Non-contact Measurement Method Research Articles

Determination of Chimney Non-Verticality from TLS Data Using RANSAC Method

The continuous monitoring of tall industrial buildings is necessary to ensure safe operation. With technological advances in terrestrial laser scanning and other non-contact measurement methods, the methods and techniques for assessing the stability of tall industrial chimneys are evolving. This paper presents a method for determining the non-verticality and straightness of chimneys that offers significant advantages over existing methods. Narrow bands of scanned point clouds are processed at selected height intervals. Using the RANSAC method, points that do not belong to the chimney shell are filtered and the centre of the circle or ellipse is adjusted using the least squares method. The proposed method enables the efficient filtering of point clouds due to frequent obstructions on the chimney shell, the determination of the regularity of the chimney shell shape, a mathematical analysis of the chimney axis curvature, and an intuitive graphical representation of chimney non-verticality. The comparison of the results with other studies confirms the efficiency of the method.

Non-contact measurement method of liquid composition using microwave radar cross-section

This study aims to provide a non-contact detection of liquid composition inside containers using Microwave Radar Cross-section (RCS) measurement technology. Firstly, it analyzes the limitations of traditional near-infrared spectroscopy methods and proposes the necessity of introducing microwave detection methods. The research demonstrates through experiments a significant correlation between polar substances like total acid content and radar scattering capability, showing microwave radar’s effectiveness in reflecting the polarity characteristics of liquids. Furthermore, theoretical derivations and experimental validations illustrate that differences in electromagnetic properties of different liquid components lead to variations in echo loss, thereby impacting RCS levels. Experimental results indicate that microwave radar RCS measurement technology achieves an accuracy level of 2%, capable of distinguishing between different concentrations of ethanol, acetic acid, and other solutions. This study highlights the significant advantages of microwave radar RCS measurement technology in non-contact detection of liquid composition, providing new methods and a technological foundation for precise liquid component detection.

A Machine Vision-Based Measurement Method for the Concentricity of Automotive Brake Piston Components

The concentricity error of automotive brake piston components critically affects the stability and reliability of the brake system. Traditional contact-based concentricity measurement methods are inefficient. In order to address the issue of low detection efficiency, this paper proposes a non-contact concentricity measurement method based on the combination of machine vision and image processing technology. In this approach, an industrial camera is employed to capture images of the measured workpiece’s end face from the top of the spring. The edge contours are extracted through the implementation of image preprocessing algorithms, which are then followed by the calculation of the outer circle center and the fitting of the inner circle center. Finally, the concentricity error is calculated based on the coordinates of the inner and outer circle centers. The experimental results demonstrate that, in comparison to a coordinate measuring machine (CMM), this method exhibits a maximum error of only 0.0393 mm and an average measurement time of 3.9 s. This technology markedly enhances the efficiency of measurement and fulfills the industry’s requirement for automated inspection. The experiments confirmed the feasibility and effectiveness of this method in practical engineering applications, providing reliable technical support for the online inspection of automotive brake piston components. Moreover, this methodology can be extended to assess concentricity in other complex stepped shaft parts.

Identification of blade vibration parameters based on improved blade tip timing technology and two-factor analysis method

Abstract Long-term complex alternating loads will significantly impact the lifespan of blades. As a non-contact measurement method, Blade tip timing (BTT) is regarded as an efficient technique for blade vibration monitoring as it reflects the condition of blades. Most of the existing parameter identification methods need several sensor and strict probe layout requirements; thus, an inappropriate sensor layout may fail BTT measurements. Using fewer sensors can reduce the impact on the compressor casing strength and the cost of test system.
Considering arrangement, cost, and safety restrictions,in this work, a novel blade vibration parameters identification method via a single BTT sensor is proposed. A method of improved BTT techonlogy is presented which obtain two-channel vibration displacement sequence using a single sensor. The elliptic model between two-channel vibration displacement sequence is established. Based on the model, the relationship between blade vibration parameters and elliptic geometric parameters is derived. Numerical simulation and experimental results show that the method is highly feasible and robust. The advantage of this method is that it utilizes only one sensor and achieves high accuracy identification of blade vibration parameter, which provides a new idea to blade vibration monitoring.

