Greensboro, NC 27411, USA

Randall J. Allemang

Structrual Dynamics Research Laboratory, 593 Rhodes Hall, Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH, 45221

Jong Won Lee

Korea Institute of Machinery and Materials, Daejeon, Korea

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 tracks the propagation of waves due to damage, and the second output channel provides the combined AE responses of the sensors. The data from these two channels are used to predict the location of damage and to qualitatively indicate the severity of the damage. Overall, this study shows that the SNS can detect damage growth in composites during operation of the structure, and the SNS architecture has the potential to tremendously simplify the AE technique for use in on-board SHM. Ten or more input neurons can be used, and still only two output channels are needed. Two levels of monitoring are possible using the SNS; a coarser SHM approach, or an on-board NDE approach. The SHM approach uses the SNS with a coarse grid of neurons to monitor and detect damage occurring in a general area during operation of the structure. The SNS will indicate where and when a more sensitive inspection is needed which can be done using ground-based NDE techniques. The on-board NDE approach uses the SNS with a fine coverage of neurons for highly sensitive NDE which continuously listens for damage and provides real-time processing and information about any damage in the structure and the performance limits and safety of the vehicle.

Key Words: structural neural system (SNS) • acoustic emission (AE) • structural health monitoring (SHM) • damage diagnostics and prognostics • real-time monitoring

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