Prevention Of Catastrophic Failure Research Articles

Monitoring Internal Oxygen Partial Pressure in YSZ Electrolyte Under Electrolysis Cell Mode Using 4-Electrode System

When solid oxide cells are operated in electrolysis mode, high oxygen pressures can be developed in electrolyte near the oxygen electrode/ electrolyte interface, leading to oxygen electrode delamination. Monitoring oxygen partial pressure in the electrolyte is necessary for the prevention of catastrophic failure in solid oxide electrolysis cells (SOECs). In this study, we applied four-electrode system to fuel electrode-supported SOECs. Four–electrode consists of air electrode, fuel electrode, Pt probe embedded in electrolyte and Pt reference electrode on the electrolyte edge. We measured internal electric potential near the oxygen electrode with respect to the reference electrode using embedded Pt probe. Based on this experimental setup, internal partial pressure of oxygen at the Pt probe location was determined. As tracing changes in internal oxygen partial pressure with varying overvoltage or current density, we investigated the relationship between operating conditions and the tendency of chemo-mechanical failure in solid oxide electrolysis cells.

Composite Airframes-Opportunities, Options and Issues

Worldwide, both in the Civil and Military Aircraft Industry, there is an increasing usage ofcomposites due to the inherent advantages they offer. However, even today, the total potentialof composites has not been realized as the designers are still conservative. Also, cost hasbecome a very important factor especially for civil aircraft. In order to realize the full potentialof these materials, extensive research is being conducted in several areas that include design,newer, faster and cost effective approaches to manufacturing, better understanding of damagetolerance and associated failure theories, crashworthiness behavior and the like. StructuralHealth monitoring has progressed significantly and could play a major role in the reductionof maintenance costs and prevention of catastrophic failures. This paper makes an effort toaddress the options, challenges and issues that are confronting the composite industry today.

Supination Adduction Vertical Medial Malleolar Fracture Fixation with Buttress Plating vs a Novel Screw-Only Construct: A Cadaveric Biomechanical Study.

Supination adduction ankle fractures are unique among rotational ankle fractures as plate constructs are more commonly used than independent screws for medial malleolar fixation. The purpose of this study was to compare fracture displacement between plate fixation to a novel screw-only construct using a cadaveric biomechanical early-weightbearing model for the treatment of vertical medial malleolus fractures. Six nonosteoporotic fresh-frozen cadaver shanks and feet in matched pairs underwent a vertical osteotomy of the medial malleolus to simulate the supination adduction type injury. Osteoporosis was measured using DEXA scans. One specimen from each pair was fixed with a one-third tubular buttress plate and the other with screw-only fixation. The specimens were then axially loaded for 100 000 cycles to simulate protected weightbearing, and subsequently loaded to failure in supination. Stiffness, fracture displacement, and load to failure were recorded. Statistical significance was set at P <.05. There were no measurable differences in displacement between the 2 constructs during axial cyclic loading after 100 000 cycles (plate, 0.74 ± 0.09 mm; screws, 0.79 ± 0.18 mm; P = .225). During supination and axial load to failure, the plate outperformed the screw construct. For load to failure (2 mm displacement) at the fracture site, the plate group failed at 716 ± 240 N, whereas the screw group failed at 567 ± 237 N (P = .015). During load to catastrophic failure, the plate group outperformed the screw group (plate, 6011 ± 1646 N; screws, 4578 ± 1837 N; P = .002). For vertical medial malleolar fractures, the screw-only construct demonstrated no statistical difference when compared to buttress plating for cyclical axial loading, simulating early weightbearing in a boot. However, buttress plating is 21% to 24% stronger than the screw-only fixation construct in overall strength and prevention of catastrophic failure when loading in a supinated position. The screw-only construct is biomechanically similar to a buttress plate when simulating early protected weightbearing. This suggests that early weightbearing as tolerated in a controlled ankle motion boot beginning 2 weeks postoperatively is mechanically safe for this fracture pattern and does not result in unacceptable amounts of fracture displacement. This construct may be useful as a less invasive treatment modality for the treatment of vertical medial malleolus fractures in select patients.

