Installation Of Strain Gauges Research Articles

Virtual sensing via Gaussian Process for bending moment response prediction of an offshore wind turbine using SCADA data

Offshore wind turbines (OWTs) can be equipped with two types of monitoring systems: (1) a Supervisory Control and Data Acquisition (SCADA) system that monitors operational data such as wind speed and power generation, and (2) vibration sensors like accelerometers and strain gauges to track structural dynamics. While strain gauges enable fatigue damage calculations, not all OWTs in a wind farm have these sensors installed. This paper proposes a Gaussian process regression (GPR) strategy to predict the bending moment time-histories of an OWT. The model takes into account various SCADA data such as wind speed, power, and nacelle acceleration as input and learns to predict the high and low-frequency dynamic response of the system. The strategy is implemented and tested to predict the bending moment response of a 6 MW offshore wind turbine in the fore-aft and side-side directions of the turbine. The accuracy and reliability of the proposed strategy are evaluated and demonstrated considering different operational conditions and multiple hotspot locations along the height of the turbine. The proposed strategy proves to be an efficient virtual sensing strategy and can be easily transferred to other turbines in the same wind farm without the need for widespread installation of strain gauges.

A Method for Measuring the Mass of a Railroad Car Using an Artificial Neural Network

The fast, convenient, and accurate determination of railroad cars’ load mass is critical to ensure safety and allow asset counting in railway infrastructure. In this paper, we propose a method for modeling the mechanical deformations that occur in the rail web under the influence of a static load transmitted through a railway wheel. According to the proposed method, a railroad car’s weight can be determined from the rail deformation values. A solid model of a track section, including a railroad tie, rail, and wheel, is developed, and a multi-physics simulation technique that allows for the determination of the values of deformations and mechanical stresses in the strain gauge installation areas is presented. The influence of the loaded mass, the temperature of the rail, and the wheel position relative to the strain gauge location is considered. We also consider the possibility of using artificial neural networks to determine railroad cars’ weight without specifying the coordinates of the wheel position. The effect of noise in the data on the accuracy of determining the railroad car weight is considered.

Crack control optimization of basement concrete structures using the Mask-RCNN and temperature effect analysis.

In order to enhance the mitigation of crack occurrence and propagation within basement concrete structures, this research endeavors to propose an optimization methodology grounded in the Mask Region-based Convolutional Neural Network (Mask-RCNN) and an analysis of temperature effects. Initially, the Mask-RCNN algorithm is employed to perform image segmentation of the basement concrete structure, facilitating the precise identification of crack locations and shapes within the structure. Subsequently, the finite element analysis method is harnessed to simulate the structural stress and deformation in response to temperature variations. An optimization algorithm is introduced to adjust geometric parameters and material properties using insights from the temperature effect analysis. This algorithm aims to minimize stress concentration and deformation within the structure, thus diminishing the incidence and proliferation of cracks. In order to assess the efficacy of the optimization approach, an authentic basement concrete structure is selected for scrutiny, and the structure is monitored in real-time through the installation of strain gauges and monitoring equipment. These instruments track structural stress and deformation under diverse temperature conditions, and the evolution of cracks is meticulously documented. The outcomes demonstrate that by adjusting the structural geometric parameters and material properties, the crack density experiences a notable reduction of 60.22%. Moreover, the average crack length and width witness reductions of 40.24% and 35.43%, respectively, thereby corroborating the efficacy of the optimization method. Furthermore, an assessment of stress concentration and deformation within the structure is conducted. Through the optimization process, the maximum stress concentration in the structure diminishes by 25.22%, while the maximum deformation is curtailed by 30.32%. These results signify a substantial enhancement in structural stability. It is evident that the optimization algorithm exhibits robustness and stability in the context of crack control, consistently delivering favorable outcomes across diverse parameter configurations.

