Non-contact Measurement Method Research Articles

Lab-based scale measurements of internal storage of crude oil tank based on non-contact infrared thermography technique

Non-contact sludge measurement methods for storage tanks can address the challenge of measuring the volume of sedimented sludge during long-term storage. While infrared thermography technology can address the issue of liquid level detection, its measurement accuracy for the undulating interface of sludge is insufficient. This study designed and constructed experimental setups for measuring sludge in storage tanks. In this study, infrared images taken by an infrared camera were used to record the temperature distribution of the outer wall of the storage tank. The threshold segmentation method is used to determine the accurate sludge boundary line in image processing. Finally, the Three-Dimensional Tank Residue Recovery Algorithm (3D-TRRA) was applied to fit the 3D distribution of the sludge and calculate accurate sludge volumes. The results indicate that the best segmentation is achieved with a threshold of 170. The measurement error for sludge volume is less than 5%. Accurate visual positioning and recognition of sludge are achieved.

A hybrid approach for vision-based structural displacement measurement using transforming model prediction and KLT

As civil infrastructures age, monitoring their health conditions has become increasingly critical. Dynamic displacement measurement is a prevalent method for assessing structural health. Traditional techniques, which often involve installing instruments and scaffolding, can interfere with the response of the structures. To address the challenge, non-contact measurement methods have been developed; however, these are typically costly and require expert operation. Advances in high-speed industrial cameras and image processing technology now enable vision-based displacement measurement. Despite their effectiveness, existing vision-based methods face significant limitations, including their inability to maintain tracking when line-of-sight is obstructed, sensitivity to lighting variations, and the need for manual intervention when feature points are lost. This study introduces a novel hybrid approach, termed ToMP-KLT, which combines the KLT tracker with a deep learning-based model. This method harnesses the precision of the KLT tracker under favorable conditions and the robustness of the deep learning-based tracker under adverse conditions. Its effectiveness is validated through simulation-based tests, lab-scale experiments, and field testing on Cheonsa Bridge, demonstrating substantial improvements in tracking robustness against occlusions and varying light conditions.

Advances in the application of non-contact temperature measurement technology for aero-engine blade.

The advancement of the aviation sector has made the temperature measurement technology for aero-engine turbine blades essential for maintaining the engine's safe and steady performance. The non-contact temperature measurement technology is a trending research focus in turbine blade temperature measurement due to its benefits of not requiring direct touch with the object being measured and its suitability for high-temperature and high-speed conditions. This paper provides a concise overview of various key non-contact temperature measurement methods for aero-engines, such as fluorescence temperature measurement, fiber-optic temperature measurement, and radiation temperature measurement. It discusses the temperature measurement principle, technical characteristics, and the current research status both domestically and internationally. Based on this, this Review further discusses the main challenges faced by the non-contact temperature measurement technology and the development trend of the future.

Using unsupervised learning based convolutional neural networks to solve Digital Image Correlation

Digital Image Correlation (DIC) is a robust, non-contact method for full-field deformation measurement widely used in photomechanics. Based on subset image matching and an iterative optimization architecture, traditional DIC deformation field analysis requires manual selection of such calculation parameters as subset size, rendering it challenging to balance spatial resolution and measurement resolution in non-uniform field measurements. The requirement of manual parameter adjustment is lowered by a recently emerged supervised learning-based neural network DIC method, as it establishes a direct correlation between images and deformation fields using deep neural networks (DNN). However, its generalization and accuracy are contingent on the scope and variety of the dataset because it requires training with a large number of simulated speckle images of deformation fields with known standards. This paper proposes an unsupervised learning neural network based DIC measurement method that circumvents the need for standard deformation fields and achieves high precision analysis of deformed fields. It constructs a loss function based on the photometric consistency assumption of speckle images before and after deformation and allows the neural network model to directly extract displacement information from the gray data of the “reference image-deformed image” pair. Meanwhile, the model is enhanced accordingly by further introducing displacement continuity constraints to enhance accuracy and mitigate noise interference. Validation through both simulated and real experiments shows that the proposed approach surpasses traditional subset DIC in measuring non-uniform deformation fields with higher precision. It also offers superior flexibility and adaptability over supervised learning-based neural network DIC methods by eliminating the need for a large training dataset, allowing for immediate application in displacement field analysis.

