Traditional Temperature Measurement Research Articles

Relationship between high-level color features and temperature mapping of magnesium alloy surface images based on the K-nearest neighbor algorithm

Numerous deformation and processing application scenarios of magnesium alloy sheets require high precision and efficiency in temperature detection. Traditional contact temperature measurement and infrared thermal imaging temperature measurement methods suffer from low accuracy and susceptibility to environmental influences. To this end, a real-time sensing method for determining the average surface temperature of magnesium alloys using visible light images is proposed in this paper. Firstly, real-time surface images of 3 mm thick magnesium alloy sheets were captured as the temperature decreased from 450 °C to 300 °C under varying light intensities. Then, calculate the high-level color features based on the grayscale frequency of the Red Green Blue color space of the image, and select the high-level color features with a strong correlation with temperature by Fisher criterion. Finally, a mapping relationship between the high-level color features of magnesium alloy images and temperature was established using the K-nearest neighbor algorithm. The results show that images in RAW format have a wider grayscale range compared to JPG, which is helpful in determining the mapping relationship between image color features and temperature. After extracting and selecting the high-level color features that have a strong correlation with temperature, the dimensions of input features are reduced. Using the K-nearest neighbor algorithm can fit the complex nonlinear relationship between high-level color features and temperature more accurately.

Development and testing of a remote calibration device for the temperature field of environmental testing equipment

Abstract Temperature is one of the important parameters for the calibration of environmental testing equipment. Due to its special working requirements and large volume and weight, the demand for on-site inspection and calibration of environmental testing equipment has always been high. Conducting research on remote calibration devices for temperature fields in environmental testing equipment is beneficial for improving the testing efficiency of environmental temperature and humidity, reducing the workload of testing personnel, and further reducing customer testing costs. By using ANT Wireless technology and using SHT3X temperature and humidity sensors to create a temperature field calibration device for environmental testing equipment, the above functions can be achieved. After testing, the maximum transmission distance of a single node can reach 70 meters, and the maximum networking distance between multiple nodes can exceed 1,000 meters. It can adapt to high and low-temperature impact tests at -20°C~60°C, and the response speed of temperature and humidity sensors is slightly slower than traditional temperature measurement thermocouples. It is suitable for calibration and detection of environmental testing equipment with good insulation performance.

Calculating the Spontaneous Combustion Temperature of Coal Based on Measured Gas concentration: A Case Study in the Huangshan Mine, China

ABSTRACT The actual coal temperature of “spontaneous combustion zone” is not convenient to obtain, which causes a lot of inconvenience to the prevention and control of coal spontaneous combustion fire. The traditional temperature measurement method does not consider the influence of gas flow in the goaf, so the measured coal temperature cannot directly reflect the actual state of coal spontaneous combustion. This paper presents a method to predict the coal temperature in the “spontaneous combustion zone” based on the theoretical data obtained in the laboratory, combined with the actual coal temperature in the “dissipation zone,” and applies it to the B4 layer coal mine in Huangshan, Jiangxi Province. The results show that the method is objective and effective, and can be used for the prediction of CSC (coal spontaneous combustion) in the goaf of coal mines.

Smoothing of the Noisy Lithium-ion Battery Surface Temperature Data Based on Savitzky–Golay (SG) Filter

As the charging current and surrounding temperatures rise concurrently in an electric vehicle's (EV) battery pack, the battery temperature increases abruptly beyond safe limits. The battery is susceptible to triggering thermal runaway at higher temperatures, leading to battery damage and even an explosion. The control system depends on these measurement data from the sensors to prevent dangerous situations and maximize the performance and cycle life of batteries. However, traditional noisy temperature measurement sensors in a real application, such as thermistors and thermocouples, are extensively used. In this paper, the experimental setup, the heat pipe-based battery thermal management system (BTMS) was designed and experimented with high input power. The battery was sandwiched with heat pipes, and heated at 30, 40, 50, and 60 W. The Savitzky-Golay filter technique is applied to the noisy temperature data of the surface temperature of the lithium-ion batteries (LIBs) used to estimate the condition of the battery. According to this study, the start-up time parameter in battery thermal management can be controlled by the Savitzky-Golay filter. This will attenuate random temperature fluctuations of battery temperature noise and avoid the cooling system from being falsely triggered. The results are measured by the signal-to-noise (SNR) to demonstrate the ability of the Savitzky-Golay filter to eliminate noise

Research on Thermocouple Integration Method of Hollow Thin-Walled Turbine Rotor Blade Surface

