Low-cost MEMS Research Articles

Structural monitoring of “Himera” viaduct by low-cost MEMS sensors: characterization and preliminary results

The term structural health monitoring (SHM) usually refers to the process of implementing a damage detection strategy for aerospace, civil or mechanical engineering infrastructures. Under an extreme event, such as an earthquake or unanticipated blast loading, SHM could be used for rapid condition screening, to provide, in near real time, reliable information about the performance of a structural system during the event and about the subsequent integrity of the system itself. On the other hand, owners of buildings and civil infrastructures need information on the actual state of the structures in order to realize effective life-cycle management plans and reduce the economic and social impact of maintenance. In fact, inspections and repairs entail huge direct and social costs due to the interruption or reduction of the structure serviceability. Such information can be collected and elaborated by means of adequate monitoring systems but their diffusion is still limited by the high cost of sensors and devices needed. In this work, the realization of a monitoring system on some bridge piers of the “Himera” viaduct, located in Italian A19 Palermo–Catania highway and recently damaged by a landslide, is presented. The proposed monitoring system aims to observe the global stability of the un-collapsed side of the viaduct during the demolition works of the other side and it is intended as a quasi-real-time alert tool able to send data and warning messages to an operations center. In order to contain costs, low-cost micro electronic mechanical system sensors have been used for the monitoring. The architecture and main features of the developed monitoring system are described in detail and the preliminary results, recorded during the first months of the campaign, are reported and discussed. Since the proposed monitoring system is still a prototype, it has been necessary to provide a metrological characterization campaign in laboratory conditions in order to define a calibration law to correct the acquired data and to estimate the sensitivity and the accuracy of the measurements. The results proved that the proposed system can be used for the monitoring of civil structures and infrastructures.

Research on the modeling of random drift error and filtering technology of low cost mems gyroscope

Research on the modeling of random drift error and filtering technology of low cost mems gyroscope

Human Location Detection System Using Micro-Electromechanical Sensor for Intelligent Fan

This paper presented the development of sensory system for detection of both the presence and the location of human in a room spaces using MEMS Thermal sensor. The system is able to detect the surface temperature of occupants by a non-contact detection at the maximum of 6 meters far. It can be integrated to any swing type of electrical appliances such as standing fan or a similar devices. Differentiating human from other moving and or static object by heat variable is nearly impossible since human, animals and electrical appliances produce heat. The uncontrollable heat properties which can change and transfer will add to the detection issue. Integrating the low cost MEMS based thermal sensor can solve the first of human sensing problem by its ability to detect human in stationary. Further discrimination and analysis must therefore be made to the measured temperature data to distinguish human from other objects. In this project, the fan is properly designed and program in such a way that it can adapt to different events starting from the human sensing stage to its dynamic and mechanical moving parts. Up to this stage initial testing to the Omron D6T microelectromechanical thermal sensor is currently under several experimental stages. Experimental result of the sensor tested on stationary and motion state of human are behaviorally differentiable and successfully locate the human position by detecting the maximum temperature of each sensor reading.

On the feasibility to integrate low-cost MEMS accelerometers and GNSS receivers

The aim of this research was to investigate the feasibility of merging the benefits offered by low-cost GNSS and MEMS accelerometers technology, in order to promote the diffusion of low-cost monitoring solutions.A merging approach was set up at the level of the combination of kinematic results (velocities and displacements) coming from the two kinds of sensors, whose observations were separately processed, following to the so called loose integration, which sounds much more simple and flexible thinking about the possibility of an easy change of the combined sensors.At first, the issues related to the difference in reference systems, time systems and measurement rate and epochs for the two sensors were faced with. An approach was designed and tested to transform into unique reference and time systems the outcomes from GPS and MEMS and to interpolate the usually (much) more dense MEMS observation to common (GPS) epochs. The proposed approach was limited to time-independent (constant) orientation of the MEMS reference system with respect to the GPS one.Then, a data fusion approach based on the use of Discrete Fourier Transform and cubic splines interpolation was proposed both for velocities and displacements: MEMS and GPS derived solutions are firstly separated by a rectangular filter in spectral domain, and secondly back-transformed and combined through a cubic spline interpolation.Accuracies around 5mm for slow and fast displacements and better than 2mm/s for velocities were assessed. The obtained solution paves the way to a powerful and appealing use of low-cost single frequency GNSS receivers and MEMS accelerometers for structural and ground monitoring applications.Some additional remarks and prospects for future investigations complete the paper.

Design of AHRS using Low-Cost MEMS IMU Sensor and Multiple Filters

Design of AHRS using Low-Cost MEMS IMU Sensor and Multiple Filters

Improvement of Low-Cost MEMS Gyroscope Characteristics by Data Filtering and Fusion

Advancement of the MEMS technology at the end of the 20th century allowed mankind to realize the things which had been unthinkable until then. Modern technological procedures have enabled a mass production of sensors and actuators of miniature dimensions with low purchase costs. The article deals with the fundamental principle of the MEMS gyroscopes as angular velocity sensors and with the necessary output data filtering and fusion with other inertial sensors to improve quality and precision of the angular velocity and position angles determination.

