Low-cost MEMS Accelerometers Research Articles

In-Depth Analysis of Low-Cost Micro Electromechanical System (MEMS) Accelerometers in the Context of Low Frequencies and Vibration Amplitudes

Shock and vibration hazards to civil structures are common and come not only from earthquakes but most often from mining operations or foundation work involving the installation of piles using hammer-driving and vibrating technology. The purpose of this study is to present test methods for low-cost MEMS accelerometers in terms of their selection for low-amplitude acceleration vibration-prone object-monitoring systems. Tests of 24 commercially available digital accelerometers were carried out on a custom-built test bench, selecting four models for detailed tests conducted on a specially built precision vibration table capable of inflicting accelerations at frequencies of 1–2 Hz, using displacements as small as a few micrometers. The analysis of the results was based, among other things, on a modified method of determining the signal-to-noise ratio (SNR) and also on the idea of the effective number of bits (ENOB). The results of the analysis showed that among low-cost MEMS accelerometers, there are some that are successfully suitable for the monitoring and warning of excessive vibration hazards in situations where objects are extremely sensitive to such impacts (e.g., treatment rooms in hospitals). Examples of accelerometers capable of detecting harmonic vibrations with amplitudes as small as 10 mm/s2 or impulsive shocks with amplitudes of at least 70 mm/s2 are indicated.

Integrating low-cost GNSS and MEMS accelerometer for precise dynamic displacement monitoring

We assess the performance of the self-developed receiver coupling dual-frequency low-cost GNSS chipset and IMU MEMS sensor. The system also comprises software dedicated to vibration monitoring based on relative kinematic positioning in a post-processing mode. The observation assessment reveals high accuracy of low-cost GNSS and MEMS accelerometer data. The Septentrio Mosaic X5 chipset exhibits competitive to high-grade receivers’ phase observation noise, outperformance in code noise level and observation auto-correlation at a level that may be neglected even in high-rate applications. The evaluation of the MEMS accelerometer data proves that the sensor accuracy is adequate for vibration monitoring. With the shake table experiment, we examine the performance of vibration recovery. An advantage of the coupled solution over a GNSS-only one is documented with a 15 % reduction of the amplitude error. The backward smoothing of coupled solutions drives the next 20 % error drop. Finally, the high performance of the coupled solution based on low-cost GNSS & accelerometer is confirmed by a mean amplitude error of 0.4 mm. The frequency analyses indicate that both coupled and single-sensor solutions precisely detect the vibration frequencies; however, they also confirm the outperformance of the former over the latter. The power spectra of the recovered displacement time series accentuate the benefits of GNSS + accelerometer fusion and backward filtering for reducing displacement noise.

Low cost MEMS accelerometer and microphone based condition monitoring sensor, with LoRa and Bluetooth Low Energy radio

Vibration-based Condition Monitoring (CM) is an essential tool for identifying potential defects in industrial machinery. However, the implementation of an efficient CM system often necessitates the use of high-cost accelerometers with a large bandwidth. To address this challenge, this study introduces a low-cost CM sensor composed of an ultrasonic MEMS microphone - SPH0641LU and an ADXL1002 MEMS accelerometer. The combination of these two sensor types allows for comparative analysis of the captured data. The SPH641LU microphone is capable of detecting audible vibration signals with a frequency range up to 80 kHz, while the ADXL1002 accelerometer can measure vibrations up to 21 kHz. In addition a three axis ultra low power accelerometer is included, allowing measurement of unbalance or rotating speed, below 200Hz. Moreover, the sensor is designed to operate on battery power and provides the capability for raw data transmission via Bluetooth Low Energy (BLE) or the transmission of pre-processed features from the raw data using LoRaWAN.

Reducing cost with MEMS sensor and improving performance of classifier using probabilistic voting method

When developing a solution for fault diagnosis, cost is a critical factor that must be considered to ensure a practical and feasible final product. To reduce expenses, the authors conducted a feasibility study using a low-cost MEMS accelerometer in conjunction with an Arduino Uno. This study aimed to determine the effect of sample length on fault classification by varying it from 50 to 5000 to identify the optimal value for this particular application. After finalizing the parameters, the vibration signal was acquired using the MEMS sensor and Arduino Uno. The resulting classification accuracy using the decision tree was 90.2%, a satisfactory result that can be further improved for industrial applications. To enhance the accuracy of decision tree classifiers, the authors proposed a novel approach known as the probabilistic voting method. By implementing this method and utilizing an Arduino Uno and MEMS sensor (ADXL335), they were able to drastically cut down costs while achieving remarkable classification accuracy. The implementation of the probabilistic voting method further elevated the accuracy to an astounding 98.5%, setting a new benchmark in the field.

