Displacement Measurement Method Research Articles

A novel peak positioning method for nanometer displacement measurement by optical linear encoder

In the field of precision measurement, subdividing displacement within the grating period to achieve high precision and high resolution remains a significant challenge. For subdivision, traditional optical linear encoders need complex coding and complicated optical systems, which struggle to meet the demands of nanometer measurement. In this paper, based on the fundamental principles of grating displacement measurement, a high-precision peak positioning method from grating images is proposed for linear displacement measurements with nanometer resolution and submicron accuracy. By utilizing a local gradient interpolation algorithm to optimize curves, it could further improve the accuracy of displacement measurement. Experiments demonstrate that this method achieves a resolution of 2 nm and an accuracy of ± 0.1 μm in a range of 50 mm using a grating with a 20 μm period. This sets the groundwork for subdividing optical linear encoders further, the resolution and accuracy could be further improved by optimizing the subdivision method.

Non-contact measurement of structural dynamic displacement based on improved edge detection and video interpolation

In structural health monitoring (SHM), smartphones are increasingly used for non-contact dynamic displacement measurement using computer vision. To overcome limitations in capturing high-frame-rate data, a non-contact measurement method of structural dynamic displacement based on an improved edge detection algorithm and video frame interpolation is proposed. Initially, a smartphone is employed to capture the structure’s vibration video and then generates intermediary frames between adjacent frames that match the structure’s motion state by using a video frame interpolation algorithm to obtain a video with a high frame rate. Subsequently, the improved edge detection algorithm is applied to measure the displacement within the interpolated video. In order to address the problem that traditional edge detection may have false edges leading to the degradation of measurement accuracy, a feature point tracking technique is proposed based on the traditional edge detection technique, which effectively excludes the interference of false edges in ROI and improves the accuracy of displacement monitoring. Experimental validation on a cantilever beam and steel frame bridge demonstrates the method’s viability and precision, highlighting its effectiveness in improving displacement measurement accuracy compared to traditional methods.

Radial Displacement Measurement Method for Magnetic Bearing Based on FPC Coils

Radial Displacement Measurement Method for Magnetic Bearing Based on FPC Coils

Accelerometer-assisted computer vision data fusion framework for structural dynamic displacement reconstruction

Accurate displacement measurement provides crucial information for evaluating the structural health condition. It is important to note that both direct and indirect displacement measurement methods have their own limitations, which can be remedied by leveraging each other’s strengths. In this study, a novel accelerometer-assisted computer vision data fusion framework is developed for accurately reconstructing structural dynamic displacement. The framework does not require a specific mathematical model or prior knowledge about the data’s characteristics, allowing it to be applied more broadly across different applications without the constraints of model assumptions. The core of this framework is to integrate successive variational mode decomposition (SVMD) and Bayesian optimization approaches to adaptively determine the weight factors of the fusion components. Importantly, an enhanced optical flow approach is presented for converting pixel movement to structural displacement from natural targets. This approach can effectively reduce the selection of mis-matched points within the ROI (Region of Interest), thereby reducing drift errors. The developed framework is verified via shaking table tests of a reinforced concrete frame structure under seismic excitation. Results indicate that the developed framework excels in accurately estimating structural dynamic displacement. Compared to single-vision displacement identification results, the proposed framework demonstrates lower peak error (< 1.65 mm) and normalized root mean square error (< 0.30). Meanwhile, the reconstructed displacement, by introducing dynamic displacement component from acceleration measurement, presents a wider frequency range than the vision-based displacement.

