Low-cost Micro Electro-mechanical System Accelerometers Research Articles

A Comprehensive Review on Low-Cost MEMS Accelerometers for Vibration Measurement: Types, Novel Designs, Performance Evaluation, and Applications

Maintenance of mechanical elements is considered to be the most crucial task in industry and affects the economics, predominantly. There are many techniques available for fault diagnosis and condition monitoring of rotatory machine elements and one of the popular tools is vibration monitoring. Analyzing vibrations would be useful in supervising equipment/machine components in an attempt to detect the significant change which is the cause of growing failures or faults. Broadly speaking, it comes under prognostics and health monitoring (PHM) and prevents unexpected failure and costly repair. Proficient predictive monitoring and on-board recognition of failures is a need of the Fourth Industrial Revolution. Several types of vibration analyzers and sensors are commercially available, however, their total budget is not affordable. This motivates reviewing the suitability of low-cost sensors for vibration monitoring. An inclusive review of a variety of applications wherein microelectromechanical system (MEMS)-based accelerometer is used is presented in this paper. This would support an open-design movement for creating open.

Landslide Monitoring using Inclinometer with Micro Electromechanical System (MEMS)

Being located on the Pacific Ring of Fire and a tectonically active country, Indonesia has to cope with the constant risk of volcanic eruptions, earthquakes, floods and landslides. Landslides and other mass movements are serious geo-environmental hazards in Indonesia. Following report from the Indonesia National Disaster Management Authority (BNPB), landslides are among high disaster death toll throughout the archipelago. It claimed 248 lives last year alone. The number of landslides in Indonesia increase steadily to 376 in 2014 from 291 in 2012. Hence, landslide monitoring system is required to determine style of landslide movement, for risk and even emergency risk management assessments and to assist with the design of mitigation works. A landslide instrumentation program including an inclinometer is designed for landslide monitoring. The inclinometer, or tilt sensor, is an instrument used for measuring slope, tilt, or inclination. In this paper we use Micro Electromechanical System (MEMS) as a sensor to measure changes in an angle. Then information is transferred to a central server soon after real-time accelerations are monitored. A data logger also used as a data recording. With low-cost MEMS accelerometers, the results show this instrument is able to provide reliable ground-motion data in network-scale deployments.

Low-cost MEMS accelerometer network for rotating machine vibration diagnostics

In this paper a wireless sensor network (WSN) with low-cost nodes (less than $US 30.00) is presented. The nodes are based on microelectromechanical systems (MEMS) accelerometers and a highly-integrated microcontroller with built-in antenna for Wi-Fi and Bluetooth Low-Energy (BLE). The system was specially designed to analyze unbalance, load, and rotor obstructions in a rotating machine. Two sensor nodes were installed on an apparatus: one in the shaft and the other on the support table. The acceleration signals were used to analyze the machine vibration in the frequency domain by its Fast Fourier Transform (FFT). This spectrum was pre-analyzed on the sensor node, and the most significant features were sent to a cloud platform. These nodes were mounted to acquire vibration from an electric bicycle motor. Three situations were simulated: unbalance, using weights at the endings of rim spokes; mechanical load, using neodymium magnets, through the principle of eddy currents; and rotor obstruction, using an object made with a nylon cable. The solution presented was used to measure vibration and calculate its spectrum and send it to the cloud. The signals were analyzed using three strategies: an FFT amplitude level comparison, logistic regression, and neural network (NN). The analyses were carried out using the signals of each sensor independently and as a sensor network, this last showing better results. It was possible to diagnose each type of fault inserted in the tests, proving that the device developed can be used in industries as a low-cost alternative to monitor the health of rotating machines.

Identification of Vibration Parameters of Mechanical System Utilizing Low-Cost MEMS Accelerometers

Multi-axial mechanical systems commonly encounter the problem of vibration while attempting to drive machining systems at high speed. Many effective methods based on feed-forward and feedback control have been proposed and applied for vibration reduction. In order to design controllers all methods require the exact knowledge of system parameters: vibration frequency and damping ratio. In recent years, low-cost Micro Electro Mechanical Systems (MEMS) accelerometers have been used for many applications in industry. This paper presents the advantage of low cost MEMS accelerometer to identify vibration parameters of mechanical systems in comparison to conventional expensive devices.