The hollow thin-walled structure is increasingly used in advanced aero-engine turbine rotor blades, and the traditional surface temperature measurement method based on groove buried couple is no longer applicable. In this paper, a new thermocouple integration method is proposed. The laser additive manufacturing process is used to generate thermocouple embedding channels on the surface of turbine blades. After the thermocouple is embedded, it is pre-fixed, and the thermocouple protection is realized by supersonic flame spraying technology. The strength of the thermocouple integrated structure of the turbine blade is verified by finite element analysis. Based on CFD analysis, the influence of thermocouple integration on the efficiency, pressure ratio and temperature distribution of the turbine blade is studied. The high-speed rotation test shows that the thermocouple integrated structure has high strength and reliability and meets the measurement requirements. This method can provide technical support for the surface temperature measurement of hollow thin-walled turbine rotor blades.

Dual-mode optical thermometers via the thermochromic Bi3+, Eu3+ co-doped La2LiSbO6 phosphors for real-time chip temperature monitoring

In recent years, with the increasing demand for non-contact temperature measurement, it is urgent to develop a new type of phosphor with double emission centers, high sensitivity and good reliability. A series of Bi3+and Eu3+ co-doped La2LiSbO6 double emission center phosphors were successfully prepared. The response characteristics of Bi3+and Eu3+ at different temperatures in the range of 293–473k were calculated by fluorescence intensity ratio (FIR) technique. The optimal relative sensitivity Sr and absolute sensitivity Sa were 1.13 % K−1 and 1.27 % K−1, respectively. Hence, La2LiSbO6:Bi3+, Eu3+ phosphors have the capability of non-contact optical temperature measurement and are a promising material for application. The phosphor is combined with the optical fiber temperature sensor to monitor the real-time temperature of the computer central processing unit. The optical fiber temperature sensor system breaks through the limitations of traditional temperature measurement, and can be used in special environments such as small size and strong electromagnetic interference. It has become an ideal choice to replace the traditional temperature sensor.

Methodology for geometry assessment of self-drilling micropiles using distributed fibre optic sensors

Self-drilling micropiles (SDMs) are gaining popularity as bearing foundation elements, as this construction technique allows for considerable time and cost savings. However, the irregular geometry and variability in the bearing capacity present uncertainty and a significant risk that has limited SDM deployment. To date, no reliable technology has been used to measure the SDM geometry. This paper proposes a methodology that uses distributed fibre optics (DFO), heat conduction modelling and inverse analysis to measure the pile geometry, and could also be extended to assess its bearing capacity. The temperature distribution during cement hydration was measured using two DFO investigative technologies and was compared to traditional temperature measurements using point sensors. An inverse analysis of SDM geometry was subsequently carried out based on the DFO measurement of temperature, a finite-element heat conduction model and a calibration pile. Finally, the calculated pile geometry was compared to the geometry of an excavated pile in the uppermost part. The methodology presented here is not only intended as a tool for designing SDMs but can also be deployed as a structural health monitoring system to detect and monitor crack formation.

Research on temperature field reconstruction technology for single-dome combustion chambers based on flame image

In order to compensate for the limitations of traditional temperature measurement methods, glass plates were installed on both sides of the single-dome combustion chamber test piece, and two CCD cameras observed the combustion flame in the furnace from different positions. The obtained flame radiation images were combined into a single image through the video capture card, and then the images were input into the reconstruction algorithm software to calculate the three-dimensional temperature field in the combustion chamber in real-time. The software could dynamically display the temperature distribution at three different positions in the chamber. This marked the first time that the temperature field in the combustion chamber was obtained using this new temperature measurement technology, which preliminarily verifies the feasibility of reconstructing the temperature field in the single-dome combustion chamber through flame image temperature measurement technology.

Diurnal to Decadal Variability in Land Surface and Air Temperature Gradient from 2002 to 2022 over the Contiguous United States

