Temperature Monitoring Method Research Articles

The Optimization of a Pipeline Temperature Monitoring Method Based on Non-Local Means with the Black Widow Optimization Algorithm

The accuracy of pipeline temperature monitoring using the Brillouin Optical Time Domain Analysis system depends on the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. The Non-Local Means noise reduction algorithm, due to its ability to use the data patterns available within the two-dimensional measurement data space, has been used to improve the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. This paper studies a new Non-Local Means algorithm optimized through the Black Widow Optimization Algorithm, in view of the unreasonable selection of smoothing parameters in other Non-Local Means algorithms. The field test demonstrates that, the new algorithm, when compared to other Non-Local Means methods, excels in preserving the detailed information within the Brillouin Gain Spectrum. It successfully restores the fundamental shape and essential characteristics of the Brillouin Gain Spectrum. Notably, at the 25 km fiber end, it achieves a 3 dB higher Signal-to-Noise Ratio compared to other Non-Local Means noise reduction algorithms. Furthermore, the Brillouin Gain Spectrum values exhibit increases of 9.4% in Root Mean Square Error, 12.5% in Sum of Squares Error, and 10% in Full Width at Half Maximum. The improved method has a better denoising effect and broad application prospects in pipeline safety.

Thermoelectric Nanofluidics Probing Thermal Heterogeneity inside Single Cells.

Accurate temperature measurement in one living cell is of great significance for understanding biological functions and regulation. Here, a nanopipet electric thermometer (NET) is established for real-time intracellular temperature measurement. Based on the temperature-controlled ion migration, the temperature change in solution results in altered ion mobilities and ion distributions, which can be converted to the thermoelectric responses of NET in a galvanostatic configuration. The exponential relationship between the voltage and the temperature promises highly sensitive thermoelectric responses up to 11.1 mV K-1, which is over an order of magnitude higher than previous thermoelectric thermometry. Moreover, the NET exhibits superior thermal resolution of 25 mK and spatiotemporal resolution of 100 nm and 0.9 ms as well as excellent stability and reproducibility. Benefiting from these unique features, both thermal fluctuations in steady-state cells and heat generation and dissipation upon drug administration can be successfully monitored, which are hardly achieved by current methods. By using NET, thermal heterogeneities of single cancer cells during immunotherapy were reported first in this work, in which the increased intracellular temperature was demonstrated to be associated with the survival benefit and resistance of cancer cells in immunotherapy. This work not only provides a reliable method for microscopic temperature monitoring but also gains new insights to elucidate the mechanism of immune evasion and therapeutic resistance.

Remote Sensing Monitoring of Drought in Southwest China Using Random Forest and eXtreme Gradient Boosting Methods

A drought results from the combined action of several factors. The continuous progress of remote sensing technology and the rapid development of artificial intelligence technology have enabled the use of multisource remote sensing data and data-driven machine learning (ML) methods to mine drought features from different perspectives. This method improves the generalization ability and accuracy of drought monitoring and prediction models. The present study focused on drought monitoring in southwest China, where drought disasters occur frequently and with a high intensity, especially in areas with limited meteorological station coverage. Several drought indices were calculated based on multisource satellite remote sensing data and weather station observation data. Remote sensing data from multiple sources were combined to build a reconstructed land surface temperature (LST) and drought monitoring method using the two different ML methods of random forest (RF) and eXtreme Gradient Boosting (XGBoost 1.5.1), respectively. A 5-fold cross-validation (CV) method was used for the model’s hyperparameter optimization and accuracy evaluation. The performance of the model was also assessed and validated using several accuracy assessment indicators. The model monitored the results of the spatial and temporal distributions of the drought, drought grades, and influence scope of the drought. These results from the model were compared against historical drought situations and those based on the standardized precipitation evapotranspiration index (SPEI) and the meteorological drought composite index (MCI) values estimated using weather station observation data in southwest China. The results show that the average score of the 5-fold CV for the RF and XGBoost was 0.955 and 0.931, respectively. The root-mean-square error (RMSE) of the LST values reconstructed using the RF model on the training and test sets was 1.172 and 2.236, the mean absolute error (MAE) was 0.847 and 1.719, and the explained variance score (EVS) was 0.901 and 0.858, respectively. Furthermore, the correlation coefficients (CCs) were all greater than 0.9. The RMSE of the monitoring values using the XGBoost model on the training and test sets was 0.135 and 0.435, the MAE was 0.095 and 0.328, the EVS was 0.976 and 0.782, and the CC was 0.982 and 0.868, respectively. The consistency rate between the drought grades identified using SPEI1 (the SPEI values of the 1-month scale) based on the observed data from the 144 meteorological stations and the monitoring values from the XGBoost model was more than 85%. The overall consistency rate between the drought grades identified using the monitoring and MCI values was 67.88%. The aforementioned two different ML methods achieved a high comprehensive performance, accuracy, and applicability. The constructed model can improve the level of dynamic drought monitoring and prediction for regions with complex terrain and topography and formative factors of climate as well as where weather stations are sparsely distributed.

