Polarization-independent high-resolution spectral interrogation of FBGs using a BFBG-CCD array for optical sensing applications

Optical fibre strain sensors using Fibre Bragg Gratings (FBGs) are poised to play a major role in structural health monitoring in a variety of application from aerospace to civil engineering. At the heart of technology is the optoelectronic instrumentation required to convert optical signals into measurands. Users are demanding compact, lightweight, rugged and low cost solutions. This paper describes development of a new device based on a blazed FBG and CCD array that can potentially meet the above demands. We have shown that this very low cost technique may be used to interrogate a WDM array of sensor gratings with highly accurate and highly repeatable results unaffected by the polarisation state of the radiation. In this paper, we present results showing that sensors may be interrogated with an RMS error of 1.7pm, drift below 0.12pm and dynamic range of up to 65nm.

Polarization-independent high-resolution spectral interrogation of FBGs using a BFBG-CCD array for optical sensing applications

Optical fibre strain sensors using Fibre Bragg Gratings (FBGs) are poised to play a major role in structural health monitoring in a variety of application from aerospace to civil engineering. At the heart of technology is the optoelectronic instrumentation required to convert optical signals into measurands. Users are demanding compact, lightweight, rugged and low cost solutions. This paper describes development of a new device based on a blazed FBG and CCD array that can potentially meet the above demands. We have shown that this very low cost technique may be used to interrogate a WDM array of sensor gratings with highly accurate and highly repeatable results unaffected by the polarisation state of the radiation. In this paper, we present results showing that sensors may be interrogated with an RMS error of 1.7pm, drift below 0.12pm and dynamic range of up to 65nm.

Polymer fibre Bragg gratings and sensing

Polymer FBGs have advantages for sensing because of low Young's modulus, high temperature sensitivity, large strain range and so on. They are attractive for many niche applications such as structural health monitoring of composite materials, biochemical and biomedical sensing. While polymer FBGs have been developed for some time, polymer microfibre Bragg gratings are developed only recently and have shown to introduce some interesting features, e.g. increased pressure sensitivity to pressure / force and improved response time to humidity. We will report and discuss the recent work on polymer FBG and polymer microfibre Bragg gratings as well as their applications such as accelerometer, humidity sensor and force and pressure sensor. © 2015 OSA.

Studio di un sistema di comunicazione ad alimentazione risonante wireless.

L'elaborato tratta della progettazione di un sistema di alimentazione wireless risonante per i nodi sensori, strumenti fondamentali per il controllo delle strutture(Structural Health Monitoring). Esso si concentra sulla realizzazione di un convertitore flyback risonante (con circuito di snubber incluso per il main switch) in grado di fornire una tensione di 5 Volt in uscita a fronte di una corrente media massima sul carico di 800mA data una tensione di 12 volt in ingresso. Dopo aver introdotto il concetto di Wireless Power Transfer (WPT) e i principi fisici su cui esso poggia (induzione elettromagnetica e risonanza elettromagnetica), si presentano i modelli circuitali più utilizzati in questo ambito. Una volta illustrate le conoscenze allo stato dell'arte dell'accoppiamento induttivo risonante, si analizza il comportamento del modello scelto, al fine di evidenziare i vantaggi dell'utilizzo del circuito alla frequenza di risonanza. Sono state effettuate simulazioni con il simulatore LTspice come controprova. Si passa quindi a dimensionare i vari elementi del circuito a fronte delle specifiche stabilite. Grazie ai risultati ottenuti, si procede alla stesura del Bill Of Materials. La tesi si conclude presentando i possibili campi di ricerca e sviluppo del sistema di alimentazione.

Tracing the behaviour of a bridge using time series modelling

Peer reviewed

A seasonal ARIMAX time series model for strain-temperature relationship in an instrumented bridge

Peer reviewed

Improved damage detectability in a wind turbine blade by optimal selection of vibration signal correlation coefficients

Piotr Omenzetter and Simon Hoell’s work within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research.

Improved image analysis based corrosion assessment using preprocessing techniques

The importance of non-destructive techniques (NDT) in structural health monitoring programmes is being critically felt in the recent times. The quality of the measured data, often affected by various environmental conditions can be a guiding factor in terms usefulness and prediction efficiencies of the various detection and monitoring methods used in this regard. Often, a preprocessing of the acquired data in relation to the affecting environmental parameters can improve the information quality and lead towards a significantly more efficient and correct prediction process. The improvement can be directly related to the final decision making policy about a structure or a network of structures and is compatible with general probabilistic frameworks of such assessment and decision making programmes. This paper considers a preprocessing technique employed for an image analysis based structural health monitoring methodology to identify sub-marine pitting corrosion in the presence of variable luminosity, contrast and noise affecting the quality of images. A preprocessing of the gray-level threshold of the various images is observed to bring about a significant improvement in terms of damage detection as compared to an automatically computed gray-level threshold. The case dependent adjustments of the threshold enable to obtain the best possible information from an existing image. The corresponding improvements are observed in a qualitative manner in the present study.