Graph Neural Network and Koopman Models for Learning Networked Dynamics: A Comparative Study on Power Grid Transients Prediction

Continuous monitoring of the spatio-temporal dynamic behavior of critical infrastructure networks, such as the power systems, is a challenging but important task. In particular, accurate and timely prediction of the (electro-mechanical) transient dynamic trajectories of the power grid is necessary for early detection of any instability and prevention of catastrophic failures. Existing approaches for prediction of dynamic trajectories either rely on the availability of accurate physical models of the system, use computationally expensive time-domain simulations, or are applicable only at local prediction problems (e.g., a single generator). In this paper, we report the application of two broad classes of data-driven learning models &#x2013; along with their algorithmic implementation and performance evaluation &#x2013; in predicting transient trajectories in power networks using only streaming measurements and the network topology as input. One class of models is based on the <i>Koopman operator theory</i> which allows for capturing the nonlinear dynamic behavior via an infinite-dimensional linear operator. The other class of models is based on the <i>graph convolutional neural networks</i> which are adept at capturing the inherent spatio-temporal correlations within the power network. Transient dynamic datasets for training and testing the models are synthesized by simulating a wide variety of load change events in the IEEE 68-bus system, categorized by the load change magnitudes, as well as by the degree of connectivity and the distance to nearest generator nodes. The results confirm that the proposed predictive models can successfully predict the post-disturbance transient evolution of the system with a high level of accuracy.

Graphical examination of dissolved gas analysis by ratio methods and Duval triangle method to investigate internal faults of power transformer

Power transformer is a pivotal high voltage electrical apparatus of power system network. It is robust, bulky and one of the costliest equipment’s among all other connected network elements. Dissolved gas analysis (DGA) is one of the most popular proven methods to detect internal faults of power transformer. This research article incorporates ratio based DGA method using fuzzy logic as well as Duval Triangle based DGA method to analyse the internal faults of power transformers. Comparison of two distinct methods of DGA analysis for prevention of catastrophic failure is presented. The industry based standard data set are used for analysis and validation.

Single Fiber Reflectance Spectroscopy for the Monitoring of Cement Paste

Reinforced concrete structures are an essential part of our modern society, and monitoring their structural health is of paramount importance. Early detection of decay allows for the reduction of repair costs and, more importantly, the prevention of catastrophic failure. For this purpose, a single fiber reflectance spectrometer was embedded in cement paste samples for the monitoring of water at the fiber tip through its sensitivity to changes in the refractive index. It monitored the curing of samples with different water-to-cement ratios (w/c), between 0.45 and 0.60, measuring the water exhaust during the hardening of the cement paste. It also measured the capillary coefficient from cement paste samples of 0.50, 0.55 and 0.60 w/c: 0.668 ± 0.002 mm/√h, 1.771 ± 0.052 mm/√h and 6.360 ± 0.269 mm/√h, respectively. The capillary coefficient values agree with gravimetric measurements of sorptivity and are further confirmed through porosity measurements made with a scanning electron microscope. Thus, single fiber reflectance spectroscopy can be a gateway to inexpensively measure the entire life cycle of cement, from its curing until its eventual decay, assessing, in situ, its durability through the capillary coefficient.

PLA toughening via bamboo-inspired 3D printed structural design

Bioinspired structures can attain mechanical properties unseen in conventional artificial materials. Specifically, the introduction of a cellular structure with a precisely designed distribution of cells, cell sizes, and cell walls is expected to enhance the mechanical response. Polylactic acid (PLA) is a biodegradable polymer produced from renewable resources with very interesting properties and good three-dimensional (3D) printing processability. However, its embrittlement during ageing at room temperature after a very short period of time (a few hours) significantly reduces its usability for advanced applications. Intense effort has been invested in improving its toughness via composition modification. However, this approach can worsen some other properties, make processing more difficult, and increase the carbon footprint. Therefore, fused deposition modelling (FDM) 3D printing was used to manufacture porous bamboo-inspired structures of unmodified PLA. The toughening of PLA solely by the pore gradient, which controlled the energy dissipation mechanism, was introduced for the first time. Improvement of the ductility and work at break was observed especially for notched specimens. Prevention of catastrophic failure could enable the use of gradient porous materials in structural components. The fundamental relationships and practical hints resulting from the work provide a foundation for the future design of toughened 3D printed structures.

