Strain Sensor Data Research Articles

Strain mode measurement of asphalt pavements using distributed fibre optic sensor data

This paper proposes a method for measuring strain modes in asphalt pavements using distributed fibre optic strain sensor data. A processing algorithm based on cross power spectral density (CPSD) was developed to utilize high spatial resolution strain data to determine the CPSD peak frequency and corresponding relative amplitude. The proposed method was applied in both laboratory and field settings, comparing the results of distributed fibre optic cables with those of accelerometers or theoretical solutions to validate accuracy. Local heating of asphalt structures was used to test the sensitivity of the measured strain modes to changes in material properties. The results demonstrate that different fibre optic cables can provide peak frequencies comparable to traditional accelerometers (±0.6 Hz). Additionally, they offer relative amplitude with a resolution of 1 cm, achieving a modal assurance criterion exceeding 0.95 when compared to theoretical strain mode shapes. The method also sensitively reflects material property changes at different positions within the asphalt structure. This indicates that the proposed approach can effectively measure strain modes in asphalt structures, offering a more detailed understanding of the distribution of structural properties compared to traditional methods.

Robust Structural Health Monitoring of CFRP Structures under Varying Loads using NSFD and Machine Learning with Combined Usage of Acceleration and Strain Data

Structural health monitoring (SHM) of objects in situ often requires a suitable combination of different methods and measurement techniques. In the field of aerospace applications, the previous research project "Combined acoustic and modal structure monitoring" dealt with the development of a robust SHM system for damage detection in carbon-fibre-reinforced polymer (CFRP) structures under varying realistic loads. The methods combined within the project were based on guided waves, acoustic emission, different vibration monitoring techniques and strain measurement. The present paper deals with the application of the nullspace-based fault detection algorithm (NSFD) combined with a machine learning approach based on strain measurements to classify different load conditions of the structure. Due to the high sensitivity to changes in the statistical properties of the measurement data, the NSFD algorithm reacts very sensitively to structural changes, but also to changes in the environmental conditions, such as changes in the external loads and the resulting stress state of the structure. In the paper, the impact of varying external loads on the NSFD damage indicator is analysed and shown. To prevent the NSFD algorithm from causing false alarms due to changes in the load conditions, but still being sensitive to small structural changes due to impact damages, the strain sensor data is used to predict the external loads on the structure. For this, machine learning is used to create clusters of similar stress states. For every cluster, a baseline for the NSFD algorithm must be made. The resulting approach is robust against changes in external loads and sensitive to small changes in the structure due to damages at the same time. The theory and the algorithms are successfully tested with measured data sets from a CFRP-airplane structure.

Automated location of steel truss bridge damage using machine learning and raw strain sensor data

Strategic major infrastructure ageing requires structural health monitoring usage to avoid critical safety issues and disasters. Machine Learning can be a valuable tool to automate the process of analysing raw monitoring data. Usually, frequency domain damage-sensitive features are extracted with data pre-processing procedures; thus these features are used as input for classification or regression problems.This paper describes a method of locating damage in steel truss railway bridges through machine learning classification tools, enabling automatic analysis of raw strain sensors signals without any pre-processing or preliminary feature extraction. Data were generated by simulating different damage scenarios with a finite element software, and then were processed by two machine learning classification tools: (a) the K-nearest Neighbours was adopted with the Dynamic Time Warping algorithm metric to select the most informative features; (b) a model suitable for high-dimensional data analysis, known as the Convolutional Neural Network, was then trained to classify strain sensors time series. The results indicate that the method applied can detect damages with an accuracy of 93% and is suitable for structural health monitoring.

HIGH-SPEED WAVE-PIERCING CATAMARAN GLOBAL LOADS DETERMINED BY FEA AND SEA TRIALS

Wave-piercing catamaran hull forms are widely used for high-speed ferry applications due to the hull slenderness, suitable for achieving high speeds. The global loads acting on these craft are of great interest as there is limited knowledge on determining the magnitude of the loads, in particular when operating in random sea conditions. Longitudinal and transverse bending moments as well as pitch connecting moments and hull torsion loads act on the hull simultaneously. This paper investigates the estimation of these global loads from full-scale catamaran sea trials strain gauge data using finite element methods. Det Norske Veritas (DNV) load cases are applied to a finite element model in order to determine the conversion between local strain values observed during sea trials and prevailing global loads. Comparisons are thus made of global loads determined from strain data collected from sea trials with DNV global load cases. The results show that this method is relatively reliable for the prediction of hull global loads in the absence of slamming. Comparisons have been made for different heading angles. The quasi-static design loads are important during the ship design stage, as they are good proxies in wavelengths comparable to the hull length for rationally determined loads obtained from a first-principles dynamic analysis. The broad aims here are to demonstrate the use of strain sensor data obtained during sea trials for determination of global sea loads, to reconcile the loads thus determined with DNV load cases and thereby to improve the accuracy of the predicted loads used in design to increase the structural efficiency of vessel design.