An improved DFD method for three-dimensional displacement measurement of vision-based tactile sensor

Currently, traditional tactile sensors based on the principles of capacitance or piezoelectricity have complex structures and difficulty in obtaining tactile information. A vision-based tactile sensor is introduced which can realize visual measurement of three-dimensional displacement in this paper. The vision-based tactile sensor is mainly composed of an elastomer embedded with marker point array, a transparent acrylic plate, 8 LED lights and a micro monocular camera. The elastomer deforms when the tactile sensor contacts an object, and the micro monocular camera is used to capture the elastomer deformation and transmit it to the computer in the form of image, and then the three-dimensional displacement information is obtained by processing the image. In order to more accurately recover the missing dimensional information in the three-dimensional displacement detection of monocular camera, an improved DFD (Depth from Defocus) method based on finite element theory is proposed in this paper. It is verified by experiments that the improved DFD method proposed in this paper can measure the three-dimensional displacement information more accurately compared with the DFD method. In addition, an experiment is conducted to prove the robustness of the improved DFD method on the robotic gripper. The experimental results demonstrate that the three-dimensional displacement measurement method proposed in this paper can provide technical support for the design and development of vision-based tactile sensors.

A visual measurement method of vibration displacement of railway bridge bearings based on phase correlation

Currently, contact displacement meters for bearing displacement measurement are prone to fatigue failure. Non-contact visual measurement has become a more effective technique. In this study, an innovative model based on phase correlation is established for the visual measurement of vibration displacement of railway bridge bearings. Firstly, it is suggested to extract the vehicle–bridge interaction time using the sliding window average detection and K-means methods. Secondly, to circumvent the limited measurement accuracy of the existing methods, the phase correlation technique is applied to efficiently calculate the displacement of bearings within a two-dimensional plane. Thirdly, an image sharpness estimation method based on the Tenengrad function is proposed, and then a line segment detector is used for updating the measurement parameters. Through comparative experiments, the measurement error of presented method is within 0.04 mm. In the process of long-term service, the proposed method exhibits no fatigue failure issues, highlighting its good technical advantage.

Optical shaping self-mixing interferometry with a neural network for displacement measurement

Based on the characteristics of optical shaping self-mixing interference (SMI) and its perfect alignment with the input requirements of neural networks (NNs) for phase extraction, a novel, to our knowledge, displacement measurement method is proposed in this work. Optical shaping involves using a static Fabry–Perot cavity to map the periodic variations of optical frequency generated by SMI, achieving fringe multiplication, signal normalization, and enhancement for SMI optically. A NN trained on simulated data is used to directly extract the phase from the spectrum-mapped SMI signal. This measurement technology achieves a relative accuracy of 10-3 and advances the development of SMI.

Experimental and Numerical Analysis of Straw Motion under the Action of an Anti-Blocking Mechanism for a No-Till Maize Planter

To address the low clearance rate issue of the anti-blocking mechanism for maize no-till planters in the Huang-Huai-Hai Plain of China, experiments and simulations were conducted to analyze the individual and collective movements of straw under the action of the round roller-claw anti-blocking mechanism. A tracer-based measurement method for straw displacement was applied firstly. Experimental results showed that the straw forward displacement could be characterized by the average horizontal displacements of longitudinal and lateral tracers, while the straw side displacement could be characterized by the lateral displacement of the longitudinal tracer. The straw forward displacement was 58.95% greater than the side displacement. Forward, side, and total displacements of straw increased as the mechanism’s forward speed increased from 3 km/h to 7 km/h, with corresponding rates of increase at 233.98%, 43.20%, and 162.47%, respectively. Furthermore, a model of straw–soil–mechanism interaction was constructed in EDEM 2022 software. The relative error between experimental and simulated straw clearance rates was 11.20%, confirming the applicability of the simulation model for studying straw–soil–mechanism interaction. Based on the simulation model, three straw tracers of different lengths were selected to study the motion behavior of straw. It was inferred that despite differences in straw length, the movement behaviors of the three straw tracers under the influence of the anti-blocking mechanism were similar. Additionally, longer straws exhibited greater displacements in all directions. This paper serves as a reference for studying straw motion behavior influenced by anti-blocking mechanisms.