A Study of the Effect of Temperature on the Capacitance Characteristics of a Metal-μhemisphere Resonant Gyroscope.

Metal-μhemispherical resonant gyros (M-μHRGs) are widely used in highly dynamic navigation systems in extreme environments due to their high accuracy and structural stability. However, the effect of temperature variations on the capacitance characteristics of M-μHRGs has not been fully investigated, which is crucial for optimizing the performance of the gyro. This study aims to systematically analyze the effect of temperature on the static and dynamic capacitances of M-μHRGs. In this study, an M-μHRG structure based on a 16-tooth metal oscillator is designed, and conducted simulation experiments using non-contact capacitance measurement method and COMSOL Multiphysics 6.2 finite element simulation software in the temperature range of 233.15 K to 343.15 K. The modeling analysis of the static capacitance takes into account the thermal expansion effect, and the results show that static capacitance remains stable across the measured temperature range, with minimal effect from temperature. The dynamic capacitance exhibits significant nonlinear variations under different temperature conditions, especially in the two end temperature intervals (below 273.15 K and above 313.15 K), where the capacitance values show local extremes and fluctuations. In order to capture this nonlinear behavior, the experimental data were smoothed and fitted using the LOESS method, revealing a complex trend of the capacitance variation with temperature. The results show that the M-μHRG has good capacitance stability in the mid-temperature range, but its dynamic performance is significantly affected at extreme temperatures. This study provides a theoretical reference for the optimal design of M-μHRGs in high- and low-temperature environments.

GMDIC: a digital image correlation measurement method based on global matching for large deformation displacement fields

The digital image correlation method is a non-contact optical measurement method, which has the advantages of full-field measurement, simple operation, and high measurement accuracy. The traditional DIC method can accurately measure displacement and strain fields, but there are still many limitations. (i) In the measurement of large displacement deformations, the calculation accuracy of the displacement field and strain field needs to be improved due to the unreasonable setting of parameters such as subset size and step size. (ii) It is difficult to avoid under-matching or over-matching when reconstructing smooth displacement or strain fields. (iii) When processing large-scale image data, the computational complexity will be very high, resulting in slow processing speeds. In recent years, deep-learning-based DIC has shown promising capabilities in addressing the aforementioned issues. We propose a new, to the best of our knowledge, DIC method based on deep learning, which is designed for measuring displacement fields of speckle images in complex large deformations. The network combines the multi-head attention Swin-Transformer and the high-efficient channel attention module ECA and adds positional information to the features to enhance feature representation capabilities. To train the model, we constructed a displacement field dataset that conformed to the real situation and contained various types of speckle images and complex deformations. The measurement results indicate that our model achieves consistent displacement prediction accuracy with traditional DIC methods in practical experiments. Moreover, our model outperforms traditional DIC methods in cases of large displacement scenarios.

A measurement method for the axial bending deformation of parts with circumferential cycling element based on laser profile sensor

Abstract The cross-section of the CPP (circumferential periodic parts) is composed of fixed shaped elements regularly arranged in the circumferential direction, examples include cylinders, positive prisms, lead screws, gear shafts, and spline shafts, it has applications in many fields. Existing research has not yet proposed a universal method for measuring the axial bending of CPP. This paper presents a non-contact measurement method using a laser profile sensor that is capable of measuring axial bending of CPP. The axis of a shaft part is determined by connecting the geometric center of each cross-section. This method is suitable for measuring the bending deformation of most CPS (circumferential periodic shaft parts). To verify the validity of this method, numerical simulation calculations are conducted on cylinders, hexagonal prisms, and lead screws. Additionally, comparative experiments are performed on lead screws using self-built experimental platform and a measuring projector to investigate the effectiveness and repeatability.

