Rohan Soman

Harvesting energy from vibrations of the underlying structure

The use of wireless sensors for structural health monitoring offers several advantages such as small size, easy installation and minimal intervention on existing structures. However the most significant concern about such wireless sensors is the lifetime of the system, which depends heavily on the type of power supply. No matter how energy efficient the operation of a battery operated sensor is, the energy of the battery will be exhausted at some point. In order to achieve a virtually unlimited lifetime, the sensor node should be able to recharge its battery in an easy way. Energy harvesting emerges as a technique that can harvest energy from the surrounding environment. Among all possible energy harvesting solutions, kinetic energy harvesting seems to be the most convenient, especially for sensors placed on structures that experience regular vibrations. Such micro-vibrations can be harmful to the long-term structural health of a building or bridge, but at the same time they can be exploited as a power source to power the wireless sensors that are monitoring this structural health. This paper presents a new energy harvesting method based on a vibration driven electromagnetic harvester. By using an improved Maximum Power Point Tracking technique on the conversion circuit, the proposed method is shown to maximize the conversion coefficient from kinetic energy to applicable electrical energy.

Kalman filter based data fusion for neutral axis tracking in wind turbine towers

Wind energy is seen as one of the most promising solutions to man’s ever increasing demands of a clean source of energy. In particular to reduce the cost of energy (COE) generated, there are efforts to increase the life-time of the wind turbines, to reduce maintenance costs and to ensure high availability. Maintenance costs may be lowered and the high availability and low repair costs ensured through the use of condition monitoring (CM) and structural health monitoring (SHM). SHM allows early detection of damage and allows maintenance planning. Furthermore, it can allow us to avoid unnecessary downtime, hence increasing the availability of the system. The present work is based on the use of neutral axis (NA) for SHM of the structure. The NA is tracked by data fusion of measured yaw angle and strain through the use of Extended Kalman Filter (EKF). The EKF allows accurate tracking even in the presence of changing ambient conditions. NA is defined as the line or plane in the section of the beam which does not experience any tensile or compressive forces when loaded. The NA is the property of the cross section of the tower and is independent of the applied loads and ambient conditions. Any change in the NA position may be used for detecting and locating the damage. The wind turbine tower has been modelled with FE software ABAQUS and validated on data from load measurements carried out on the 34m high tower of the Nordtank, NTK 500/41 wind turbine.

Numerical evaluation of multi-metric data fusion based structural health monitoring of long span bridge structures

Abstract This work focuses on structural health monitoring of long span bridges for damage detection. A feature extraction level data fusion based damage isolation strategy is presented using multi-metric sensing. The multi-metric sensing uses two types of sensors, namely strain sensors and accelerometers. The methodology combines the advantages offered by each type of sensors, while at the same time overcomes their limitations. The flexibility index method is applied and the flexibility matrices based on the strain and displacement data are combined after performing co-ordinate transformation. A study has been carried out on a simulated finite element model of the Great Belt East Bridge where realistic damage scenarios like damage in the girder, breaking of hanger cables, pier settlement, and loss of cable pretension were introduced on the structure. The study indicates that multi-metric sensing is indeed necessary as it reduces the possibility of false detections and increases the sensitivity and robustness of the methodology.

Assessment of delamination in composite beam using infrared thermography, optical sensors and terahertz technique

Composite materials find wide ranging applications due to their high strength-to-weight ratio. Due to this increasing dependence on composite materials there is a need to study their mechanical behavior in case of damage. There are several ENDT and SHM methods for the assessment of the mechanical properties each with their set of advantages and disadvantages. The paper presents a comparative study of three distinct damage detection methods (infrared thermography, neutral axis method based on optical strain sensor measurements and terahertz spectroscopy) for the detection of delamination in a simple GFRP beam-like structure. Each of the methods have their own set of advantages and disadvantages. The terahertz spectroscopy is a specialized technique suitable for detecting deterioration inside the structure, but is not suitable for in performance monitoring. Similarly the infrared thermography technique in the active domain may be used for in situ monitoring but not in in-service assessment. Both methods allow the visualization of the internal structure and hence allow identification of the type and the extent of damage. Fibre optic sensors (especially FBG) due to their small diameter and no need of calibration can be permanently integrated within the sample and applied for continuous dynamic strain measurements. The measured strain is treated as an input for neutral axis (NA) method, which as a damage sensitive feature may be used for in-service monitoring but gives absolutely no information about the type and extent of damage. The results for damage detection based on proposed comparative studies give a complete description of the analyzed structure.