Parallel camera network: Motion-compensation vision measurement method and system for structural displacement

This study proposes a motion-compensation vision measurement method and system for structural displacement, termed the parallel camera network (PCN). Based on the fixedly connected calibration cameras and measurement cameras, the PCN method effectively address the optical measurement problems of the unstable observation platform. Unlike existing ego-motion compensation methods, the PCN method allows for reference or stationary points that are distant from the camera stations, significantly reducing motion-induced measurement errors of the observation platform. Laboratory tests showed that compensating for platform motion tripled measurement accuracy using the PCN method. In practical applications, the system has been successfully deployed for monitoring subsidence of operational high-speed rail piers and the arch axis during large-span arch bridge construction. The proposed method and system significantly enhance the engineering flexibility of visual measurements.

Structural displacement measurement using deep optical flow and uncertainty analysis

Displacement serves as a crucial indicator in the field of structure health monitoring for assessing the condition of civil engineering structures. Computer vision is commonly employed for this purpose due to its cost-effectiveness and convenience. Traditional non-contact computer vision methods, such as optical flow, have been applied to obtain the displacement of bridges. However, real-time monitoring remains challenging due to the computational complexity involved. Therefore, optical flow based on deep learning (referred to as deep optical flow) has gained significant attention. Its main advantage lies in its ability to achieve real-time displacement monitoring, which is of great significance. Furthermore, the accuracy of deep optical flow is comparable to that of traditional optical flow methods for displacement measurement. In this paper, the deep optical flow, RAFT-GOCor, is proposed and applied to extract the displacement of structures, then Bayesian RAFT-GOCor based on Monte Carlo dropout is also presented and applied to analyze the uncertainty of the displacement. The algorithms are verified by laboratory simulated experiments and field bridge loading test. The results indicate that both deep optical flow and uncertainty analysis are feasible methods for measuring structural displacement.

Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors

The piston-type accumulator is an energy storage device in hydraulic–pneumatic systems, playing a significant role in industries such as petrochemicals, heavy machinery, and steel metallurgy. The displacement parameters of the piston-type accumulator are vitally important for fault diagnosis and early warning in hydraulic systems. Traditional displacement measurement methods cannot meet the requirements of the internal testing environment of the accumulator. Therefore, this paper proposes an accumulator piston displacement signal compensation method based on rotational factors and support vector regression. Firstly, empirical mode decomposition is utilized to denoise the signal. Then, rotational factors are used to generate a delay compensation module to compensate for the signal attenuation and time delay caused by metallic reflection and scattering within the cylinder of the radar signal. The support vector regression model is improved based on a hash table to enhance its computational efficiency and achieve radar displacement signal compensation. Finally, a simulation experiment is designed to verify the effectiveness of the proposed method.

Thermography measurement for bridge displacement in the darkness using power-free target

Vision-based displacement measurement has become increasingly popular in bridge health assessment. However, this technology faces challenge that a complicated power-consuming supply system must be installed in the darkness, making it unable to meet the needs of low energy consumption and low cost. This paper proposes a bridge displacement measurement method using power-free target and thermography to address it. First, a thermography measurement system for bridge displacement in the darkness is developed. The power-free target with a hollowed-out circular feature is adopted to form the contrast to the background, and graphene is coated on the target surface to enhance the infrared emissivity and contrast further. Second, a sub-pixel elliptic center positioning algorithm based on thermography enhancement and data optimization is proposed to improve the accuracy and the ability to resist occlusion. The feasibility of the method is verified by laboratory tests and bridge field tests. When the measurement distance is within 15 m, the results meet the engineering error requirements of 5%, with the prospect of popularization in small and medium-sized bridges displacement measurement.

Grating (Moiré) Microinterferometric Displacement/Strain Sensor with Polarization Phase Shift.

Grating (moiré) interferometry is one of the well-known methods for full-field in-plane displacement and strain measurement. There are many design solutions for grating interferometers, including systems with a microinterferometric waveguide head. This article proposes a modification to the conventional waveguide interferometer head, enabling the implementation of a polarization fringe phase shift for automatic fringe pattern analysis. This article presents both the theoretical considerations associated with the proposed solution and its experimental verification, along with the concept of in-plane displacement/strain sensing using the described head.

