Temperature Compensation Method Research Articles

Fast Dynamic Time Warping for Temperature Compensation in Guided Waves

The paper discusses Structural Health Monitoring (SHM) based on ultrasonic guided waves for damage detection in structures. Guided waves allow inspection over long distances and inaccessible features, but are also sensitive to changes in environmental and operating conditions (EOC). The focus of this paper is on temperature compensation methods for guided waves. The compensation techniques include Baseline Signal Stretch (BSS), Optimal Baseline Selection (OBS), OBS+BSS, and Dynamic Time-Warping (DTW). In particular, a new, fast approximation of DTW is evaluated and compared with conventional but computationally expensive DTW. The FDTW algorithm utilizes a multilevel approach inspired by graph bisection principles to achieve precise warping path determination with linear computational complexity. The study evaluates the compensation performance of FDTW using a single baseline at a single temperature, thus addressing the complexity and inaccessibility issues of obtaining an extensive database of baseline signals in practical applications. The Open Guided Wave (OGW) dataset is employed for a fair comparison with other compensation methods. Results indicate that FDTW performs well, demonstrating comparable warping performance to DTW but with significantly reduced computational complexity. The analysis also includes comparisons with BSS, OBS+BSS, and DTW across a range of temperatures, highlighting the effectiveness of FDTW in mitigating errors introduced by larger temperature variations.

Temperature compensation model for non-dispersive infrared CO2 gas sensor based on WOA-BP algorithm

Temperature compensation is the main measure to solve the problem that the detection accuracy of non-dispersive infrared CO2 gas sensor is affected by temperature. As the measurement accuracy of the non-dispersive infrared CO2 gas sensor is easily affected by the ambient temperature, this article analyzes the reasons why the sensor is affected by temperature, and proposes a temperature compensation method that integrates the Whale Algorithm (WOA) and BP neural network. The whale algorithm is used to optimize the weights and thresholds of the BP neural network to build a temperature compensation model for the non-dispersive infrared CO2 gas sensor and compare the superiority with the traditional BP neural network model and particle swarm optimization (PSO) BP neural network model. The experimental results show that the temperature compensation model error of WOA-BP algorithm is lower than 30 ppm, and the average absolute error percentage is 3.86%, which is far better than BP neural network and PSO-BP neural network, and effectively reduces the influence of temperature on the accuracy of the sensor.

Numerical analysis and experimental comparison of temperature-compensation method for large length–diameter ratio thermal mass flowmeter

Thermal mass flowmeters (TMFs) are inherently sensitive to temperature fluctuations, which can result in inaccurate measurements. This study focuses on a novel TMF named the large length–diameter ratio thermal mass flowmeter (LLRTMF), which was previously proposed for its insensitivity to the distribution of radial flow velocity and implemented via a variable-speed, variable-temperature experimental setup. The power characteristics of the LLRTMF are investigated over velocity and temperature ranges of 1.00–6.00 m/s and 283.0–319.0 K, respectively. The experimental results indicate that the maximum relative error of the experimental power consumption is 21.96 %. Additionally, a full-size numerical model of the LLRTMF is developed to simulate its power characteristics across wider velocity and temperature ranges of 0.25–16.00 m/s and 283.0–353.0 K, respectively. Experimental data are used to verify the accuracy of the simulation model. Using the numerical model, an empirical correlation equation between power and velocity is derived and polynomially fitted to establish a temperature-compensation equation. The results of the temperature compensation equations and experiment were compared. The maximum relative error of the absolute value was 20.84 %, i.e., less than the maximum error in the results of the experiment due to temperature change, confirming the validity of the formula.

A Self-Temperature Compensation Barometer Based on All-Quartz Resonant Pressure Sensor.

This paper reports a self-temperature compensation barometer based on a quartz resonant pressure sensor. A novel sensor chip that contains a double-ended tuning fork (DETF) resonator and a single-ended tuning fork (SETF) resonator is designed and fabricated. The two resonators are designed on the same diaphragm. The DETF resonator works as a pressure sensor. To reduce the influence of the temperature drift, the SETF resonator works as a temperature compensation sensor, which senses the instantaneous temperature of the DETF resonator. The temperature compensation method based on polynomial fitting is studied. The experimental results show that the accuracy is 0.019% F.S. in a pressure range of 200~1200 hPa over a temperature range of -20 °C~+60 °C. The absolute errors of the barometer are within ±23 Pa. To verify its actual performance, a drone flight test was conducted. The test results are consistent with the actual flight trajectory.