Intelligent Structural Health Monitoring and Noncontact Measurement Method of Small Reservoir Dams Using UAV Photogrammetry and Anomaly Detection

Intelligent Structural Health Monitoring and Noncontact Measurement Method of Small Reservoir Dams Using UAV Photogrammetry and Anomaly Detection

Thermodynamic Calculations of the Main Characteristics of the Combustion Process of Pyrotechnic Nitrate-Metallized Mixtures with Additives of Organic and Inorganic Substances under External Thermal Influences

The aim of the article is to determine the dependencies of the combustion product temperatures of four-component mixtures, the content of high-temperature condensate, and unoxidized metal on the excess oxidizer coefficient, the number of additives, and external pressure using thermodynamic calculations. Modern methods of physicochemical analysis (such as cinephotography and microcinephotography methods, X-ray analysis methods, non-contact and contact temperature measurement methods), nonlinear thermal conductivity and thermal stability methods, as well as mathematical and experimental-statistical modeling, were employed to investigate the influence of elevated heating temperatures (up to 800 K) and external pressures (up to 107 Pa) on the ignition and combustion processes of pyrotechnic products. Standard pyrotechnic equipment was used to test pyrotechnic product samples under specified conditions. Calculations based on models were conducted in real-time and dialog mode on computer devices that meet modern requirements for the use of specialized software. The results of thermodynamic calculations are presented for the dependencies of combustion product temperatures of multicomponent pyrotechnic mixtures based on powders of metal fuels (magnesium, aluminium), nitrate oxidizer (sodium nitrate), additives of organic and inorganic substances (paraffin, sodium fluoride), and the content of high-temperature condensate and unoxidized metal depending on the excess oxidizer coefficient α = 0.1…4.0, the amount of paraffin additive εp = 0…0.2, sodium fluoride additive εf = 0…0.06, and external pressure P = 105…107 Pa. Based on the obtained results of thermodynamic calculations, statistical models in the form of regression dependencies of the specified combustion characteristics on their technological parameters and external conditions have been developed. These models enable the determination of fire hazardous properties of mixtures in real-time dialog and PC-based scenarios, considering premature initiation of products based on them under external thermal actions.

Thermal radiation imaging technology and sensor assisted calculation of sports training technology simulation

With the development of sports training science and technology, the real-time monitoring of athletes’ physical condition and training effect is becoming more and more important. As a non-contact measurement method, thermal radiation imaging technology can capture the changes of athletes’ body surface temperature efficiently and in real time, and provide relevant information about physiological state and performance indicators. This paper discusses the application of thermal radiation imaging technology in sports training to help measure athletes’ physical state and training effect, so as to provide scientific basis for improving training efficiency and reducing sports injuries. In this paper, the high resolution thermal imaging equipment is used to systematically analyze the thermal radiation imaging of athletes, and the physiological index monitoring and data processing technology are combined to dynamically observe the temperature distribution and change characteristics of athletes under different training states. Through comparative analysis, the correlation between the data provided by thermal radiation imaging technology in the training process and the traditional physiological indicators was evaluated. The results show that thermal radiation imaging technology can effectively reflect the heat distribution and local body temperature changes of athletes during training. Compared to traditional physiological feedback data, the technology has significant advantages in quickly identifying excessive fatigue and potential injuries in athletes, especially during high-intensity training, and can provide early warning of associated risks. The application of sensor-assisted measurement system will further enhance the accuracy and effectiveness of sports scientific research and provide important support for athletes’ scientific training and health management.

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.

