Non-contact Measurement Method Research Articles

Design Method for Automatic Assembly Production Line of Electric Valves in Space Propulsion Systems

This article proposes a design method for a valve automatic assembly production line in response to the automation assembly requirements of electric valve products in space propulsion systems and the engineering problems of inaccurate loading force control and low valve measurement accuracy in existing process methods. This method can achieve five assembly processes during the assembly process of electric valves, including pre-tightening force control, valve-core stroke measurement, performance testing, and shell structure welding. The article introduces the design of platform components such as process execution, positioning, and transportation, as well as the design and operation process of workstations. By combining the design of a three-axis motion mechanism, a small turntable, and a robotic arm, the product can achieve professional, positioning, full process automation, and equipment miniaturization design across multiple workstations. Through the design of precise control of loading force and non-contact optical measurement method of moving structure, compared with the original method, the parameters affecting product performance are precisely controlled and the precision is improved. And the multivariable decoupling of valve product performance is realized by this method. Through application verification, this automatic assembly production line can significantly improve the assembly efficiency of electric valve products and solve difficult problems in product engineering.

Noncontact Evaluation of HTS Cylinder for Compact Cryogen-Free NMR

Herein, a compact cryogen-free nuclear magnetic resonance (NMR) system is developed using a stacked high-temperature superconducting (HTS) bulk system. To compensate for magnetic field uniformity, an HTS cylinder is installed inside the measurement space. The cylinder is wound with REBCO tapes placed on the surface of a copper cylinder. The uniformity of its critical current density is important for NMR signal detection. Such an operation requires an examination of the quality of the critical-current-density distribution of the cylinder. A noncontact measurement method is required for such an examination. When a magnetic field is applied, the superconductor exhibits a demagnetization effect. Moreover, if any defect exists in any part of the superconducting tape, that particular part would contain no demagnetization, thus disturbing the relevant magnetic field distribution. In this study, this phenomenon is investigated using DC and AC magnetic fields. The magnets and sensor are placed inside the HTS cylinder, and magnetic fields are applied to the HTS cylinder surface. The results confirm that the proposed method is effective and applicable for evaluating the critical-current distribution.

In-Water Fish Body-Length Measurement System Based on Stereo Vision.

Fish body length is an essential monitoring parameter in aquaculture engineering. However, traditional manual measurement methods have been found to be inefficient and harmful to fish. To overcome these shortcomings, this paper proposes a non-contact measurement method that utilizes binocular stereo vision to accurately measure the body length of fish underwater. Binocular cameras capture RGB and depth images to acquire the RGB-D data of the fish, and then the RGB images are selectively segmented using the contrast-adaptive Grab Cut algorithm. To determine the state of the fish, a skeleton extraction algorithm is employed to handle fish with curved bodies. The errors caused by the refraction of water are then analyzed and corrected. Finally, the best measurement points from the RGB image are extracted and converted into 3D spatial coordinates to calculate the length of the fish, for which measurement software was developed. The experimental results indicate that the mean relative percentage error for fish-length measurement is 0.9%. This paper presents a method that meets the accuracy requirements for measurement in aquaculture while also being convenient for implementation and application.

DEVELOPMENT OF A METHOD FOR CONTACTLESS TEMPERATURE MEASUREMENT IN 3 SPECTRAL RANGES

A 3-spectral method for non-contact temperature measurement has been developed. Three silicon photodiodes were used. The separation of the spectrum is done by optical bandpass filters and beamspliters. It is possible to work in three modes: three-spectral, two-spectral (spectral ratio) and one-spectral. In the first mode, the temperature of objects with an unknown and variable emissivity can be measured. The last two modes give good results respectively for gray bodies and for objects with known emissivity.

Research on accurate non-contact temperature measurement method for telescope mirror.

Non-contact temperature measurement for a solar telescope mirror is critical for improving the mirror seeing and thermal deformation of solar telescopes, a long-standing challenge in astronomy. This challenge arises from the telescope mirror's inherent weak thermal radiation, often overwhelmed by reflected background radiations due to its high reflectivity. In this work, an infrared mirror thermometer (IMT) is equipped with a thermally-modulated reflector, and a measurement method based on an equation for extracting mirror radiation (EEMR) has been developed for probing the accurate radiation and temperature of the telescope mirror. Using this approach, we can extract the mirror radiation from the instrumental background radiation via the EEMR. This reflector has been designed to amplify the mirror radiation signal incident on the infrared sensor of IMT, while inhibiting the radiation noise from the ambient environment. In addition, we also propose a set of evaluation methods for IMT performance based on EEMR. The results reveal that the temperature measurement accuracy of IMT to the solar telescope mirror using this measurement method can be achieved better than ±0.15°C.