Measurement method of spherical radius size of differential planetary gear tooth surface based on 3D vision

Abstract In order to accurately measure the spherical radius dimensions of planetary gear tooth surfaces, we propose a non-contact measurement method based on 3D vision technology. Firstly, the point cloud data of the planetary gear captured by the laser 3D profiler is preprocessed, and the tooth surface point cloud is extracted. Next, based on the structural characteristics of the planetary gear tooth surface, a uniform slicing and sampling algorithm is used to extract circular contour points where the tooth surface point cloud intersects with the slicing planes. These points are then projected onto a two-dimensional plane, where an improved RANSAC algorithm is used to precisely fit each circular cross-section. Contour points associated with significant fitting errors are removed based on a set tolerance value. Finally, an improved differential evolution algorithm is used to perform a three-dimensional spherical fitting on the contour points that meet the tolerance criteria, achieving accurate measurement of the spherical radius of the planetary gear tooth surfaces. Experimental results demonstrate that the repeatability error of the proposed method is ±0.02 mm, with a maximum absolute error of 0.026 mm and an average measurement time of 4.76 s. The method exhibits high robustness and measurement accuracy, meeting practical engineering measurement requirements.

Experimental analysis on the progressive failure mechanism of soft rock subjected to tunnel excavation

The failure of the soft rock can seriously threaten the construction and operation of tunnels, which may suffer from larger deformation and instability. The knowledge of the failure process and mechanism of soft rock can help to optimize the tunnel design and construction. In the present paper, a large-scale physical model test with the use of both contact and non-contact measurement methods was conducted to investigate the progressive failure mechanism of soft rock. The results show that a notable pattern of progressive failure was observed in the soft rock, whereby failure initiates in the proximal regions and propagates to the distal regions within the surrounding rock. The occurrence of the failure intensifies the deformation of the soft rock, and the tunnel suffers from the cross-section flattening finally. The effectiveness of the non-contact measurement method was accessed by the comparison of the strain measurements between embedded sensors and digital photogrammetry analysis. Subsequently, the radius of the failure zone determined by the stress distribution and mechanical analysis is between two and three times the radius of the tunnel. The results of model tests were then verified by numerical analysis. Eventually, the failure modes were evaluated by local relative deformation. The shear failure takes place in the tunnel inverted arch which suffers from large shear stress, while compression failure appears on the tunnel sidewall, and the tension failure occurs at the top of the tunnel arch.

Measurement of outer diameter parameters of manual grinding cable joints based on 3D point cloud processing

For the problems associated with low measurement accuracy caused by surface shrinkage, uneven grinding, and irregular shapes in manual grinding cable joints, as well as the limitations of existing machine vision measurement techniques for the outer diameter parameters, this paper proposes a non-contact outer diameter parameters measurement method of manual grinding cable joints based on three-dimensional (3D) point cloud processing. Firstly, statistical filtering is used to remove discrete noise, and coordinate transformation preprocessing is conducted to ordered the point cloud. Secondly, a bidirectional point cloud slicing method is proposed based on the cable joint structure and point cloud distribution to preserve the original features for effective measurement. Subsequently, the normalized mean curvature is utilized as the weight of point cloud coordinate averaging to construct key points for surface slices, which reduces the amount of data in the sliced point cloud and improves the matching efficiency. Finally, the key points are matched according to the position of the surface slices, and the distance of point pairs is calculated to complete the measurement of the outer diameter parameters. Experiments and error analysis are conducted on multiple manual grinding 110 kV and 500 kV real cable joints. The absolute error in the measurement of the outer diameter is less than 0.2 mm, with a relative error within 1 %. The absolute error of the XY-axis outer diameter deviation is less than 0.1 mm. The experiments demonstrate that the proposed method exhibits superior performance in cable joint outer diameter measurement.