Non-contact measurement of conveyor belt speed based on fast point cloud registration of feature block

Abstract Non-contact, real-time measurement of conveyor belt speed is critical for energy-saving speed regulation and efficient development of coal mine conveyor systems. Existing speed measurement technologies for conveyor systems are often limited by the slippage and wear in contact measurement and complex environmental disturbance. This study introduces three-dimensional point cloud technology into coal flow information detection and innovatively presents a non-contact measurement method of conveyor belt speed based on fast point cloud registration of feature blocks. In the proposed method, a three-dimensional camera is used to capture point cloud data of the dynamically running conveyor belt, and the raw point cloud is preprocessed and segmented into blocks. Then, the C-MANV (curvature and mean angle of normal vectors) features of block point clouds are constructed based on the point cloud neighborhood curvature and the mean angle of neighborhood normal vectors. Finally, an improved block point cloud registration method based on C-MANV features is adopted to achieve the accurate measurement of the dynamic running speed of conveyor belt. Experimental results demonstrate that the proposed method achieves an average relative error of less than 1.8% in high-speed conveyor belt operation with an average processing time of less than 35 ms, which fulfils the accuracy and real-time requirements for conveyor belt speed detection in coal mines. This study provides an effective technical solution for the speed monitoring of coal mine conveyor systems.

Automated body measurement of beef cattle based on keypoint detection and local point cloud clustering

Abstract Body size parameters of beef cattle are crucial for assessing growth status and breeding value. In actual farming environments, the various postures of beef cattle and complex backgrounds can affect the accuracy and stability of non-contact body measurement methods. Therefore, this paper proposes a novel method called the cattle body measurement method (CBMM), which combines keypoint detection with local point cloud clustering. First, a keypoint detection model based on YOLOv8-SimBiFPN is constructed. This model enhances the feature extraction and fusion capabilities of YOLOv8-pose by introducing SimAM and BiFPN into the backbone and neck networks, respectively, and realizes 2D keypoint detection for beef cattle in various postures. Second, a 3D keypoint-locating algorithm based on Density-based spatial clustering of applications with noise (DBSCAN) is proposed. This algorithm utilizes 2D keypoints, depth maps and camera parameters to generate local point clouds, which are then clustered using DBSCAN to segment cattle body point clouds, thereby relocating the 3D keypoints based on their positional features. Finally, body size parameters are calculated based on the 3D keypoints and distance formulae. In our experiment, the mean average precision (mAP@0.5) of YOLOv8-SimBiFPN reached 99.1% on an Angus beef cattle keypoint detection dataset. The mean absolute percentage errors for measuring beef cattle withers height, hip height, body depth, body length, and oblique body length using the CBMM were 4.37%, 4.96%, 6.47%, 4.84%, and 4.14%, respectively. In summary, our method can achieve non-contact body measurement for beef cattle in a free-moving state with high accuracy and stability.

Investigation of Impedance Determination using Two-Point High Frequency Injection Technique for Non-Contact Voltage Measurement System

Energy management facilities are becoming more necessary due to the growth of smart energy and microgrid technologies. Convenient access to information on energy consumption is an advantage for energy planning and management and secure for the operators. Consequently, these conceive the idea of developing non-contact sensor approach to replace the conventional energy measurement while maintain full accessibility to voltage and current information. This paper proposes a method of non-contact conductive voltage measurement with an automated calibration system. The previous application of non-contact voltage measurement systems in the low-voltage range used a method based on the capacitive coupling principle by covering a metal plate around an insulated power line cable. The important factor of measurement accuracy depends on the determination of impedance occurring between the conductors within the cable and the metal plates attached to the cable surface by insulating as a dielectric. The proposed impedance determination process employs a two-point high-frequency signal injection technique that transmits a reference signal to calculate the established parasitic impedance value. From the high-frequency injection technique, the resulting impedance can be calibrated throughout the reverse calculation to measure the actual voltage of the power line. The experimental results of the proposed voltage measurement system can measure the actual voltage of the power line with the capability to calibrate the impedance due to the variation in the environment and eliminate noise errors from the instability under various conditions.