Digital Image Correlation Analysis of Strain Fields in Fibre-Reinforced Polymer–Matrix Composite under ±45° Off-Axis Tensile Testing

This study presents an experimental investigation of an in-plane shear of a glass lamina composite using a ±45° off-axis tension test. Typically, the shear stress curve, shear modulus, and in-plane shear strength for composite lamina-type materials are identified. Previous research indicated that a loading rate affects the strength of this composite. This study extends the existing literature by utilising a non-contact optical digital image correlation (DIC) method to measure strain distribution during the test. Two cross-head displacement rates were examined. The obtained strain maps reveal an uneven distribution resembling fabric texture. As the deformation progresses, the differences in the strain pattern increase. Subsequently, a quantitative analysis of the differences between regions with extreme (minimum and maximum) strain values and regions with average values was conducted. Based on these measurements, shear stress–strain curves, indicating variations in their courses, were constructed. These differences may reach several percent and may influence the analysis of numerical simulations. The DIC results were validated using strain gauge measurements, a commonly utilised method in this test. It was demonstrated that the location of the strain gauge installation impacts the results. During the tests, the occurrence of multiple microcracks in the resin was observed, which can contribute to the nonlinearity observed in the shear stress–shear strain curve.

Case Study: Modeling a Grain Bin for Safe Entry Retrofit

All new grain bins produced after 2018 are recommended to have anchor points capable of handling a 2000 lb loading for attachment of bin entry lifeline systems. This study aims to assess the feasibility of a safe entry anchor point retrofit by using finite element analysis (FEA). We used a grain bin owned by Penn State for 3D FEA modeling in SolidWorks. To validate the model results from the FEA model, first strain and then deflection measurements were conducted on the grain. Strain gauges were applied to the grain bin in five locations and strain values were obtained after applying static loads. The strain gauge measurements from the experimental study were compared to the strain output from the FEA simulation. The error seen was far greater than was expected. The most pertinent error source was strain gauge installation error and equipment failure. Then, the vertical roof deflection of the bin was measured using a precision phase-comparison laser while applying incremental static loads to the retrofitted rescue anchor points. The FEA model results were compared to the experimentally measured deflection results. A 3D FEA model of a grain bin was created. A high amount of error was observed in deflections between the measured and FEA modeling. The errors have resulted from the assumptions made during the model creation. However, the SolidWorks Simulation model still may be used to estimate loading scenarios in a safe and non-destructive way. Based on the research findings, the project team recommends that the suitability of any bin to safely accommodate a lifeline and anchor point system must be verified on a case-by-case basis. Evaluation by a professional structural engineer and consulting with the manufacturer are recommended. This recommendation extends to all-grain bins, including those post-2018.

Probabilistic temporal extrapolation of fatigue damage of offshore wind turbine substructures based on strain measurements

Abstract. Substructures of offshore wind turbines are becoming older and beginning to reach their design lifetimes. Hence, lifetime extensions for offshore wind turbines are becoming not only an interesting research topic but also a relevant option for industry. To make well-founded decisions on possible lifetime extensions, precise fatigue damage predictions are required. In contrast to the design phase, fatigue damage predictions can be based not only on aeroelastic simulations but also on strain measurements. Nonetheless, strain-measurement-based fatigue damage assessments for lifetime extensions have been rarely conducted so far. Simulation-based approaches are much more common, although current standards explicitly recommend the use of measurement-based approaches as well. For measurement-based approaches, the main challenge is that strain data are limited. This means that measurements are only available for a limited period and only at some specific hotspot locations. Hence, spatial and temporal extrapolations are required. Available procedures are not yet standardised and in most cases not validated. This work focusses on extrapolations in time. Several methods for the extrapolation of fatigue damage are assessed. The methods are intended to extrapolate fatigue damage calculated for a limited time period using strain measurement data to a longer time period or another time period, where no such data are available. This could be, for example, a future period, a period prior to the installation of strain gauges or a period after some sensors have failed. The methods are validated using several years of strain measurement data from the German offshore wind farm Alpha Ventus. The performance and user-friendliness of the various methods are compared. It is shown that fatigue damage can be predicted accurately and reliably for periods where no strain data are available. Best results are achieved if wind speed correlations are taken into account by applying a binning approach and if a least some winter months of strain data are available.