Seismic damage and loss evaluation in precast industrial buildings through low-cost accelerometers

Past seismic events, in particular the Emilia earthquake (2012), showed how the damage caused in highly industrialized areas could cause serious problems with large-scale economic effects. It is interesting to highlight how these effects are both immediate, due to the seismic vulnerability of old precast industrial buildings, and in the long term since the restoration operations are typically long due to the difficulty of assessing the real structural health state, particularly for the difficulty in reaching and inspecting components sensitive to damage, such as the connections. This work has the objective of finding a procedure, through relatively low-cost MEMS accelerometers, which allows to record and process the seismic accelerations experienced by the various structural elements. The paper considers the application of a procedure that allows identifying the state of health of the system through the application of damage indexes found in the literature and a first estimate of costs and recovery times to bring the building back to its pre-earthquake conditions. The procedure has been applied to a selected case study

Cost-effective classification of tool wear with transfer learning based on tool vibration for hard turning processes

This paper presents a novel application of transfer learning for tool wear detection in turning processes using one-dimensional (1D) convolutional neural network (CNN). The work also investigates the applications of low-cost MEMS as well as IEPE accelerometers in tool wear detection. Tool wear detection is performed using a classification method in which two classes of tool wear sizes are defined: class one and class two which correspond to tool wear sizes smaller or equal to 0.1 mm and bigger than 0.1 mm respectively. The advantage of transfer learning is that it utilizes knowledge from previously learned tasks and applies them to the related ones. In the case of CNC machines, tool wear mechanism in the turning process and the working principles are similar thus, transfer learning can be used to generalize the specific cutting condition to a broader use case. The transfer learning model in this paper uses a pretrained tool wear prediction model which was composed of 1D CNN layers with full connection layers and sufficient amount of data on a source CNC machine. The CNN layers of the pretrained model are frozen at first and then the full connection layers are trained with the new data from the target CNC machine with different cutting insert types. In this study, the application of transfer learning in tool wear size classification showed that the pretrained tool wear detection models can be transferred to other similar processes while maintaining a high accuracy. The accuracy of the transfer learning model was evaluated by comparing it with the results of a model developed from scratch using only the target machine data. It is demonstrated that the transfer learning maintained an accuracy of higher than 80%. In addition to this, the transfer learning model significantly increased the tool wear classification accuracy using the single-axis low-cost MEMS accelerometer from 58% to 85%. Moreover, the tool wear classification model using transfer learning significantly reduced the amount of data required for model development. To evaluate this, the accuracy of the transfer learning model was tested by further reducing the amount of the training data by maximum 80% and the model still showed an accuracy higher or equal to 80%.

Soil mass movement monitoring for landslide detection using low-cost accelerometer sensor as inclinometer

This paper presents soil mass movement monitoring for landslides detection using low-cost MEMS accelerometer as inclinometer. Commercial inclinometers for geotechnical ground observations are quite expensive. This research aims to study and develop low-cost inclinometer as an alternative using accelerometer. The output of the low-cost accelerometer is noisy and fluctuated make it not suitable for accurate measurement device. We solved this problem in this paper using moving average filter. The digital filter algorithm was tested and showed promising results.