Multi-verse optimizer for thermal error modeling approach of spindle system based on thermal image

Since the spindle thermal error of CNC machine tools has a significant impact on machining precision, this paper introduces a unique approach for modeling spindle thermal error. Several key steps are involved in the proposed approach. First, the Fluke thermal imaging camera is employed for acquiring thermal image information of the spindle system. Second, the Gaussian filter is employed to denoise the thermal image sequence. Next, the temperature values at the measurement points are extracted from the thermal image sequence according to the mapping relationship between the grayscale value and the temperature value. Subsequently, critical temperature points are identified from thermal images using the density-based spatial clustering of applications with noise (DBSCAN) algorithm and the correlation coefficient method. Finally, the multi-verse optimized NARX neural network is employed to investigate the nonlinear prediction of thermal deformation. The research is conducted on the VMC-850E vertical machining center as the subject of study. The performance of the model is validated under conditions of idle spindle and 5000 r/min, comparing prediction results against traditional algorithms. The findings demonstrate that the non-contact measurement method based on thermal imaging successfully establishes the thermal error model, achieving a prediction accuracy of 0.1517 μm for the MVO-NARX model.

Interferometric-scale full-field vibration measurement by a combination of digital image correlation and laser vibrometer

Digital Image Correlation (DIC) is a crucial noncontact full-field optical measurement method used in various fields. However, in practical applications, DIC is affected by systematic and random noises, leading to experimental resolutions lower than theoretical ones. In this study, we proposed a laser Doppler vibrometer guided DIC to perform vibration measurements. 3D-DIC obtains a sequence of out of displacement field initially. A three-dimensional frequency domain collaborative filtering (3D-FDCF) method that utilizes Laser Doppler vibrometer (LDV) single-point data to assist in processing of the displacement field sequence pixel-wise is adopted. The 3D-FDCF method combines lowpass filtering in the spatial frequency domain with LDV-guided bandpass filtering in the temporal frequency domain. The effectiveness of the 3D-FDCF method is demonstrated through a comparison among DIC data, the filtered DIC data, and continuously scanning LDV data. The experiment results demonstrate the 3D-FDCF method's capability in measuring vibration amplitudes of several hundred nanometers with the size of a test sample of about 100 mm × 100 mm, supporting the statement of interferometric scale full-field vibration measurement by DIC with the guidance of the LDV data.

A Noncontact Method for Simultaneous Measurement of di/dt and dv/dt

This letter proposes a non-contact method for simultaneously measuring the current and voltage change rates by Rogowski coils. The dv/dt measurement capability is achieved based on electric field coupling, which is incorporated into the state-of-the-art Rogowski coil based current sensing solutions. The application of the proposed method for power semiconductor devices is demonstrated by a case study of an IGBT switching circuit. The Rogowski coils are integrated with a Printed Circuit Board (PCB) and fit with the IGBT module terminals. The comparable results from high-performance commercial instruments verify the di/dt and dv/dt measurement accuracy of the presented compact and cost-effective solution.

Numerical simulation and experimental analysis of foreign object impact on aero-engine fan rotor blade

The detection and identification of fan rotor foreign object impact events during the working process of aero-engines are crucial to the safety and security of aircraft flight. Through the numerical simulation method based on finite element and the foreign object impact test platform to simulate the process of real engine impact by foreign objects, the rule of fan rotor blade foreign object impact and the monitoring and identification method were studied. Meanwhile, aiming at the problems of high difficulty and low accuracy in identifying the impact of foreign objects on the fan by the airborne parameters and added vibration parameters, the non-contact vibration measurement method based on blade tip timing was used to obtain the blade vibration displacement under different fan speed, foreign matter mass and impact velocity. The numerical simulation and experimental results show that the change trend of the vibration displacement of the rotor tip of the fan is consistent with that of the numerical simulation. And with the increase of the foreign object projectile mass, the vibration displacement of the blade tip caused by the impact of the foreign object on the fan blade increases. In addition, the impact velocity of the foreign object has little effect on the vibration displacement of the blade tips after impact. Furthermore, the vibration displacement of the blade tip caused by the impact of foreign objects first increases and then decreases with increasing speed.

Research on non-contact dynamic stress calibration method of fan blade on simulation

In order to establish the non-contact measurement method of fan blade dynamic stress, the most important thing is to establish the method of fan blade dynamic stress calibration. In this paper, the static virtual loading and harmonious response analysis of fan blades in the simulation platform, and the calibration equation between the blade tip displacement and the blade dynamic stress is established. Finally, the calibration equation is verified and the error analysis is carried out by simultaneously carrying out contact and non-contact dynamic stress measurement tests on the rotor test bench.