Accuracy estimation of low-current voltage drop method for junction temperature monitoring under DC power cycling

A thorough accuracy estimation of the well-known low-current voltage drop method is carried out in this paper. A high-performance infrared camera is utilized as the reference in a DC power-cycling test on a commercial IGBT power module. The result shows that the low-current voltage drop method produces generally a higher temperature than the actual, in particular at the bond wire's joint point. However, a simple compensation method with a linear function can be easily adopted to compensate the temperature difference. After compensation, the maximum error of temperature swing is 0.28 °C.

Junction temperature monitoring for cascode GaN devices using the Si MOSFET's body diode voltage drop

In this paper, a new online junction temperature monitoring method is proposed for cascode GaN devices using the body diode of Si MOSFET. The reverse current during soft-switching mode is utilized to extract the voltage drop of the Si MOSFET's body diode. It is pointed out that, to accurately obtain the voltage drop of Si MOSFET's body diode, the VDS voltage of cascode GaN device should be measured when the reverse current is small during the dead-time period. An experimental platform has been designed to validate the proposed method. The proposed method exhibits an accuracy within 0.6 °C and a R2 = 0.99 goodness of linear fit on a test Buck converter, showing that this method is practical for real applications.

Towards interfacial nanothermometry in chemical reactions by total internal reflection wide-field diamond microscopy

In situ monitoring interfacial temperatures at microscopic scale is crucial in chemical reactions involving two immiscible phases. However, such measurement is challenging for most sensing methods due to harsh chemical conditions. Nanodiamond (ND) containing nitrogen-vacancy centers provides a promising method for sensitive temperature monitoring at nanoscale in a chemical reaction zone without perturbing the ongoing reactions. In this study, we developed a total internal reflection (TIRF) wide-field microscopy combined with ND quantum sensors for interfacial temperature monitoring in chemical reactions. The stability and measurement uncertainty of this method were demonstrated by monitoring the interfacial temperature in zinc electrodeposition with dendrite growth induced internal short circuit (ISC). For the first time, the surface scattering of growing Zn dendrites was revealed, which would induce significant photon counts fluctuations of NDs when the surface morphology of Zn dendrite dynamically changed. Fluorescence stability maintained by adopting TIRF mode wide-field microscope, which enabled real time monitoring of the interfacial temperature at intervals of hundreds of seconds with a measurement uncertainty of ∼1 K during the entire process of zinc dendrite induced ISC. This work represents a critical advancement in the investigation of surface optical properties and thermal dynamics across a wide range of chemical reactions.

Numerical and experimental research on electromagnetic-thermal and leakage defects characteristics of the high voltage cable terminal

Thermal and insulation performance of the High Voltage Cable Terminal (HVCT) could effectively improve the reliability and economy of power energy transmission. In this paper, a numerical model of the electromagnetic heat flow field was proposed to analyze the heat transfer and temperature characteristics of HVCT under oil leakage defects. This model was the first validated using operation data from the actual equipment and the relative error was less than 1.51%. The effects of oil leakage fault on the global and local temperature distribution of the HVCT were then investigated under different load currents. In addition, the local temperature rise effect of different ambient temperatures on the HVCT oil leakage defect was also considered. Finally, combined with reference indicators such as oil level, temperature, and temperature gradient, a qualitative and quantitative evaluation of the oil leakage fault of HVCT was performed. The results showed that the temperature of the core, surface, and their maximum temperature gradient were linearly related to the ambient temperature. However, the temperature of the core at the oil leak exceeded the normal temperature by up to 7.2 °C with 1000A load current, and the temperature gradient change reaches 681.3 °C/m, which indicated that the convection heat dissipation effect of the oil-deficient part was poor, and the heat accumulates near the cable core. Therefore, the numerical model and analytical results presented in this paper provided an important method for online temperature monitoring and fault analysis of HVCTs.