An innovative binocular vision-based method for displacement measurement in membrane structures

Abstract This article presents a new binocular vision method for accurate deformation measurements of flexible membrane structures. Using enhanced marker points on the membrane, the method identifies areas for displacement measurements, filtering out unwanted image features with scale-invariant feature transform and threshold correlation. It integrates Canny edge recognition and quadratic weighted averaging for precise positioning of measurement points. By comparing reference images and utilizing the principle of minimum distance between matching points, the method achieves fast matching and determines the three-dimensional coordinates of marker points, enhancing measurement efficiency and robustness. This approach has been empirically tested on membrane structures, providing new insights. The results highlight that our novel algorithm can achieve high-precision measurements down to millimeters, and its accuracy increases with the actual displacement of the membrane structure. Notably, this groundbreaking measurement method remains unaffected by the form of the membrane surface, addressing a long-standing challenge in the field.

Research on precision linear displacement measurement method based on closed loop phase-shift control of single alternating light field

Abstract A high-precision linear displacement measurement method based on the digital phase shift of a single light source is proposed. In this method, the light intensity of a four-channel sinusoidal transmission surface with a 90° difference in the spatial phase is modulated by a single channel alternating light, and four-channel alternating light intensity signal with the same time phase can be obtained. Two channels of standing wave signals with a time difference of 90° are obtained through micro-control digital phase shift processing. After differential amplification, an electric travelling wave signal containing position information of measurement object is synthesized by these two standing wave signals. By measuring the phase difference between the travelling wave and the reference signal, the high precision linear displacement can be measured. The principle of displacement measurement based on the modulation of a single-alternating light field and the principle of digital phase-shift of micro-control are introduced in detail. The feasibility of the method is verified through experiments. Finally, the sensor is optimized by analyzing the causes of error, and the measurement accuracy of ±0.2 μm is achieved within the 100 mm range with a 0.6 mm grating period.

Vibration displacement measurement method based on vision Gaussian fitting and edge optimisation for rotating shafts

The vibrations of rotating machinery contain crucial information for monitoring the operational status of machines and diagnosing potential faults. Measuring vibration displacement using machine vision is currently a promising approach for machine monitoring in industries. However, the existing vision-based vibration displacement measurement methods cannot satisfy the high-accuracy requirements for detecting micrometre-level displacements generated by low-amplitude vibrations of rotating machinery. Therefore, this study proposes an improved noncontact-type vibration displacement measurement method based on Gaussian fitting and edge optimisation for rotating shafts. The real number-type edge positions of the rotating shafts within the selected region of interest (ROI) are accurately extracted via Gaussian edge fitting. Subsequently, edge-linking schemes, double-threshold detection and edge fitting are preformed to optimise the real number-type position of the edges. Finally, continuous displacement extraction of rotating shafts is achieved by monitoring the edge positions in the ROI. The coefficient of determination is used to evaluate a series of simulated and actual experiments quantitatively. The experimental results verify the feasibility, effectiveness, and robustness of the proposed method. An eddy current sensor is used in the actual experiments to verify the accuracy of the proposed method. The result shows that the coefficient of determination reaches 0.9792 when the amplitude of the measured displacement signals is only 22.7 μm.

A DIC-UAV based displacement measurement technique for bridge field testing

Load rating of old or deteriorated bridges is performed either analytically or experimentally to determine their safe passing loads. Due to high costs of field operations and testing, analytical load rating is currently the preferred method of practice. To promote experimental load rating through bridge field testing, a novel displacement measurement technique as an alternative to conventional sensor-based measurements is proposed in the present study incorporating digital image correlation (DIC), unmanned aerial vehicle (UAV or drone), opensource computer programs, and cost-effective cameras. The development of the proposed DIC-UAV displacement measurement method including mission strategy, camera system trade-off, DIC software, drone, and payload configuration is discussed. Furthermore, the accuracy of the proposed technique is evaluated by performing more than 70 tests on a scaled truss bridge in a laboratory. The proposed DIC-UAV based displacement measurement strategy and computational tools were found feasible with submillimeter accuracies, significantly advancing the state-of-the-art methodologies.