Fatigue Damage Monitoring of Wind Turbine Blades by Using the Kalman Filter

Fatigue damage accumulation needs to be monitored for extending the lifetime of horizontal axis wind turbines. To monitor accumulation of fatigue damage, this paper proposes a blade load and fatigue damage monitoring system consisting of physics models coupled with the Kalman filter (KF). The proposed monitoring system estimates bending moment distribution along the blade as a time series from 1 Hz data collected by a supervisory control and data acquisition (SCADA) system and from 20 Hz strain sensor data collected at the blade root. Static load is computed on the basis of blade element and momentum theory from SCADA data, whereas dynamic response of the blade is computed by assuming a mass–spring–damper system. The KF corrects the estimated moment distribution with moment measured at the blade root. The monitoring system is demonstrated and validated by installing it on a 2 MW floating downwind turbine and estimating strain and fatigue damage at the medium span position of the blades, where a strain sensor is installed. The validation results show that strain wave forms and fatigue damage estimated by the monitoring system with the KF are consistent with those computed from the strain measurement with sufficient accuracy for structural health monitoring.

Noise Cancellation of Helicopter Blade Deformations Measurement by Fiber Bragg Gratings.

The work presents data treatment methods aimed at eliminating the noise in the strain sensor data induced by vibrations of the helicopter blade in flight conditions. The methods can be applied in order to enhance the metrological performance of the helicopter weight estimation system based on the deformation measurement of the main rotor blades. The experimental setup included a composite plate fixed to the vibrating stand on the one end, with six fiber-optic strain sensors attached to its surface. In this work, the procedure of the optimal linear smoothing (POLS) and 3D-invariant methods were used to obtain monotone calibration curves for each detector, thereby making it possible to distinguish the increase of load applied to the free end of the plate with an increment of 10 g. The second method associated with 3D invariants took into account 13 quantitative parameters defined as the combination of different moments and their intercorrelations up to the fourth-order inclusive. These 13 parameters allowed the calculation of the 3D surface that can serve as a specific fingerprint, differentiating one set of initial data from another one. The combination of the two data treatment methods used in this work can be applied successfully in a wide variety of applications.

A Comparative and Review Study on Shape and Stress Sensing of Flat/Curved Shell Geometries Using C0-Continuous Family of iFEM Elements.

In this study, we methodologically compare and review the accuracy and performance of C0-continuous flat and curved inverse-shell elements (i.e., iMIN3, iQS4, and iCS8) for inverse finite element method (iFEM) in terms of shape, strain, and stress monitoring, and damage detection on various plane and curved geometries subjected to different loading and constraint conditions. For this purpose, four different benchmark problems are proposed, namely, a tapered plate, a quarter of a cylindrical shell, a stiffened curved plate, and a curved plate with a degraded material region in stiffness, representing a damage. The complexity of these test cases is increased systematically to reveal the advantages and shortcomings of the elements under different sensor density deployments. The reference displacement solutions and strain-sensor data used in the benchmark problems are established numerically, utilizing direct finite element analysis. After performing shape-, strain-, and stress-sensing analyses, the reference solutions are compared to the reconstructed solutions of iMIN3, iQS4, and iCS8 models. For plane geometries with sparse sensor configurations, these three elements provide rather close reconstructed-displacement fields with slightly more accurate stress sensing using iCS8 than when using iMIN3/iQS4. It is demonstrated on the curved geometry that the cross-diagonal meshing of a quadrilateral element pattern (e.g., leading to four iMIN3 elements) improves the accuracy of the displacement reconstruction as compared to a single-diagonal meshing strategy (e.g., two iMIN3 elements in a quad-shape element) utilizing iMIN3 element. Nevertheless, regardless of any geometry, sensor density, and meshing strategy, iQS4 has better shape and stress-sensing than iMIN3. As the complexity of the problem is elevated, the predictive capabilities of iCS8 element become obviously superior to that of flat inverse-shell elements (e.g., iMIN3 and iQS4) in terms of both shape sensing and damage detection. Comprehensively speaking, we envisage that the set of scrupulously selected test cases proposed herein can be reliable benchmarks for testing/validating/comparing for the features of newly developed inverse elements.

Crack Detection Zones: Computation and Validation.