Abstract The surface and air temperature gradient (TS00 − Tair) drives the development of the convective boundary layer and the occurrence of clouds and precipitation. However, its variability is still poorly understood due to the lack of high-quality observations. This study fills in this gap by investigating the diurnal to decadal variability in TS00 − Tair from 2002 to 2022 based on hourly observations collected at over 100 stations of the U.S. Climate Reference Network. It is found that TS00 − Tair reaches its maximum at noon with an average of 6.85°C over the contiguous United States, which decreases to 4.28°C when the soil moisture exceeds 30%. The daily minimum of TS00 − Tair has an average of −2.08°C, which generally occurs in the early evening but is postponed as the cloud fraction decreases. Moreover, while existing studies have used the near-surface soil temperature, such as the 5-cm soil temperature (TS05), to calculate TS05 − Tair, we find that TS00 − Tair and TS05 − Tair have opposite diurnal cycles, and their amplitudes differed drastically. The daily minimum of TS00 − Tair has a significant decreasing trend (−0.50° ± 0.007°C decade−1) from 2002 to 2022 due to Tair increasing at a higher rate than TS00 during the nighttime. The occurrence frequency of near-surface stable conditions (TS00 − Tair < 0) increases significantly, and the frequency of unstable conditions (TS00 − Tair > 0) decreases notably throughout the year except for winter. When it is stable, the magnitude of TS00 − Tair tends to decrease while the TS00 − Tair tends to increase when it is unstable, which is consistent with the drying condition caused by the precipitation deficit. This study provides the first observational evidence on how TS00 − Tair responds to warming. Significance Statement The impact of global warming on surface-air temperature gradients is a crucial scientific issue that needs to be addressed. These gradients determine changes in cloud and precipitation, affecting water resources. However, traditional surface temperature measurements from weather stations are of high uncertainty due to direct exposure to the insolation. Satellite retrieval of surface temperature is limited by the availability of clear sky conditions, with low accuracy for temperature gradient calculations. Despite their importance, high-accuracy data of land surface temperature are still lacking. To address this issue, the U.S. Climate Reference Network (USCRN) uses infrared radiometers to continuously monitor surface temperature with high accuracy and sampling frequency. This study reports on surface-air temperature gradients at more than 100 U.S. stations, providing insight into diurnal to decadal variability over the contiguous United States. The study also highlights the significant difference between the land surface temperature–air temperature gradient and soil temperature–air temperature gradient.

A deep learning-based detection method for pig body temperature using infrared thermography

Body temperature is one of the important indicators that reflect the health of pigs. The traditional rectal temperature measurement is inconvenient and time-consuming, and also easy to cause stress responses to pigs. Infrared Thermography (ITG) was supposed to have potentials to realize non-invasive and rapid temperature detection for pigs in intensive pig farming. In this paper, an automatic temperature detection method base on ITG was developed. First, temperatures on six regions on pig body surface (i.e., forehead (FH), eyes, nose, ear root (ET), back and anus) were measured by ITG for the region of interest (ROI) selection; Then, an improved model of YOLOv5s-BiFPN was developed for automatic ROI detection and temperature extraction. A dataset with 2797 images that collected from sixteen pigs for 30 days was used for model development. It was shown that temperatures on FH and ET were supposed to be the ROI for ITG temperature detection and because of their strong correlations with the rectal temperature among the six parts on pig body surface. Also, the maximum temperature (MaxT) on FH and ET could reflect pig’s body temperature variations the best. The proposed model of YOLOv5s-BiFPN achieved optimal performances (e.g., the mAP was 96.36%, outperformance was 20 MB, target detection speed was up to 100 frame/s), and the mAPs were increased by 4.85%, 4.38%, 1.60%, 1.56%, 31.52%, and 22.42% compared with models of CenterNet, Faster R-CNN, YOLOv4, YOLOv5s, Nanodet and YOLOv5n, respectively. Therefore, it is a feasible way for pig body temperature automatic detection and facilitates early staged disease warning and environmental control.

Design of an Intelligent High-Temperature Infrared Temperature Measurement System

The system is designed to address the problem of untimely transmission of temperature measurement signals in the sampling process at the LF refinery location, as well as the subjective involvement of the operator in the traditional temperature measurement method, i.e. the technology and experience of the operation, which results in a certain systematic error in the temperature measurement results. In this paper, a steel plant is used as an experimental object to test the performance of the device and the practicality of the system in combination with the on-site operating environment; and the system differs from the traditional contact temperature measurement by visualizing the steel image and generating an intelligent temperature profile while measuring the temperature with the help of an infrared camera. The experimental results show that the system can be remotely controlled to achieve intelligent temperature measurement, and the percentage of temperature measurement error is controlled within 1.5%, which is of practical significance to the automation and intelligence of the LF refining furnace temperature measurement in the iron and steel metallurgical industry.

Research on Reconstruction Technology of Temperature Field in Engine Combustion Chamber Based on Image Thermometry

In order to make up for the defects of traditional temperature measurement methods, image temperature measurement technology is gradually applied to the measurement of temperature field of high-temperature objects. By analysing and demonstrating the feasibility of three-dimensional temperature field reconstruction in engine combustion chamber, the algorithm of temperature field reconstruction was put forward in this paper, the model between flame radiation energy and temperature field in engine combustion chamber was established, and the application of radiation imaging temperature measurement technology in aero engine annular combustion chamber test was preliminarily realized.