A Junction Temperature Monitoring Method for IGBT Modules Based on Turn-Off Voltage With Convolutional Neural Networks

Junction temperature monitoring (JTM) is essential for reliability evaluation and health management for insulated-gate bipolar transistor (IGBT) modules, and thus is extensively focused on in power electronics converters. However, many JTM methods for IGBT modules are criticized for providing inaccurate junction temperature information with strong load current dependence. To address this, a JTM method based on the turn-off voltage (TOV) and convolutional neural networks (CNN) is proposed in this paper. In this method, the TOV is used as the junction temperature indicator, and the characterization behavior of the TOV during turn-off transient process is thoroughly analyzed. Then, the parameter dependence of the TOV is investigated. Considering the proposed JTM method may be subject to the issue of load current dependence and show an undesirable performance, the CNN is adopted to maintain the accuracy of junction temperature prediction due to its excellent global and local feature recognition capability. With this regards, the proposed JTM method enables to provide accurate junction temperature information under different conditions. Finally, the double-pulse tests and experimental tests are carried out to validate the effectiveness of the proposed JTM method.

A Novel Junction Temperature Estimation Method Independent of Bond Wire Degradation for IGBT

The insulated-gate bipolar transistor (IGBT) junction temperature is crucial for condition monitoring, reliability assessment, and health management. However, the existing IGBT temperature monitoring methods are affected by the aging of IGBT bond wires. A novel junction temperature estimation method independent of bond wire degradation for IGBT is proposed in this paper. On-state voltage drop and turn-on gate voltage overshoot are analyzed for dependence on the junction temperature and bond wire degradation. These two temperature and bond wire monitoring electrical parameters are combined by the Artificial Neural Network (ANN), to eliminate the effect of bond wire degradation. A double pulse test platform is built to verify the influence of temperature and bond wire degradation on on-state voltage drop and turn-on gate voltage overshoot, which agrees with the theoretical analysis. Lots of experimental data at various collector-emitter voltages, load currents, IGBT junction temperature, and bond wire degradation are tested. A BP neural network is trained for junction temperature monitoring and its optimal number of hidden layers is studied. The experimental results show that the junction temperature estimation method based on the neural network can effectively eliminate the effect of bond wire degradation. The estimation error is around 4.219°C. The proposed method has the advantages of high accuracy and a wide application range for junction temperature monitoring.

A Novel Temperature Sensitive Electrical Parameter of IGBT Modules Without Involvement of Operating Parameters

The junction temperature estimation of the insulated gate bipolar transistor (IGBT) module is crucial for reliability assessment and health management of converters. However, most of the existing methods need to measure the collector-emitter voltage and collector current for the IGBT junction temperature estimation, which increases the cost and complexity of the monitoring and calibration process. In this article, a novel junction temperature monitoring method for IGBT modules without involving operating condition calibration based on the pre-overshoot of gate voltage is proposed. The gate pre-overshoot is influenced by the internal gate resistor, which has a linear relationship with the IGBT junction temperature. Besides, the gate pre-overshoot is only temperature dependent, while not affected by the collector-emitter voltage and collector current. Therefore, the gate pre-overshoot is a promising temperature-sensitive electrical parameter (TSEP). Because the collector-emitter voltage and collector current are not involved, the gate pre-overshoot-based method for junction temperature has the advantages of a simple signal monitoring structure, a simple calculation process, and a simple calibration process.

A High-Frequency Online Junction Temperature Monitoring Method for SiC mosfets Based on on-State Resistance With Aging Compensation

A High-Frequency Online Junction Temperature Monitoring Method for SiC mosfets Based on on-State Resistance With Aging Compensation

Evaluation of Different Registration Algorithms to Reduce Motion Artifacts in CT-Thermography (CTT).