During the development of aerospace structures, typically many fatigue tests are conducted. During these tests, much effort is put into inspections in order to detect the onset of failure before complete failure. Strain sensor data may be used to reduce inspection effort. For this, a sufficient number of sensors need to be positioned appropriately to collect the relevant data. In order to minimize cost and effort associated with sensor positioning, the method proposed here aims at minimizing the number of necessary strain sensors while positioning them such that fatigue-induced damage can still be detected before complete failure. A suitable detection criterion is established as the relative change of strain amplitudes under cyclic loading. Then, the space of all possible crack lengths is explored. The regions where the detection criterion is satisfied before complete failure occurs are assembled into so-called detection zones. One sensor in this zone is sufficient to detect criticality. The applicability of the approach is demonstrated on a representative airplane structure that resembles a lower wing section. The method shows that four fatigue critical spots can be monitored using only one strain sensor in a non-intuitive position. Furthermore, we discuss two different strain measures for crack detection. The results of this paper can be used for reliable structural health monitoring using a minimum number of sensors.

Wearable strain sensor suit for infants to measure limb movements under interaction with caregiver

Development of motion capture technology has enabled the measurement of body movements over long periods of time in daily life. Although accelerometers have been used as primary sensors, problems arise when they are used to measure the movements of infants. Because infants and caregivers interact frequently, accelerometer data from infants may be significantly distorted by a caregiver’s movement. To overcome this problem, a strain sensor suit was developed for infants to measure flexion and extension movements of the limbs. A case study was performed to analyze the strain sensor data of an infant in relation to the accelerometer data of the infant’s and a caregiver’s body under various types of infant–caregiver interaction. The results demonstrated that the strain sensor data had low correlation with the accelerometer data of the infant and caregiver while the accelerometer data between infant and caregiver had higher correlation. This suggests that the strain sensor is suitable to detect limbs’ angular displacements mostly independent from the translational body movements exerted by a caregiver.

A Fiber Bragg Grating Force Sensor with Sensitization Structure

This article presents a fiber Bragg grating (FBG) force sensor with enhanced sensitivity. a strain-reinforcing mechanism comprised of a linear structure and loop-shaped structure is proposed. When external tension is applied, the strain directions are opposite in the two structures. Two FBGs were adhered to the surfaces of positive and negative strain bodies and sensor data were compared. Then, the sensing principle of the FBG force sensor was deduced based on the theory of material mechanics. Results of theoretical calculations and finite element analysis (FEA) show that sensitivity of the proposed sensor is about 5.07 times higher than an FBG directly adhered to the surface of the substrate. Comprehensive testing of the sensor prototype was conducted. Experimental results show that the sensor can achieve a sensitivity of 38.25 pm/kN with a repeatability error and hysteresis error of 2.11% and 1.76%, respectively. The enhanced coefficient is 4.84, which is basically consistent with theoretical design value of 5.07. In addition, favorable temperature compensation and creep resistance were obtained during performance tests. Good capability for alternating strain measurement has also been demonstrated. Our results demonstrate the potential for superior structural health monitoring in civil engineering applications using the proposed sensor structure.

High-Speed Wave-Piercing Catamaran Global Loads Determined by FEA and Sea Trials

Wave-piercing catamaran hull forms are widely used for high-speed ferry applications due to the hull slenderness, suitable for achieving high speeds. The global loads acting on these craft are of great interest as there is limited knowledge on determining the magnitude of the loads, in particular when operating in random sea conditions. Longitudinal and transverse bending moments as well as pitch connecting moments and hull torsion loads act on the hull simultaneously. This paper investigates the estimation of these global loads from full-scale catamaran sea trials strain gauge data using finite element methods. Det Norske Veritas (DNV) load cases are applied to a finite element model in order to determine the conversion between local strain values observed during sea trials and prevailing global loads. Comparisons are thus made of global loads determined from strain data collected from sea trials with DNV global load cases. The results show that this method is relatively reliable for the prediction of hull global loads in the absence of slamming. Comparisons have been made for different heading angles. The quasi-static design loads are important during the ship design stage, as they are good proxies in wavelengths comparable to the hull length for rationally determined loads obtained from a first-principles dynamic analysis. The broad aims here are to demonstrate the use of strain sensor data obtained during sea trials for determination of global sea loads, to reconcile the loads thus determined with DNV load cases and thereby to improve the accuracy of the predicted loads used in design to increase the structural efficiency of vessel design.

