Microelectromechanical Systems Accelerometer Research Articles

Robust Orientation Estimation from MEMS Magnetic, Angular Rate, and Gravity (MARG) Modules for Human-Computer Interaction.

While the availability of low-cost micro electro-mechanical systems (MEMS) accelerometers, gyroscopes, and magnetometers initially seemed to promise the possibility of using them to easily track the position and orientation of virtually any object that they could be attached to, this promise has not yet been fulfilled. Navigation-grade accelerometers and gyroscopes have long been the basis for tracking ships and aircraft, but the signals from low-cost MEMS accelerometers and gyroscopes are still orders of magnitude poorer in quality (e.g., bias stability). Therefore, the applications of MEMS inertial measurement units (IMUs), containing tri-axial accelerometers and gyroscopes, are currently not as extensive as they were expected to be. Even the addition of MEMS tri-axial magnetometers, to conform magnetic, angular rate, and gravity (MARG) sensor modules, has not fully overcome the challenges involved in using these modules for long-term orientation estimation, which would be of great benefit for the tracking of human-computer hand-held controllers or tracking of Internet-Of-Things (IoT) devices. Here, we present an algorithm, GMVDμK (or simply GMVDK), that aims at taking full advantage of all the signals available from a MARG module to robustly estimate its orientation, while preventing damaging overcorrections, within the context of a human-computer interaction application. Through experimental comparison, we show that GMVDK is more robust to magnetic disturbances than three other MARG orientation estimation algorithms in representative trials.

Enhancing Wireless Sensor Network in Structural Health Monitoring through TCP/IP Socket Programming-Based Mimic Broadcasting: Experimental Validation

This paper presents the implementation of a synchronous Structural Health Monitoring (SHM) framework utilizing wireless, low-cost, and off-the-shelf components. Vibration-based condition monitoring plays a crucial role in assessing the reliability of structural systems by detecting damage through changes in vibration parameters. The adoption of low-cost Micro-Electro-Mechanical Systems (MEMS) sensors in Wireless Sensor Networks (WSNs) has gained traction, emphasizing the need for precise time synchronization to schedule wake-up times of multiple sensor nodes for data collection. To address this challenge, our proposed method introduces a TCP/IP socket programming-based mimic broadcasting mechanism and a scalable sensing network controlled by a central gateway, leveraging the Raspberry Pi Python platform. The system operates using Internet of Things (IoT) concepts and adopts a star topology, where a packet is transmitted from the gateway to initiate measurements simultaneously on multiple sensor nodes. The sensor node comprises a MEMS accelerometer, a real time clock DS3231 module and Raspberry Pi Zero 2W (RPi0-2W), while the gateway employs a Raspberry Pi 4 (RPi4). To ensure accurate time synchronization, all Pi0-2W nodes were configured as Network Time Protocol (NTP) clients, synchronizing with an RPi4 server using chrony, the reliable implementation of the NTP. Through experimental evaluations, the system demonstrates its effectiveness and reliability in achieving initial time synchronization. This study addresses the challenge of achieving precise time alignment between sensor nodes through the utilization of the Dynamic Time Wrapping (DTW) method for Frequency Domain Decomposition (FDD) applications. The contribution of this research significantly enhances the field by improving the accuracy and reliability of time-aligned measurements, with a specific focus on utilizing low-cost sensors. By developing a practical and cost-effective SHM framework, this work advances the accessibility and scalability of structural health monitoring solutions, facilitating more widespread adoption and implementation in various engineering applications

Data-Driven Approaches for Vibroacoustic Localization of Leaks in Water Distribution Networks

This study aims to propose Micro-electromechanical System (MEMS) accelerometers for leak localization in the water distribution network and assess the performance of machine learning models in accurately estimating leak locations. Intensive field experimentation was conducted to collect data for model development. Machine learning algorithms were employed to develop leak localization models, specifically artificial neural network (ANN) and support vector machine (SVM). Seventeen time-domain and frequency-domain features were extracted, and feature selection was performed using the backward elimination method. The results indicate that the ANN and SVM models are suitable classifiers for localizing leak distance. Both models achieved leak location predictions with over 80% accuracy, and the mean absolute errors were measured at 0.858 and 0.95 for the ANN and SVM models, respectively. The validation results demonstrated that the models maintained accuracies close to 80% when the distance between sensors and the leak was less than 15 m. However, the performance of the model deteriorates when leaks occur at distances greater than 15 m. This study demonstrates the applicability of MEMS accelerometers for leak localization in water distribution networks. The findings highlight the promising potential of employing MEMS accelerometers-based ANN and SVM models for accurate leak localization in urban networks, even under real-world, uncontrolled conditions. However, the current model exhibits limited performance in long-distance leak localization, requiring further research to address and resolve this issue.