Kalman Filter Based Data Fusion for Bi-Axial Neutral Axis Tracking in Wind Turbine Towers

Structural Health Monitoring (SHM) systems allow early detection of damage which allows maintenance planning and reduces the maintenance cost. So there is a lot of active research in the area for SHM of civil and mechanical structures. The SHM system uses a damage sensitive feature, and any change in the structure is reflected by a change in this feature. If this change is above a threshold the structure is said to be damaged. In most applications the determination of this threshold is based on engineering judgment and the previous experience of the operator. These practices are highly subjective to the skill of the operator and the uncertainty is more in case of unique structures or in unique conditions, like in the case of the newer high capacity wind turbines. So there is a need for a formal methodology for the selection of the thresholds for the chosen damage detection technique. This paper demonstrates a methodology for the selection of threshold for damage detection based on qualitative data acquired from several damage scenarios of a 10 MW wind turbine. The damage indicator is the change of neutral axis (NA) which is tracked using Kalman Filter (KF). Based on the level of damage to be detected and the probability of occurrence of false positive and false negative detections, a threshold value is selected. This threshold is then applied to strain data from the Nordtank NTK500/41 wind turbine for validation. doi: 10.12783/SHM2015/345

Comparative study of deterioration of composite due to moisture using strain, electro-mechanical impedence, and guided waves

Glass fibre reinforced plastic (GFRP) composites are finding increasing application in aerospace structures. The monitoring of these structures is not only necessary but also mandatory by the safety codes. The present state of the art allows isolation of damage (level II) and the quantification of damage (level III) is the next challenge. The quantification of the damage may allow for better maintenance scheduling and as a result lower downtime for airplanes, yachts and wind turbine which makes it significant in the different disciplines. The paper presents a comparative study of three distinct damage detection methods on a sample of GFRP composite. The aim of the research is to compare the performance of the three methods for the assessment of the deterioration of the composite samples due to the influence of moisture. The electromechanical impedance (EMI) and guided waves (GW) based methods have been shown to be sensitive to moisture induced deterioration. The dynamic strain based damage detection using neutral axis (NA) as a damage sensitive feature is sensitive to moisture induced deterioration as well. In addition to the detection of deterioration, the use of measured strains provides an intuitive way for the quantification of the moisture induced deterioration in the sample. Thus, the present study allows the calibration of the NA based structural health monitoring (SHM) technique using already established SHM methods like EMI and GW based techniques. Hence, it may be seen as a necessary step for the standardization, validation and development of the strain based method for SHM.

Preliminary studies for the optimization of sensor placement for electro-mechanical impedance based damage detection

Electro-mechanical impedance based damage detection has been shown to detect small changes in structure. Due to the large frequency band which may be assessed with the EMI technique, it has been shown to detect very local damage irrespective of the change in the boundary conditions. Studies have also been carried out to compensate for ambient temperature effects. Unfortunately, the drawback of the local nature of the EMI approach is the relatively low range of sensing. As a result most of the studies using EMI approach are limited to applications where the sensitive region is apriori known. This is not always the case, and thus the study of the technique at the array level is necessary. Thus, the present study tries to establish the approach for optimization of the array of PZTs for EMI based approach. The primary task is to establish the range of the sensors to damage in an anisotropic GFRP plate. The range of the sensors and their directionality then may be used to optimize the sensor placement to ensure the maximum coverage of the plate for damage detection. Once the cost function of the optimization has been established, genetic algorithm (GA) is employed for optimization. GA offers several advantages over brute force based methods as well as other optimization approaches. GA is ideally suited for multi-objective optimization which then paves the way for incorporating other optimization objectives in the search.

Moisture contamination detection in adhesive layer using embedded fibre Bragg grating sensors

The paper presents an application of embedded fibre Bragg grating (FBG) sensors for moisture contamination detection in an adhesive layer between composite elements. Due to their high corrosion resistance as well as their small size and weight, FBG sensors are a great tool for Structural Health Monitoring of composite structures. Adhesive bonds are very popular in many industrial sectors (e.g. automotive, aerospace). One of the major problems limiting the use of adhesive joints is their sensitivity to moisture from its surroundings. Even 1% of moisture can negatively affect the adhesive bond layer. The experimental and numerical investigations were performed on two rectangular samples of two glass fibre reinforced composite elements bonded together using an adhesive commonly used in the bonding or repair of aircraft elements. Moisture contamination due to diffusion process changes the volumetric properties of the material induced strain. This strain was measured by FBG sensors embedded in the adhesive layer parallel to the main axis of the sample. The behaviour of the adhesive layer in the analysed sample was also modelled using the finite element commercial code ABAQUS. Numerical and experimental results confirm the utility of FBG sensors for moisture detection in the adhesive layer even when the amount of moisture is around 2% of the sample weight.

Performing modal analysis for multi-metric measurements: a discussion

This work addresses the severe lack of literature in the area of modal analysis for multi-metric sensing. The paper aims at providing a step by step tutorial for performance of modal analysis using Fiber Bragg Grating (FBG) strain sensors and Laser Doppler Vibrometer (LDV) for displacement measurements. The paper discusses in detail the different parameters which affect the accuracy of the experimental results. It highlights the often implied, and un-mentioned problems, that researchers face while performing experiments. The paper tries to bridge the gap between the theoretical idea of the experiment and its actual execution by discussing each aspect including the choice of specimen, boundary conditions, sensors, sensor position, excitation mechanism and its location as well as the post processing of the data. The paper may be viewed as a checklist for performing modal analysis in order to ensure high quality measurements by avoiding the systematic errors to creep in.