Study on Temperature Measurement Model of Rare Earth Metal Electrolysis Based on Infrared Image

The temperature of rare earth electrolysis plays an important role in the process of electrolysis control. It is the main parameter to control the efficiency of electrolysis. In production practice, most of the process parameters are controlled by the artificial thermocouple temperature measurement method. This extensive technical management method greatly affects the accuracy of production control. And the traditional thermocouple temperature measurement is artificial and slow, not timely, short life, and has other issues. Therefore, it is extremely urgent to explore a continuous and safe surface temperature measurement method to achieve precise control of process parameters. Therefore, based on the principle of infrared temperature measurement, this paper introduces the theory of infrared temperature measurement, uses Matlab software to extract the average gray value of the infrared image, and obtains the infrared temperature measurement model by least square method and bp neural network for temperature and gray fitting curve. At the same time, the experimental data are measured, and the model is verified. This method provides a reference for online temperature measurement of electrolytic molten metal.

Measurement of weld penetration for variable-groove weldment using dual-band imaging and neural network

For the process of gas metal arc welding, the online measurement of weld penetration has always been a research hotspot, and high-precision penetration monitoring can lay the foundation for penetration control. This study developed a novel method for measuring the weld penetration of variable-groove weldments using dual-band imaging and a neural network. First, a welding temperature field measurement system was constructed using a colour charge-coupled device (CCD), and the feasibility of using the output images from the red and green channels of the CCD for colorimetric temperature measurement was investigated. Then, a welding temperature field measuring algorithm based on a network model was investigated. Compared with the traditional temperature measurement algorithm, the proposed algorithm can measure the welding temperature field with higher precision. The distribution characteristics of the temperature field were extracted by means of the histograms of oriented gradients (HOG) algorithm. Finally, based on a back-propagation (BP) neural network, a weld penetration prediction model for variable-groove weldments was built, and the distribution features of the temperature field were used as the model input. The experimental results reveal that the proposed back bead penetration measurement method for variable-groove weldments can achieve the high-accuracy penetration measurement, which has broad application potential.

Three-dimensional temperature field measurement method based on light field colorimetric thermometry

This paper proposes a novel method for three-dimensional (3D) temperature measurement using light field colorimetric thermometry, aiming to overcome the challenges associated with the intricate system structure and the limited availability of 3D information in traditional radiation temperature measurement methods. Firstly, the correlation between corresponding image points and the positions of 3D object in the light field image system is analyzed using the ray tracing method. The 3D position acquisition model and the light field colorimetric thermometry model are established, enabling simultaneous acquisition of the spatial coordinates and radiometric information of the 3D object. Then, the light field camera radiation calibration experiment was conducted, and the 3D temperature field will be obtained by employing colorimetric thermometry for each corresponding image point of the same object point. Finally, the experiment employed a light field camera for temperature measurement and reconstruction of candle flames. The accuracy of the temperature measurement is 3.31%, thus confirming the feasibility and effectiveness of the proposed method.

Upconversion luminescence and effect of pump power on optical thermometry of Yb3+/Er3+ co-doped YOF self-crystallization glass ceramics

The optical thermometry based on the fluorescence intensity ratio (FIR) method overcomes many disadvantages of traditional contact temperature measurement, and appropriate host materials can improve the temperature sensing sensitivity. Herein, Yb3+/Er3+ co-doped YOF glass ceramics (GC) were prepared, and the luminescence and temperature sensing properties were researched under 980 nm excitation with different pump power. The FIR of thermal couple energy level (TCEL) and non-thermal couple energy level (NTCEL) was used to evaluate the temperature sensing sensitivity. The higher pump power results in a decrease in relative sensitivity (SR) due to the thermal effect and increased rate of energy transfer and non-radiative relaxation process between dopants and host. The maximum relative sensitivity of this sample reaches 1.59 × 10−2 K−1 at 310 K with a pump power of 0.2 W, which shows that this GC is a prospective application in optical thermometry.

An Intelligent Medical Isolation Observation Management System Based on the Internet of Things.