A field experiment on a steel Gerber-truss bridge for damage detection utilizing vehicle-induced vibrations

A field experiment was conducted on a real continuous steel Gerber-truss bridge with artificial damage applied. This article summarizes the results of the experiment for bridge damage detection utilizing traffic-induced vibrations. It investigates the sensitivities of a number of quantities to bridge damage including the identified modal parameters and their statistical patterns, Nair’s damage indicator and its statistical pattern and different sets of measurement points. The modal parameters are identified by autoregressive time-series models. The decision on bridge health condition is made and the sensitivity of variables is evaluated with the aid of the Mahalanobis–Taguchi system, a multivariate pattern recognition tool. Several observations are made as follows. For the modal parameters, although bridge damage detection can be achieved by performing Mahalanobis–Taguchi system on certain modal parameters of certain sets of measurement points, difficulties were faced in subjective selection of meaningful bridge modes and low sensitivity of the statistical pattern of the modal parameters to damage. For Nair’s damage indicator, bridge damage detection could be achieved by performing Mahalanobis–Taguchi system on Nair’s damage indicators of most sets of measurement points. As a damage indicator, Nair’s damage indicator was superior to the modal parameters. Three main advantages were observed: it does not require any subjective decision in calculating Nair’s damage indicator, thus potential human errors can be prevented and an automatic detection task can be achieved; its statistical pattern has high sensitivity to damage and, finally, it is flexible regarding the choice of sets of measurement points.

Optical sensing of the fatigue damage state of CFRP under realistic aeronautical load sequences

Abstract: We present an optical sensing methodology to estimate the fatigue damage stateof structures made of carbon fiber reinforced polymer (CFRP), by measuring variations on the surface roughness. Variable amplitude loads (VAL), which represent realistic loads during aeronautical missions of fighter aircraft (FALSTAFF) have been applied to coupons until failure. Stiffness degradation and surface roughness variations have been measured during the life of the coupons obtaining a Pearson correlation of 0.75 between both variables. The data were compared with a previous study for Constant Amplitude Load (CAL) obtaining similar results. Conclusions suggest that the surface roughness measured in strategic zones is a useful technique for structural health monitoring of CFRP structures, and that it is independent of the type of load applied. Surface roughness can be measured in the field by optical techniques such as speckle, confocal perfilometers and interferometry, among others.

Hardware-software design of embedded systems for intelligent sensing applications

This Thesis wants to highlight the importance of ad-hoc designed and developed embedded systems in the implementation of intelligent sensor networks. As evidence four areas of application are presented: Precision Agriculture, Bioengineering, Automotive and Structural Health Monitoring. For each field is reported one, or more, smart device design and developing, in addition to on-board elaborations, experimental validation and in field tests. In particular, it is presented the design and development of a fruit meter. In the bioengineering field, three different projects are reported, detailing the architectures implemented and the validation tests conducted. Two prototype realizations of an inner temperature measurement system in electric motors for an automotive application are then discussed. Lastly, the HW/SW design of a Smart Sensor Network is analyzed: the network features on-board data management and processing, integration in an IoT toolchain, Wireless Sensor Network developments and an AI framework for vibration-based structural assessment.

Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.

Monitoring of Bridges using Computer Vision Methods

This paper is an overview of the development and application of Computer Vision for the Structural Health
Monitoring (SHM) of Bridges. A brief explanation of SHM is provided, followed by a breakdown of the stages of computer
vision techniques separated into laboratory and field trials. Qualitative evaluations and comparison of these methods have been
provided along with the proposal of guidelines for new vision-based SHM systems.

Structural FRF acquisition via electric impedance measurement applied to damage location

Continuing development of new materials makes systems lighter and stronger permitting more complex systems to provide more functionality and flexibility that demands a more effective evaluation of their structural health. Smart material technology has become an area of increasing interest in this field. The combination of smart materials and artificial neural networks can be used as an excellent tool for pattern recognition, turning their application adequate for monitoring and fault classification of equipment and structures. In order to identify the fault, the neural network must be trained using a set of solutions to its corresponding forward Variational problem. After the training process, the net can successfully solve the inverse variational problem in the context of monitoring and fault detection because of their pattern recognition and interpolation capabilities. The use of structural frequency response function is a fundamental portion of structural dynamic analysis, and it can be extracted from measured electric impedance through the electromechanical interaction of a piezoceramic and a structure. In this paper we use the FRF obtained by a mathematical model (FEM) in order to generate the training data for the neural networks, and the identification of damage can be done by measuring electric impedance, since suitable data normalization correlates FRF and electrical impedance.

[Health Status of Young Men in Switzerland: Monitoring Results from Conscription].

Abstract Due to their representativeness and consistent measurement standards the medical und sports data of the Swiss conscripts provide a valuable basis for a continuous health monitoring of young Swiss men. During three to four years, the prevalence of overweight and obesity seems to stabilise on a high level. After a longer period of decreasing performance at the endurance test between the 1980s and 2002, the level of physical performance in the fitness test does no longer decrease since 2006. However, health and health behaviour show significant regional and socioeconomic inequalities among young Swiss men. Besides economic resources and education, major driving factors behind these inequalities can be identified in health knowledge, values, and attitudes.