Influence of part temperature on in-situ monitoring of powder bed fusion of metals using eddy current testing

AbstractPowder bed fusion of metals (PBF-LB/M) is currently the most widely adopted additive manufacturing technology for the fabrication of metal parts. However, the inconsistent quality of PBF-LB/M-manufactured parts and high costs for part certification are impeding wider industrial adoption. In-situ monitoring technologies are expected to enable process control in order to ensure consistent quality, and to replace some of the post-process inspection steps, therefore, reducing part certification costs. Eddy current testing (ECT) is a standardized nondestructive testing technique, which can be used as an in-situ monitoring technology to measure the part quality during the PBF-LB/M build cycle. However, the process-induced complex temperature fields in PBF-LB/M parts during the build cycle are among the most relevant disturbances due to the temperature dependence of the electrical conductivity. This study investigates the process-induced temperature influence on in-situ monitoring of relative density using ECT. Parts made from AlSi10Mg were manufactured on a PBF-LB/M machine and the build cycle was monitored using ECT and an infrared camera, which was used to extract the part surface temperature right before the ECT measurement. The results demonstrate that the temperature increase of the parts during the build cycle decreases the electrical conductivity independently of the relative part density, which was measured via micro-computed tomography. Therefore, a temperature compensation method was proposed and applied demonstrating that a layer-to-layer difference of 0.15 % relative density can be detected via ECT. Consequently, it has been demonstrated that ECT is an effective in-situ monitoring technology for PBF-LB/M, even in the presence of temperature disparities within parts.

Damage Location Determination with Data Augmentation of Guided Ultrasonic Wave Features and Explainable Neural Network Approach for Integrated Sensor Systems

Machine learning techniques such as deep learning have already been successfully applied in Structural Health Monitoring (SHM) for damage localization using Ultrasonic Guided Waves (UGW) at various temperatures. However, a common issue arises due to the time-consuming nature of collecting guided wave measurements at different temperatures, resulting in an insufficient amount of training data. Since SHM systems are predominantly employed in sensitive structures, there is a significant interest in utilizing methods and algorithms that are transparent and comprehensible. In this study, a method is presented to augment feature data by generating a large number of training features from a relatively limited set of measurements. In addition, robustness to environmental changes, e.g., temperature fluctuations, is improved. This is achieved by utilizing a known temperature compensation method called temperature scaling to determine the function of signal features as a function of temperature. These functions can then be used for data generation. To gain a better understanding of how the damage localization predictions are made, a known explainable neural network (XANN) architecture is employed and trained with the generated data. The trained XANN model was then used to examine and validate the artificially generated signal features and to improve the augmentation process. The presented method demonstrates a significant increase in the number of training data points. Furthermore, the use of the XANN architecture as a predictor model enables a deeper interpretation of the prediction methods employed by the network.

A comparison of temperature compensation methods in a diaphragm-embedded FBG

This paper presents the comparison of temperature compensation techniques for Fiber Bragg Grating (FBG)-based pressure sensor. Such temperature compensation is obtained through a physical assembly using a continuous FBG pair embedded in a rubber diaphragm through a vulcanization method in conjunction with different signal processing methods. The so-called continuous FBG pair is based on two consecutive FBGs (with different Bragg wavelengths) inscribed with physical separation up to 10 mm. The sensor system is tested as a function of temperature and pressure, where the temperature sensitivities of both FBGs are around 50 pm/°C. Pressure estimation errors around 148.22 Pa/°C were observed when this variation in sensitivity was not taken into account by the compensation technique. Furthermore, there is a comparison between techniques that consider the pressure sensitivity variation, namely multiple linear regression using the response of both FBGs and exponential regression using the sum of FBGs. In this case, the exponential regression indicated a smaller temperature cross-sensitivity (3.07 Pa/°C), higher determination coefficient (0.97) and smaller root mean squared error (0.40 kPa). Therefore, the exponential regression using the response of both FBGs is the suitable technique for temperature compensation when the continuous FBG pair embedded in the diaphragm assembly is considered. The results obtained in this work provide a guideline for the development of diaphragm-embedded sensors with temperature compensation and suitable for large-scale production in practical applications.