An Innovative Sensor Integrated with GNSS and Accelerometer for Bridge Health Monitoring

This paper presents an innovative integrated sensor that combines GNSS and a low-cost accelerometer for bridge health monitoring. GNSS and accelerometers are both significant and effective sensors for structural monitoring, but they each have limitations. The sampling rate of GNSS data is relatively low, making it challenging to capture high-frequency vibrations, while accelerometers struggle with low-frequency signals and are susceptible to environmental changes. Additionally, GNSS receivers and accelerometers are often installed separately, leading to challenges in data fusion processing due to differing temporal and geospatial references. The proposed integrated sensor addresses these issues by synchronizing GNSS and an accelerometer’s time and geospatial coordinate reference. This allows for a more accurate and reliable deformation and vibration measurement for bridge monitoring. The performance of the new sensor was assessed using a high-quality/cost Leica GM30 GNSS receiver and a Sherborne A545 accelerometer. Experiments conducted on the Wilford suspension bridge demonstrate the effectiveness of this innovative integrated sensor in measuring deformation and vibration for bridge health monitoring. The limitation of the low-cost MEMS (Micro Electromechanical System) accelerometer for the weak motion frequency detection is also pointed out.

A Bayesian statistical method for large-scale MEMS-based sensors calibration: a case study on 100 digital accelerometers

Low-cost sensors and in particular micro-electro-mechanical systems (MEMS) devices are widely used in many applications, including consumer electronics, healthcare, automotive, and industrial automation. Their large-scale production (typically in the order of millions per week in a single factory) would require the calibration of a huge number of devices that would be costly and time-consuming. A solution can be found in the use of statistical methods in order to (at least partially) substitute for the typical calibration procedures. In this work, we propose a Bayesian method to statistically calibrate large batches of sensors using probabilistic models and prior knowledge. The method involves experimentally calibrating only a small sample of sensors, then infer the number of reliable sensors in the entire batch and assign an appropriate uncertainty to all the sensors. Therefore, it can be considered as a statistical calibration of the batch. The Bayesian nature of this approach allows reducing the number of experimental calibrations by incorporating the prior knowledge coming from the previous calibration of a ‘benchmark’ batch, which is performed ‘once and for all’ and is representative of the whole production process. The application and validation of the method are performed through the calibration of 100 digital MEMS accelerometers. Validation results showed an acceptable agreement between experimental-based bootstrap and theoretical values, with relative differences within ±7%.

Optical Interferometric MEMS Accelerometers

AbstractLaser interferometry is one of the most accurate measurement technologies whose displacement resolution can reach the femtometer (fm) level. With the development of Micro‐Electro‐Mechanical Systems(MEMS) technology, many excellent optical interferometric MEMS sensors have emerged, which play an essential role in intelligent manufacturing, aerospace, and many other fields. Among them, optical interferometric MEMS accelerometers develop rapidly due to their high sensitivity, low noise, and anti‐electromagnetic interference advantages. Lots of research in optical interferometric chip design, micro‐nano fabrication process, signal demodulation algorithm, and range extension technology are performed, and different types of MEMS accelerometers based on the optical interferometric principles are developed, which have been demonstrated and applied in the fields of disaster monitoring, equipment operation and maintenance, and resource exploration. This paper reviews the development status of optical interferometric MEMS accelerometers, mainly focusing on the critical problems and solutions that need to be solved in the development process of optical interferometric accelerometers, and finally introduces the typical application scenarios.