TransPhys: Transformer-based unsupervised contrastive learning for remote heart rate measurement

Remote Photoplethysmography (rPPG) is a non-contact heart rate measurement method based on facial video. Because the rPPG signal is highly susceptible to interference from external conditions such as light changes and head movements, the rPPG signal extracted in natural scenes has a low signal to noise ratio and cannot accurately calculate the heart rate value. Aiming at these problems, this paper constructs a self-supervised machine learning network based on Transformer, TransPhys, to achieve robust measurement of heart rate. Using the self-supervised method of contrastive learning, first perform data augmentation, input the enhanced frame into Stem, and extract coarse local spatial features. Through Spatial-Temporal Transformer, calculate the pulse signal of the face area, and finally output the PPG signal. Compared with other supervised machine learning methods, this method can be trained and achieve ideal results without using any labels, which means that this method can be well generalized to practical applications. The experimental results show that this method is better than the current mainstream self-supervised methods.

Errors of non-contact temperature measurement

In recent years, there is a trend of improving the performance and efficiency of all existing measuring instruments due to a leap in technology. Almost every industry uses a variety of technologies that apply temperature control. Temperature of a heated body can be estimated by measuring the parameters of its thermal radiation, which are electromagnetic waves of different lengths. Temperature measurement is necessary for comfortable automatic control and management of production processes. The use of non-contact means makes it possible to measure the temperature of, firstly, moving objects, secondly, objects in inaccessible places, thirdly, to avoid damage to the measuring instruments when controlling large temperatures. High speed, the possibility of measuring temperature without disconnecting the object from the technological process, ensuring personnel safety, temperature measurement up to 3000 °C – these are the advantages of non-contact temperature measurement method. To obtain reliable values when measuring thermophysical quantities it is necessary to know the processes occurring in interaction of the measuring device or sensor with the object of measurement. These processes affect the magnitude of the measurement error, that is, magnitude of the result deviation from the true value of the measured parameter. This paper describes the errors of non-contact temperature measurement of pyrometers, namely total radiation pyrometer, partial radiation pyrometer, spectral ratio pyrometer, as well as shows the results of comparative calculations between them. Expressions for the evaluation of methodical errors of total radiation, partial radiation and spectral ratio pyrometers are given, as well as the results of comparative calculations of errors are shown.

Remote sensing of human skin temperature by AI speckle pattern analysis

Analysis of dynamic differential speckle patterns, scattered from human tissues illuminated by a laser beam, has been found by many researchers to be applicable for noncontact sensing of various biomedical parameters. The COVID-19 global pandemic brought the need for massive rapid-remote detection of a fever in closed public spaces. The existing non-contact temperature measurement methods have a significant tradeoff between the measurement distance and accuracy. This paper aims to prove the feasibility of an accurate temperature measurement system based on speckle patterns analysis, enabling the sensing of human temperature from an extended distance greater than allowed by the existing methods. In this study, we used speckle patterns analysis combined with artificial intelligence (AI) methods for human temperature extraction, starting with fever/no fever binary classification and continuing with temperature measurement at higher resolution.

A Dual-Beam Differential Method Based on Feedback Interferometry for Noncontact Measurement of Linear and Angular Displacement

A dual-beam differential method for noncontact measurement of linear and angular displacement is reported in this paper. Compared to other optical sensors, the system based on the frequency-modulated feedback interferometry has higher sensitivity for non-cooperative targets and a wider range with respect to the angle measurement. Performance of the proposed method is evaluated via testing a prototype system. The experimental results show that the prototype has a stability of 35 nm and 0.15“ over 1 hour with a resolution of 1 nm and 0.02” respectively. The linearity is 5.58×10^-6 in the range of 100 mm and 1.34×10^-4 in the range of 360°. The experiments for PZT deformation measurement and rotary motor monitor indicate that the proposed method possesses considerable potential for high-precision metrological applications, such as lathe calibration and motor control.

