Conventional Displacement Sensor Research Articles

Multi-vision-based displacement monitoring using global-local deep deblurring and Rauch-Tung-Striebel smoother

Measuring structural vibrations help assess dynamic performances of civil structures and infrastructure. Although conventional displacement sensors have been widely adopted, they are contact-based methods which lack scalability. Recently, computer vision (CV) has been applied as a noncontact method to measure displacements. However, fast speed of structural vibration (e.g., in shake table tests) can inevitably cause motion blur that imposes challenges in all image-based object/feature detections, especially for normal portable cameras (without high-speed shutters). To address such issue, the study proposed a multi-vision, full-field sensing framework with affordable cameras using a novel global–local detection and deblurring (GLDD) module, which was designed with a generative adversarial network (GAN)-based deblurring model to enhance detection efficiency and accuracy by restoring blemished videos from multiple perspectives. Rauch-Tung-Striebel (RTS) smoother was studied for data fitting using incomplete observations caused due to severe motion-induced blurs. A shake table test was conducted on an aluminum frame with cameras and conventional sensors monitoring the structural vibrations. Fiducial markers were used to track the movement of the key locations on the structure. Results showed that the proposed method is satisfactory to monitor shake table tests when compared to conventional measurements with root-mean-square errors of 0.51–0.95 mm. The proposed deblurring module restored misdetection by 92.1 %, 50.6 %, and 25.2 % for mild-, medium-, and severe-level motion blurs, respectively. Smoother-based data fitting outperformed filter-based one when dealing with highly blemished images. The proposed monitoring system with GLDD and RTS smoother-based data fitting provides a robust measurement solution when dealing with motion blurs.

Rapid and high-precision displacement sensing based on the multiple mode dip areas in a SNAP microresonator.

Whispering gallery mode (WGM) microresonators offer significant potential for precise displacement measurement owing to their compact size, ultrahigh sensitivity, and rapid response. However, conventional WGM displacement sensors are prone to noise interference, resulting in accuracy loss, while the demodulation process for displacement often exhibits prolonged duration. To address these limitations, this study proposes a rapid and high-precision displacement sensing method based on the dip areas of multiple resonant modes in a surface nanoscale axial photonics microresonator. By employing a neural network to fit the nonlinear relationship between displacement and the areas of multiple resonant dips, we achieve displacement prediction with an accuracy better than 0.03µm over a range of 200µm. In comparison to alternative sensing approaches, this method exhibits resilience to temperature variations, and its sensing performance remains comparable to that in a noise-free environment as long as the signal-to-noise ratio is greater than 25dB. Furthermore, the extraction of the dip area enables significantly enhanced speed in displacement measurement, providing an effective solution for achieving rapid and highly accurate displacement sensing.

Reliability Assessment of a Vision-Based Dynamic Displacement Measurement System Using an Unmanned Aerial Vehicle.

In recent years, many studies have been conducted on the vision-based displacement measurement system using an unmanned aerial vehicle, which has been used in actual structure measurements. In this study, the dynamic measurement reliability of a vision-based displacement measurement system using an unmanned aerial vehicle was examined by measuring various vibrations with a frequency of 0 to 3 Hz and a displacement of 0 to 100 mm. Furthermore, free vibration was applied to model structures with one and two stories, and the response was measured to examine the accuracy of identifying structural dynamic characteristics. The vibration measurement results demonstrated that the vision-based displacement measurement system using an unmanned aerial vehicle has an average root mean square percentage error of 0.662% compared with the laser distance sensor in all experiments. However, the errors were relatively large in the displacement measurement of 10 mm or less regardless of the frequency. In the structure measurements, all sensors demonstrated the same mode frequency based on the accelerometer, and the damping ratios were extremely similar, except for the laser distance sensor measurement value of the two-story structure. Mode shape estimation was obtained and compared using the modal assurance criterion value compared with the accelerometer, and the values for the vision-based displacement measurement system using an unmanned aerial vehicle were close to 1. According to these results, the vision-based displacement measurement system using an unmanned aerial vehicle demonstrated results similar to those of conventional displacement sensors and can thus replace conventional displacement sensors.