Reliability assessment method for MEMS accelerometers based on uncertainty process

In this paper, according to the degradation data characteristics of MEMS (Micro-Electro-Mechanical Systems) accelerometers of small samples, the uncertainty theory is introduced into, and the reliability model of the product is established by deduction according to the three basic scientific principles of reliability analysis. In this paper, the parameter estimation and model validation problems are solved under the framework of uncertainty theory by using the great likelihood estimation and hypothesis testing method under the algorithm of uncertainty theory. It is analyzed that the MEMS accelerometer reliability model based on uncertainty theory is a new method for reliability assessment applicable to dealing with uncertainty problems in the absence of large samples of degraded data.

Condition Monitoring of Ball Bearing Using MEMS-based Accelerometer

Industry 4.0 has attracted a lot of attention in recent times. Intelligent machines are at the heart of Industry 4.0. Condition monitoring and predictive maintenance of critical components in rotating machines are crucial for intelligent machines. This paper studies the use of a commercially available Micro Electro Mechanical System (MEMS) accelerometer for the condition monitoring of ball bearings. Embedding the sensor in the housing reduces the transmission path between the sensor and the fault providing efficient condition monitoring for low-speed applications. A comparison between MEMS and piezoelectric accelerometers has been made. Defects are induced artificially into the bearing and fault classification of the bearing has been done using a machine learning algorithm.

IoT Integrated Accelerometer Design and Simulation for Smart Helmets

This article delves into the transformative potential of micro-electromechanical systems (MEMS) accelerometers, particularly their role in revolutionizing helmet safety. Accelerometers, ubiquitous in vehicle manufacturing, computer technology, and audio-video systems, play a crucial role in measuring acceleration. This work focuses on the design and simulation of a rotating accelerometer integrated into football helmets. The introduction of rotational acceleration addresses specific challenges in detecting and mitigating brain injuries that linear accelerometers may encounter. Additionally, the article explores the concept of piezoresistive football helmets, designed to resist force and reduce the impact of concussions. Piezoresistive helmets, designed to resist force and minimize the impact of concussions, when augmented with Internet of Things (IoT) capabilities can create a comprehensive smart safety solution. The incorporation of IoT sensors and connectivity into piezoresistive helmets enables real-time monitoring and data analysis of impact forces. This integration not only enhances player safety by providing immediate insights into potential risks but also opens avenues for injury prevention strategies. Leveraging COMSOL simulation software, this research not only conceives but also realizes the innovative integration of rotational accelerometers and piezoresistive materials in football helmet design, advancing safety standards in sports technology.

Smartphone MEMS Accelerometer and Gyroscope Measurement Errors: Laboratory Testing and Analysis of the Effects on Positioning Performance.

Embedding various sensors with powerful computing and storage capabilities in a small communication device, smartphones have become a prominent platform for navigation. With the increasing popularity of Apple CarPlay and Android Auto, smartphones are quickly replacing built-in automotive navigation solutions. On the other hand, smartphones are equipped with low-performance Micro Electro Mechanical Systems (MEMS) sensors to enhance their navigation performance in Global Navigation Satellite System (GNSS)-degraded or -denied environments. Compared with higher-grade inertial navigation systems (INS), MEMS-based INSs have a poor navigation performance due to large measurement errors. In this paper, we present laboratory test results on the stochastic and deterministic errors observed in MEMS inertial sensor measurements of five different smartphones from different manufacturers. Then, we describe and discuss the short-term effects of these errors on the pure inertial navigation performance and also on the navigation performance based on the tight coupling of INS with GNSS measurements using a smartphone.

A 100- to- 10-kHz 5.4- to- 216- μW Power-Efficient Readout Circuit Employing Closed-Loop Dynamic Amplifier for MEMS Capacitive Accelerometer

Micro-electromechanical systems (MEMS) capacitive accelerometer for the Internet of Things (IoT) applications is required to operate at various sampling frequencies while keeping high power efficiency. To meet this requirement, dynamic amplifiers are used in the readout circuit of the accelerometer. However, the dynamic amplifier’s open-loop gain is low and varies with the sampling frequency of the readout circuit. This significantly deteriorates the gain accuracy of the readout circuit. In this article, the dynamic-amplifier-specific oversampling successive approximation (DA-OSA) readout technique is proposed to overcome this gain variation problem and make the dynamic amplifier applicable to the readout circuit. The DA-OSA-based readout circuit is fabricated in a commercial 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $</tex-math> </inline-formula> m 1.8-V CMOS process and tested with the MEMS accelerometer sensing element. The measurement results show that, with the sampling frequency varying from 10 kHz to 1 MHz, the proposed readout circuit achieves a variable signal bandwidth from 100 Hz to 10 kHz, a variable power consumption from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.4$</tex-math> </inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm{216}\mu \mathrm{W}$</tex-math> </inline-formula> , and a low gain variation of 1.2%.