Tool-integrated thin-film sensor systems for measurement of cutting forces and temperatures during machining

Effective in-process control mechanisms are vital for the future development of production processes and ask for reliable monitoring of process variables. For this, new tool-integrated sensor systems for measuring cutting forces and temperature for machining of hardened steel are proposed. Thin-film technology is used to apply a thermoresistive temperature sensor in the chip-workpiece contact area for the first time on industrial cutting inserts with chip breaker geometry. Complementary piezoresistive thin-film sensors based on diamond-like carbon (DLC) and manganin layers were developed and deposited on a washer. It was placed beneath the indexable insert to measure the cutting forces. Turning experiments were conducted to study sensor life and accuracy. All sensors showed an adequate lifetime for laboratory purposes. Temperature compensation methods were investigated for force measurement and results compared to forces recorded by a dynamometric platform. While temperature compensation for the DLC-based sensor needs further optimization, results of the manganin-based sensor compared well with the expected cutting forces. The manganin thin-film sensor exhibited rapid responsiveness and great reproducibility, underscoring its prospective utility for real-time monitoring of cutting forces in machining operations.

Application of synchro spline-kernelled chirplet transformation in nonlinear ultrasonic wave modulation: self-sensing method with temperature protection

The purpose of this work is to investigate the synchro spline-kernelled chirplet extracting transformation (SSCET) method for defect detection in the nonlinear ultrasonic wave modulation (NUWM) method. To implement the NUWM method using only one piezoelectric transducer, the self-sensing method is used. Self-sensing has many advantages, but it is sensitive to temperature changes. To reduce the temperature effects, a novel circuit has been designed and implemented. A sandwich beam with bolt loosening and an aluminum beam with boundary loosening damages are used to test the results of the SSCET method on the signal received from the self-sensing circuit. First, an aluminum beam with a loosened boundary is modeled based on beam theory including self-sensing circuits with temperature effects. In this study, SSCET results are compared with those of other time–frequency methods for the detection of the loosened boundary. Based on the temperature effects, the theoretical results are compared with the experimental results of the circuit. To quantify defect detection, a damage index calculated based on instantaneous frequency is provided. Three piezoelectric configurations are also compared with the self-sensing method damage index results. SSCET is then used to check bolt loosening in a sandwich beam for further investigation. The proposed method is compared to a previous self-sensing temperature compensation method. Based on the SSCET method, the severity of the bolt loosening is determined.

Research on Temperature Compensation Method for CRAFT Dot Matrix Hall High Current Sensor

Research on Temperature Compensation Method for CRAFT Dot Matrix Hall High Current Sensor

RGBW LED mixing temperature compensation method with high output consistency

This paper proposes a temperature compensation method for RGBW LED mixing based on fast non-dominated sorting genetic algorithm(NSGA-II). The proposed method can achieve compensation for temperature-induced changes in LED correlated color temperature (CCT), color fidelity (Rf) and color gamut index (Rg) by predicting the spectral power distribution (SPD) at different temperatures. The SPD-temperature model of the RGBW LED light source is established by measuring the spectral power distribution of LEDs at different temperatures. Based on the established model, NSGA-II is used to compensate the mixing temperature of the RGBW LED light source according to the spectral superposition theorem. The experimental results show that the fit of the established temperature-spectral model is R2 > 0.98, and the deviation of the compensated mixing results from the initial state of the light source is less than 10 K in CCT; the deviation value of Rf is less than 4 % in the range of 2000 K-7000 K, and less than 2.15 % in the range of 3000 K-7000 K; and the deviation value of Rg in the range of 2000 K-7000 K is less than 4.46 %.

Research on temperature compensation method for photoelectric sensors

In order to make the temperature error of fiber-optic current sensor (FGB) based on polarization modulation principle meet the requirements of engineering applications, the temperature error characteristics of FGB are analyzed theoretically, and the optimized BP neural network is used for the temperature compensation of FGB, which realizes the nonlinear temperature error correction of the sensor, and compares and analyzes the experimental results with those of other types of temperature compensation algorithms. The results show that the temperature compensation results based on the neural network algorithm are better than other compensation effects. Finally, the repeatability of the FGB was experimentally verified using its full temperature experiment, and the temperature errors of the FGB in the range of 20 ℃~ 100 ℃ were less than 0.5% after the correction of the neural network algorithm.

Determination of difluoromethane (R32) by non-dispersive infrared (NDIR) spectroscopy with temperature compensation by an improved whale optimization algorithm (IWOA)

To reduce the influence of temperature on difluoromethane (R32) gas sensors using non-dispersive infrared (NDIR), a temperature compensation method based on improved whale optimization algorithm (IWOA) and back propagation neural network (BPNN) is proposed. The IWOA that uses tent chaotic mapping to optimize the initial population of the whale optimization algorithm (WOA) and incorporates adaptive weights in the population iterations is used to optimize the weights and thresholds between nodes in each layer of the BPNN. The optimized BPNN is used to train the temperature compensation model. The experimental results show that the back propagation neural network optimized with improved whale optimization algorithm (IWOA-BPNN) reduces the maximum relative error to 1.33%, compared to 12.46 and 11.66% for the traditional BPNN and the back propagation neural network optimized with whale optimization algorithm (WOA-BPNN), respectively. IWOA-BPNN is also compared with previously reported algorithms, and the results show that the algorithm improves the accuracy of the sensor.