P-Detector: Real-Time P-Wave Detection in a Seismic Waveform Recorded on a Low-Cost MEMS Accelerometer Using Deep Learning

Recently, the Internet of Things (IoT) systems have been widely used for earthquake detection because of the easy construction of a dense seismic network, communication capabilities, and low cost of sensors. However, when utilizing MEMS sensors as seismic sensors, earthquake detection capabilities are often affected by various types of noises because such sensors are installed in heterogeneous environments. In earthquakes, P-waves first arrive, but their lengths are only a few seconds, and their amplitudes are also relatively smaller than to S-waves. As a result, it is difficult to accurately detect P-waves in IoT systems where environmental noises are always present. Furthermore, when using deep learning approaches for earthquake detection, inference time usually becomes a critical factor for real-time processing because of the complex architecture of a detection model. To that end, in this letter, we present a deep learning model that can detect P-waves in noisy environments. The model outputs the detection probability before the arrival of strong shakes. We tested our model on earthquakes recorded by the IoT-based seismic sensors deployed in South Korea. Our model can detect P-waves within 1.5–2.5 s after the first arrival of P-wave with the accuracy of 98.8%, making it applicable in real-time earthquake detection.

Improved Resolution and Cost Performance of Low-Cost MEMS Seismic Sensor through Parallel Acquisition.

In earthquake monitoring, an important aspect of the operational effect of earthquake intensity rapid reporting and earthquake early warning networks depends on the density and performance of the deployed seismic sensors. To improve the resolution of seismic sensors as much as possible while keeping costs low, in this article the use of multiple low-cost and low-resolution digital MEMS accelerometers is proposed to increase the resolution through the correlation average method. In addition, a cost-effective MEMS seismic sensor is developed. With ARM and Linux embedded computer technology, this instrument can cyclically store the continuous collected data on a built-in large-capacity SD card for approximately 12 months. With its real-time seismic data processing algorithm, this instrument is able to automatically identify seismic events and calculate ground motion parameters. Moreover, the instrument is easy to install in a variety of ground or building conditions. The results show that the RMS noise of the instrument is reduced from 0.096 cm/s2 with a single MEMS accelerometer to 0.034 cm/s2 in a bandwidth of 0.1–20 Hz by using the correlation average method of eight low-cost MEMS accelerometers. The dynamic range reaches more than 90 dB, the amplitude–frequency response of its input and output within −3 dB is DC −80 Hz, and the linearity is better than 0.47%. In the records from our instrument, earthquakes with magnitudes between M2.2 and M5.1 and distances from the epicenter shorter than 200 km have a relatively high SNR, and are more visible than they were prior to the joint averaging.

Simplification of calibration of low-cost MEMS accelerometer and its temperature compensation without accurate laboratory equipment

A nonlinear cost function is defined for field calibration of the accelerometer, using the rule that the norm of the measured vector in a static state is equal to the magnitude of the gravity vector. To solve this cost function, various optimization methods like Newton and Levenberg–Marquardt have been presented in different references. However, these methods are complicated, time-consuming, and require an initial value. This study presents a method that simplifies the cost function and obtains the error coefficients, including bias, scale factor, and non-orthogonality using the linear least-squares method which is simpler and faster than other optimization methods and does not need initial values. Also, the output of the low-cost MEMS accelerometer depends on temperature due to its silicon property. Thus, by finding the dependency of the error coefficients on temperature, they can be compensated. This paper models dependency of error coefficients on temperature using cubic spline interpolation and minimizes the temperature effect. Simulation results of MATLAB and the proposed field calibration method and temperature compensation on the low-cost MPU6050 sensor show its good performance.

Condition monitoring of critical industrial assets using high performing low-cost MEMS accelerometers

The high investment cost, which is in majority introduced by the sensor costs, is one of the barriers for adopting Predictive Maintenance/Condition-Based Maintenance strategy in the industry. To lower this barrier, the use of cost-effective sensors for such an application is, therefore, a necessity. This paper presents experiences in setting-up two different remote vibration monitoring systems using low-cost MEMS accelerometers available on the market in two different industrial settings. Some technical challenges and the state-of-the-art algorithms used to analyze the measured raw vibration signals are presented. The installed vibration monitoring systems have successfully detected faults on two different critical assets, namely an induction motor and an expander machine.

