Processing signals for damage detection in structures using neural networks

Julian E. Chance; Keith Worden; Geoffrey R. Tomlinson

doi:10.1117/12.173946

1 May 1994 Processing signals for damage detection in structures using neural networks

Julian E. Chance, Keith Worden, Geoffrey R. Tomlinson

Proceedings Volume 2191, Smart Structures and Materials 1994: Smart Sensing, Processing, and Instrumentation; (1994) https://doi.org/10.1117/12.173946
Event: 1994 North American Conference on Smart Structures and Materials, 1994, Orlando, FL, United States

Abstract

The effective use of neural networks for fault detection, location, and classification requires training data. Distinguishing features in the data can be enhanced by pre-processing. Feature vectors that often prove advantageous in training networks for fault detection are modeshapes and curvatures; however, the procedures and sensors used to determine these can introduce problems. This paper uses numerical and practical experiments to investigate the use of acceleration, displacement, and strain response signals to extract modeshape and curvature functions from a cantilever plate and beam with localized damage. The importance of spatial accuracy, noise, and fault severity for fault detection is studied. It is shown that limiting spatial conditions occur with direct dynamic displacement measurements that have to be differentiated to obtain curvatures that can be overcome by using strain gauges that directly return quantities proportional to the curvature.

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Julian E. Chance, Keith Worden, and Geoffrey R. Tomlinson "Processing signals for damage detection in structures using neural networks", Proc. SPIE 2191, Smart Structures and Materials 1994: Smart Sensing, Processing, and Instrumentation, (1 May 1994); https://doi.org/10.1117/12.173946

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