Thermoelastic wave generation and its longitudinal wave propagation measurement by a microscopic optical interferometer

Laser ultrasonics is a noncontact measurement method that uses a laser-induced elastic wave source in combination with an optical surface displacement-tracking system. This study compared the performances of two optical interferometers with different characteristics when applied to measurement of pulsed thermoelastic waves. The surface displacement-tracking system was designed to measure the center of the microscopic view. A pulsed laser beam irradiated a black ink layer to generate the thermoelastic waves. The out-of-plane displacement on the axially opposite side was then measured using either a Michelson interferometer or a Sagnac interferometer. The objective lens of the system was of a type commonly used in biological observations. The Michelson interferometer estimated a maximum displacement of 0.43 nm and a maximum sound pressure of 24.7 kPa. The signal-to-noise ratios from 16 averages were 14.9 dB (Michelson interferometer) and 19.2 dB (Sagnac interferometer). Furthermore, this paper compares the performance of the numerically estimated Sagnac interferometer outputs calculated from the measured Michelson interferometer outputs with the experimentally obtained Sagnac interferometer outputs. The numerically estimated Sagnac interferometer’s output was shown to be identical to the experimentally acquired output. The Michelson interferometer requires a higher average operating frequency (i.e., it needs a longer data acquisition time), although this interferometer does offer superior displacement output linearity. This property enables calculation of the sound pressure from the displacement amplitude. These findings indicated that combination of the measurement points of the Sagnac interferometer with those of the sparsely distributed Michelson interferometer reduced the measurement time when compared with a single use of the Michelson interferometer while also maintaining the data acquisition quality.

Nonlinear optimization DIC method inspired by unsupervised learning for high order displacement measurement

Digital Image Correlation (DIC) is the most widely used non-contact full-field strain measurement method, but due to the influence of subset calculations in principle, measuring complex deformation with high strain gradients within the subset remains a challenge. DIC based on neural networks performs well in complex deformation measurement scenarios, but its measurement accuracy and generalization ability depend on extensive training sets. This paper proposes a nonlinear optimization DIC method inspired by unsupervised learning that requires only a single pair of images. Unlike traditional unsupervised algorithms of optical flow estimation or Particle Image Velocimetry (PIV), the shape function in traditional DIC is incorporated into the loss function. This allows the use of only the reference image and the deformed image as input dataset, and the correct displacement field can be output after parameter optimization through training. This method does not require additional training sets to measure high-order strain fields like traditional neural networks-based DIC. The proposed method has demonstrated promising results in simulation experiments for high-order strain measurement and does not require additional datasets of the same type. In the measurement of the star-shaped displacement fields, its accuracy is better than the traditional DIC based on the subset, and it maintains convergence even when the strain is large, a situation where traditional DIC fails to converge. In addition, compared with the network obtained by supervised training as a pre-trained model, the method proposed in this paper can achieve superior results. The methods of the article can theoretically be applied to the result optimization of other networks beyond those discussed in this paper.

Nondestructive measurement of terahertz optical thin films by machine learning based on physical consistency

Optical scattering measurement is one of the most commonly used methods for non-contact online measurement of film properties in industrial film manufacturing. Terahertz photons have low energy and are non-ionizing when measuring objects, so combining these two methods can enable online nondestructive testing of thin films. In the visible light band, some materials are transparent, and their thickness and material properties cannot be measured. Therefore, a method based on physical consistency modeling and machine learning is proposed in this paper, which realizes the method of obtaining high-precision thin film parameters through single-frequency terahertz wave measurement, and shows good performance. Through the experimental measurement of organic material thin films, it is proved that the proposed method is an effective terahertz online detection technology with high precision and high throughput.