Analysis of Low-Cost MEMS Accelerometer and Gyroscope Characteristics for Stochastic Sensor Simulation within Motorcycle Models

Analysis of Low-Cost MEMS Accelerometer and Gyroscope Characteristics for Stochastic Sensor Simulation within Motorcycle Models

A flexible triboelectric-piezoelectric hybrid nanogenerator based on P(VDF-TrFE) nanofibers and PDMS/MWCNT for wearable devices.

This paper studied and realized a flexible nanogenerator based on P(VDF-TrFE) nanofibers and PDMS/MWCNT thin composite membrane, which worked under triboelectric and piezoelectric hybrid mechanisms. The P(VDF-TrFE) nanofibers as a piezoelectric functional layer and a triboelectric friction layer are formed by electrospinning process. In order to improve the performance of triboelectric nanogenerator, the multiwall carbon nanotubes (MWCNT) is doped into PDMS patterned films as the other flexible friction layer to increase the initial capacitance. The flexible nanogenerator is fabricated by low cost MEMS processes. Its output performance is characterized in detail and structural optimization is performed. The device’s output peak-peak voltage, power and power density under triboelectric mechanism are 25 V, 98.56 μW and 1.98 mW/cm3 under the pressure force of 5 N, respectively. The output peak-peak voltage, power and power density under piezoelectric working principle are 2.5 V, 9.74 μW, and 0.689 mW/cm3 under the same condition, respectively. We believe that the proposed flexible, biocompatible, lightweight, low cost nanogenerator will supply effective power energy sustainably for wearable devices in practical applications.

An Efficient Attitude Reference System Design Using Velocity Differential Vectors under Weak Acceleration Dynamics

This paper proposes a new method achieving computationally efficient attitude reference system for low cost strapdown sensors and microprocessor platform. The main idea in this method is to define and compare velocity differential vectors, geometrically computed from INS and GPS data with different update rate, for generating attitude error measurements which is further used for filter construction. A quaternion based Kalman filter configuration is applied for the attitude estimation with the adapted measurement model of differential vector comparison. Linearized model for Extended Kalman Filter and low pass filtered characteristics of measurement greatly extend the affordability of the proposed algorithm to the field of simple low cost embedded systems. For performance verification, experiment are done employing a practical low cost MEMS IMU and GPS receiver specification. Performance comparison with a high grade navigation system demonstrated good estimation result.

Heading Estimation with Real-time Compensation Based on Kalman Filter Algorithm for an Indoor Positioning System

The problem of heading drift error using only low cost Micro-Electro-Mechanical (MEMS) Inertial-Measurement-Unit (IMU) has not been well solved. In this paper, a heading estimation method with real-time compensation based on Kalman filter has been proposed, abbreviated as KHD. For the KHD method, a unified heading error model is established for various predictable errors in magnetic compass for pedestrian navigation, and an effective method for solving the model parameters is proposed in the indoor environment with regular structure. In addition, error model parameters are solved by Kalman filtering algorithm with building geometry information in order to achieve real-time heading compensation. The experimental results show that the KHD method can not only effectively correct the original heading information, but also effectively inhibit the accumulation effect of positioning errors. The performance observed in a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirms that apply KHD method to PDR(Pedestrian Dead Reckoning) algorithm can reliably achieve meter-level positioning using a low cost MEMS IMU only.

English

English

A low cost MEMS based NDIR system for the monitoring of carbon dioxide in breath analysis at ppm levels

The molecules in our breath can provide a wealth of information about the health and well-being of a person. The level of carbon dioxide (CO2) is not only a sign of life but also when combined with the level of exhaled oxygen provides valuable health information in the form of our metabolic rate. We report upon the development of a MEMS-based non-dispersive infrared CO2 sensor for inclusion in a hand held portable breath analyser. Our novel sensor system comprises a thermopile detector and low power MEMS silicon on insulator (SOI) wideband infrared (IR) emitter. A lock-in amplifier design permits a CO2 concentration of 50ppm to be detected on gas bench rig. Different IR path lengths were studied with gases in dry and humid (25% and 50% RH) in order to design a sensor suitable for detecting CO2 in breath with concentrations in the range of 4–5%. A breath analyser was constructed from acetal and in part 3D printed with a side-stream sampling mechanism and tested on a range of subjects with two data-sets presented here. The performance of the novel MEMS based sensor was validated using a reference commercial breath-by-breath sensor and produced comparable results and gave a response time of 1.3s. Further work involves the detection of other compounds on breath for further metabolic analysis and reducing the overall resolution of our MEMS sensor system from ca. 250ppm to 10ppm.

The Development and Performance Characterization of Turbine Prototypes for a MEMS Spirometer

The design, optimization, and performance characterization of turbine prototypes for the development of a Microelectromechanical Systems spirometer is reported. Four different turbines were designed based on large-scale Savonius turbine architecture. The number of turbine blades was optimized through finite-element simulations to maximize the induced moment and rotational speed at normal breathing flow rates. The turbines were manufactured and tested for their speed performance with respect to input flow rate, pressure difference, and actuation power, showing good agreement with the simulation results. The highest rotational speed was achieved with involute-bladed turbine design with eight blades, and was measured to be 10.56 kr/min at the peak 25-lpm flow rate. Real-life testing with this design was carried out on healthy subjects to demonstrate its capability of performing respiration flow rate measurements. The turbine prototypes presented in this paper allow for the development of low-cost and portable MEMS spirometers for remote- and self-monitoring of lung malfunctions in chronic obstructive pulmonary disease and asthma diseases.