Theories, Techniques and Materials for Sealing Coalbed Methane Extraction Boreholes in Underground Mines: A Review

To further enhance the intelligent technology, platformisation, and systematisation of coalbed methane extraction sealing technology, this paper analyses the research progress of theories, technologies, and sealing materials related to coalbed methane extraction sealing and systematically summarises the latest achievements of the basic theories, key technologies, and sealing materials of coalbed methane extraction. Considering the increasing mining depth, advancements in intelligent technology, and the evolving landscape of coalbed methane development, it is particularly important to establish a more comprehensive coalbed methane extraction borehole sealing system. Based on this, future development trends and research prospects are proposed: In terms of coalbed-methane-extraction-related theories, there should be a stronger focus on fundamental research such as on gas flow within the coal matrix. For coalbed methane extraction borehole sealing technologies and devices, efforts should be made to enhance research on intelligent, platform-based, and systematic approaches, while adapting to the application of directional long borehole sealing processes. In terms of coalbed methane extraction borehole leakage detection, non-contact measurement and non-destructive monitoring methods should be employed to achieve dynamic monitoring and early warning of methane leaks, integrating these technologies into coalbed methane extraction system platforms. For coalbed methane extraction borehole sealing materials, further development is needed for liquid sealing materials that address borehole creep and the development of fractures in surrounding rock, as well as solid sealing materials with Poisson’s ratios similar to that of the surrounding rock mass.

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 prediction model of gear radial composite deviation based on digital twin mesh

The intelligent development of traditional detection technology through the application of emerging technologies is a crucial trend in achieving multi-information gear detection. In this paper, digital twin mesh (DTM) is proposed, and according to the proposed DTM, the prediction model (DTM model) of gear radial composite deviation is established. The DTM model is established by merging the mechanism and the data model with a parallel structure. The mechanism model is established based on the measurement principle. The data model is established through the Gaussian process regression algorithm. Comparing the predicted results of DTM model with the measured results, it is observed that the DTM model demonstrates superior precision. The DTM model achieves a prediction accuracy exceeding 87 % for total radial composite deviation and exceeding 90 % for tooth-to-tooth radial composite deviation. This DTM model holds significant engineering application value and offers insights for the non-contact dynamic measurement method of gears.

Background radiation compensation calibration method for film cooling infrared temperature measurement based on BP neural network

The precise non-contact temperature measurement method is essential for studying gas turbine component heat transfer. While infrared thermal imaging provides high-resolution two-dimensional temperature fields, errors arise from the measurement environment's complexity, emissivity variations, and reflection radiation effects. This study utilizes BP neural networks to correct infrared radiation on gas film cooled plates. By modeling background radiation and analyzing infrared emission processes, factors influencing temperature distribution are identified. Experimental data from uncooled plate are used for network training. Experimental measurements were conducted on uncooled plates to obtain training data. A BP neural network was established based on Planck's law and the radiation transmission process to relate the mainstream temperature to the background radiation. Calibration analysis was performed based on experimental data from the cooled plate, achieving the restoration of the two-dimensional temperature field under multiple operating conditions and significantly reducing measurement errors. This method comprehensively considers the entire process of radiation transmission, thereby eliminating background radiation embedded in the infrared images. The obtained measurement results vividly depict the real temperature distribution, offered significant support for gas turbine engineering applications.

Ultrasensitive detection of remote acoustic vibrations at 300 m distance by optical feedback enhancement

Sensitive detection of remote vibrations at nanometer scale owns promising potential applications such as geological exploration, architecture, and public security. Nevertheless, how to detect remote vibration information with high sensitivity and anti-disturbance has become a major challenge. Reported current non-contact measurement methods are difficult to simultaneously possess characteristics of high light intensity sensitivity, long working distance, high vibration response sensitivity, and anti-disturbance of ambient light. Here, we propose a polarization-modulated laser frequency-shifted feedback interferometry method with the above characteristics, to obtain remote vibration information. The method can directly measure non-cooperative targets without the need for any cooperative markers. In each interference cycle, the energy as low as 2.3 photons can be effectively responded to, and the vibration amplitude sensitivity at 300 m can reach 0.72 nm/Hz1/2 at 1 kHz. This approach provides a strategy for the ultrasensitive detection of remote vibration that is immune to electromagnetic interference.

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.