Ultrasensitive Inductive Debris Sensor With a Two-Stage Autoasymmetrical Compensation Circuit

Inductive oil debris sensors have been widely used in the health monitoring of mechanical equipment. Such sensors are important for fault diagnosis, life prediction, and prevention of catastrophic failures. In this article, the concept that the residual voltage caused by the asymmetry of two incentive coils hinders the improvement of the sensor resolution is theoretically demonstrated for the first time. In order to achieve ultrasensitive detection, a novel two-stage autoasymmetrical compensation circuit (AACC) is introduced into the triple-coil debris sensor to automatically suppress the residual voltage in real time. The experimental results show the successful detection of 70 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m (diameter or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</i> ) ferrous particles and 165 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</i> ) nonferrous particles in a sensing channel with a diameter of 43 mm. The AACC is verified effectively to maintain the residual voltage at an ultrasmall value even in the presence of environmental fluctuations, and the sensitivity of the sensor using AACC is more than 31 times that of the sensor without AACC.

Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines.

Railway axles are critical to the safety of railway vehicles. However, railway axle maintenance is currently based on scheduled preventive maintenance using Nondestructive Testing. The use of condition monitoring techniques would provide information about the status of the axle between periodical inspections, and it would be very valuable in the prevention of catastrophic failures. Nevertheless, in the literature, there are not many studies focusing on this area and there is a lack of experimental data. In this work, a reliable real-time condition-monitoring technique for railway axles is proposed. The technique was validated using vibration measurements obtained at the axle boxes of a full bogie installed on a rig, where four different cracked railway axles were tested. The technique is based on vibration analysis by means of the Wavelet Packet Transform (WPT) energy, combined with a Support Vector Machine (SVM) diagnosis model. In all cases, it was observed that the WPT energy of the vibration signals at the first natural frequency of the axle when the wheelset is first installed (the healthy condition) increases when a crack is artificially created. An SVM diagnosis model based on the WPT energy at this frequency demonstrates good reliability, with a false alarm rate of lower than 10% and defect detection for damage occurring in more than 6.5% of the section in more than 90% of the cases. The minimum number of wheelsets required to build a general model to avoid mounting effects, among others things, is also discussed.

Bio‐electrochemical response to sense implant degradation

AbstractIn vivo monitoring of biomedical implants to detect early stages of failure is currently unavailable. State‐of‐the‐art imaging techniques do not provide information about small‐scale localized changes in the implant and surrounding environment, which are key to early detection of failure. Here, we discuss different electrochemical responses of implants during degradation which may be utilized to develop biosensors to monitor implant degradation in vivo. This review is focused on identifying the need, potential measurement techniques and degradation response obtained from sensors based on electrochemical signature of biomedical implants. Benefits of designing these novel sensors include continuous monitoring, early failure detection, reduced post‐surgery and prevention of catastrophic failure from implant degradation.

Fiber Bragg Grating-Based Fabry-Perot Interferometer Sensor for Damage Detection on Thin Aluminum Plate

Structural Health Monitoring of engineering structures has become an essential measure for the prevention of catastrophic failures. This study presented a feasibility study of Fiber Bragg Grating-based Fabry-Perot interferometer (FBG-FPI) sensor for damage detection on an aluminum plate, by acoustic Lamb wave technique. In the investigation, a rectangular damage slot was introduced to the plate, at 15 cm from the actuator. Two FBG-FPI sensors were surface-mounted on the aluminum plate for the detection. In the analysis, we observed an additional wave packet with attenuated amplitude and time delay in the time-domain signal for the defect plate. Similar features are observed in the time-frequency spectrograms and they are important signatures that can be used for detecting the defect. The findings show the applicability of FBG-FPI sensor for damage detection on a thin-walled metallic structure.

Changing Device Regulations in the European Union: Impact on Research, Innovation and Clinical Practice.

