Use of FBG sensors for SHM in aerospace structures

This paper details some significant findings on the use of the fiber Bragg grating (FBG) sensors for structural health monitoring (SHM) in aerospace fiber reinforced polymer (FRP) structures. A diminutive sensor provides a capability of imbedding inside FRP structures to monitor vital locations of damage. Some practical problems associated with the implementation of FBG based SHM systems in the aerospace FRP structures such as the difficulty of embedding FBG sensors during the manufacturing process and interrelation of distortion to FBG spectra due to internal damage, and other independent effects will be thoroughly studied. An innovative method to interpret FBG signals for identifying damage inside the structures will also be discussed

Indexing damage using distortion of embedded FBG sensor response spectra

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

Estimation of strain of distorted FBG sensor spectra using a fixed FBG filter circuit and an artificial neural network

Fibre Bragg Grating (FBG) sensors are extremely sensitive to changes of strain, and are therefore an extremely useful candidate for Structural Health Monitoring (SHM) systems of composite structures. Sensitivity of FBGs to strain gradients originating from damage was observed as an indicator of initiation and propagation of damage in composite structures. To date there have been numerous research works done on distorted FBG spectra due to damage accumulation under controlled environments. Unfortunately, a number of related unresolved problems remain in FBG-based SHM systems development, making the present SHM systems unsuitable for real life applications. This paper reveals a novel configuration of FBG sensors to acquire strain reading and an integrated statistical approach to analyse data in real time. The proposed configuration has proven its capability to overcome practical constraints and the engineering challenges associated with FBG-based SHM systems. A fixed filter decoding system and an integrated artificial neural network algorithm for extracting strain from embedded FBG sensor were proposed and experimentally proved. Furthermore, the laboratory level experimental data was used to verify the accuracy of the system and it was found that the error levels were less than 0.3% in strain predictions

Detecting delamination in a composite structure using an embedded FBG-AE hybrid system

Distorted spectra of Fibre Bragg Gratings (FBG) sensors have been using in most of research on identifying delaminations in composites which is the most common cause of failures of laminated composite structures. However, it has been shown that there are multiple causes which can produce a similar response spectrum of an FBG sensor. As a consequence an integrated additional monitoring system would enhance the prediction power of FBG based monitoring system. In this research paper we introduce a 'FBG-AE hybrid system' concept for the detection of delaminations in composite structures which uses same FBG sensor network for monitor damage using two independent responses from the sensors. The proposed system use spectral responses from FBG sensors and extract strain and acoustic emission data for monitoring purpose. The proposed concept has experimentally investigated with convincing results

Optimized FBG sensor network for efficient detection of a delamination in FRP structures

Delamination is a potential cause of failure of composite components. Due to the hidden nature of propagation, the detection of delaminations in composites is a time consuming and extremely difficult task. A few decades of research have shown the effectiveness of the embedded fibre Bragg grating (FBG) sensors to detect such damage in fibre reinforced polymeric (FRP) structures. However, a number of sensors are required to detect delaminations within a particular region of a composite structure due the limited receptive range of an FBG sensor. The complexity and the cost of manufacturing increases with the number of sensors attached and therefore, estimation of the optimum number of sensors for efficient identification of damage is an equally important factor to investigate. This paper details a study on optimization of the number of sensors used to monitor damage in a critical region of an FRP structure. A detailed finite element analysis (FEA) was used for the investigation. A delamination and several FBG sensors were simulated in FEA. The strain values at simulated FBG sensors were used as an input for the development of an optimization algorithm, using artificial neural network (ANN). The number of FBG sensors was decreased until the prediction of the algorithm was reached within a 0.1% error level. The optimal number of FBGs was taken at 0.1% error level with a minimum number of epoch. Furthermore, the effect of obsolete sensors of an optimized sensor network on prediction of the delamination, was also investigated

NIR fibre bragg grating as dynamic sensor: an application of 1D digital wavelet analysis for signal denoising

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

Prediction of obsolete FBG sensor using ANN for efficient and robust operation of SHM systems

