Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method

Zhu, Min; Pang, Lixin; Zhang, Xiao; Shen, Chong; Cao, Huiliang; Shi, Yunbo; Liu, Jun

doi:10.3390/app9040695

Cited by 21 publications

(21 citation statements)

References 25 publications

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“…To ensure the accuracy of the parameters, the tests must be performed for each working temperature and inclination. Alternative methods consists of using neural networks to predict and compensate the thermal behavior [ 25 ]. In both methods, the calibration process for each DUT (Device Under Test) is time consuming and hard to compute, increasing with each degree of the polynomial model or with each neuron added.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Thermal Compensation Technique for MEMS Capacitive Accelerometer Oriented to Quasi-Static Measurements

Martinez¹,

Asiain²,

Beltrán

2021

Sensors

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The application of MEMS capacitive accelerometers is limited by its thermal dependence, and each accelerometer must be individually calibrated to improve its performance. In this work, a light calibration method based on theoretical studies is proposed to obtain two characteristic parameters of the sensor’s operation: the temperature drift of bias and the temperature drift of scale factor. This method requires less data to obtain the characteristic parameters, allowing a faster calibration. Furthermore, using an equation with fewer parameters reduces the computational cost of compensation. After studying six accelerometers, model LIS3DSH, their characteristic parameters are obtained in a temperature range between 15 °C and 55 °C. It is observed that the Temperature Drift of Bias (TDB) is the parameter with the greatest influence on thermal drift, reaching 1.3 mg/°C. The Temperature Drift of Scale Factor (TDSF) is always negative and ranges between 0 and −400 ppm/°C. With these parameters, the thermal drifts are compensated in tests with 20 °C of thermal variation. An average improvement of 47% was observed. In the axes where the thermal drift was greater than 1 mg/°C, the improvement was greater than 80%. Other sensor behaviors have also been analyzed, such as temporal drift (up to 1 mg/h for three hours) and self-heating (2–3 °C in the first hours with the corresponding drift). Thermal compensation has been found to reduce the effect of the latter in the first hours after power-up of the sensor by 43%.

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Section: Introductionmentioning

confidence: 99%

Lightweight Thermal Compensation Technique for MEMS Capacitive Accelerometer Oriented to Quasi-Static Measurements

Martinez¹,

Asiain²,

Beltrán

2021

Sensors

View full text Add to dashboard Cite

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“…In fact, they are being increasingly used in monitoring applications also in association with open-source controlling platforms such as Arduino ® [13,14,15,16]. Despite advantages over traditional high precision electromechanical sensor such as smaller size and power consumption and sufficient resolution for many monitoring problems, IMU MEMS sensors have the disadvantage to be very sensible to temperature variation [17,18]. This makes IMU MEMS not readily suitable for mid-to-high precision monitoring applications in uncontrolled environment.…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Chamber Prototype for Automatic Thermal Analysis of MEMS IMU Sensors in Tilt Measurements Perspective

Ruzza

Guerriero

Revellino

et al. 2019

Sensors

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In this work, a low-cost, open-source and replicable system prototype for thermal analysis of low-cost Micro Electro-Mechanical Systems (MEMS) Inertial Measurement Unit (IMU) sensors in tilt measurement perspective is presented and tested. The system is formed of a 3D printed frame, a thermal cell consisting in a Peltier element mounted over a heat sink, and a control and power system. The frame is designed to allow the independent biaxial tilting of the thermal cell through two servomotors. The control board is formed by an Arduino® and a self-made board including a power drive for controlling the thermal unit and servomotors. We tested the chamber analyzing the behavior of multiple MEMS IMU onboard accelerometers suitable for measuring tilt. Our results underline the variability of the thermal behavior of the sensors, also for different sensor boards of the same model, and consequently the need for the adoption of a thermal compensation strategy based on thermal analysis results. These data suggesting the need for the analysis of the thermal behavior of MEMS-based sensors, indicate the potential of our system in making low-cost sensors suitable in medium-to-high precision monitoring applications.

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“…Recently, this approach has been progressively stepping into the MEMS inertial sensors field. For instance, in reference [ 59 , 60 ], RBF models were developed and implemented for temperature compensation in the area of MEMS inertial sensors. The experimental results from the studies proved the method would make big progress in compensating temperature drift of MEMS gyroscopes and MEMS accelerometers.…”

Section: Resultsmentioning

confidence: 99%

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

et al. 2020

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Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

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Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method

Cited by 21 publications

References 25 publications

Lightweight Thermal Compensation Technique for MEMS Capacitive Accelerometer Oriented to Quasi-Static Measurements

Lightweight Thermal Compensation Technique for MEMS Capacitive Accelerometer Oriented to Quasi-Static Measurements

A Low-Cost Chamber Prototype for Automatic Thermal Analysis of MEMS IMU Sensors in Tilt Measurements Perspective

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

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