FBG-based three-dimensional micro-force sensor with axial force sensitivity-enhancing and temperature compensation for micro-forceps.

During retinal microsurgery, excessive interaction force between surgical instruments and intraocular tissue can cause serious accidents such as tissue injury, irreversible retinal damage, and even vision loss. It is essential to accurately sense the micro tool-tissue interaction force, especially for the Ophthalmic Microsurgery Robot. In this study, a fiber Bragg grating (FBG) three-dimensional (3-D) micro-force sensor for micro-forceps is proposed, which is integrated with the drive module as an end-effector and can be conveniently mounted onto the ophthalmic surgical robot. An innovative axial force sensitivity-enhancing structure is proposed based on the principles of flexure-hinge and flexible levers to overcome the low sensitivity of axial force measurement. A dual-grating temperature compensation method is adopted for axial force measurement, which considers the differential temperature sensitivity of the two FBGs. Three FBGs are arranged along the circumference of the guide tube in this study to measure transverse forces and compensate for effects caused by changes in temperature. The experimental results demonstrate that the micro-forceps designed in this study achieved a resolution of 0.13 mN for transverse force and 0.30 mN for axial force. The temperature compensation experiments show that the 3-D micro-force sensor can simultaneously compensate for temperature effects in axial and transverse force measurement.

The temperature effects on embedded PZT signals in structural health monitoring for composite structures with different thicknesses

Ultrasonics guided waves (GWs) are established as an effective structural health monitoring technique for detecting damage in composite structures. However, for GWs to be applied reliably in service, the effect of temperature needs to be accounted for baseline damage detection approaches. Previous studies have focused on developing temperature compensation methods for surface-mounted transducers. In this paper, an extensive investigation has been carried out to develop an effective temperature compensation method for GWs generated by embedded transducers in different thickness composites. To accomplish this, Laser Doppler Vibrometer experiments were carried out to obtain wavenumbers generated by embedded transducers and to compare them against surface mounted cases. The consistent trend observation of dispersion curves between surface-mounted and embedded PZTs serves as a foundation for conducting analysis of the embedded signals with the effect of temperature. Following that, GW propagation has been studied in three quasi-isotropic composites of 2 mm, 4 mm and 9 mm thicknesses. Based on the established temperature database with measurements from − 50 °C to 70 °C, a temperature compensation methodology has been developed and validated with the temperature difference of 30 °C. It demonstrates that the temperature effect on the embedded PZTs is lower in comparison to surface mounted PZTs, and the temperature compensation of a single thickness composite is scalable to other panels thickness. This novel methodology proves beneficial in reducing the scale of the experimental campaign required, which holds significant value for actual aeronautical structures characterized by variable composite thicknesses across different sections, such as in fuselage panels or wing boxes.

A Novel Temperature Compensation Method for Surface Strain of Cylindrical Lithium-ion Batteries

In order to ensure the safe operation of lithium-ion batteries, real-time monitoring of battery status is necessary. The surface strain signal of lithium-ion batteries has the potential to evaluate the battery’s state, but it is significantly affected by temperature. Generally, measuring the battery surface temperature and the thermal expansion coefficient can be performed to quantify and eliminate the influence of temperature on strain, but this increases the cost and complexity of strain measurement. This article proposes a method that eliminates the need to measure the battery temperature and material parameters. By simultaneously measuring the circumferential and axial strains on the battery surface and calculating their difference, the influence of temperature on strain can be minimized. Furthermore, the effectiveness of the proposed method is experimentally tested. Results demonstrate that after applying temperature compensation to commercial lithium-ion batteries, the influence of temperature on strain can be reduced from 16.4 ppm/°C to 1.7 ppm/°C. The strain no longer exhibits sensitivity to current, making it more suitable for evaluating the state of lithium-ion batteries.