Development and experimental verification of an IoT sensing system for drive-by bridge health monitoring

Vehicles equipped with various types of sensors have the great potentials to effectively evaluate the health conditions of a population of bridges at a low cost. However, existing drive-by structural health monitoring (SHM) methods acquire vehicle vibration responses offline and export them to a computer for postprocessing. Furthermore, the vehicle trajectory information on the bridge is important for scaling up the drive-by SHM for in situ applications, which is not synchronously measured by existing systems. Therefore, a single-board computer-based IoT sensing system for continuous and real-time drive-by bridge health monitoring is developed in this study. The developed IoT sensing system integrates a triaxial microelectromechanical system (MEMS) accelerometer, temperature sensor, GPS and 4G module on Raspberry Pi 4 Model B. The sensor node can be mounted on a moving vehicle to collect the triaxial acceleration responses, temperature and GPS information. A graphical user interface (GUI) is developed based on the Python Tkinter package to remotely control the sensor node and visualise the collected data in real time. The fast Fourier transform of the measured acceleration responses is performed on the sensor node inboard processor. The raw data are sent to both the cloud server and remote terminal computer through a 4G module. The goal is to provide a low-cost, accurate and scalable sensing system for easy implementation of drive-by bridge health monitoring. The system architecture and workflow of the developed IoT sensing system are presented in detail. A series of experimental tests are conducted to validate the accuracy of the measured acceleration responses and feasibility of using the developed IoT sensing system for drive-by SHM applications.

Multimode TED-tuned beam mass damper for low-g capacitive MEMS accelerometers VRE reduction

Capacitive Micro-Electro-Mechanical Systems (MEMS) accelerometers for quasi-static applications are susceptible to vibration rectification error (VRE) when the ambient AC vibration signals get rectified to DC. Studies to mitigate the VRE have relied on the sigma-delta filtering algorithm with closed-loop feedback as well as the adoption of vibration-insensitive frequency modulation (FM) instead of conventional amplitude modulation. In this study, we propose a novel solution to VRE caused by asymmetric railing introducing multimode thermoelastic damping (TED) tuned beam mass damper (TBMD) integrated into the MEMS accelerometer effectively functioning as a compensator. Our contribution breaks through the microfabrication limitation of damping elements by utilizing intrinsic TED in MEMS material. The damper elements were alternately tuned up to the third flexural mode due to MEMS manufacturing constraints. This was done through parametric sweeps in numerical simulations on single-axis capacitive MEMS accelerometer integrated with multimode TED-damped TBMD. According to simulation results, the higher the mode shapes of the TED-damped tuned beam mass damper, the higher the damping effect and the better the vibration rectification factor.

