Structural Health Monitoring Systems based on embedded FBG sensors, to identify damage conditions, are largely dependent on the spectral distortion of the sensors. The uneven stress gradient occurring along the grating of FBG sensors, due to damage inside composite structures can be estimated by analysing significant changes that appear in the FBG response spectra. However, the stochastic nature of the distorted shape of the FBG spectra makes it difficult to interpret and quantify the existing damage at the location of the FBG sensors. There are several indexing methods proposed by researchers. We have previously presented a novel concept of the “Distortion Index (DI)” which is defined using distorted spectra of FBG sensors. It was observed that the DI increases with the increase in damage size. The Distortion Index (DI) is introduced to create a correlation between the damage and the distortion of the response spectra of a FBG sensor. This index provides the ability to generalise the distortion of FBG spectra for a particular structure. The index can be used to quantify the damage in the structure relative to its original condition, which can be the condition of structure during a regulated time, i.e. a month uninterrupted operation or first hours in operation, of a structure can be used as no damage condition. In this paper we discuss the application of distortion index and comparison with available several other indexes.
During the past decade, many successful studies have evidently shown remarkable capability of Fiber Bragg Gratings (FBG) sensor for dynamic sensing. Most of the research works utilized the 1550 nm wavelength range of FBG sensors. However near infra-red (NIR) FBG sensors can offer the lower cost of Structural health Monitoring (SHM) systems which uses cheaper silicon sources and detectors. Unfortunately, the excessive noise levels that experienced in NIR wavelengths have caused the rejection of sensor that operating in this range of wavelengths for SHM systems. However, with the appropriate use of signal processing tools, these noisy signals can be easily ‘cleaned’. Wavelet analysis is one of the powerful signal processing tools nowadays, not only for time-frequency analysis but also for signal denoising. This present study revealed that the NIR FBG range gave good response to impact signals. Furthermore, these ‘noisy’ signals’ response were successfully filtered using one dimensional wavelet analysis.