Temperature Drift Error Research Articles

Half-wave voltage controllable optical voltage sensor with arbitrary electric field direction modulation*

Abstract An optical voltage sensor with an arbitrary electric field direction modulation (AEFDM) mode is proposed to increase the half-wave voltage. The mode is realized by heterogeneous electrodes arranged in a center-symmetric way, and generates an electric field with a direction at an arbitrary angle to the light propagation direction. The finite element method and coupling wave theory are used to design and optimize the electrodes and field distribution. The experimental results show that heterogeneous electrodes and AEFDM mode are able to regulate the sensor’s half-wave voltage in a range from 1 to 30 times (47–1409 kV), with 0.5 class accuracy and 99.99% linearity. This novel AEFDM method proposes a new electrode shape and position, reducing temperature drift to 50% compared to solid-state voltage divider method.

High-precision and wide-range temperature measurement and control system of satellite-borne calibration blackbody

The accurate measurement and control of blackbody temperature can play an important role in radiometric calibration. A novel circuit suitable for the aerospace environment is designed to measure and control the temperature of the blackbody. A coarse-fine measurement method considering the wire resistance and self-heating effect of the platinum resistance is presented to calculate the resistance values of the platinum resistors. The precision of temperature measurement is improved using a line fitting algorithm with error calibration and temperature drift error compensation. A fuzzy self-adaptive PID controller is designed to improve the temperature control effect. The performances of the developed system are implemented on an FPGA/A3PE3000-FG484I controller board. The test results show that the device temperature measurement precision can reach ±0.03K for the entire temperature range 238 K to 367 K, the temperature stability is better than 0.02 K/20 s, and the temperature uniformity is better than 0.1 K.

A Novel Temperature Drift Error Estimation Model for Capacitive MEMS Gyros Using Thermal Stress Deformation Analysis.

Because the conventional Temperature Drift Error (TDE) estimation model for Capacitive MEMS Gyros (CMGs) has inadequate Temperature Correlated Quantities (TCQs) and inaccurate parameter identification to improve their bias stability, its novel model based on thermal stress deformation analysis is presented. Firstly, the TDE of the CMG is traced precisely by analyzing its structural deformation under thermal stress, and more key decisive TCQs are explored, including ambient temperature variation ∆T and its square ∆T2, as well its square root ∆T1/2; then, a novel TDE estimation model is established. Secondly, a Radial Basis Function Neural Network (RBFNN) is applied to identify its parameter accurately, which eliminates local optimums of the conventional model based on a Back-Propagation Neural Network (BPNN) to improve bias stability. By analyzing heat conduction between CMGs and the thermal chamber with heat flux analysis, proper temperature control intervals and reasonable temperature control periods are obtained to form a TDE precise test method to avoid time-consuming and expensive experiments. The novel model is implemented with an adequate TCQ and RBFNN, and the Mean Square Deviation (MSD) is introduced to evaluate its performance. Finally, the conventional model and novel model are compared with bias stability. Compared with the conventional model, the novel one improves CMG's bias stability by 15% evenly. It estimates TDE more precisely to decouple Si-based materials' temperature dependence effectively, and CMG's environmental adaptability is enhanced to widen its application under complex conditions.

Accurate Cutting-Force Measurement with Smart Tool Holder in Lathe.

Cutting force in lathe work is closely related to tool wear and affects the turning quality. Direct measurement of the cutting force by measuring the strain of the tool holder is challenging because the tool holder design aims to be highly rigid in order to undertake large cutting forces. Accordingly, the most popular dynamometer designs modify the standard tool holder by decreasing the structural rigidity of the holder, which reduces the machining precision and is not widely accepted. In order to solve the issue of the low stiffness of the dynamometer reducing the machining precision, in this paper, the ultra-low strain on the tool holder was successfully detected by the highly sensitive semiconductor strain gauges (SCSG) adjacent to the blade cutting insert. However, the cutting process would generate much heat, which increases the force measuring area temperature of the tool holder by about 30 °C. As a result, the readout drifted significantly with the temperature changes due to the high temperature coefficient of SCSG. To solve this problem, the temperature on the tool holder was monitored and a BP neural network was proposed to compensate for temperature drift errors. Our methods improved the sensitivity (1.14 × 10-2 mV/N) and the average relative error of the BP neural network prediction (≤1.48%) while maintaining the original stiffness of the tool holder. The smart tool holder developed possesses high natural frequency (≥6 kHz), it is very suitable for dynamic cutting-force measurement. The cutting experiment data in the lathe work show comparable performance with the traditional dynamometers and the resolution of the smart tool holder is 2 N (0.25% of total range).

