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An improved impedance-based damage classification using self-organizing maps

 
: Junior, P.O.; Conte, S.; D'Addona, D.M.; Aguiar, P.; Bapstista, F.

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Volltext ()

Procedia CIRP 88 (2020), S.330-334
ISSN: 2212-8271
Conference on Intelligent Computation in Manufacturing Engineering (ICME) <13, 2019, Gulf of Naples>
Englisch
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer J LEAPT ()

Abstract
The identification and severity of structural damages, especially in the early stage, is critical in structural health monitoring (SHM) systems. Among several approaches used to accomplish this goal, the electromechanical impedance (EMI) technique has taken place within nondestructive evaluation (NDE) methods. On the other hand, neural networks (NN) based on self-organizing maps (SOM) has been a promising tool in many engineering classification problems. However, there is a gap of application regarding the combination of the EMI technique and SOM NN. To encourage this, an enhanced EMI-based damage classification method using self-organizing features is proposed in the present research paper. A SOM NN architecture was implemented whose inputs were derived from representative features of the impedance signatures. As a result, self-organizing maps can be used as an effective tool to enhance the damage classification in EMI-based SHM applications. For the present application, the results indicated a promising and useful contribution to the grinding field.

: http://publica.fraunhofer.de/dokumente/N-602606.html