Fully Integrated Line Array Angular Displacement Sensing Chip

The angular displacement sensor is a digital angular displacement measurement device that integrates optics, mechanics, and electronics. It has important applications in communication, servo control, aerospace, and other fields. Although conventional angular displacement sensors can achieve extremely high measurement accuracy and resolution, they cannot be integrated because complex signal processing circuitry is required at the photoelectric receiver, which limits their suitability for robotics and automotive applications. The design of a fully integrated line array angular displacement-sensing chip is presented for the first time using a combination of pseudo-random and incremental code channel designs. Based on the charge redistribution principle, a fully differential 12-bit, 1 MSPS sampling rate successive approximation analog-to-digital converter (SAR ADC) is designed for quantization and subdivision of the incremental code channel output signal. The design is verified with a 0.35 μm CMOS process and the area of the overall system is 3.5 × 1.8 mm2. The fully integrated design of the detector array and readout circuit is realized for the angular displacement sensing.

A study of multi-target image-based displacement measurement approach for field testing of bridges

ABSTRACT As the demand for field testing of bridges grows, so does the need to optimize field testing procedures to allow for a simplified testing strategy that can be employed more effectively. With the advent of computer vision, there has been limited research exploring Feature-Based Image Registration (FBIR) methods for structural testing of bridges. In this paper, the potential of a simple FBIR approach to accurately capture submillimeter displacements using consumer-grade cameras will be demonstrated through a field test on a reinforced concrete slab bridge and on a full-scale bridge deck specimen in the laboratory. The internal and external parameters that influence the results of this measurement strategy were investigated by using various camera positions during the laboratory tests and applying different threshold parameters to the Speeded-Up Robust Features algorithm used for the feature detection and matching. The FBIR method demonstrates great potential, producing an average measurement accuracy within 1.6% of conventional displacement sensors during the field test and 3.3% during the laboratory tests. Altogether, the advantages to this image-based measurement approach enhance the load testing strategy to be implemented by bridge owners at much lower costs and with minimal complication and field setup.

Cost-effective, vision-based multi-target tracking approach for structural health monitoring

The displacement response of structures is an important parameter in structural health monitoring (SHM). Displacement responses can be applied in both structural performance monitoring and structural dynamic characteristics monitoring. To overcome the shortcomings of traditional contact sensors, a vision-based multi-point structural displacement measurement system equipped with an inexpensive surveillance camera and a consumer camera was developed herein. In addition, to reduce the computing time of target tracking, an improved region-matching algorithm based on the prior knowledge of structural deformation was proposed. Numerical results revealed that the improved region-matching algorithm could save computing time without reducing location accuracy. Moreover, static and dynamic loading tests were conducted on a scale model of a suspension bridge to validate the effectiveness of the proposed vision-based measurement system. Displacement responses and modal parameters obtained from the vision-based measurement system were compared with those of traditional contact sensors, and a satisfactory consistency was obtained. Hence, the proposed vision-based measurement system could be a cost-effective alternative to conventional displacement sensors and accelerometers for SHM.

3D structural vibration identification from dynamic point clouds

Video-based measurement has received increased attention for modal analysis and nondestructive evaluation, playing an important role in the development of the next-generation structural sensing technologies. As these techniques have evolved, more quantitative approaches based on computer vision techniques have emerged on full-field unsupervised structural identification, exploiting the benefits provided by the use of video cameras such as high spatial sensor density and low installation costs. More recent work has started to explore the use of laser point cloud data for 3D mapping of scenes and structures. Sensors such as LIDAR provide huge amounts of measurements at high spatial resolution from which it is possible to estimate accurate structural geometry for applications such as the generation of CAD models. Unfortunately to-date, the frame rate and depth resolution of LIDAR and other sensors capable of 3D geometry measurements has not been sufficient for measuring structural dynamics. In this paper, we introduce an approach for efficient and extremely high resolution 3D structural dynamic identification/modal analysis from point cloud data acquired using a commercial, low-cost, time-of-flight imager. Vibration mode shapes and modal coordinates are extracted from this data by creating virtual Lagrangian sensors based on the point clouds parameters. First, time-varying point cloud data are collected from a vibrating structure. Then, a mesh of virtual sensors is created based on the dynamic point cloud data for tracking the structure’s displacement over time. Next solutions to the blind source separation problem are employed to estimate high resolution 3D mode shapes, modal coordinates, and resonant frequencies. We demonstrate the potential of our proposed approach on laboratory tests and compare the results to the data collected from conventional laser displacement sensors. This technique represents an advance towards efficiently exploring the full advantages of using dynamic point cloud data for practical monitoring applications and has the potential to be extended for a wide range of 3D motion decomposition problems.