Temporary cable force monitoring techniques during bridge construction-phase: the Tajo River Viaduct experience

This article deals with the comparative analysis of current cable force monitoring techniques. In addition, the experience of three cable stress monitoring techniques during the construction phase is included: (a) the installation of load cells on the active anchorages of the cables, (b) the installation of unidirectional strain gauges, and (c) the evaluation of stresses in cables applying the vibrating wire technique by means of the installation of accelerometers. The main advantages and disadvantages of each technique analysed are highlighted in the Construction Process context of the Tajo Viaduct, one of the most singular viaducts recently built in Spain.

Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups

Abstract. The sustained development over the past decades of the offshore wind industry has seen older wind farms beginning to reach their design lifetime. This has led to a greater interest in wind turbine fatigue, the remaining useful lifetime and lifetime extensions. In an attempt to quantify the progression of fatigue life for offshore wind turbines, also referred to as a fatigue assessment, structural health monitoring (SHM) appears as a valuable contribution. Accurate information from a SHM system can enable informed decisions regarding lifetime extensions. Unfortunately direct measurement of fatigue loads typically revolves around the use of strain gauges, and the installation of strain gauges on all turbines of a given farm is generally not considered economically feasible. However, when we consider that great numbers of data, such as supervisory control and data acquisition (SCADA) and accelerometer data (of cheaper installation than strain gauges), are already being captured, these data might be used to circumvent the lack of direct measurements. It is then highly relevant to know what is the minimal sensor instrumentation required for a proper fatigue assessment. In order to determine this minimal instrumentation, a data-driven methodology is developed for real-world jacket-foundation offshore wind turbines (OWTs). In the current study the availability of high-frequency SCADA (1 Hz) and acceleration data (>1 Hz) as well as regular 10 min SCADA is taken as the starting point. Along these measured values, the current work also investigates the inclusion of an estimate of the quasi-static thrust load using the 1 s SCADA using an artificial neural network (ANN). After data collection all data are transformed to features on a 10 min interval (feature generation). When considering all possible variations a total of 430 features was obtained. To reduce the dimensionality of the problem this work performs a comparative analysis of feature selection algorithms. The features selected by each method are compared and related to the sensors to decide on the most cost-effective instrumentation of the OWT. The variables chosen by the best-performing feature selection algorithm then serve as the input for a second ANN, which estimates the tower fore–aft (FA) bending moment damage equivalent loads (DELs), a valuable metric closely related to fatigue. This approach can then be understood as a two-tier model: the first tier concerns itself with engineering and processing 10 min features, which will serve as an input for the second tier that estimates the DELs. It is this two-tier methodology that is used to assess the performance of eight realistic instrumentation setups (ranging from 10 min SCADA to 1 s SCADA, thrust load and dedicated tower SHM accelerometers). Amongst other findings, it was seen that accelerations are essential for the model's generalization. The best-performing instrumentation setup is looked at in greater depth, with validation results of the tower FA DEL ANN model showing an accuracy of around 1 % (MAE) for the training turbine and below 3 % for other turbines, with a slight underprediction of fatigue rates. Finally, the ANN DEL estimation model – based on two intermediate instrumentation setups (combinations of 1 s SCADA, thrust load, low quality accelerations) – is employed in a farm-wide setting, and the probable causes for outlier behaviour are investigated.

Application of Principal Component Analysis-Assisted Neural Networks for the Rotor Blade Load Prediction

This paper presents a novel approach of principal component analysis- (PCA-) assisted back propagation (BP) neural networks for the problem of rotor blade load prediction. 86.5 hours of real flight data were collected from many steady-state and transient flight maneuvers at different altitudes and airspeeds. Prediction of the blade loads was determined by the PCA-BP model from 16 flight parameters measured and monitored by the flight control computer already present in the helicopter. PCA was applied to reduce the dimension of the flight parameters influencing the component load and eliminate the correlation among flight parameters. Thus, obtained principal components were used as input vectors of the BP neural network. The combined PCA-BP neural network model was trained and tested by real flight data. Comparison of this model and to a BP neural network model as well as to a multiple linear regression (MLR) model was also done. The results of comparison demonstrate that the PCA-BP model has higher prediction precision with an average error of 2.46%, while 4.49% for BP and 10.20% for MLR. The results also reveal that the PCA-BP model has a shorter convergence path than the BP model. This method not only is useful in establishing the load spectra of helicopter rotor in-service where installation of strain gauges is impractical but also can reduce the cost of installation and maintenance measured by strain gauges.