Temperature Drift Error Correction of F-P Filter Based on Attention Mechanism and LSTM Network

Temperature Drift Error Correction of F-P Filter Based on Attention Mechanism and LSTM Network

A Novel Temperature Drift Error Precise Estimation Model for MEMS Accelerometers Using Microstructure Thermal Analysis.

Owing to the fact that the conventional Temperature Drift Error (TDE) precise estimation model for a MEMS accelerometer has incomplete Temperature-Correlated Quantities (TCQ) and inaccurate parameter identification to reduce its accuracy and real time, a novel TDE precise estimation model using microstructure thermal analysis is studied. First, TDE is traced precisely by analyzing the MEMS accelerometer’s structural thermal deformation to obtain complete TCQ, ambient temperature T and its square T2, ambient temperature variation ∆T and its square ∆T2, which builds a novel TDE precise estimation model. Second, a Back Propagation Neural Network (BPNN) based on Particle Swarm Optimization plus Genetic Algorithm (PSO-GA-BPNN) is introduced in its accurate parameter identification to avoid the local optimums of the conventional model based on BPNN and enhance its accuracy and real time. Then, the TDE test method is formed by analyzing heat conduction process between MEMS accelerometers and a thermal chamber, and a temperature experiment is designed. The novel model is implemented with TCQ and PSO-GA-BPNN, and its performance is evaluated by Mean Square Error (MSE). At last, the conventional and novel models are compared. Compared with the conventional model, the novel one’s accuracy is improved by 16.01% and its iterations are reduced by 99.86% at maximum. This illustrates that the novel model estimates the TDE of a MEMS accelerometer more precisely to decouple temperature dependence of Si-based material effectively, which enhances its environmental adaptability and expands its application in diverse complex conditions.

Development of MEMS composite sensor with temperature compensation for tire pressure monitoring system

The pressure and temperature inside the tire is mainly monitored by the tire pressure monitoring system (TPMS). In order to improve the integration of the TPMS system, moreover enhance the sensitivity and temperature-insensitivity of pressure measurement, this paper proposes a microelectromechanical (MEMS) chip-level sensor based on stress-sensitive aluminum–silicon hybrid structures with amplified piezoresistive effect and temperature-dependent aluminum–silicon hybrid structures for hardware and software temperature compensations. Two types of aluminum–silicon hybrid structures are located inside and outside the strained membrane to simultaneously realize the measurement of pressure and temperature. The model of this composite sensor chip is firstly designed and verified for its effectiveness by using finite element numerical simulation, and then it is fabricated based on the standard MEMS process. The experiments indicate that the pressure sensitivity of the sensor is between 0.126 mV/(V·kPa) and 0.151 mV/(V·kPa) during the ambient temperature ranges from −20 °C to 100 °C, while the measurement error, sensitivity and temperature coefficient of temperature-dependent hybrid structures are individually ±0.91 °C, −1.225 mV/(V °C) and −0.150% °C−1. The thermal coefficient of offset (TCO) of pressure measurement can be reduced from −3.553%FS °C−1 to −0.375%FS °C−1 based on the differential output of the proposed sensor. In order to obtain the better performance of temperature compensation, Elman neural network based on ant colony algorithm is applied in the data fusion of differential output to further eliminate the temperature drift error. Based on which, the overall measured error is within 3.45 kPa, which is less than ±1.15%FS. The TCO is −0.017%FS °C−1, and the thermal coefficient of span is −0.020%FS °C−1. The research results may provide a useful reference for the development of the high-performance MEMS composite sensor for the TPMS system.

