It has been proven that the embedment of optical fibers into a composite material could offer an alternative to robust piezoelectric transducers used for Acoustic Emission (AE) monitoring. In this configuration optical fibers are used as intensity-modulated sensors. A set of propagating elastic waves is generated whenever damage occurs in the composite material. These waves locally modify the optical and geometrical properties of the optical fiber and hence can be detected by them as a transient signal that modulates the light intensity. In this paper a method for detecting the transients by on-line signal processing is presented. It is then applied to optical signals resulting from tensile tests performed on CFRP composites material with embedded optical fibers. By means of the Short-Time Fourier Transform (STFT), the level of the noise added to the signal is estimated by filtering the time trajectories. This filter is continuously adapted according to the principle of minimization
of the mean squared error. Finally the detection is achieved by a constant false alarm rate power-law detector. This technique is fast and doesn't take into account neither the statistical distribution of the noise nor the frequency content of the transients as long as the frequency component distribution can be approximated by an exponential law. The detected transient features can be correlated with the AE results but an off-line analysis and classification is still needed.
An intensity modulated sensor, based on the microbending concept, has been incorporated in laminates produced from a C/epoxy prepreg. Pencil lead break tests (Hsu-Neilsen sources) and tensile tests have been performed on this material. In this research study, fibre optic sensors will be proven to offer an alternative for the robust piezoelectric transducers used for Acoustic Emission (AE) monitoring. The main emphasis has been put on the use of advanced signal processing techniques based on time-frequency analysis. The signal Short Time Fourier Transform (STFT) has been computed and several robust noise reduction algorithms, such as Wiener adaptive filtering, improved spectral subtraction filtering, and Singular Value Decomposition (SVD) -based filtering, have been applied. An energy and frequency -based detection criterion is put forward to detect transient signals that can be correlated with Modal Acoustic Emission (MAE) results and thus damage in the composite material. There is a strong indication that time-frequency analysis and the Hankel Total Least Squares (HTLS) method can also be used for damage characterization. This study shows that the signal from a quite simple microbend optical sensor contains information on the elastic energy released whenever damage is being introduced in the host material by mechanical loading. Robust algorithms can be used to retrieve and analyze this information.
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