Simulation and Optimization of Embedded Voltage Sensor in High Voltage Insulator

In the smart grid, the voltage measurement of the power system is of great significance to the measurement of electric energy, the correct operation of the relay protection device, and the measurement of the voltage of the transmission line. The main methods of high voltage (35-220kV) measurement are contact and non-contact. Contact measurement has many defects, and non-contact voltage measurement has become a research hotspot. While capacitive voltage sensors are the main non-contact measurement method, currently known capacitive voltage sensors have some disadvantages, such as separation of the resin from the elements constituting the capacitive sensor due to resin bubbles of the dielectric material arranged around the sensor assembly, Unnecessary partial discharge occurs because the resin does not fully adhere and/or clamp the sensor device completely. In view of the above problems, this paper proposes a built-in integrated voltage sensor, which is placed inside the insulator. The multi-layer and through-hole holes perfectly avoid the occurrence of bubbles in the resin medium around the sensing component and the complete fit between the medium and the sensor. Partial Discharge. This paper describes the modeling and simulation analysis of the designed sensor using the finite element software COMSOL multiphysics, and the performance of the sensor in terms of electric field distribution is optimized through modeling.

Noncontact measurement of tire deformation based on computer vision and Tire-Net semantic segmentation

Tire deformation is a contributor to vehicle driving safety and an important parameter reflecting the vehicle-road/bridge interaction. Existing methods for measuring tire deformation require measuring devices in contact with the tires, which has many limitations in practice. Thus, the present study proposes a noncontact measurement method of tire deformation based on computer vision and deep learning techniques. Firstly, the diverse dataset of tire images is established based on tire images collected from roadside cameras. Next, a new semantic segmentation Tire-Net is developed to segment the tire images. Then, the quantification algorithm including the subpixel-level edge detection, key point positioning, and scale factor determination is proposed to calculate the physical value of tire deformation. Finally, field tests are carried out on the tires of cars, buses, light trucks, and heavy trucks to verify the proposed method. The results show that it performs well as a means of measuring tire deformation.

A real-time heart rate estimation framework based on a facial video while wearing a mask.

The imaging photoplethysmography (iPPG) method is a non-invasive, non-contact measurement method that uses a camera to detect physiological indicators. On the other hand, wearing a mask has become essential today when COVID-19 is rampant, which has become a new challenge for heart rate (HR) estimation from facial videos recorded by a camera. The aim is to propose an iPPG-based method that can accurately estimate HR with or without a mask. First, the facial regions of interest (ROI) were divided into two sub-ROIs, and the original signal was obtained through spatial averaging with different weights according to the result of judging whether wearing a mask or not, and the CDF, which emphasizes the main component signal, was combined with the improved POS suitable for real-time HR estimation to obtain the noise-removed BVP signal. For self-collected data while wearing a mask, MAE, RMSE, and ACC were 1.09 bpm, 1.44 bpm, and 99.08%, respectively. Experimental results show that the proposed framework can estimate HR stably in real-time in both cases of wearing a mask or not. This study expands the application range of HR estimation based on facial videos and has very practical value in real-time HR estimation in daily life.

Vibration Parameters Identification of Rotating Blades Based on Blade Tip-Timing Sensor Waveforms

Blade tip-timing is one of the most widely used non-contact measurement methods for rotating blade vibration. The classic blade tip-timing method is to identify the vibration parameters of the blade based on the time of blade tip arrival to the tip-timing sensor installed on the casing. Since the vibration measurement of blade tip-timing is a serious under-sampling method, the blade resonance frequency is usually higher than the sampling frequency of the BTT data acquisition system, resulting in a large identification error of blade vibration parameters. In view of the above shortcomings, a blade vibration parameters identification method is proposed based on the mapping matching between the static waveform of the sensor and the dynamic waveform of the blade tip-timing. The modulation effects of different vibration displacements, velocities and vibration amplitudes on the tip-timing waveform of the sensor are analyzed. The vibration displacements and velocities of the blade tip passing through the sensor are obtained by the waveform mapping method, and then the vibration parameters of the blade are identified. The influence of the number of blade tip-timing sensors on the identification accuracy of the blade vibration parameters identification algorithm is observed based on the waveform mapping theory. The identification accuracy of waveform mapping method is compared with other methods. The results show that compared with the under-sampled time of arrival (TOA) data of classic BTT applications, the modulation of the sensor waveform output by blade vibration can provide additional data, and the waveform mapping method can not only reduce the number of sensors but also improve the identification accuracy of blade vibration parameters. The waveform mapping method has higher identification accuracy of blade vibration parameters compared with the waveform method in the reference.