Computed tomography (CT)-based Thermography (CTT) is currently being investigated as a non-invasive temperature monitoring method during ablation procedures. Since multiple CT scans with defined time intervals were acquired during this procedure, interscan motion artifacts can occur between the images, so registration is required. The aim of this study was to investigate different registration algorithms and their combinations for minimizing inter-scan motion artifacts during thermal ablation. Four CTT datasets were acquired using microwave ablation (MWA) of normal liver tissue performed in an in vivo porcine model. During each ablation, spectral CT volume scans were sequentially acquired. Based on initial reconstructions, rigid or elastic registration, or a combination of these, were carried out and rated by 15 radiologists. Friedman's test was used to compare rating results in reader assessments and revealed significant differences for the ablation probe movement rating only (p = 0.006; range, 5.3-6.6 points). Regarding this parameter, readers assessed rigid registration as inferior to other registrations. Quantitative analysis of ablation probe movement yielded a significantly decreased distance for combined registration as compared with unregistered data. In this study, registration was found to have the greatest influence on ablation probe movement, with connected registration being superior to only one registration process.

An Online Junction Temperature Monitoring Method for SiC MOSFET Based on Drain-source Voltage Change Rate During Turn-on Transient

This paper analyzes the turn-on transient of silicon carbide (SiC) metal oxide semiconductor field effect transistor (MOSFET) based on the basic physical equation, and the analysis model of drain-source voltage change rate (dv ds/dt ) during turn-on transient is derived. The change rule of dv ds/dt of SiC MOSFET during turn-on transient and its temperature-dependent characteristics are studied, and the calculations generally fit the measurements well. Experimental results show that the magnitude of turn-on dv ds/dt increases with increasing temperature and exhibits good linearity. The influence of driving conditions on the temperature dependence of the turn-on dv ds/dt is also analyzed through the model, finding that the temperature sensitivity can be improved by reducing the driving forward bias voltage and increasing the driving resistance. Finally, the online junction temperature monitoring experiments are used to verify the accuracy and effectiveness of the proposed method.

Online Monitoring Method of P-I-N Diode Temperature Based on Maximum Recovery Current

The P-i-N diode is as important as insulated gate bipolar translators (IGBTs) in power electronic devices, and its junction temperature is a significant indicator when evaluating the reliability of power electronics. Thus, an online junction temperature monitoring method with the maximum recovery current for P-i-N diodes is proposed in this paper. Firstly, the relationship among maximum recovery current, on-state current and junction temperature of diode has been deduced in theory. The linear correlation among the maximum recovery current, temperature, and on-state current have been illustrated. Secondly, a sampling circuit based on a current transformer has been designed. And then, the mathematical model of junction temperature online monitoring based on the maximum recovery current and sampling circuit has been obtained. Finally, the experimental platform based on the DSP TMS320F28335 has been developed. The experimental results verify the correctness of the relationship among maximum recovery current, on-state current and junction temperature of the diode, and high accuracy of the proposed online monitoring method in practice application, where the maximum temperature error and error percentage are 4.7℃ and 4.49%, respectively. In addition, the proposed method has advantages of simplicity, small size, low cost, convenient installation, and high linearity.

Wind Turbine Fault Detection With Multimodule Feature Extraction Network and Adaptive Strategy

Unexpected failures in wind turbines (WTs) lead to tremendous economic losses and safety hazards to wind farms. Intelligent condition monitoring and fault detection can prevent unscheduled downtime and improve reliability and efficiency. However, the variational external environments and operation states result in time-varying correlations and fluctuations of variables, which pose challenges to precise modeling and detection for WTs. This article proposes a dual-stage temperature monitoring and fault detection method, which can be applied to multiple key components in WTs. First, a novel multimodule feature extraction network (MMFENet) is designed to model the normal behavior of temperature data in the monitoring component. The proposed model decomposes the correlations into distinct patterns and devises different modules for quantification. Specifically, the static module aims to characterize the stationary correlations resulting from the inherent structure to provide reference expectations of fluctuations in the dynamic module and accelerate the model training. The dynamic module is devoted to depicting time-varying interdependence caused by variational operation states to pursue precise modeling. In addition, the proposed network also uses a heterogeneous data fusion module to use external information. Second, an adaptive detection strategy is devised for converting the prediction errors into detection results. Based on the evaluation results in the training procedure, parameter ranges during online detection can be adaptively determined to contain multiple rational values, which can enhance robustness and scalability. Experimental results using field data from real wind farms demonstrate the effectiveness and superiority of the proposed method.