Since COVID-19 (coronavirus disease 2019) was discovered in December 2019, it has spread worldwide. Early isolation and medical observation management of cases and their close contacts are the key to controlling the spread of the epidemic. However, traditional medical observation requires medical staff to measure body temperature and other vital signs face to face and record them manually. There is a general shortage of human and personal protective equipment and a high risk of occupational exposure, which seriously threaten the safety of medical staff. We designed an intelligent crowd isolation medical observation management system framework based on the Internet of Things using wireless telemetry and big data cloud platform remote management technology. Through a smart wearable device with built-in sensors, vital sign data and geographical locations of medical observation subjects are collected and automatically uploaded to the big data monitoring platform on demand. According to the comprehensive analysis of the set threshold parameters, abnormal subjects are screened out, and activity tracking and health status monitoring for medical observation and management objectives are performed through monitoring and early warning management and post-event data traceability. In the trial of this system, the subjects wore the wristwatches designed in this study and real-time monitoring was conducted throughout the whole process. Additionally, for comparison, the traditional method was also used for these people. Medical staff came to measure their temperature twice a day. The subjects were 1,128 returned overseas Chinese from Europe. Compared with the traditional vital sign detection method, the system designed in this study has the advantages of a fast response, low error, stability, and good endurance. It can monitor the temperature, pulse, blood pressure, and heart rate of the monitored subject in real time. The system designed in this study and the traditional vital sign detection method were both used to monitor 1,128 close contacts with COVID-19. There were six cases of abnormal body temperature that were missed by traditional manual temperature measurement in the morning and evening, and these six cases (0.53%) were sent to the hospital for further diagnosis. The abnormal body temperature of these six cases was not found in time when the medical staff came to check the temperature on a twice-a-day basis. The system designed in this study, however, can detect the abnormal body temperature of all these six people. The sensitivity and specificity of our system were both 100%. The system designed in this study can monitor the body temperature, blood oxygen, blood pressure, heart rate, and geographical location of the monitoring subject in real time. It can be extended to COVID-19 medical observation isolation points, shelter hospitals, infectious disease wards, and nursing homes.

Optimization Method of Temperature Measuring Point Layout for Steel-Concrete Composite Bridge Based on TLS-IPDP

An optimization method of temperature measurement point layout for steel-concrete composite bridges based on the total least squares improved piecewise Douglas–Peucker (TLS-IPDP) algorithm was proposed to solve the problem that the traditional temperature measurement data cannot reflect the actual temperature gradient (TG) due to the position of measurement points on different paths is not reasonable. The characteristic curves of TG for the most unfavorable period and annual period are extracted from the finite element model. The rationality of the proposed method is illustrated by two typical steel-concrete composite beams with steel plates and steel boxes. By improving the classical Douglas–Peucker (DP) algorithm, the TLS-IPDP algorithm proposed in this paper has a better approximation effect on the original data. Compared with the traditional temperature measuring point arrangement method, the TLS-IPDP algorithm optimized arrangement in this paper realized the measuring point arrangement with different variable spacing under different paths; the temperature gradient curve obtained was closer to the real temperature distribution, and had higher accuracy in the region with a large gradient. In addition, the proposed method has the function of manually specifying the location of feature points and reserving the required number. The optimized arrangement of measuring points can meet the requirements of measuring points number and measurement accuracy. The method presented in this paper can provide a useful reference for temperature data acquisition and sensor layout for health monitoring of steel-composite bridges.

Identity and body temperature detection system based on image registration

In view of the normalization of epidemic prevention and control work, the low detection efficiency of traditional temperature measurement methods, and the existence of infection risks, an infrared thermal imaging temperature measurement system that can measure and identify the temperature of more than two people is designed. The system uses the Raspberry Pi as the core controller, using visible light and infrared cameras to capture identity and body temperature information. Identity recognition is based on OpenCv, using the Face Recognition face recognition framework to complete the identity recognition; body temperature detection is based on image registration, and the face position is mapped to the infrared image in real time through affine transformation, and finally the temperature of the human face area is obtained and displayed on the screen. The final experimental results show that it can simultaneously identify multiple people and detect body temperature with high accuracy, which can basically meet the requirements of rapid body temperature screening in intensive situations.

Design of a new anti-metal RFID temperature tag antenna based on short-circuit stub structure

With the increasing maturity of the power Internet of things (pIoT), radio frequency identification technology (RFID), is more applied to the fault monitoring of power equipment, of which the temperature monitoring of power equipment is one of the focuses of fault monitoring. When the traditional RFID temperature measurement tag is applied to high-voltage electrical equipment, it often produces metallic interference to the RFID temperature measurement tag antenna because it is attached to the surface of metal conductor, such as large synchronous machine and high-voltage transmission line, which greatly shortens the identification range of the traditional RFID temperature measurement tag on the metal surface and even cannot work normally. To solve the problems above, a new anti-metal RFID temperature tag antenna is designed in this paper. The internal antenna of the RFID temperature measurement tag adopts the anti-metal design based on the short-circuit stub structure, so that the temperature measurement tag can achieve the purpose of good anti-metal performance. Finally, through the simulation of HFSS, this paper verifies that the designed RFID temperature measurement tag can not only resist metal effect, but also be well applied to complex metal environment.