A multi-resolution deep feature framework for dynamic displacement measurement of bridges using vision-based tracking system

Structural displacement is an imperative indicator for safety evaluation and maintenance. To address the limitations of conventional displacement sensors, advanced non-contact vision-based trackers offer a promising alternative. Based on the reconstructed Efficient Convolution Operator (ECO), a more powerful multi-resolution deep feature framework is proposed to efficiently encode the informative representation. The fusion of the shallow convolutional and the deep convolutional features is discriminative while preserving spatial and structural information. Furthermore, the discrete feature map is transferred to the continuous spatial domain by introducing an interpolation operator to achieve accurate sub-pixel registration. A careful comparison of the results on the steel suspension bridge demonstrates the high accuracy of the multi-resolution deep feature tracker (MDFT) for displacement measurement. The performances in the time and frequency domain show decent agreement with the results acquired by the laser displacement sensor (LDS), which confirm the low-cost, target-free, high resolution, and non-contact measurement capacities.

Evaluation of a novel video- and laser-based displacement sensor prototype for civil infrastructure applications

Deflection measurements on structures continue to be a challenge with current sensor technologies. Material degradation and changes in the mechanical properties over time (e.g. creep and shrinkage in concrete bridges) directly impact the deflections exhibited by a structure. In this article, we introduce and discuss the evaluation of a novel laser- and video-based displacement sensor prototype to monitor displacements and rotations on structures remotely. The sensor is inexpensive, using off-the shelf components, but also accurate and practical for situations that do not allow the use of conventional displacement sensors, which require a reference base. In contrast to other image-based approaches such as digital image correlation (DIC) or Eulerian-based virtual video sensors (VVS), the digital camera of our proposed solution is located at the measurement location on the structure. The sensor was evaluated using laboratory tests to determine the practicality, accuracy, and sensitivity to lighting conditions. The accuracy of the sensor was found to be approximately ± 0.9 mm (± 0.035 in) (95% prediction limits) for a 30.5 m (100 ft) measurement distance under laboratory conditions. Finally, we applied and evaluated the sensor under real-world conditions on a concrete deck/single steel box girder pedestrian bridge under static and dynamic loading conditions as well as on a five-story steel moment-frame building under ambient conditions. Essential for field applications, the results demonstrate the prototype offers an inexpensive yet practical and accurate solution for monitoring displacements and rotations remotely.

Study on High Lift-Off of Eddy Current Type Rail Displacement Sensor by High Quality Factor

As the conventional eddy current type rail displacement sensor has a small distance (liftoff) from the railhead top surface to a sensor, there is the possibility that a sensor wakes up trouble and malfunction for a collision with the snow and the gravel. As the conventional triangle coil has low Q level, a change of the impedance to depend on rail displacement for when I raise a liftoff is not provided enough. Therefore, the problem that the detection precision of the rail displacement falls to occurs. In this article, I improved a Q level by becoming examination, the high frequency of the drive circuit of the coil structure and enabled measuring it at 50mm that was conventional double size by a liftoff.

An Improved Vision Method for Robust Monitoring of Multi-Point Dynamic Displacements with Smartphones in an Interference Environment.

Current research on dynamic displacement measurement based on computer vision mostly requires professional high-speed cameras and an ideal shooting environment to ensure the performance and accuracy of the analysis. However, the high cost of the camera and strict requirements of sharp image contrast and stable environment during the shooting process limit the broad application of the technology. This paper proposes an improved vision method to implement multi-point dynamic displacement measurements with smartphones in an interference environment. A motion-enhanced spatio-temporal context (MSTC) algorithm is developed and applied together with the optical flow (OF) algorithm to realize a simultaneous tracking and dynamic displacement extraction of multiple points on a vibrating structure in the interference environment. Finally, a sine-sweep vibration experiment on a cantilever sphere model is presented to validate the feasibility of the proposed method in a wide-band frequency range. In the test, a smartphone was used to shoot the vibration process of the sine-sweep-excited sphere, and illumination change, fog interference, and camera jitter were artificially simulated to represent the interference environment. The results of the proposed method are compared to conventional displacement sensor data and current vision method results. It is demonstrated that, in an interference environment, (1) the OF method is prone to mismatch the feature points and leads to data deviated or lost; (2) the conventional STC method is sensitive to target selection and can effectively track those targets having a large proportion of pixels in the context with motion tendency similar to the target center; (3) the proposed MSTC method, however, can ease the sensitivity to target selection through in-depth processing of the information in the context and finally enhance the robustness of the target tracking. In addition, the MSTC method takes less than one second to track each target between adjacent frame images, implying a potential for online measurement.

A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation.