Increased use of FRP composites for critical load bearing components and structures in recent years has raised the alarm for urgent need of a comprehensive health mentoring system to alert users about integrity and the health condition of advanced composite structures. A few decades of research and development work on structural health monitoring systems using Fibre Bragg Grating (FBG) sensors have come to an accelerated phase at the moment to address these demands in advanced composite industries. However, there are many unresolved problems with identification of damage status of composite structures using FBG spectra and many engineering challenges for implementation of such FBG based SHM system in real life situations. This paper details a research work that was conducted to address one of the critical problems of FBG network, the procedures for immediate rehabilitation of FBG sensor networks due to obsolete/broken sensors. In this study an artificial neural network (ANN) was developed and successfully deployed to virtually simulate the broken/obsolete sensors in a FBG sensor network. It has been found that the prediction of ANN network was within 0.1% error levels

An application of near infra-red fibre bragg grating as dynamic sensor in SHM of thin composite laminates

Vibration testing is an essential component in Structural Health Monitoring (SHM). It can provide vital information regarding the integrity of critical structure; for instance, it can provide information on progressive failure monitoring of composites structure in the aerospace industry. Over the past decade, there have been many successful researches showing extraordinary ability of Fiber Bragg grating (FBG) sensors as a dynamic sensor. Ability of acquiring both static and dynamic strain measurements, make FBG sensor as a good alternative to replace the conventional vibration sensors. In addition the physical size of FBG sensor provides greater access to embed them in composite structures without affecting to any properties of the composite. However, in most applications to date, people have used only the FBG with wavelength 1550 nm. Moreover, FBG sensors with this wavelength are commonly use in industries such as telecommunications and medical industries. However, there is an option of using near infra-red (NIR) FBG range which comparably cheap in term of total system design. This paper details the use of near infra-red (NIR) FBGs as dynamic sensors; a part of SHM system for the monitoring of the damages in a thin glass fiber composite plates. Results reveal that the NIR FBG range gives good response to an impact and; also to applied high frequency vibrations

Extraction and processing of real time strain of embedded FBG sensors using a fixed filter FBG circuit and an artificial neural network

Fibre Bragg Grating (FBG) sensors have been used in the development of structural health monitoring (SHM) and damage detection systems for advanced composite structures over several decades. Unfortunately, to date only a handful of appropriate configurations and algorithms are available for using in SHM systems have been developed. This paper reveals a novel configuration of FBG sensors to acquire strain reading and an integrated statistical approach to analyse data in real time. The proposed configuration has proven its capability to overcome practical constraints and the engineering challenges associated with FBG-based SHM systems. A fixed filter decoding system and an integrated artificial neural network algorithm for extracting strain from embedded FBG sensor were proposed and experimentally proved. Furthermore, the laboratory level experimental data was used to verify the accuracy of the system and it was found that the error levels were less than 0.3% in predictions. The developed SMH system using this technology has been submitted to US patent office and will be available for use of aerospace applications in due course

Earthquake Damage Repair Loss Estimation in New Zealand: What Other Variables Are Essential Based on Experts’ Opinions?

Major earthquakes can cause extensive damage to buildings and alter both the natural and built environments. Accurately estimating the financial impact from these events is complex, and the damage is not always visible to the naked eye. PACT, SLAT, and HAZUS are some of the computer-based tools designed to predict probable damage before an earthquake. However, there are no identifiable models built for post-earthquake use. This paper focuses on verifying the significance and usage of variables that specifically need to be considered for the post-earthquake cost estimation of earthquake damage repair work (CEEDRW). The research was conducted using a questionnaire survey involving 92 participants who have experience in cost estimating earthquake damage repair work in New Zealand. The Weighted Average, Relative Importance Index (RII), and Exploratory Factor Analysis were used to analyse the data. The research verified that eleven major variables that are significant to the CEEDRW and should be incorporated to cost estimation models. Verified variables can be used to develop a post-earthquake repair cost estimation tool and can be used to improve the pre-earthquake loss prediction tools