High reliability damage imaging under non-uniform environmental temperature variations based on modified dynamic time warping

In practical applications of Lamb wave monitoring, the environmental temperature fields usually vary non-uniformly over the monitored structures, which can inconsistently affect the wavepackets of every diagnostic signal and reduce the reliability of final detection results. A number of temperature compensation methods have been reported. However, under the complicated non-uniform temperature variations, many methods are deteriorated in their performance or require large amount of Lamb wave data. To effectively improve the reliability of Lamb wave damage detection under non-uniform temperature variations, modified dynamic time warping (MDTW) is presented in this paper. In MDTW, with the flexible time alignment ability of dynamic time warping (DTW), the non-uniform temperature variation effects on Lamb wave signals can be compensated just requiring one set of baselines. Furthermore, to preserve the damage information in the compensated signals, the warping adjusting mechanism is introduced to resolve the temperature over-compensation problem of traditional DTW. This makes MDTW a robust non-uniform temperature compensation method for high reliability Lamb wave detection. After the basic principle of DTW is analyzed, MDTW is proposed and numerically validated. Then, a MDTW-based Lamb wave imaging method is developed for high reliability damage imaging, in which a triple-suppression of non-uniform temperature variations can be accomplished. Finally, the efficiency of MDTW and MDTW-based imaging is demonstrated by the temperature experiment of A0 mode Lamb wave damage imaging on an aluminum plate with its temperature field non-uniformly varied by 13℃∼43℃.

A Novel Catheter Shape-Sensing Method Based on Deep Learning with a Multi-Core Optical Fiber.

In this paper, we propose a novel shape-sensing method based on deep learning with a multi-core optical fiber for the accurate shape-sensing of catheters and guidewires. Firstly, we designed a catheter with embedded multi-core fiber containing three sensing outer cores and one temperature compensation middle core. Then, we analyzed the relationship between the central wavelength shift, the curvature of the multi-core Fiber Bragg Grating (FBG), and temperature compensation methods to establish a Particle Swarm Optimization (PSO) BP neural network-based catheter shape sensing method. Finally, experiments were conducted in both constant and variable temperature environments to validate the method. The average and maximum distance errors of the PSO-BP neural network were 0.57 and 1.33 mm, respectively, under constant temperature conditions, and 0.36 and 0.96 mm, respectively, under variable temperature conditions. This well-sensed catheter shape demonstrates the effectiveness of the shape-sensing method proposed in this paper and its potential applications in real surgical catheters and guidewire.

Temperature Compensation of Wind Tunnel Balance Signal Detection System Based on IGWO-ELM.

The wind tunnel balance signal detection system is widely employed in aerospace applications for the accurate and automated measurement of aerodynamic forces and moments. However, measurement errors arise under different environmental temperature. This paper addresses the issue of measurement accuracy under different temperature conditions by proposing a temperature compensation method based on an improved gray wolf optimization (IGWO) algorithm and optimized extreme learning machine (ELM). The IGWO algorithm is enhanced by improving the initial population position, convergence factor, and iteration weights of the gray wolf optimization algorithm. Subsequently, the IGWO algorithm is employed to determine the optimal network parameters for the ELM. The calibration decoupling experiment and high-low temperature experiment are designed and carried out. On this basis, ELM, GWO-ELM, PSO-ELM, GWO-RBFNN and IGWO-ELM are used for temperature compensation experiments. The experimental results show that IGWO-ELM has a good temperature compensation effect, reducing the measurement error from 20%FS to within 0.04%FS. Consequently, the accuracy and stability of the wind tunnel balance signal detection system under different temperature environments are enhanced.

Combined Temperature Compensation Method for Closed-Loop Microelectromechanical System Capacitive Accelerometer.

This article describes a closed-loop detection MEMS accelerometer for acceleration measurement. This paper analyzes the working principle of MEMS accelerometers in detail and explains the relationship between the accelerometer zero bias, scale factor and voltage reference. Therefore, a combined compensation method is designed via reference voltage source compensation and terminal temperature compensation of the accelerometer, which comprehensively improves the performance over a wide temperature range of the accelerometer. The experiment results show that the initial range is reduced from 3679 ppm to 221 ppm with reference voltage source compensation, zero-bias stability of the accelerometer over temperature is increased by 14.3% on average and the scale factor stability over temperature is increased by 88.2% on average. After combined compensation, one accelerometer zero-bias stability over temperature was reduced to 40 μg and the scale factor stability over temperature was reduced to 16 ppm, the average value of the zero-bias stability over temperature was reduced from 1764 μg to 36 μg, the average value of the scale factor stability over temperature was reduced from 2270 ppm to 25 ppm, the average stability of the zero bias was increased by 97.96% and the average stability of the scale factor was increased by 98.90%.