Failure cause analysis of an expansion bellow of integrated turbine unit

The bellow is a flexible component which compensates an expansion allowance imposed by axial strain during high temperature service. The bellow failure during turbine operation of boiler unit has been systematically investigated. The metallurgy of bellow was AISI 316L stainless steel. The comprehensive laboratory investigation includes visual inspection, chemical analysis, mechanical tests, metallographic and fractographic studies. Visual inspection in the field revealed multiple cracks in the bellow and failed debris were collected for laboratory investigation. The mechanical tests of the expansion bellow confirmed to material property specification. The microstructure revealed austenitic structure and fracture surface revealed striation marks. The striation marks attributed to fatigue failure. Finite element analysis revealed that maximum stress induced in the trough region and stress could be higher as compared to ASME VIII Div 1 design equations. It can be concluded that actual operating pressure range could resulted in sufficient stress cycles to initiate fatigue cracks from surface of the bellow. The large number of stress cycles owing to actual varied pressure range attributed to fatigue failure. It is recommended to reduce many intermittent operating conditions by good operational practice and installation of strain gauges at critical locations of the bellow. It is further proposed to increase the existing ply thickness and install reinforced expansion bellow & sandwich/layered plies for prevention of fatigue failure.

On the use of neural networks for dynamic stress prediction in Francis turbines by means of stationary sensors

Nowadays, one of the major mechanical issues of hydraulic turbines and particularly Francis turbines are the failures produced by fatigue. Due to the massive entrance of new renewable energies such as wind or solar, hydraulic turbines have to withstand off-design conditions and multiple transients, which greatly increase the risk of fatigue failures. Fatigue damage and crack propagation models are based on the static and dynamic stresses on the turbine blades. Therefore, an accurate and realistic determination of these stresses is of paramount importance although it is yet a challenging task. Numerical simulations have still limitations when predicting static and dynamic stresses in the most harmful conditions such as deep part load conditions and transients, which are highly stochastic. The installation of strain gauges on the blades gives accurate stress measurement but it involves long and very expensive measurement campaigns, as the turbine runner is submerged, confined and rotating. In this paper we propose a neural network-based method to determine the magnitude of static and dynamic stresses based on the measurements of stationary sensors, which greatly reduces the complexity and costs of strain gauge testing. Inputs of the neural networks are selected based on previous experience monitoring Francis turbines. The trained network can be implemented in advanced monitoring systems that could continuously evaluate the stress level of the turbine and determine the risk of possible fatigue damage.

Experience of using tensoresistive strain gauges in corrosive environments

This work is devoted to the analysis of the design, installation and use of strain gauges in highly aggressive environments of potassium salts. The strain gauge system is a part of an integrated automated system for monitoring the deformation of a complex building structure. This structure is a special (industrial) building of the mining and processing plant of the Petrikovsky potassium salt deposit in the Republic of Belarus. Ensuring the performance of monitoring systems is one of the main problems in aggressive conditions. Strain gages are susceptible to salt exposure, as they are very poorly protected in the conventional design. Conventional strain-strain sensors are not designed to operate in such conditions and require the development of new solutions that will provide the desired measurement accuracy and will be durable. This paper presents new solutions for measuring deformations with high accuracy. The created sensors were tested both in the laboratory and in the test conditions at the potash plant site for two years.

Measuring scheme for determination of loads acting on the side frame of the bogie from a wheelset

The study aims to develop a measuring circuit to determine the loads acting on the side frame of the bogie from the wheelset when the wagon is moving. The paper reviews and analyzes experimental methods for measuring the vertical and lateral forces acting on the side frame of the bogie from the wheelset when the wagon is moving. Theoretical studies of the stress-strain state of the side frame of a freight wagon bogie were carried out using the finite element method under the action of loads on the axle opening from the wheelset. As a result of theoretical studies by the finite element method, the places for the installation of strain gauges were determined, and a method for processing the received signals was selected. The developed measuring scheme makes it possible to determine the spatial force effect acting on the side frame of the bogie from the wheelset, which makes it possible, without increasing the number of measuring channels in the equipment, to reduce the number of strain gauges for measuring the considered loads while running dynamic tests. In addition, it improves the accuracy of measurements of vertical loads, with the help of which the coefficient of dynamic addition of unsprung parts of the bogie is calculated.