Minimum Variance Optimal Filter Design for a 3x3 MEMS Gyroscope Cluster Configuration

Abstract Gyroscopes are physical sensors that detect and measure the angular motion of an object relative to an inertial frame of reference. The low cost MEMS gyroscopes are known to have a smaller size, lower weight and less power consumption than their discrete counterparts. However the current state-of-the-art MEMS gyroscopes have low grade performance and cannot compete with established sensors in high accuracy navigational and guidance applications. By integrating large numbers of MEMS gyroscopes on a single circuit board in a defined configuration, the collective behavior of these devices can be improved. Kalman filtering technique provides a discrete estimation algorithm to fuse the individual gyroscope outputs to a single virtual gyroscope output. The combined gyro drift is reduced and their performance is improved from low grade to high grade aiding in much wider applications.

Calibration of low-cost triaxial magnetometer

Heading angle of magnetic compasses is estimated using the triaxial magnetometer readings. The accuracy of these readings is influenced by many factors such as sensor errors (scale factors, non-orthogonality, and offsets), and magnetic deviations (soft-iron and hard-iron interference). To calculate the true heading angle, the magnetic calibration of magnetometer is necessary. This research paper describes a calibration procedure for triaxial low-cost MEMS magnetometer. The proposed calibration method determines twelve calibration parameters in 3D arbitrary rotations of magnetometer.

A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

A MAN-PORTABLE, IMU-FREE MOBILE MAPPING SYSTEM

Abstract. Mobile mapping systems are commonly mounted on cars, ships and robots. The data is directly geo-referenced using GPS data and expensive IMU (inertial measurement systems). Driven by the need for flexible, indoor mapping systems we present an inexpensive mobile mapping solution that can be mounted on a backpack. It combines a horizontally mounted 2D profiler with a constantly spinning 3D laser scanner. The initial system featuring a low-cost MEMS IMU was revealed and demonstrated at MoLaS: Technology Workshop Mobile Laser Scanning at Fraunhofer IPM in Freiburg in November 2014. In this paper, we present an IMU-free solution.

Navigation algorithm combining building plans with autonomous sensor data

This paper presents an approach to navigation system’s position and heading correction using building floor plans. The algorithm includes three steps: (a) autonomous sensors data processing to obtain position and heading, (b) map-matching correction, and (c) navigation system errors estimation. A particle filter is used to incorporate the building plan information and a Kalman filter estimates the dead reckoning error states. This algorithm was designed for vehicle navigation systems operating inside buildings with known floor plans and can be adapted for implementation on real-time navigation systems using low-cost MEMS gyroscope and speed sensor as dead reckoning instruments. The real-world data collected from the vehicle indoor tests has shown that the proposed algorithm is able to correct significant errors in dead reckoning position and heading by applying the map constraints.

Stochastic Characterization of a MEMs based Inertial Navigation Sensor using Interval Methods

The aim here remains to introduce effectiveness of interval methods in analyzing dynamic uncertainties for marine navigational sensors. The present work has been carried out with an integrated sensor suite consisting of a low cost MEMs inertial sensor, GPS receiver of moderate accuracy, Doppler velocity profiler and a magnetic fluxgate compass. Error bounds for all the sensors have been translated into guaranteed intervals. GPS based position intervals are fed into a forward- backward propagation method in order to estimate interval valued inertial data. Dynamic noise margins are finally computed from comparisons between the estimated and measured inertial quantities It has been found that the intervals as estimated by proposed approach are supersets of 95% confidence levels of dynamic errors of accelerations. This indicates a significant drift of dynamic error in accelerations which may not be clearly defined using stationary error bounds. On the other side bounds of non-stationary error for rate gyroscope are found to be in consistence with the intervals as predicted using stationary noise coefficients. The guaranteed intervals estimated by the proposed forward backward contractor, are close to 95% confidence levels of stationary errors computed over the sampling period. Index Terms—Stochastic, dynamic, error, MEMs, inertial, interval, methods, INS, GPS.

Improving the performance of tightly-coupled GPS/INS navigation by using time-differenced GPS-carrier-phase measurements and low-cost MEMS IMU

To avoid the difficulties in fixing the carrier phase ambiguities, the time difference carrier phase approach is applied to a GPS/IMU tightly-coupled navigation system to eliminate the ambiguity between two successive GPS epochs, which can provide high velocity estimation accuracy. The carrier phases are carefully corrected before use. A modified method is proposed by using the system matrix in each time update to calculate the integration of the velocity errors in the measurement update equation. A Cubature Kalman Filter (CKF) is applied to the integrated navigation system to improve the attitude estimation accuracy. The navigation result and comparison show the accuracy improvement after applying the carrier phase corrections, modified measurement update method and the CKF.