A Temperature Monitoring Method For Power Electronic Converter Based on Infrared Image and Object Detection Algorithm

Power electronic converters are more and more widely used, and abnormal temperature of converter components is the most important factor in converter failure. To improve the reliability of the converter design, it is necessary to monitor the temperature of key components in the converter during the prototype test stage. The temperature measurement method of infrared thermal images has rich temperature information, wide detection range, and does not affect the original circuit design. However, in the current automatic temperature measurement methods, it is necessary to manually establish a standard matching template for the infrared thermal image of the circuit to be tested, which indicates a large workload and poor versatility. This paper proposes a method for fully automatic temperature monitoring of converter components. This method is based on a deep learning object detection algorithm, which can automatically identify the type of converter components, obtain partial infrared thermal images of components through heterogeneous image registration, achieve accurate component temperature monitoring and facilitate the converter state monitoring and fault detection. The advantages of this method are: 1) there is no need to manually establish a standard template for each converter; 2) it can monitor the converter temperature without manual intervention; 3) combining the temperature information and circuit prior knowledge, the state monitoring and fault diagnosis can further be realized. The experimental results also verify the feasibility and accuracy of this method.

An Additional Method for Temperature Monitoring in the Intensive Care Unit

An Additional Method for Temperature Monitoring in the Intensive Care Unit

Analysis and verification of signal propagation as method for temperature monitoring of underground power cables

• Signal propagation velocity can be employed as a measure for the cable temperature. • Conversion of the velocity to temperature is based on the insulation permittivity. • PILC and XLPE insulated power cables show opposite relationship with temperature. • Thermal overloading of XLPE cables can be detected online for cables in service. • The proposed method can be retrofitted to existing power cable connections. Underground power cables are increasingly becoming a bottleneck in the medium-voltage grid due to congestion. Limitations arise from the lack of knowledge on their thermal state. This paper investigates for two commonly applied cable types, PILC and XLPE, how signal propagation velocity can be exploited as means for thermal monitoring. To this end, the transit time of signals sent along a cable is interpreted in terms of the temperature dependency of the cable insulation permittivity. The sensitivity is verified by laboratory and field tests. Shifts in waveforms can be distinguished with a resolution down to 1 °C, but uncertainties in cable parameters hamper a direct conversion of the propagation characteristics to an absolute temperature scale. Instead, changes in waveforms need to be calibrated with respect to a reference signal at a known temperature. Parameter uncertainties then only arise in the relative shift and an accuracy below 5 °C is obtained. The method is demonstrated for a multi-conductor power cable exhibiting distinct signal propagation modes.

Research on junction temperature monitoring technology of IGBT modules

Modular Multilevel Converter (MMC) is widely used in flexible HVDC transmission systems, IGBT module as its core power device, its failure process seriously affects the operation reliability of MMC. The high junction temperature is an important reason for the failure of the IGBT module, so monitoring the junction temperature of IGBT module is an important means to ensure the stable operation of the system. According to the topological structure of IGBT module, the current junction temperature monitoring methods of IGBT module are summarized and compared. According to the shortcomings of the current research and the current research status, the research direction of IGBT module junction temperature monitoring is prospected.

MicroLED neural probe for effective in vivo optogenetic stimulation.

The MicroLED probe enables optogenetic control of neural activity in spatially separated brain regions. Understanding its heat generation characteristics is important. In this study, we investigated the temperature rise (ΔT) characteristics in the brain tissue using a MicroLED probe. The ΔT strongly depended on the surrounding environment of the probe, including the differences between the air and the brain, and the area touching the brain tissue. Through animal experiments, we suggest an in situ temperature monitoring method using temperature dependence on electrical characteristics of the MicroLED. Finally, optical stimulation by MicroLEDs proved effective in controlling optogenetic neural activity in animal models.