Strength Tests of Carbon Plastic Samples Using Dynamic Tensometry

Comparative analysis of results of determining the stress-strain state (SSS) of a specimen with the laying of [± 45/90/03/90/± 45] Torayca T700 prepreg with geometric dimensions of 500 × 100 × 1.7 mm during its tension was performed. Stress concentrator was a hole with a diameter of 14 mm located in the center. The loading was carried out on a MTS Landmark test machine. The load increased stepwise through the interval ΔР = 10 kN until complete destruction of the sample, which occurred at P = 97 kN. The stress value was determined by calculation method and experimentally. The Compass 3D system was used, in which the VAT of the sample was simulated at each loading stage. Stress distribution in the model with increasing load is shown, which reflects the process of sample destruction during testing. Experimental values of relative deformations and corresponding stresses were determined using the dynamics-3 multichannel microprocessor-based fast-acting tensometric system. Strain gages were glued to the sample, two of which were located near the edge of the hole, the other two at a distance of 44 mm and 40 mm. It was established that the pre-destructive state corresponded to the redistribution of relative deformations with their subsequent double increase. The loads at which destruction of the sample material appears in the area of installation of strain gauges were determined

Calibration of Steel Rings for the Measurement of Strain and Shrinkage Stress for Cement-Based Composites.

Concrete shrinkage is a phenomenon that results in a decrease of volume in the composite material during the curing period. The method for determining the effects of restrained shrinkage is described in Standard ASTM C 1581/C 1581M–09a. This article shows the calibration of measuring rings with respect to the theory of elasticity and the analysis of the relationship of steel ring deformation to high-performance concrete tensile stress as a function of time. Steel rings equipped with strain gauges are used for measurement of the strain during the compression of the samples. The strain is caused by the shrinkage of the concrete ring specimen that tightens around steel rings. The method allows registering the changes to the shrinkage process in time and evaluating the susceptibility of concrete to cracking. However, the standard does not focus on the details of the mechanical design of the test bench. To acquire accurate measurements, the test bench needs to be calibrated. Measurement errors may be caused by an improper, uneven installation of strain gauges, imprecise geometry of the steel measuring rings, or incorrect equipment settings. The calibration method makes it possible to determine the stress in a concrete sample leading to its cracking at specific deformation of the steel ring.

Nano-Hybrid Versus Micro-Hybrid Composite as Reinforced Resin for Ribbon Bar Attachment

Purpose: The goal of this study was to compare and evaluate stress transmitted to an overdenture abutment tooth supported by polyethylene ribbon bar reinforced composite resin of hybrid type, the hybrid type includes Nano-hybrid composite and Micro-hybrid composite. Methods: Edentulous educational acrylic model modified to receive two canine teeth and duplicated to ten models. Polyvinyl silicon impression material was packed to simulate mucosa covering the ridge. The canines prepared to receive a metal jacket and then infibra ribbon bar constructed on the abutments, mandibular overdenture constructed to each model over the bar, the ten models divided equally into two groups, Group I: Five models received mandibular overdenture supported by two canines splinted with ribbon fiber bar reinforced with Nano-hybrid composite. Group II: Five models received mandibular overdenture supported by two canines, splinted with ribbon fiber bar reinforced with Micro-hybrid composite. Installation of strain gauges around each surface of the abutment, Universal testing machine used to apply load to each model Unilateral (50 N) and bilateral (100 N) vertical static load applied at 1st molar area to measure µ-strains at buccal, lingual, mesial and distal surfaces of the abutment. the outcomes collected and statistically analyzed to compare between the two groups. Result: -Infibra ribbon bar reinforced by Nano-hybrid composite and infibra ribbon bar reinforced by micro-hybrid composite exhibits No significant difference of microstrain transmitted to overdenture abutment except the labial surface. - Under bilateral loading, Infibra ribbon bar reinforced with a Nano-hybrid composite bar transmit higher microstrain to all labial, lingual, and mesial surfaces of overdenture abutment than infibra ribbon bar reinforced with Micro-hybrid composite except the distal surface. Conclusion: -Infibra ribbon bar reinforced with hybrid type composite decrease stresses transmitted to mandibular overdenture abutment -Regarding stress distribution, Micro-hybrid composite is the material of choice to construct infibra ribbon bar over Nano-hybrid composite.