Up until 2017, medical devices were placed on the European Union's (EU) single market in accordance with either Medical Device Directive 93/42/EEC for general medical devices or Medical Device Directive 90/385/EEC for active implantable devices. However, some devices that complied with these directives still failed catastrophically. In the orthopaedic device field, these failures were most pronounced in metal-on-metal hip devices causing severe patient morbidity with increased need for revision surgery which had unpredictable outcomes. Subsequently, the newly introduced Medical Device Regulations 2017/745 are aimed at addressing patient safety based on previous experience and thorough device assessment prior to and post-release on the EU single market; to accommodate for this they are substantially different (and more stringent). This poses a greater challenge for manufacturers and regulatory bodies in terms of time and resources. A review of the EU directives and published literature was undertaken. This review provides the rationale behind this change and its potential impact on research, industry, and clinical practice. The change in legal requirements for the medical devices to be put on the EU single market ultimately leads to increased patient safety, which is supported by clinical professionals. The new requirements for data transparency, post-market surveillance, and implant information availability increase the chance of catastrophic failure prevention. However, the exact method of implementation remains uncertain, and some essential rules on the data requirements for compliance have not yet been published by the EU. These limitations may limit the availability of products on the market including withdrawal of existing devices and a decrease in new medical device innovation. It is speculated that lack of new technologies within the medical device area can dramatically affect patient safety itself by not allowing potentially safer materials and methods on the EU single market, as the focus for the manufacturer becomes existing devices.

Acoustic diagnostics of electrical origin fault modes with readily available consumer‐grade sensors

Acoustic diagnostics, traditionally associated with mechanical fault modes, can potentially solve a wider range of monitoring applications. Typically, fault modes are induced purposefully by the researcher through physical component damage whilst the system is shutdown. This study presents low-cost real-time fault diagnostics of a previously unreported acute electrical origin fault that manifests sporadically during system operation with no triggering intervention. A suitability study into acoustic measurements from readily available consumer-grade sensors for low-cost real-time diagnostics of audible faults, and a brief overview of the theory and configuration of the wavelet packet transform (including optimal wavelet selection methods) and empirical mode decomposition processing algorithms is also included. The example electrical origin fault studied here is an unpredictable current instability arising with the pulse-width modulation controller of a BrushLess DC motor. Experimental trials positively detect 99.9% of the 1160 resultant high-bandwidth torque transients using acoustic measurements from a USB microphone and a smartphone. While the use of acoustic techniques for detecting emerging electrical origin faults remains largely unexplored, the techniques demonstrated here can be readily adopted for the prevention of catastrophic failure of drive and power electronic components.

Investigations of the Structural and Electrochemical Properties of Overheated Li-Ion Batteries and Its Effects in Single Cells Vs. Multi-Cells Packs

Abuse of electrochemical cells and batteries can have a wide range of effects depending on the properties of the battery as well as the nature of the damage caused. From a safety perspective, simple passive monitoring is often unable to identify the onset of failure until it is too late to intervene. Once a significant voltage change or self-heating of a cell is identified, a catastrophic runaway is often inevitable. The ultimate weakness of traditional monitoring techniques, typically based on voltage and temperature, is that they detect only the symptoms of cell failure. Once a significant change has been registered, irreversible internal changes have likely already occurred within the cell. Compounding the issue is the fact that cells in a battery are comprised of all the elements needed to release their stored energy, meaning that once a catastrophic failure has initiated no amount of intervention can keep the reaction from proceeding to its conclusion. The prevention of catastrophic failure requires detection of internal faults well before they have developed to the point of no return. This makes techniques that can monitor the internal health of a cell essential to provide dependable protection from catastrophic failure. Safety issues are exacerbated by the use of multi-cell modules. Monitoring a pack of multiple cells with traditional techniques can be misleading, a battery pack that otherwise appears healthy could have a single problematic cell within the pack. Abusive conditions may also be unintentionally created, as voltage imbalance can create overcharge/overdischarge conditions, while the poor thermal design of a module can generate thermally abusive conditions. However, monitoring the changes to the impedance would aid in identifying ailing cells before a dangerous and costly failure. This work examines the application of Electrochemical Impedance Spectroscopy (EIS) and Differential Capacity calculations (dQ/dV) as tools for determining the state of stability (SOS) of an electrochemical cell and multi-cell battery pack. The cells used for this study were commercial 10 Ah NMC cells subjected to thermal abuse coupled with EIS monitoring. This aims to not only provide a deeper understanding of how abused cells and batteries fail but also form the technical basis of a tool that could ultimately be used to interrogate cells of unknown stability and even monitor active cells for early signs of damage or failure. Fast impedance monitoring hardware previously developed at Idaho National Laboratory is used to provide not only monitoring after an abusive battery test but also look for changes in the cell while abusive conditions are applied. Differential capacity calculations are explored both before tests and after exposing single cells to different temperature ranges to examine any noticeable changes that may be monitored during charge and discharge operations. Lastly, X-ray diffraction measurements are performed on the cathode/anode before and after the abuse test to understand the effect of overheat in the structural changes. Figure 1 and Figure 2 presents the EIS and dQ/dV measurements for the NMC single cells heated from 50°C-150°C. Markers within the data can be identified and applied to cross-examine batteries of unknown stability as well as provide the basis for an active diagnostic method as part of a battery management system. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. Figure 1