Development of a small-size laser triangulation displacement sensor and temperature drift compensation method

In practical applications, some special working scenarios need to take into account both narrow space and extreme temperature, which puts more strict requirements on the size and temperature adaptability of the laser triangulation displacement sensor (LTDS). In this paper, a small-size LTDS was developed and the measurement error caused by temperature drift of the sensor was corrected. Firstly, based on direct projection laser triangulation imaging with a reflector unit, a mathematical model of the compact geometry optical structure was established; then, an optimization process of the optical parameters was formulated, and the sensor was assembled. Secondly, a temperature drift error database was built by an extreme temperature experiment, and a general regression neural network model was constructed for error compensation. In addition, root mean square error and execution time were used to evaluate and compare different regression methods. Finally, the experimental results showed that the repeatability accuracy of the proposed sensor was ±2.3 µm, and the temperature linearity was 0.001% F.S. °C−1.

Modification of MTEA-Based Temperature Drift Error Compensation Model for MEMS-Gyros.

The conventional temperature drift error (TDE) compensation model cannot decouple temperature dependence of Si-based materials because temperature correlated quantities (TCQ) have not been obtained comprehensively, and Micro-Electro-Mechanical System gyros’ (MEMS-gyros’) environmental adaptability is reduced in diverse, complicated conditions. The study presents modification of TDE compensation model of MEMS-gyros based on microstructure thermal effect analysis (MTEA). First, Si-based materials’ temperature dependence was studied in microstructure with thermal expansion effect and TCQ that determines the structural deformation were extracted to modify the conventional model, including temperature variation and its square. Second, a precise TDE test method was formed by analyzing heat conduction process between MEMS-gyros and thermal chamber, and temperature experiments were designed and conducted. Third, the modified model’s parameters were identified based on radical basis function artificial neural network (RBF ANN) and its performance was evaluated. Last, the conventional and modified models were compared in performance. The experimental results show MEMS-gyros’ bias stability was up to 10% of the conventional model, the temperature dependence of Si-based materials was decoupled better by the modified one and the environmental adaptability of MEMS-gyros was improved to expand their application in diverse complicated conditions.

Temperature Drift Compensation for Exponential Hysteresis Characteristics of High-Temperature Eddy Current Displacement Sensors

The output stability of the displacement sensors is essential to enhance the reliability of active magnetic bearings in the high-temperature applications. This paper proposes a novel temperature drift compensation method for exponential hysteresis characteristics of high-temperature eddy current displacement sensors. The transient thermal analysis for the sensing assemblies demonstrate that the temperature drift errors are mainly induced by the temperature variation hysteresis effect: one is the dynamic lag between the probe coil and the air gap, and the other is the dynamic lag between the target material and the air gap. An auxiliary device consisting of compensation probe and compensation plate is designed to solve the hysteresis effect. The thickness of the compensation plate is determined by simulating the transient thermal analysis of the compensation plate and the measured target region. The model analyses also find that the exponential temperature drift at different determined displacement states is similar under the same temperature gradient. With this error characteristic, the differential circuit is used to eliminate the exponential hysteresis temperature drift. Experimental investigations show the effectiveness of the proposed solution.

Temperature-Insensitive Structure Design of Micromachined Resonant Accelerometers.

Micromachined resonant accelerometers (MRAs), especially those devices fabricated by silicon on glass technology, suffer from temperature drift error caused by inherent thermal stress. This paper proposes two structure designs to attenuate the effect of thermal stress. The first MRA structure is realized by optimizing the locations of the bonding anchors and utilizing a special-shaped substrate to isolate the thermal stress generated during the die attach process. The second structure is designed using an isolation frame fixed by a single anchor to replace all dispersed anchors associated with the suspension beams and micro-levers. Simulated and experimental results show that both of the MRA structures can effectively reduce the thermal stress effect. The experimental results on one MRA prototype indicate that the differential temperature sensitivity reduces down to 1.9 μg/°C and its 15-day bias stability reaches 1.4 μg.

Error characteristic analysis and experimental research on a fiber optic current transformer.

To solve the operation precision problem of a fiber optic current transformer (FOCT), we have established models of the polarization error based on the Jones matrix and a model of the temperature drift error taking into account the bending characteristics. The polarization study shows that a greater extinction ratio of the polarizer and analyzer and a smaller deviation of the polarization direction lead to a smaller output error. Temperature analysis suggests that greater intrinsic linear birefringence, a smaller bending radius, and more winding turns of the sensing fiber should exacerbate temperature drift and reduce the temperature range to meet an accuracy of class 0.2. Furthermore, we have constructed an experimental platform and performed current tests for different extinction ratios, polarization direction deviations, and temperature conditions. The experimental results confirm the effectiveness of the theoretical analysis.