Strain sensing characteristics of 3D‐printed conductive plastics

Three types of commercially available conductive three-dimensional (3D) printing filament are electrically characterised for use in 3D-printed functional devices. The three plastics were carbon dispersed acrylonitrile butadiene styrene, carbon dispersed polylactic acid (PLA) and graphene dispersed PLA. The method of 3D printing used was material extrusion and prints were made in both single and dual extrusion modes. The plastics were found to be piezoresistive, enabling them to be characterised as strain sensors. The electrical characteristics of these materials enabled the measurement of strain using low cost, readily available prototyping equipment and minimising the requirement for dedicated instrumentation components (e.g. Wheatstone bridges). Increasing the thickness of the plastics improved conductivity. However, this also decreased the reliability and reproducibility of strain sensor data due to a complex internal 3D structure. The recommendation for reliable use in prototyping and manufacturing is to print tracking under 0.8 mm thickness, thus producing resistance measurements that are predictable and follow a linear regression up to R2 = 0.9991. A dual extruded 3D print was fabricated as a final demonstration. A force sensing resistor interface was created. The final demonstration uses a PIC18F45K20 microcontroller to process sensor inputs, outputting to an alphanumeric LCD.

Main Lobe Control of a Beam Tilting Antenna Array Laid on a Deformable Surface

The projection method (PM) is a simple and low-cost pattern recovery technique that already proved its effectiveness in retrieving the radiation properties of different types of arrays that change shape in time. However, when dealing with deformable beam-tilting arrays, this method requires to compute new compensating phase shifts every time that the main lobe is steered, since these shifts depend on both the deformation geometry and the steering angle. This tight requirement causes additional signal processing and complicates the prediction of the array behavior, especially if the deformation geometry is not a priori known: this can be an issue since the PM is mainly used for simple and low-cost systems. In this letter, we propose a simplification of this technique for beam-tilting arrays that requires only basic signal processing. In fact the phase shifts that we use are the sum of two components: one can be directly extracted from strain sensor data that measure surface deformation and the other one can be precomputed according to basic antenna theory. The effectiveness of our approach has been tested on two antennas: a 4 × 4 array (trough full-wave simulations and measurements) and on an 8 × 8 array (trough full-wave simulations) placed on a doubly wedge-shaped surface with a beam tilt up to 40 degrees.

Application of a type of strain block FBG sensor for strain measurements of squeezing rock in a deep-buried tunnel

Tunneling in squeezing rock often encounters large deformation, which threatens the safety and stability of tunnel support during tunnel construction. In this work, to acquire a greater amount of strain sensor data to fully understand the stress/strain state of surrounding rock during tunnel excavation, a special type of strain block with fiber Bragg grating (FBG) sensors has been introduced, which is characterized by three groups of FBG strain rosettes adhered to three adjacent surfaces of the block, respectively, and each strain rosette distributed in the form of a 0°–45°–90° arrangement. Applying this type of FBG strain block sensor to a deep-buried tunnel in squeezing rock, six strain components in one strain block (representative of strain state of a point in rock) could be obtained with the processing method. The monitoring results can reflect the effect of tunnel excavation due to abrupt changes of strain monitored in rock, and were verified as being in reasonable agreement with numerical simulation. Therefore, the strain block with FBG sensors can be applicable in measuring strains in squeezing rock.

Prediction of Impact Contact Forces of Composite Plates Using Fiber Optic Sensors and Neural Networks

Real-time determination of contact forces due to impact on composite plates is necessary for on-line impact damage detection and identification. We demonstrate the use of fiber optic strain sensor data as inputs to a neural network to obtain contact force history. An experimental and theoretical study is conducted to determine the in-plane strains of a clamped graphite/epoxy composite plate upon low-velocity impacts using surface-mounted extrinsic Fabry-Perot interferometric (EFPI) strain sensors. The plate is impacted with a semispherical impactor with various impact energies using the drop-weight technique. The significant features of the strain and contact force response are contact duration, peak strain, and strain rise time. We have designed and built an instrumented drop-weight impact tower to facilitate the measurement of contact force during an impact event. The impact head assembly incorporates a load cell to measure the contact forces experimentally. An in-house finite-element program is used to establish the validity of the EFPI fiber optic sensor contact force response. The finite-element model is based on a higher-order shear deformation theory and accounts for geometric nonlinearity. Experimental load cell data and finite-element impact-induced contact force responses are in close agreement. The load cell data is used to train a three-layer feed-forward neural network which utilizes the Delta Bar Delta back-propagation algorithm. The output of the neural network simulation is the contact force history and the inputs are fiber optic sensor data in two different locations and time in 10-ms intervals. The efficiency and accuracy of the neural network method is discussed. The neural network scheme recovers the impact contact forces without using any complex signal processing techniques.