DESCRIPTIVE STATISTICAL CALIBRATION METHOD OF TRIAXIAL DIGITAL ACCELEROMETER ADXL345 AS EARTHQUAKES SENSOR

Seismic monitoring networks are the crucial elements in strong motion seismology for effective risk reduction. Low scale lateral variation of high intensity ground movement caused by earthquakes will be detected more effectively with densely located networks. However, the limitations of developing such project are rooted in expensive costs associated with the construction and installation in addition to bulky size of the conventional seismic observation system. Recently, micro-electromechanical system (MEMS) has being recognized in the applications of seismological and earthquake engineering due to the high precision obtained in these micron size semiconductor instruments and cheaper alternative for traditional seismic detector. ADXL345 is a type of digital triaxial MEMS accelerometer that is ideal for measurement of low-frequency vibrations and static accelerations of gravity, which makes it suitable for ground motion detection. Thus, this study aims at calibrating ADXL345 sensor that is required as sensing component in an affordable earthquake monitoring system with the Earthquake Benchmarking System (Penanda Aras Gempa Bumi, PAG) available in the inventory of Department of Mineral and Geoscience Malaysia, Sabah. Soil vibrations in EW (east-west or x-axis), NS (north-south or y-axis), and UD (up-down or z-axis) directions during random forces hit on the surface are recorded by both accelerometers. Acceleration magnitudes recorded by PAG and ADXL345 are extracted and data exploration is performed. Predominantly, ADXL345 measurements in horizontal and vertical ground movements are on a higher scale than the reference device. Subsequently, evaluation by using descriptive statistical analysis is chosen to produce numerical equations for data correction operations. İmplementation of the mathematical functions in ADXL345 for observing land movements in EW, NS, and UD directions resulted in decreasing the range values of output readings. Higher approximation of magnitudes of ground motion with the PAG system is achieved.

Bayesian Calibration of MEMS Accelerometers

This study aims to investigate the utilization of Bayesian techniques for the calibration of micro-electro-mechanical systems (MEMS) accelerometers. These devices have garnered substantial interest in various practical applications and typically require calibration through error-correcting functions. The parameters of these error-correcting functions are determined during a calibration process. However, due to various sources of noise, these parameters cannot be determined with precision, making it desirable to incorporate uncertainty in the calibration models. Bayesian modeling offers a natural and complete way of reflecting uncertainty by treating the model parameters as variables rather than fixed values. Additionally, Bayesian modeling enables the incorporation of prior knowledge, making it an ideal choice for calibration. Nevertheless, it is infrequently used in sensor calibration. This study introduces Bayesian methods for the calibration of MEMS accelerometer data in a straightforward manner using recent advances in probabilistic programming.

Improving the thermo-electro-mechanical responses of MEMS resonant accelerometers via a novel multi-layer perceptron neural network

Microelectromechanical systems (MEMS) accelerometers have been considerably developed since their wide use in current industrial applications. MEMS resonant accelerometers have attracted considerable attention in recent years by introducing some advantages over analog ones in terms of supporting a large range of dynamic movements, providing good sensitivity, considerably reducing the effects of interferences, etc. However, since silicon-based designs have shown some temperature-related disadvantages, there are always significant errors in such sensors due to the variable temperature. In this paper, we propose a novel multi-layer perceptron (MLP) neural network to improve the accuracy temperature compensation model for MEMS resonant accelerometers. To do so, we propose a novel metaheuristic algorithm by improving the searching ability of an artificial bee colony (ABC), named global search artificial bee colony (GSABC) to optimally train the MLP for gaining a better temperature compensation model. We then first evaluate GSABC on engineering and some well-known benchmark datasets in comparison with some widely-used and novel metaheuristic algorithms. After that, we perform various experiments to train the proposed MLP. Our experiments show that the proposed GSABC-derived MLP outperforms the state-of-the-art in providing higher accuracy for temperature compensation of MEMS resonant accelerometers. It is shown that within the calibration measuring experiment of the MEMS system, the environmental temperature was varied from 0 °C to 90 °C with a temperature interval of 10 °C.

Design and simulation of dual-axis MEMS accelerometer

Micro electro Mechanical systems (MEMS) are integrated electro-mechanical systems with microscale features. These devices are effective and low power consumers, making them one of the prospective technologies for use in a variety of industries and fields, including as the automotive, consumer, industrial, optical, robotic, biotechnology, medical engineering, and military sectors. MEMS accelerometer is one of the prominent inertial sensor allied in different applications.The proposed work is intended on design, modelling and simulation of a dual axis crab-legged suspension system. By using the SOI MUMPs fabrication process flow formulated by MEMSCAP Foundry a capacitive-based MEMS accelerometer is designed. Natural frequency, cross axis sensitivity, frequency response, and static capacitance analysis are the main design characteristics.Sensor is designed for a low frequency applicatons by assuming a natural frequency of 4KHz, he actual bandwidth vs sensitiviy is about 2KHz and 0.088um/g respectively. The measured stiffness along × and y are kx = 176.089 N/m ky = 176.089 N/m respectively.The proposed design of the capacities sensing comb drive structure is capable of producing linear change of the displacement for applied accelerations between 1 and 1000 g (1 g = 9.8 m/s2), over which the stress values exceed the tensile strength of the silicon material. By using stationary,frequency domain studies and electro-mechanics physics, the simulation is carried out in the COMSOL Multiphysics tool.