Design, Fabrication, and Implementation of an Array-Type MEMS Piezoresistive Intelligent Pressure Sensor System.

To meet the radiosonde requirement of high sensitivity and linearity, this study designs and implements a monolithically integrated array-type piezoresistive intelligent pressure sensor system which is made up of two groups of four pressure sensors with the pressure range of 0–50 kPa and 0–100 kPa respectively. First, theoretical models and ANSYS (version 14.5, Canonsburg, PA, USA) finite element method (FEM) are adopted to optimize the parameters of array sensor structure. Combing with FEM stress distribution results, the size and material characteristics of the array-type sensor are determined according to the analysis of the sensitivity and the ratio of signal to noise (SNR). Based on the optimized parameters, the manufacture and packaging of array-type sensor chips are then realized by using the standard complementary metal-oxide-semiconductor (CMOS) and microelectromechanical system (MEMS) process. Furthermore, an intelligent acquisition and processing system for pressure and temperature signals is achieved. The S3C2440A microprocessor (Samsung, Seoul, Korea) is regarded as the core part which can be applied to collect and process data. In particular, digital signal storage, display and transmission are realized by the application of a graphical user interface (GUI) written in QT/E. Besides, for the sake of compensating the temperature drift and nonlinear error, the data fusion technique is proposed based on a wavelet neural network improved by genetic algorithm (GA-WNN) for average measuring signal. The GA-WNN model is implemented in hardware by using a S3C2440A microprocessor. Finally, the results of calibration and test experiments achieved with the temperature ranges from −20 to 20 °C show that: (1) the nonlinear error and the sensitivity of the array-type pressure sensor are 8330 × 10−4 and 0.052 mV/V/kPa in the range of 0–50 kPa, respectively; (2) the nonlinear error and the sensitivity are 8129 × 10−4 and 0.020 mV/V/kPa in the range of 50–100 kPa, respectively; (3) the overall error of the intelligent pressure sensor system is maintained at ±0.252% within the hybrid composite range (0–100 kPa). The involved results indicate that the developed array-type composite pressure sensor has good performance, which can provide a useful reference for the development of multi-range MEMS piezoresistive pressure sensor.

An Optical Voltage Sensor Based on Wedge Interference

The optical voltage sensor (OVS) based on the Pockels effect and its light intensity detection mode has a limitation of half-wave voltage and optical power correlation. In this paper, a new type of OVS which can achieve linear measurements of a wide range electrooptic (EO) phase delay was proposed. It uses a crystal wedge to convert EO phase delay to a displacement image of light stripes, and an image acquisition system that captures the spot and calculates the displacement. Mathematical derivation regarding the linear relationship between the stripes displacement and phase delay is given; a suitable image sensor and a stripe location algorithm are selected. Experimental verification is carried out, and several key issues are discussed. Compared with light intensity detection mode, the experimental results show that this measuring mode is independent of light source. An imaging mode also offers a large dynamic range and a good linear measurement of EO phase delay in the range of 291° with a measurement error of less than 0.5%. This mode is not limited by the half-wave voltage of crystal. Temperature drift errors on measurement results are also reduced.

Electronic frequency compensation of AlN-on-Si MEMS reference oscillators

In this paper we report on the design of a frequency compensation system for AlN-on-Si MEMS reference oscillator to replace temperature compensated crystal oscillators (TCXOs) in cellular handsets. A 76.8MHz, 105ppm Temperature Stable, AlN-on-Si MEMS Oscillator is designed to demonstrate the frequency compensation scheme. Double layers of SiO2 are used for passive temperature compensation. The oscillator consumes 850μA, with phase noise of −127dBc/Hz at 1kHz frequency offset. Temperature drift errors and initial frequency offset of ±8000ppm are combined and further tackled electronically. A simple digital compensation circuitry generates a compensation word to a 21-bit MASH 1-1-1 ΔΣ modulator included in LTE fractional N-PLL for frequency compensation. Temperature is sensed using 4.6μA, 11.5 bit temperature to digital converter TDC with resolution of 0.1°C in 100ms conversion time. The paper presents the first AlN-on-Si oscillator platform with ±0.5ppm frequency stability over temperature ranges from −40°C to 85°C. The system runs on 1.8V supply in 32nm CMOS.