An Adaptive Radon-Transform-Based Marker Detection and Localization Method for Displacement Measurements Using Unmanned Aerial Vehicles.

UAVs have been widely used in deformation monitoring because of their high availability and flexibility. However, the quality of UAV images is affected by changing attitude and surveying environments, resulting in a low monitoring accuracy. Cross-shaped markers are used to improve the accuracy of UAV monitoring due to their distinct center contrast and absence of eccentricity. However, existing methods cannot rapidly and precisely detect these markers in UAV images. To address these problems, this paper proposes an adaptive Radon-transform-based marker detection and localization method for UAV displacement measurements, focusing on two critical detection parameters, namely, the radius of marker information acquisition and the edge width of the cross-shaped scoring template. The experimental results show that the marker detection rate is 97.2% under different combinations of flight altitudes, radius ratios of marker information acquisition, and marker sizes. Furthermore, the root mean square error of detection and localization is 0.57 pixels, significantly surpassing the performance and accuracy of other methods. We also derive the critical detection radius and appropriate parameter combinations for different heights to further improve the practicality of the method.

Computer vision-based real-time deflection monitoring of complex and sizeable steel structures

Deflection, as an intuitive index, plays a pivotal role in assessing the load-bearing capability and structural integrity of complex and sizable steel structures. Notwithstanding, the conventional contact measurement is limited to static deflection and necessitates work stands and manual readings. Therefore, it hinders the attainment of dynamic deflection and fails to cater to the engineering demands of real-time and prolonged monitoring. By leveraging the advancements in computer vision technology, we propose an innovative system for real-time deflection monitoring of complex and sizable steel structures, particularly suitable for monotonic deformation induced by prolonged loading. Specifically, the off-axis-based displacement measurement method was adopted to surmount the constraint of requiring the optical axis of the camera to be perpendicular to the target. Additionally, the inverse compositional Gauss-Newton (IC-GN) algorithm and parallel computation based on seed point diffusion were exploited to boost the matching and computing efficiency for attaining real-time monitoring of multi-points. To validate the efficacy of the proposed system, we conducted static load monitoring tests on a Bailey beam, with a height of 29.5 m and length of 16.5 m per span, as part of the Alibaba Jiangsu headquarters project in Nanjing. The collected test data were compared with the results from a laser displacement sensor and the ABAQUS model. The outcomes substantiate that the system is capable of non-contact, expeditious, and straightforward installation, besides achieving high accuracy and real-time deflection monitoring. This system serves as a sophisticated solution for health monitoring in constructing complex and sizeable steel structures and further contributes to realizing construction intelligence.

Non-contact measurement method of displacement of spherical joints in long-span space steel structures

For long-span space steel structures after a long period of operation, it is crucial to quickly and accurately measure the deformation for structural safety evaluation. To address the challenges in deformation measurement of space steel structures, this paper presents a non-contact, three-dimensional coordinate measurement method for key ball joints in their spatial positions. A high-precision Laser Total Station is utilized to measure the surface of the ball joints at multiple points. Three-dimensional coordinates of the center of the ball joints are then accurately determined by using three-dimensional analytical method and the least square error method. Subsequently, the deformation of key joints of the long-span space structures is obtained by comparing the determined coordinates with their initial coordinates. An engineering application by adopting the propose method to measure the deformation of a large-span steel structure gymnasium in Guangdong Province is presented. This method not only can be used to evaluate the safety of existing space steel structures, but also can be applied to rapid measurements and positioning of new long-span steel structures to improve the measurement efficiency and personnel safety.