A sigma-delta interface built-in self-test and calibration for microelectromechanical system accelerometer's utilizing interpolation method

This work presents the capacitive micromechanical accelerometer with a completely differential high-order switched capacitor sigma-delta modulator interface. Such modulation interface circuit generates one-bit output data using a third sigma-delta modulator low-noise front-end, doing away with the requirement for a second enhanced converter of resolution to encode the feedback route analog signal. A capacitive micromechanical sensor unit with just a greater quality factor has been specifically employed to give greater resolution. The closed-loop and electrical correction control are used to dampen the high-Q values to get the system's stability with high-order. This microelectromechanical system (MEMS) capacitive accelerometer was calibrated using a lookup table and Akima interpolation to find manufacturing flaws by recalculating voltage levels for the test electrodes. To determine the proper electrode voltages for fault compensation, COMSOL software simulates a number of defects upon that spring as well as the fingers of the sensor system. When it comes time for the feedback phase of a proof mass displacement correction, these values are subsequently placed in the lookup table.

A High-Sensitivity MEMS Accelerometer Using a Sc0.8Al0.2N-Based Four Beam Structure.

In this paper, a high-sensitivity microelectromechanical system (MEMS) piezoelectric accelerometer based on a Scandium-doped Aluminum Nitride (ScAlN) thin film is proposed. The primary structure of this accelerometer is a silicon proof mass fixed by four piezoelectric cantilever beams. In order to enhance the sensitivity of the accelerometer, the Sc0.2Al0.8N piezoelectric film is used in the device. The transverse piezoelectric coefficient d31 of the Sc0.2Al0.8N piezoelectric film is measured by the cantilever beam method and found to be -4.7661 pC/N, which is approximately two to three times greater than that of a pure AlN film. To further enhance the sensitivity of the accelerometer, the top electrodes are divided into inner and outer electrodes; then, the four piezoelectric cantilever beams can achieve a series connection by these inner and outer electrodes. Subsequently, theoretical and finite element models are established to analyze the effectiveness of the above structure. After fabricating the device, the measurement results demonstrate that the resonant frequency of the device is 7.24 kHz and the operating frequency is 56 Hz to 2360 Hz. At a frequency of 480 Hz, the sensitivity, minimum detectable acceleration, and resolution of the device are 2.448 mV/g, 1 mg, and 1 mg, respectively. The linearity of the accelerometer is good for accelerations less than 2 g. The proposed piezoelectric MEMS accelerometer has demonstrated high sensitivity and linearity, making it suitable for accurately detecting low-frequency vibrations.

On the suitability of redundant accelerometers for the implementation of smart oscillation monitoring system: Preliminary assessment

Structural health monitoring (SHM) is an essential aspect to ensure the safety and longevity of civil infrastructure. In recent years, there has been a growing interest in developing SHM systems based on Micro-Electro-Mechanical Systems (MEMS) technology. MEMS-based sensors are small, low-power, and cost-effective, making them ideal for large-scale deployment in structural monitoring systems. However, the use of MEMS-based sensors in SHM systems can be challenging due to their inherent errors, such as drift, noise, and bias instability; these errors can affect the accuracy and reliability of the measured data, leading to false alarms or missed detections. Therefore, several methods have been proposed to compensate for these errors and improve the performance of MEMS-based SHM systems. For this purpose, the authors propose the combined of a redundant configuration of cost-effective MEMS accelerometers and a Kalman Filter approach to compensate MEMS inertial sensor errors and data filtering; the performance of the method is preliminarily assessed by means of a custom-controlled oscillation generator and compared with that granted by a high-cost, high-performance MEMS reference system where amplitude differences of 0.02 m/s2 have been experienced. Finally, a sensor node for real-time monitoring has been proposed that exploits LoRaWAN and NFC protocols to access the structure information to be monitored.