Simultaneous Temperature Compensation and Synchronous Error Elimination for Axial Displacement Sensors Using an Auxiliary Probe

The output stability of the displacement sensors is an important issue for safe operations in magnetic bearing supported high-speed rotor system. This paper proposes an effective postprocessing solution to the problems of temperature drift and synchronous measuring error for the axial displacement sensor in a 10-kW magnetically suspended motor. The proposed configuration of the axial displacement sensor consists of three probes: one pair is used to eliminate the synchronous measuring error, and the third probe is employed for the consideration of temperature drift. First, the generating mechanism of the synchronous measuring error caused by the improper assemble is presented, and an operational amplifier is proposed to obtain an accurate position of the axial center by adjusting the weights of sensing signals from one pair of probes. Then the scheme of temperature compensation using an auxiliary probe is presented. In order to obtain accurately the characteristics of temperature drift at the operating point, the detailed procedure is given to determine the temperature–drift ratio. Finally, the terms related to temperature drift and synchronous error in the final output of the displacement sensor are both eliminated. Experimental results on a magnetically suspended motor test rig show the effectiveness of the proposed solution.

A Temperature Compensation Model for Low Cost Quartz Accelerometers and Its Application in Tilt Sensing

Although the quartz accelerometer has made great advances, the performance, in some specific applications such as tilt sensing, needs to be well compensated in high temperature environment. Based on the high temperature testing of low cost quartz accelerometers, we found that the normalized positive and negative parts are asymmetrical at high temperature and the temperature curve of zero sensor output is related to the roll angle of the sensor. Traditional temperature compensation method only considers the temperature factor and ignores the roll sensitivity, which leads to deteriorated accuracy. To solve this problem, this paper proposes a novel and simple mathematical model to obtain a more accurate expression of zero sensor output, which makes the sensor output more robust at high temperature. Experimental results on two low cost quartz accelerometers demonstrate that the proposed model is feasible and effective, which could reduce the temperature drift error of the sensor output typically from 0.01 g to 0.001 g. Furthermore, we introduce the compensated sensors in the three-axis inclinometer system for tilt sensing, and the evaluation results show that the temperature drift error of the inclination in the range (20∘C,150∘C) is reduced typically from0.4∘to0.1∘compared to the traditional method.

Temperature Drifts Compensation of Strain-Temperature Method for the Measurement of Continuous Welded Rail Temperature Force

Temperature drift error usually exists in the strain-temperature measurement of continuous welded rail (CWR) temperature force. A piece of invar with low (close to zero) coefficient of thermal expansion is used to simulate the actual CWR. Then the temperature drift of the strain sensor node is measured by using a programmable temperature chamber. The temperature drift error is compensated during the monitoring of the temperature force in actual CWR. Results show that the measured temperature force vibration of the CWR is-681kN~149kN and the stress-free temperature vibration is-2.4~1.0°Cwithin a month after temperature drift error compensation. The temperature force and stress-free temperature after compensation meets the stability condition of the CWR.

Online Calibration of Visual Measurement System Based on Industrial Robot

Visual measurement system based on industrial robot is applied to the online and real-time monitoring and inspection of key dimensions in the process of automobile body assembly and subassembly.Temperature variations of the manipulator and environment lead to expansion or deformation of links and joints,therefore cause the temperature drift error. An online calibration approach is proposed.Firstly,positioning error model of the end-effector is established by adopting D-H forward kinematics and differential kinematics.Secondly,combining with thermal experiments on axis rotation,link parameters that change significantly because of robot's temperature variation are determined by using multivariate Unear regression method.At last,thermal error is compensated by using a reference sphere.Practical measurement results show that the measurement errors caused by temperature variation can be reduced to about 0.05 mm and the approach can meet the requirement of the production tact.

Compensation of Temperature-Drift Errors with No Additional Hardware

A new approach that does not require additional hardware for temperature-drift compensation is reviewed. The efficiency of the new approach depends on the repeatability of the input transducer temperature-dependent transfer characteristics. A simple polynomial approximation of the measurand model was used in the application example. To bring the temperature-compensation efficiency to the level limited by the repeatability of the transfer characteristics of the input transducer, advanced fitting methods can be used.