Calibration of a micro-electro mechanical system-based accelerometer for vehicle navigation

Micro-electro mechanical system-based inertial sensors have broad applications in moving objects including in vehicles for navigation purposes. The low-cost micro-electro mechanical system sensors are normally subject to high dynamic errors such as linear or nonlinear bias, misalignment errors and random noises. In the class of low cost sensors, keeping the accuracy at a reasonable range has always been challenging for engineers. In this paper, a novel method for calibrating low-cost micro-electro mechanical system accelerometers is presented based on soft computing approaches. The method consists of two steps. In the first step, a preliminary model for error sources is presented based on fuzzy subtractive clustering algorithm. This model is then improved using adaptive neuro-fuzzy systems. A Kalman filter is also used to calculate the vehicle velocity and its position based on calibrated measured acceleration. The performance of the presented approach has been validated in the simulated and real experimental driving scenarios. The results show that this method can improve the accuracy of the accelerometer output, measured velocity and position of the vehicle by 79.11%, 97.63% and 99.28%, in the experimental test, respectively. The presented procedure can be used in collision avoidance and emergency brake assist systems.

How Well Can We Extract the Permanent Displacement from Low-Cost MEMS Accelerometers?

Following the recent establishment of a high-density seismic network equipped with low-cost micro-electro-mechanical system (MEMS) P-wave-alert-device (P-Alert) by the earthquake early warning (EEW) research group at the National Taiwan University, a large quantity of strong-motion records from moderate-magnitude earthquakes (ML > 6) around Taiwan has been accumulated. Using a data preprocessing scheme to recover the dynamic average embedded within the P-Alert data, we adopted an automatic baseline correction approach for the P-Alert accelerograms to determine the coseismic deformation (Cd). Comparisons between the Cd values determined using global positioning system (GPS) data, strong-motion records from the P-Alert network, and data from the Taiwan Strong Motion Instrumentation Program (TSMIP) demonstrates that the near-real-time determination of Cd values (>2 cm), which provide crucial information for seismic hazard mitigation, is possible using records from low-cost MEMS accelerometers.

An Algorithm for the In-Field Calibration of a MEMS IMU

Recently, micro electro-mechanical systems (MEMS) inertial sensors have found their way in various applications. These sensors are fairly low cost and easily available but their measurements are noisy and imprecise, which poses the necessity of calibration. In this paper, we present an approach to calibrate an inertial measurement unit (IMU) comprised of a low-cost tri-axial MEMS accelerometer and a gyroscope. As opposed to existing methods, our method is truly infield as it requires no external equipment and utilizes gravity signal as a stable reference. It only requires the sensor to be placed in approximate orientations, along with the application of simple rotations. This also offers easier and quicker calibration comparatively. We analyzed the method by performing experiments on two different IMUs: an in-house built IMU and a commercially calibrated IMU. We also calibrated the in-house built IMU using an aviation grade rate table for comparison. The results validate the calibration method as a useful low-cost IMU calibration scheme.

Stand-alone tsunami alarm equipment

Abstract. One of the quickest means of tsunami evacuation is transfer to higher ground soon after strong and long ground shaking. Ground shaking itself is a good initiator of the evacuation from disastrous tsunami. Longer period seismic waves are considered to be more correlated with the earthquake magnitude. We investigated the possible application of this to tsunami hazard alarm using single-site ground motion observation. Information from the mass media is sometimes unavailable due to power failure soon after a large earthquake. Even when an official alarm is available, multiple information sources of tsunami alert would help people become aware of the coming risk of a tsunami. Thus, a device that indicates risk of a tsunami without requiring other data would be helpful to those who should evacuate. Since the sensitivity of a low-cost MEMS (microelectromechanical systems) accelerometer is sufficient for this purpose, tsunami alarm equipment for home use may be easily realized. Amplitude of long-period (20 s cutoff) displacement was proposed as the threshold for the alarm based on empirical relationships among magnitude, tsunami height, hypocentral distance, and peak ground displacement of seismic waves. Application of this method to recent major earthquakes indicated that such equipment could effectively alert people to the possibility of tsunami.

Error Analysis and Stochastic Modeling of Low-cost MEMS Accelerometer

This paper presents the error analysis and stochastic modeling of commercial low-cost MEMS Accelerometer. Although Micro Electro Mechanical Systems (MEMS) based sensors have been utilized for the development of low-cost integrated navigation systems on the benefits of low inherent cost, small size, low power consumption, and solid reliability, it is significantly important to characterize the error behaviors of MEMS-based sensors and to construct more sophisticated mathematical modeling methods. The errors of MEMS-based accelerometer have been identified into deterministic and stochastic error sources and the stochastic error part was the focus to be discussed in this paper using discrete parameter models of stationary random process. Appropriate Autoregressive (AR) models have been analyzed which can be used to help the development of appropriate optimal algorithm for multiple sensor integration.