Study on fracture characteristics of anchored sandstone with precast crack based on double K criterion

Fracture toughness is an important fracture mechanics parameter of rocks, the double K criterion can reasonably explain the fracture mechanism of rocks. To investigate the fracture mechanics behavior of the anchored rock with a pre-crack, three-point bending test was performed to analyze the mode I fracture process of the rock. A series of pre-cracked sandstone beams of the same size, strength, and different anchor parameters were tested. The evolution characteristics of deformation field and crack tip propagation during the entire fracture process were investigated using a non-contact optical measurement method, and rock fracture mechanics parameters corresponding to different anchoring conditions were obtained. The toughening mechanism of anchor bolt was discussed based on stress intensity factor theory. The double K criterion can be used to describe the fracture failure characteristics of rocks. By regarding the anchored rock as the composite material of bolt and rock, the anchoring composite fracture toughness can be obtained, and the stability of the anchored rock can be determined using the double K criterion. The toughening mechanism of the anchor bolt increases the threshold of crack initiation and instability of the entire anchored rock by increasing the stress intensity factor at the crack tip of the rock. The crack arrest mechanism of the bolt retards crack propagation via the transverse closing force. Investigating the anchoring effect of different anchoring parameters allows, the rock anchoring control mechanism to be revealed from the perspective of fracture mechanics, which can provide a reference for mining and tunnel engineering support.

A novel non-contact heart rate measurement method based on EEMD combined with FastICA

Objective. The imaging photoplethysmography (IPPG), which is applied to heart rate measurement, is susceptible to interference caused by illumination variations during photography. To improve the accuracy, robustness, and real-time performance of IPPG, a new method combined with modified ensemble empirical mode decomposition (EEMD) and fast independent component analysis (FastICA) was proposed. Approach. On the modified EEMD, to reduce the error caused by the residual white noise of the algorithm, it was characterized by recurrently updating zero-mean white noise for ensemble averaging based on the standard deviation of the input signal. On the modified FastICA, its constructed nonlinear function was superseded by Huber’s approximation function to improve the robustness and running speed. Main results. Comparison experiments were conducted between the MAHNOB-HCI database and own collected data. In the hybrid natural light and computer screen light scenario, the mean absolute error (MAE) of heart rate amounted to 0.93 beats per minute and the correlation coefficient reached 0.85. In the experiments of MAHNOB-HCI database, the MAE amounted to 6.03 beats per minute and the correlation coefficient reached 0.75. Furthermore, the modified method decreased the running time by approximately ten times compared to the original algorithms. Significance. Various experimental results demonstrated that the proposed method significantly improves the accuracy, timeliness, and interference resistance.

Improving surface smoothness in FDM parts through ironing post-processing

Additive manufacturing using fused deposition modelling (FDM) is an affordable and efficient way to produce functional parts and prototypes. However, the FDM-manufactured parts, particularly those made of polylactic acid (PLA), has some drawbacks regarding accuracy and surface finish. To address this, post-processing techniques such as ironing can be applied. However, little is known about the effect of ironing process parameters on surface roughness, and there is a lack of comparative studies on contact and non-contact measurement methods. This study aims to address these gaps by investigating the impact of path speed, ironing spacing, and flow rate on the surface roughness of FDM-produced PLA samples using both contact and non-contact measurement methods. The results show that the ironing process can lead to a significant reduction in surface roughness values, and careful optimization of process parameters can further minimize roughness. The study highlights the advantages and limitations of contact and non-contact methods for surface roughness analysis and provides guidance on selecting the appropriate method for a given application and material.

Optical Measurements of the thickness of the Gallium Indium free surface jet for the SARAF beam dump and neutron source

The Soreq Applied Research Accelerator Facility (SARAF) is a proton/deuteron RF superconducting linear accelerator. Phase II of the project is underway and includes the development of the accelerator to its final specifications: energy of 40 MeV proton/deuteron, and a current of up to 5 mA. A beam dump and a neutron source will be required for the commissioning stage, daily operation, and basic physics experiments. A liquid metal target based on a high-velocity windowless gallium–indium jet has been designed to absorb a beam of 200 kW by generating a stable 6 mm-thick film flowing at a velocity of up to 5 m/s into a concave supporting wall (nozzle). The main goal of the present research was to demonstrate that the thickness and variations of the Ga–In flow satisfy the design requirement in the area corresponding to the beam footprint. This research also presents a novel method for optical non-contact measurements of the liquid Ga–In thickness by using a chromatic confocal sensor. The advantage of our system is that it uses an “off the shelf” non-expensive product, non-contact, very accurate, and with very high resolution.The results show that the average Ga–In thickness was 5.6 ± 0.3 mm at a flow speed of 5 m/s. These results are in agreement with the expected thickness of the nozzle.