Currently, the majority of studies on vision-based measurement have been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect measurement accuracy. This paper developed a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal contextual aspects. To validate the feasibility, stability, and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination changes and fog interference were simulated experimentally in the laboratory. The results of the proposed method were compared to conventional displacement sensor data and current vision-based method results. It was demonstrated that the proposed method gave better measurement results than the current ones under illumination change and fog interference.

Target-free vision-based technique for vibration measurements of structures subjected to out-of-plane movements

Vibration measurements have been widely used for structural health monitoring (SHM). Usually, wired sensors are required to attach on the testing structure, which may be arduous, costly and sometimes impossible to install those sensors on the remote and inaccessible part of the structure to be monitored. To overcome the limitations of contact sensors based vibration measurement methods, computer vision and digital image processing based methods have been proposed recently to measure the dynamic displacement of structures. Real-life structure subjected to bi-directional dynamic forces is susceptible to significant out-of-plane movement. Measuring the vibrations of structures under the out-of-plane movements using target-free vision-based methods have not been well studied. This paper proposes a target-free vision-based approach to obtain the vibration displacement and acceleration of structures subjected to out-of-plane movements from minor level excitations. The proposed approach consists of the selection of a region of interest (ROI), key-feature detection and feature extraction, tracking and matching of the features along the entire video, while there is no artificial target attached on the structure. The accuracy of the proposed approach is verified by conducting a number of experimental tests on a reinforced concrete structural column subjected to bi-directional ground motions with peak ground accelerations (PGA) ranging from 0.01 g to 1.0 g. The results obtained by the proposed approach are compared with those measured by using the conventional accelerometer and laser displacement sensor (LDS). It is found that the proposed approach accurately measures the displacement and acceleration time histories of the tested structure. Modal identification is conducted using the measured vibration responses, and natural frequencies can be identified accurately. The results demonstrate that the proposed approach is reliable and accurate to measure the dynamic responses and perform the system modal identification for structural health monitoring.

Development of Marker-Free Night-Vision Displacement Sensor System by Using Image Convex Hull Optimization.

Vision-based displacement sensors (VDSs) have the potential to be widely used in the structural health monitoring field, because the VDSs are generally easier to install and have higher applicability to the existing structures compared to the other conventional displacement sensors. However, the VDS also has disadvantages, in that ancillary markers are needed for extracting displacement data and data reliability is significantly lowered at night. In this study, a night vision displacement sensor (NVDS) was proposed to overcome the aforementioned two limitations. First, a non-contact NVDS system is developed with the installation of the infrared (IR) pass filter. Since it utilizes the wavelength of the infrared region and it is not sensitive to the change of a visible ray, it can precisely extract the shape information of the structure even at night. Second, a technique to extract the feature points from the images without any ancillary marker was formulated through an image convex hull optimization. Finally, the experimental tests of a three-story scaled model were performed to investigate the effectiveness of proposed NVDS at night. The results demonstrate that the NVDS has sufficiently high accuracy even at night and it can precisely measure the dynamic characteristics such as mode shapes and natural frequencies of the structure. The proposed system and formulation would extend the applicability of vision sensor not only into night-time measure but also marker-free measure.

Self-Balancing Signal Conditioning Circuit for a Floating-Wiper Resistive Displacement Sensor

The limited operating life of conventional resistive displacement sensor is due to wear and tear because the wiper has to rub against the resistive element during operation. This problem can be eliminated by using a floating wiper. However, the absence of electrical contact between the wiper and the resistive element demands the use of complex signal conditioning circuitry to obtain an output proportional to displacement. We now propose a simple self-balancing signal conditioning circuit suitable for a floating wiper resistive displacement sensor that provides an output linear to the displacement. The proposed signal conditioning circuit employs only a single dc reference voltage and provides an output that is independent of the coupling capacitance that arises between the floating wiper and the resistive element. Hence, errors due to mismatch between reference voltages and variations in the coupling capacitance are completely eliminated. The results obtained from simulation studies and the tests conducted on a prototype built, establish the efficacy of the proposed method. Analysis of the proposed scheme presented here establishes the ground rules for avoiding pitfalls in the design stage itself.