Experimental and numerical investigation of sandwich structures with balsa core and hybrid corrugated composite/balsa core under three-point bending using digital image correlation

Sandwich structures are commonly used in automotive, aerospace, and civil infrastructure applications due to their high strength-to-weight and stiffness-to-weight ratios. In the present work, the performance of sandwich structures with aluminum facings and two different types of core, including balsa wood core and hybrid corrugated composite/balsa core subjected to three-point bending, is investigated, both experimentally and numerically. The face sheets are 6061-T6 aluminum alloy and the hybrid core has been made of balsa wood with average density of 125 kg m−3 and E-glass woven fabric, bonded together using epoxy resin. For data acquisition during the tests, a linear variable differential transformer has been placed under the mid-span of the specimens and a digital image correlation technique has been used for monitoring the full-field strain distributions and deflection pattern as well. Continuum damage mechanics has been employed for the finite element model analysis of the three-point bending tests. In order to predict the maximum load-bearing capacity and failure modes, a wood material behavior model for balsa and a combination of Hashin and Puck failure criterion for corrugated composite were imported into ABAQUS/Explicit via VUMAT subroutines. It was found that combining balsa core with corrugated composite results in prevention of catastrophic failure and consequently gradual decrease of load-bearing capacity. Furthermore, the results demonstrate that the hybrid core increases the strength-to-mass density and stiffness-to-mass density ratios by 34.7% and 28.2%, respectively. Numerical findings have been successfully compared to experimental data, which reveals its suitability for parametric analysis of the proposed hybrid core sandwich structure.

Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines.

A phase field formulation for hydrogen assisted cracking

We present a phase field modeling framework for hydrogen assisted cracking. The model builds upon a coupled mechanical and hydrogen diffusion response, driven by chemical potential gradients, and a hydrogen-dependent fracture energy degradation law grounded on first principles calculations. The coupled problem is solved in an implicit time integration scheme, where displacements, phase field order parameter and hydrogen concentration are the primary variables. We show that phase field formulations for fracture are particularly suitable to capture material degradation due to hydrogen. Specifically, we model (i) unstable crack growth in the presence of hydrogen, (ii) failure stress sensitivity to hydrogen content in notched specimens, (iii) cracking thresholds under constant load, (iv) internal hydrogen assisted fracture in cracked specimens, and (v) complex crack paths arising from corrosion pits. Computations reveal a good agreement with experiments, highlighting the predictive capabilities of the present scheme. The work could have important implications for the prediction and prevention of catastrophic failures in corrosive environments. The finite element code developed can be downloaded from www.empaneda.com/codes.

Investigations of the Structural and Electrochemical Properties of Overheated Li-Ion Batteries and Its Effects in Single Cells Vs. Multi-Cells Packs