A Lever-Type Method of Strain Exposure for Disk F-Shaped Torque Sensor Design.

Disk-shaped torque sensors are widely used in robotic joints and wheel driving. However, in terms of conventional spoke-type geometries, there is always a trade-off between sensitivity and stiffness, because their strain exposure depends upon a bending deformation mode which causes strain nonuniformity. This paper presents a lever-type method of strain exposure that performs a uniaxial tension and compression deformation mode to optimize the strain uniformity and improve the trade-off. Moreover, on the basis of this approach, the proposed disk F-shaped torque sensor enjoys has axial thinness, easy installation of strain gauges and flexible customization. The simulation and experimental results have validated the basic design idea.

Corrosion damage to cantilever MR walls – Representation in tests and calculations

AbstractAfter the failure of a cantilever retaining wall with counterfort on the Brenner Autobahn, intensive investigation of existing structures showed considerable corrosion damage to cantilever walls with and without counterforts, at the location of construction and day joints. This is difficult to detect and assess due to the placing of the main reinforcement at the back of the construction. The present paper presents a monitoring concept, which enables detection of corrosion damage and also provides a gain of information about the behaviour of the structure. The basis for this concept is the installation of inclination and strain gauges on the front of a retaining wall. In order to explain the functioning of the concept, a rudimentary cantilever model of the effect of corrosion damage is first described. Then the results and findings from a test series with artificial corrosion representation to reinforced concrete structures to validate the presented monitoring concept is described.

Prediction of strain values in reinforcements and concrete of a RC frame using neural networks

The level of strain in structural elements is an important indicator for the presence of damage and its intensity. Considering this fact, often structural health monitoring systems employ strain gauges to measure strains in critical elements. However, because of their sensitivity to the magnetic fields, inadequate long-term durability especially in harsh environments, difficulties in installation on existing structures, and maintenance cost, installation of strain gauges is not always possible for all structural components. Therefore, a reliable method that can accurately estimate strain values in critical structural elements is necessary for damage identification. In this study, a full-scale test was conducted on a planar RC frame to investigate the capability of neural networks for predicting the strain values. Two neural networks each of which having a single hidden layer was trained to relate the measured rotations and vertical displacements of the frame to the strain values measured at different locations of the frame. Results of trained neural networks indicated that they accurately estimated the strain values both in reinforcements and concrete. In addition, the trained neural networks were capable of predicting strains for the unseen input data set.

Identification of moving vehicle parameters using bridge responses and estimated bridge pavement roughness

Passing vehicles cause bridge deformation and vibration. Overloaded vehicles can result in fatigue damage to, or even failure of, the bridge. The bridge response is related to the properties of the passing vehicles, particularly the vehicle weight. Therefore, a bridge weigh-in-motion system for estimating vehicle parameters is important for evaluating the bridge condition under repeated load. However, traditional weigh-in-motion methods, which involve the installation of strain gauges on bridge members and calibration with known weight truck, are often costly and time-consuming. In this paper, a method for the identification of moving vehicle parameters using bridge acceleration responses is investigated. A time-domain method based on the Bayesian theory application of a particle filter is adopted. The bridge pavement roughness is estimated in advance using vehicle responses from a sensor-equipped car with consideration of vehicle-bridge interaction, and it is utilized in the parameter estimation. The method does not require the calibration. Numerical simulations demonstrate that the vehicle parameters, including the vehicle weight, are estimated with high accuracy and robustness against observation noise and modeling error. Finally, this method is validated through field measurement. The resulting estimate of vehicle mass agrees with the measured value, demonstrating the practicality of the proposed method.