On-machine multi-directional laser displacement sensor using scanning exposure method for high-precision measurement of metal-works

We propose a multi-directional triangulation optical system and scanning exposure method for precise height displacement measurement of metal-works. It is difficult to measure metal surfaces precisely with a conventional triangulation-laser displacement sensor due to machining traces. Because reflection characteristics vary by position on the workpiece, measurement errors occur even with positions of the same height. Our multi-directional triangulation optical system performs triangulation using a lens array consisting of four lenses located in a cross. Since our optical system can detect reflected light from four directions, we can achieve high-precision measurement even if the reflection characteristics vary. In addition, our scanning exposure method accumulates reflected light from many positions by scanning a projection beam onto a workpiece during the image sensor’s exposure time. Because the shape of a spot image on an image sensor is the sum of the reflected light from many positions, measurement error due to measurement position is reduced. Our proposed methods can achieve on-machine deflection measurement of metal-works with a target accuracy of ±1μm.

Model and Control of a Compact Long-Travel Accurate-Manipulation Platform

Stick-slip accurate manipulator (composed of piezo and slider) is widely applied in scanning electron microscope (SEM). However, for its positioning control in SEM, conventional displacement sensors are hard to be used because of their big physical dimensions or magnetic working principles. To solve these problems, this paper proposes to use strain gauge as the accurate manipulator's positioning sensor and develops a displacement-prediction method to achieve a long-travel accurate positioning (positioning error no more than 2% of the traveling range) in SEM. The slider's feedback displacement is obtained indirectly, which is calculated and predicted by the LS friction model and the piezo's motion statements obtained from the strain gauge. Meanwhile, the application of strain gauge can minimize the manipulator's geometric dimension guarantee a nonmagnetic working environment and avoid the thermal radiation caused by sensor in SEM. An LS friction model with the main friction characteristics is developed to provide the friction calculation between the slider and the piezo. Feedforward PID control method is employed to improve the system's dynamic characteristics. Experiments are carried out to validate the effectiveness of displacement-prediction control applying on the positioning platform.

Experimental validation of cost-effective vision-based structural health monitoring

Monitoring structural displacement responses can provide quantitative information for both structural safety evaluations and maintenance purposes. To overcome the limitations of conventional displacement sensors, advanced noncontact vision-based systems offer a promising alternative. This study validates the potentials of the vision displacement sensor for cost-effective structural health monitoring. The results of laboratory experiments on simply-supported beam structures demonstrate the high accuracy of the vision sensor for dense full-field displacement measurements. The identified natural frequencies and mode shapes from measurements by using one camera match well with those from an array of accelerometers. Moreover, the smoother mode shapes make possible the noncontact damage detection based on the conventional mode shape curvature index. This study also discusses the issues concerning the practical applications of the vision displacement sensors, such as the scaling factor determination, measurement with small camera tilt angles, tradeoffs between the measurement resolution and measurement points or field of view, etc. Furthermore, the remote, real-time and multi-point measurement capacities of the vision sensor are confirmed through field tests of Manhattan Bridge during train passing.

Seismic Response Measurement of an Under-Water Model Through High Speed Camera and Feature Tracking

Shake table testing is a viable validation tool for bench-marking the analytically computed seismic response of under-water structural models with fluid–structure interaction effects. Conventional displacement sensors like LVDTs (linearly variable differential transformer) cannot be used for under-water response measurements as they require a stationary platform near the measurement point as a reference. This rather limiting application of LVDTs in several situations warrants the use of high speed camera as a potential measurement tool. This article presents the vision based displacement measurement of an under-water model tested on a shake table. High speed video camera is used to record the motion during shaking. The video images are processed using a special motion tracking algorithm and displacements are measured. The sloshing effect of water on the test model and their free vibration at the end of the shaking are studied to calculate natural frequency and damping. Vital information on the allowable under-water motion of the test model is obtained and discussed.

Fiber Optic Radial Displacement Sensor-Based a Beam-Through Technique

A fiber optics displacement sensor based on a beam-through technique has wide application due its simplicity, high accuracy, and immune to electromagnetic interference. The fingerprint for such a sensor system is established through the longitudinal displacement. However, it is known that the highest intensity modulation is normally fall at zero distance for the beam-through technique. Thus, it is valuable to take advantage at such optimization position. A novel method is introduced by conducting a fiber optic sensor-based radial displacement. Three types of fiber optic, including 1000, 500, and $265~\mu \text{m}$ core diameters, were employed to optimize the probe. A Gaussian profile is identified to be the fingerprint for the radial displacement sensor, which entirely different with a linear one for longitudinal displacement. The radial displacement fiber optic sensor is a core diameter-dependent. The bandwidth at full width half maximum tends to be broader with the core diameter of a fiber optic. Higher response of the sensor is achieved at the negative side of the Gaussian curve comparable with positive part. It is realized that the sensitivity of the radial sensor is 7 times higher correspond to core diameter and 3 times better performance than the conventional displacement sensor.