Non-contact near-field cavity perturbation method for quantitative dielectric measurement and metallic defect inspection

Low dielectric constant (low-k) materials are essential in electronic industry aiming at new generation high-speed communications. Dielectric constant of these materials are measured by standard procedures and instruments with conventional contact configuration. Non-contact and in-situ techniques for low-k dielectric measurement are highly demanded in practice and worth of investigation. We hereby proposed a near-field microwave probe (NFMP) to accurately measure dielectric constant of low-k samples in a non-contact manner with a quantitative electromagnetic (EM) cavity perturbation theory. An NFMP set was designed, optimized by finite-element method (FEM) and fabricated with handy materials in the lab. Frequency response of the NFMP was tested experimentally and further used for subsequent frequency displacement analysis. Various low-k samples were tested by the non-contact NFMP set and dielectric constant were extracted based on the field perturbation theory. The relative error was obtained less than 0.5 % for low-k materials. The NFMP was then fixed on a micrometer stage and worked in a line-scan mode to inspect conductive defects in a low-k substrate. Both surface and subsurface defects were inspected by analyzing displaced central frequencies. The NFMP system is promising to facilitate quantitative and non-contact dielectric property measurement in electronics.

An acoustic weighing method based on oscillation signal and feature enhanced network

Acoustic weighing is a promising method for non-contact mass measurement of tiny objects as it avoids contamination and contact losses. However, due to the highly nonlinear nature of the acoustic field, some parameters of the mechanism model of acoustic weighing cannot be accurately simulated, thereby reducing the accuracy of acoustic weighing. To improve the accuracy of acoustic weighing, we propose an acoustic weighing method based on oscillating signals and feature enhancement network. Firstly, to drive the object oscillation and collect oscillation data, an acoustic levitation-based data acquisition system is constructed. Then, to break the limitations of the mechanism model, a feature enhancement network named CNN-BiLSTM-SE is proposed, which directly establishes the correlation between oscillating signals and actual mass. Finally, these data are used to train and test the proposed network model, validating the effectiveness of the model. Experimental results show that the method achieves high accuracy in measuring object mass, following the actual measurements with remarkable consistency. In addition, our approach is also suitable for acoustic weighing of small and sensitive objects, opening up new perspective for the study and application of nonlinear acoustic systems.

Auto-Alignment Non-Contact Optical Measurement Method for Quantifying Wobble Error of a Theodolite on a Vehicle-Mounted Platform

Auto-Alignment Non-Contact Optical Measurement Method for Quantifying Wobble Error of a Theodolite on a Vehicle-Mounted Platform

Application of non-contact video quantitative measurement method in reservoir bank landslide monitoring

The geological structure of the reservoir bank landslide is complex and intricate. After deformation and damage, it causes river blockage, surges, and loss of people’s lives and property, posing a huge threat. At present, in academia and engineering applications, a large number of techniques such as inclinometers, rain gauges, and surface GNSS deformation monitoring are still used for monitoring landslides on reservoir banks. This type of monitoring method has the problem of “point to surface” which can easily lead to missed detection and reporting in some areas, some disaster points are close to water and steep, difficult to reach, and equipment installation is difficult. This work designs and implements a non-contact video quantitative monitoring system for surface deformation of geological disaster. By constructing a deep learning neural network, deformation area recognition and displacement quantitative calculation are achieved; By obtaining continuous images for a long time, draw the surface displacement-time curve, and output the surface deformation data and landform changes of the disaster. Meanwhile, this work explores the impact of different lighting conditions on the recognition results of target areas. This work can provide non-contact monitoring methods and dynamic warning support for large-scale monitoring of geological disasters.

Non-destructive characterization of pore structure and chloride diffusion coefficient of cementitious materials with modified non-contact electrical resistivity method

The intricate pore structure of cement-based materials passively influences chloride ion transport and mechanical properties. Existing non-contact electrical resistivity measurement (NC-ERM) methods lack precision for rigorous studies. the NC-ERM was upgraded in this research, which is suitable for assessing the pore structure and transport characteristics of hardened cementitious substrates. The results obtained from this method closely correspond to those obtained from CEMHYD3D, Mercury intrusion porosimetry (MIP), and other methods. Additionally, the impact of mixture proportions on the resistivity, pore structure parameters, and chloride ion diffusion coefficient of cementitious materials is discussed. With an increase in sand ratio and sand-to-stone ratio, the resistivity exhibits an initial rise followed by a decline. Chloride diffusion of 5 wt% NaCl-saturated specimens is 1.2–1.3 times higher than 3.5 wt% NaCl. This study provides a novel non-destructive, convenient and accurate method for characterising the pore structure and testing the transport properties of cementitious materials.