Failure analysis on a water pump based on a low-cost MEMS accelerometer and machine learning classifiers

This work presents a failure diagnosis tool for a water pump using a low-cost MEMS accelerometer. It was inserted three types of failures: rotor blade (new and damaged), pump soleplate tightness (stiff or loose), and cavitation, in this case on three conditions: none, incipient and severe, totaling twelve fault combinations. These conditions were tested under two different speeds to perform the diagnosis, totaling twenty-four tests. In all cases, the vibration signals from axes X, Y, and Z were acquired. Some features extracted from the vibration spectra from X-axis were used to compose the dataset. These data were analyzed employing logistic regression, a linear support vector machine (SVM), and an artificial neural network multilayer perceptron (ANN-MLP). We compared these three techniques of machine learning and evaluated which one was able to obtain the most accurate result. Using the ANN-MLP, the system was able to detect all three types of failures inserted, with about 100% of accuracy on the rotor blade condition, 92% for anchorage faults, and about 99% accuracy on cavitation state. As a conclusion, it is demonstrated that this classifier algorithm can be used to process the data from the low-cost MEMS accelerometer in predictive maintenance as an accurate tool.

A Low-Cost Calibration Method for Low-Cost MEMS Accelerometers Based on 3D Printing.

A ubiquitous sensor in embedded systems is the accelerometer, as it enables a range of applications. However, accelerometers experience nonlinearities in their outputs caused by error terms and axes misalignment. These errors are a major concern because, in applications such as navigations systems, they accumulate over time, degrading the position accuracy. Through a calibration procedure, the errors can be modeled and compensated. Many methods have been proposed; however, they require sophisticated equipment available only in laboratories, which makes them complex and expensive. In this article, a simple, practical, and low-cost calibration method is proposed. It uses a 3D printed polyhedron, benefiting from the popularisation and low-cost of 3D printing in the present day. Additionally, each polyhedron could hold as much as 14 sensors, which can be calibrated simultaneously. The method was performed with a low-cost sensor and it significantly reduced the root-mean-square error (RMSE) of the sensor output. The RMSE was compared with the reported in similar proposals, and our method resulted in higher performance. The proposal enables accelerometer calibration at low-cost, and anywhere and anytime, not only by experts in laboratories. Compensating the sensor’s inherent errors thus increases the accuracy of its output.

A Field Calibration Method for Low-Cost MEMS Accelerometer Based on the Generalized Nonlinear Least Square Method

This paper proposes a field calibration method for an accelerometer without the need of having any external devices for calibration. In the proposed calibration method, generalized nonlinear least-square (GNLS) is used to estimate deterministic errors. A novel sensor’s data collection procedure is developed to collect data of an accelerometer along all three axes and all possible orientations where the expectation of influence of all possible errors is very high. The proposed calibration method is verified by applying it to two different accelerometers. The proposed calibration method achieved an accurate estimation of calibration parameters. The results of the proposed GNLS based calibration method are compared with two other commonly used algorithms, such as Levenberg–Marquardt (LM) and Gauss–Newton (GN). Simulation and experimental results show that the proposed GNLS-based calibration method is slightly more accurate than the LM and GN. The GNLS convergence rate for estimating the calibration parameters is also faster than the LM and GN.

Globally Asymptotic Stable Attitude Estimation with Application to MEMS Sensors

Aiming at the requirement of attitude information module with high precision, small size and low power consumption for the control of miniature UAV, a practical attitude estimation algorithm based on the micro-electro-mechanical sensor is proposed in this paper, which realizes the accurate estimation of the attitude of the UAV under the condition of low acceleration. A low-cost MEMS gyroscope, accelerometer, and magnetometer are used in the system. The Euler angle is obtained by the state observer method based on Direction Cosine Matrix (DCM) which can be got by fusing the sensor data. Firstly, based on the basic idea of TRIAD algorithm, a method to determine the attitude rotation matrix by accelerometer and magnetometric measurement is proposed. Compared with the traditional method, this method does not have to calculate the inverse of the matrix. Secondly, a state observer is intended to estimate the attitude of the system. The state observer doesn't have to observe the bias of the gyroscope, but still ensures the convergence of the Euler angle. Finally, the simulation based on the actual sampling data of the MEMS sensor shows that the output of the state observer designed in this paper still has high accuracy and good dynamic characteristics under the condition of gyroscope noise and bias.