A non-contact continuous blood pressure measurement method based on video stream

Hypertension is the primary disease that endangers human health. A convenient and accurate blood pressure measurement method can help to prevent the hypertension. This paper proposed a continuous blood pressure measurement method based on facial video signal. Firstly, color distortion filtering and independent component analysis were used to extract the video pulse wave of the region of interest in the facial video signal, and the multi-dimensional feature extraction of the pulse wave was preformed based on the time-frequency domain and physiological principles; Secondly, an integrated feature selection method was designed to extract the universal optimal feature subset; After that, we compared the single person blood pressure measurement models established by Elman neural network based on particle swarm optimization, support vector machine (SVM) and deep belief network; Finally, we used SVM algorithm to build a general blood pressure prediction model, which was compared and evaluated with the real blood pressure value. The experimental results showed that the blood pressure measurement results based on facial video were in good agreement with the standard blood pressure values. Comparing the estimated blood pressure from the video with standard blood pressure value, the mean absolute error (MAE) of systolic blood pressure was 4.9 mm Hg with a standard deviation (STD) of 5.9 mm Hg, and the MAE of diastolic blood pressure was 4.6 mm Hg with a STD of 5.0 mm Hg, which met the AAMI standards. The non-contact blood pressure measurement method based on video stream proposed in this paper can be used for blood pressure measurement.

Data recovery of 2D lifetime-based phosphor thermometry using deep neural networks

Thermographic phosphor (TP) thermometry has been widely used as one of the newly developed non-contact surface temperature measurement methods. However, temperature information is frequently lost locally because chemical bonding coatings are easily damaged during the measurement. This limits its application, such as in the case of jet impact, high-speed motion, high vibration, etc. We proposed to use of deep neural networks (DNNs) as a tool for recovering lost data. In this study, we captured the dynamic two-dimensional (2D) temperature field of jet impingement cooling a high temperature plate by TP thermometry. Different parts of the temperature information, including jet impact area and non-impact area, were then removed to assume the coating was damaged. A prediction model was established by the DNNs using the 2D space and time coordinates as the input dataset and using the temperature information corresponding to the coordinates as the output dataset. The removed temperature information was then recovered using the developed regression model and compared with the raw temperature field to evaluate predicted results. The results showed that the removed data can be successfully recovered using the established DNN prediction model, in which the predicted accuracy was greater than 94.94%. While in the central and the boundary area of the jet, the model has a relatively poor performance, which is mainly due to the large temperature gradients. These results indicate that the established DNN model can be used to recover the lost temperature information but it is limited for applications in regions with drastic temperature changes.

Efficiency Study of Combined UAS Photogrammetry and Terrestrial LiDAR in 3D Modeling for Maintenance and Management of Fill Dams

Low-cost unmanned aerial system (UAS) photogrammetry and terrestrial laser scanner (TLS, terrestrial LiDAR) technologies are being used as noncontact measurement methods for collecting unstructured data for the maintenance of construction infrastructure facilities. This study investigated the possibility of settlement, which is a maintenance condition evaluation item for fill-dam bodies, using point clouds based on the UAS (unmanned aerial system) structure from motion (UAS-SfM) and TLS (terrestrial laser scanner) point clouds. Specifically, the Z-axis RMSE of the point cloud improved to 0.012 m and the shape reproducibility rate to 98.53% by complementing the heterogeneous data of the UAS and TLS by combining the two systems with block coordination and ICP algorithms. The maximum settlement height and volume (heaving) of the dam crest and upstream and downstream slopes were derived from the combined UAS/TLS point-cloud-based 3D model. The quantitative values for the settlement of the fill-dam body were derived using the combined 3D model with high accuracy and density. This result verified the possibility of using the combined 3D model for evaluation of the maintenance condition.