Abuse of electrochemical cells and batteries can have a wide range of effects depending on the properties of the battery as well as the nature of the damage caused. From a safety perspective, simple passive monitoring is often unable to identify the onset of failure until it is too late to intervene. Once a significant voltage change or self-heating of a cell is identified, a catastrophic runaway is often inevitable. The ultimate weakness of traditional monitoring techniques, typically based on voltage and temperature, is that they detect only the symptoms of cell failure. Once a significant change has been registered, irreversible internal changes have likely already occurred within the cell. Compounding the issue is the fact that cells in a battery are comprised of all the elements needed to release their stored energy, meaning that once a catastrophic failure has initiated no amount of intervention can keep the reaction from proceeding to its conclusion. The prevention of catastrophic failure requires detection of internal faults well before they have developed to the point of no return. This makes techniques that can monitor the internal health of a cell essential to provide dependable protection from catastrophic failure. Safety issues are exacerbated by the use of multi-cell modules. Monitoring a pack of multiple cells with traditional techniques can be misleading, a battery pack that otherwise appears healthy could have a single problematic cell within the pack. Abusive conditions may also be unintentionally created, as voltage imbalance can create overcharge/overdischarge conditions, while the poor thermal design of a module can generate thermally abusive conditions. However, monitoring the changes to the impedance would aid in identifying ailing cells before a dangerous and costly failure. This work examines the application of Electrochemical Impedance Spectroscopy (EIS) and Differential Capacity calculations (dQ/dV) as tools for determining the state of stability (SOS) of an electrochemical cell and multi-cell battery pack. The cells used for this study were commercial 10 Ah NMC cells subjected to thermal abuse coupled with EIS monitoring. This aims to not only provide a deeper understanding of how abused cells and batteries fail but also form the technical basis of a tool that could ultimately be used to interrogate cells of unknown stability and even monitor active cells for early signs of damage or failure. Fast impedance monitoring hardware previously developed at Idaho National Laboratory is used to provide not only monitoring after an abusive battery test but also look for changes in the cell while abusive conditions are applied. Differential capacity calculations are explored both before tests and after exposing single cells to different temperature ranges to examine any noticeable changes that may be monitored during charge and discharge operations. Lastly, X-ray diffraction measurements are performed on the cathode/anode before and after the abuse test to understand the effect of overheat in the structural changes. Figure 1 and Figure 2 presents the EIS and dQ/dV measurements for the NMC single cells heated from 50°C-150°C. Markers within the data can be identified and applied to cross-examine batteries of unknown stability as well as provide the basis for an active diagnostic method as part of a battery management system. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. Figure 1

The Financial Benefits of Various Catastrophic Failure Prevention Strategies in a Wind Farm: Two market studies (UK-Spain)

Operation of wind farms is driven by the overall aim of minimising costs while maximising energy sales. However, in certain circumstances investments are required to guarantee safe operation and survival of an asset. In this paper, we discuss the merits of various catastrophic failure prevention strategies in a Spanish wind farm. The wind farm operator was required to replace blades in two phases: temporary and final repair. We analyse the power performance of the turbine in the different states and investigate four scenarios with different timing of temporary and final repair during one year. The financial consequences of the scenarios are compared with a baseline by using a discounted cash flow analysis that considers the wholesale electricity market selling prices and interest rates. A comparison with the UK electricity market is conducted to highlight differences in the rate of return in the two countries.

Feasibility of using digital image correlation for unmanned aerial vehicle structural health monitoring of bridges

Quantifying the condition of aging structures is important to verify structural integrity and long-term reliability. Structural health monitoring plays a key role in the prevention of catastrophic failure, in improving the safety of infrastructure, and in reducing the downtime and costs associated with their maintenance. Bridges are typically designed to have a lifespan on order of 50 years; therefore, bridge monitoring is important since many of them are near to or have already exceeded their design life. Conventional sensors and examination techniques such as accelerometers and strain gages produce results at only a discrete number of points. Visual inspection only provides qualitative information and is subject to human variability and inconsistencies between inspectors. Moreover, both approaches are labor intensive and time-consuming. In recent years, three-dimensional digital image correlation systems have proven their efficiency in being able to provide accurate quantitative information of structural deformations, full-field strain, and geometry profiles of large-scale structures. At the same time, unmanned aerial vehicles have emerged as valuable tools for remotely performing measurements in places, which are either difficult or dangerous to access. With regard to bridge inspection, unmanned aerial vehicles have the capability to expedite the measurement process, offer increased accessibility, and reduce interference with the structures’ functionality. In this study, a novel approach that combines the use of an unmanned aerial vehicle and three-dimensional digital image correlation is developed to perform non-contact, optically based measurements to monitor the health of bridges. Extensive laboratory tests and a long-term monitoring campaign on two in-service concrete bridges demonstrated the accuracy of this system in detecting structural changes. Results show that this system is able to detect changes to the bridge geometry with an uncertainty on the order of 10−5 m while improving accessibility. The feasibility of the approach, best practices, and lessons learned is presented.