Damped Vibration Analysis of Extrinsic Fabry-Perot Interferometric Sensors using Artificial Neural Networks


Health monitoring of a structure entails regular strain sensing. Vibrational strain, characterized as functions of damped sinusoids, is a typical case of strain that can act on a structure. Past research has developed a demodulation technique, employing Artificial Neural Networks (ANN) as the processing element, for Extrinsic Fabry-Perot Interferometric (EFPI) sensors, attached to a vibrating structure, exposed to un-damped sinusoidal strain. The work employed two ANN to perform the demodulation. The first ANN was trained to extract the harmonic content from the EFPI modulated output and the second ANN was trained to predict the maximum strain acting, from the predicted harmonic content, during a vibration event. This project extends the study to a damped sinusoidal strain acting on the sensor. The ANN demodulation system predicts the maximum strain level from the spectral content of the sensor output, during a vibration event. Instead of employing an ANN to extract the spectral content, as done in the past research, simple Fast Fourier Transforms (FFT) is used. This paper develops the demodulation technique using computer simulations. Results are presented for different ANN architectures employed. An algorithm fusion system is presented that shows an improved accuracy in maximum strain prediction.

Meeting Name

2007 International Joint Conference on Neural Networks, IJCNN 2007 (2017: Aug. 12-17, Orlando, FL)


Electrical and Computer Engineering

Keywords and Phrases

Artificial intelligence; Computational methods; Computer networks; Computer simulation; Demodulation; Fabry-Perot interferometers; Fast Fourier transforms; Forecasting; Fourier transforms; Geodetic satellites; Industrial research; Interferometry; Lattice vibrations; Optical variables measurement; Project management; Sensor data fusion; Sensor networks; Sensors; Structural health monitoring; Vibrations (mechanical), Extrinsic Fabry-Perot interferometric sensors; Joint conference; Spectral contents, Neural networks

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International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version


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© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Aug 2007