Improving GNSS Landslide Monitoring with the Use of Low-Cost MEMS Accelerometers

Observation and monitoring of landslides and infrastructure is a very important basis for land planning, human activities, and safety. Geomatic techniques for deformation monitoring have usually involved GNSS and total station measurements or, more generally, expensive geodetic instruments, but other techniques, such as SAR (Synthetic Aperture Radar), can be efficiently applied. Using low-cost sensors could be an interesting alternative solution if the accuracy requirements can be satisfied. This paper shows the results obtained for tilt measurements using MEMS accelerometers, which were combined with mass-market GNSS sensors for monitoring five sites located on landslides. The use of a MEMS-like inclinometer requires an important calibration process to remove bias and improve the solution’s accuracy. In this paper, we explain the MEMS calibration procedure employed, with a simple and cheap solution. The results indicate that with a simple calibration, it is possible to improve measurement accuracy by one order of magnitude, reaching an angular accuracy of a few hundredths of a degree, verified by an independent technique.

Evaluation of low-cost MEMS accelerometers for SHM: frequency and damping identification of civil structures

Sensing techniques based on accelerometers for modal parameters identification are among the most studied and applied in Structural Health Monitoring of civil structures. The advent of low-cost MEMS accelerometers and open-source electronic platforms, such as Arduino, have facilitated the design of low-cost systems suitable for modal identification, although there is still a lack of studies regarding practical application and comparison of commercially available low-cost accelerometers under SHM conditions. This work presents an experimental performance evaluation of six low-cost MEMS accelerometers for the identification of natural frequencies and damping ratios of a three-storey frame model and a reinforced concrete slab, as well as their noise characteristics. A low-cost Arduino-based data acquisition system was used. The results showed an overall good performance of the MEMS accelerometers, with identified natural frequencies errors within 1.02% and 7.76% of reference values, for the three-storey frame and concrete slab, respectively, and a noise density as low as 108 g/√Hz.

Uncertainty evaluation in calibration of low-cost digital MEMS accelerometers for advanced manufacturing applications

Advanced manufacturing applications often involve the use of vibration control systems for both process control and defect prevention. Recently, the use of networks of low-cost digital MEMS accelerometer has become a widespread practice, being an economically competitive and promising way to improve quality and productivity effectiveness. Nevertheless, due to the lack of metrological traceability, these devices are currently not reliable to quantify amplitude and frequency of the actual vibrational motion. In this work, contributions of uncertainty of a calibration procedure by comparison for digital MEMS accelerometers at high frequency are investigated in order to provide traceability to primary standard.

Thermal Compensation of Low-Cost MEMS Accelerometers for Tilt Measurements.

Low-cost MEMS accelerometers have the potential to be used in a number of tilt-based monitoring applications but have the disadvantage of being very sensitive to temperature variation (thermal drift). In this paper, we analyze the thermal behavior of a low-cost sensor in the range −10 to +45 °C in order to provide a simple compensation strategy to mitigate this problem. For sensor analysis, we have developed a miniaturized thermal chamber, which was mounted on a tilting device to account for tilt angle variation. The obtained raw data were used to construct low degree polynomial equations that by relating the measurement error induced by thermal drift (i.e., acceleration residuals) to temperature and inclination (of each specific axis), can be used for thermal compensation. To validate our compensation strategy, we performed a field monitoring test and evaluated the compensation performance by calculating RMS errors before and after correction. After compensation, the RMS errors calculated for both the X and Y axes decreased by 96%, indicating the potential of using a simple set of equations to solve common drawbacks that currently make low-cost MEMS sensors unsuitable for tilt-based monitoring applications.

Fast and precise solving of non‐linear optimisation problem for field calibration of triaxial accelerometer

Unlike laboratory calibration methods, filed calibration methods are not time consuming, costly and do not need laboratory equipment, so they can be used in any environment for calibration of low-cost MEMS accelerometers. The magnitude of gravity vector in static state is used as a reference in the calibration process. Then a cost function is defined which is non-linear and complex. So, in order to simplify this equation, a novel change of variables is proposed which converts this non-linear optimisation problem to a linear least square (LLS) method and a non-linear equation solving. After minimisation of this cost function using LLS method, bias, scale factor and non-orthogonality of the accelerometer are estimated and it is compared with two other field calibration methods. The results of simulations show good performance of proposed method. For online estimation of these coefficients, recursive least squares method is also utilised. Finally, simulations in MATLAB have shown that they converge to the true values.