Abstract

As carbon fiber reinforced polymer composites (CFRPs) are multifunctional materials in which the damage is coupled with the change in material electrical resistance, the use of electrical conductivity monitoring can provide real-time information concerning the damage state through examining the change in resistance/electric potential. It has been shown that, in some recent work, resistance measurement allows the monitoring of the in-situ evolution of various internal damage nucleation and growth phenomena such as fiber fractures, interply matrix cracks and interply delamination. However, one of the common difficulties in measuring the small changes of electrical signals due to failures in the CFRPs is the contamination of extraneous noises during the detection process.

In this paper, signal analysis procedures are applied to minimize or eliminate the contaminated noise during the damage detection process, as to maximize the accuracy of the damage detection process. In addition to the traditional time signal averaging technique, the Fast Fourier Transform (FFT) is applied to filter at the narrow band near the input frequency as to eliminate the unwanted noises. However, based on laboratory experiments, the authors have found that the frequency filtering techniques will retain significant inaccuracy when extraneous noises are in the neighborhood of the input frequency, and, in some cases, accurate measurement of the electrical signals can be susceptable when noise components are significantly higher than the original input signal. To overcome this drawback, the use of a joint time-frequency technique with wavelet transforms has been introduced. As a joint time-frequency signal analysis tool, the wavelet transforms offer simultaneous interpretation of the signal in both time and frequency domains which results in signal filtering effects in both time and frequency domains. Experimental results by the authors have shown significant improvement in accuracy when using the wavelets than those of the FFT and the time averaging techniques. This paper demonstrates the wavelet transform applications in electrical signal noise reduction of CFRPs as well as the use of both continuous wavelet (CWT) and discrete wavelet (DWT) transforms. It has been found that when noise is small, the DWT is more appropriate as the original signal can be reconstructed to offer better comparative information. The use of CWT will provide better results when random noises are significantly higher. Although the use of CWT does not have the ability revert back to the original signal, the comparison of the CWT coefficients can offer more valuable information concerning the damage occurrences while the repeated applications of DWT will result in accumulated errors such that the original shapes of the wavelet cannot be correctly reconstructed.

This work is licensed under a Creative Commons Attribution 4.0 License.