Sensor Measurement Errors Research Articles

Research on Strain Measurements of Core Positions for the Chinese Space Station.

The Chinese space station is designed to carry out manned spaceflight, space science research, and so on. In serious applications, it is a common operation to inject gas into the hull, which can produce strain of the bulkhead. Accurate measurement of strain for the bulkhead is one of the key tasks in evaluating the health condition of the space station. This is the first work to perform strain detection for the Chinese space station bulkhead by using optical fiber Bragg grating. In the period of measurements, the resistance strain gauge is used as the strain standard. The measurement error of the fiber optical sensor in the circumferential direction is very small, being less than 4.52 με. However, the error in the axial direction is very large with the highest value of 28.93 με. Because the measurement error of bare fiber in the axial direction is very small, the transverse effect of the substrate of the fiber optical sensor likely plays a role. The comparison of the theoretical and experimental results of the transverse effect coefficients shows that they are fairly consistent, with values of 0.0271 and 0.0287, respectively. After the transverse effect is compensated, the strain deviation in the axial detection is smaller than 2.04 με. It is of great significance to carry out real-time health assessment for the bulkhead of the space station.

Online Open-Phase Fault Detection for Permanent Magnet Machines With Low Fault Harmonic Magnitudes

Conventional permanent magnet (PM) machine open-phase fault detection relies on the estimation of current harmonics using available phase current sensors. Because the magnitudes of current harmonic are proportional to the machine load condition, it is still a challenge to detect a phase fault at light load when fault-induced current harmonics are too small to estimate. This paper investigates the phase fault detection specifically for the conditions under low fault harmonic magnitudes. Both fault-induced harmonics in dq currents and neutral point (NP) voltage are analyzed to find a suited signal for the fault detection. By adding a voltage sensor to measure the NP voltage in a PM machine, a better fault detection performance is achieved because the fault harmonic in NP voltage results in the reduced effect on torque loads and sensor measurement errors. A 50-W PM machine is tested to verify the phase fault detection performance under low fault harmonic magnitudes. This paper includes the experimental evaluation on the detection limitation in terms of load capability using current and voltage fault signals.

Analysis and correction for measurement error of edge sensors caused by deformation of guide flexure applied in the Thirty Meter Telescope SSA

The Thirty Meter Telescope (TMT) project will design and build a 30-m-diameter telescope for research in astronomy in visible and infrared wavelengths. The primary mirror of TMT is made up of 492 hexagonal mirror segments under active control. The highly segmented primary mirror will utilize edge sensors to align and stabilize the relative piston, tip, and tilt degrees of segments. The support system assembly (SSA) of the segmented mirror utilizes a guide flexure to decouple the axial support and lateral support, while its deformation will cause measurement error of the edge sensor. We have analyzed the theoretical relationship between the segment movement and the measurement value of the edge sensor. Further, we have proposed an error correction method with a matrix. The correction process and the simulation results of the edge sensor will be described in this paper.

Control and estimation of a quadcopter dynamical model

The main motivation for this paper is to apply LQ and LQG methodologies for quadcopter control system. The developed control system is for both the rectangular position (xy) and altitude (z) as well as the orientation (attitude - angles around the axes) based on 6-Degree of Freedom (6DOF) mathematical model. 6DOF refers to the model with 3 linear and 3 angular motions. The altitude and attitude controllers are designed and the results presented in both the continuous and the discrete time cases. For the controller design, a nonlinear mathematical model was obtained first for 6DOF. The next step was to linearize the nonlinear model in hovering mode, and the final step was the reduction of the resulted linear model to be used as starting model for the controller design. The reduced linear model was tested for controllablity and observability. The control goal was to track a spatial trajectory with the quadcopter center of gravity under environment disturbances and sensor measurement errors. For this purpose, designed LQ controller was augmented by Kalman Filter state observer. The resultant controllers provide precise and robust performance for an input reference signal and for a regulation problem. After the transient response (of order of few seconds) the tracking error is acceptable which provides safe handling even under disturbances and measurement noises. The transient response can be further reduced by controllers fine tuning.

Development of localization system using ultrasonic sensor for an underwater robot to survey narrow environment

ABSTRACT This paper describes a localization system for a swimming robot to survey underwater narrow environments. In that environment, external sensors cannot be set up to localize the robot position, as there are many structures and the robot moves three-dimensionally. Therefore, the position needs to be calculated only by internal sensors. In this work, a new localization method based on map-matching is proposed, referring to cross-sectional shape data cut from a three-dimensional computer-aided design (CAD) data as an environmental map and structural shapes measured by a range sensor. As a range sensor, an ultrasonic sensor which is two-dimensional scanning-type was developed. The reflected signals of the ultrasonic sensor have some noise. Only structural shape data are extracted from the reflected signals. The image correlation is used as the matching method. Experiments to evaluate the performance of the proposed system were implemented at a mock-up environment. As a result, it was confirmed that the position was detected with an accuracy of 100 mm. The error is mainly caused by measurement error of the ultrasonic sensor that is used to calculate structural shapes. We concluded to improve the measurement accuracy of the ultrasonic sensor to reduce localization error.

A Computationally Efficient Framework for Automatic Inertial Sensor Calibration

The calibration of (low-cost) inertial sensors has become increasingly important over the past years, since their use has grown exponentially in many applications going from unmanned aerial vehicle navigation to 3-D animation. However, this calibration procedure is often quite problematic since, aside from compensating for deterministic measurement errors due to physical phenomena such as dynamics or temperature, the stochastic signals issued from these sensors in static settings have a complex spectral structure and the methods available to estimate the parameters of these models are either unstable, computationally intensive, and/or statistically inconsistent. This paper presents a new software platform for calibration of the stochastic component in inertial sensor measurement errors based on the generalized method of wavelet moments, which provides a computationally efficient, flexible, user-friendly, and statistically sound tool to estimate and select from a wide range of complex models. In addition, all this is possible also in a robust framework allowing to perform sensor calibration when the data are affected by outliers. The software is developed within the open-source statistical software R and is based on C++ language allowing it to achieve high computational performance.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

Center-Pivot-Mounted Sensing System for Monitoring Plant Height and Canopy Temperature

Abstract. Easy-to-use data acquisition methods are required for variable-rate irrigation (VRI) decision support systems. Plant canopy temperature is related to plant water stress. Plant height is useful as an indicator of plant health conditions and can be used to estimate yield potential. Therefore, measurements of plant canopy temperature and plant height coupled with spatial information in the field can be used for determining VRI water application depths. A center-pivot-mounted wireless data acquisition (WDAQ) system was developed to collect plant canopy temperature and plant height data in the field. Each WDAQ unit consisted of a GPS receiver, programmable data logger, infrared temperature sensor, ultrasonic distance sensor, solar power supply, and wireless data transmitter/receiver. The system included two WDAQ units installed on a four-span center-pivot VRI system. One unit was mounted at the middle of the third span, and the other was mounted at the middle of the fourth span from the pivot. The infrared temperature sensors were used to detect the canopy temperature, while the ultrasonic distance sensors were used to measure plant height. The WDAQ system was designed to continuously and simultaneously measure plant canopy temperature and plant height and record the spatial coordinates at each measurement location as the center pivot moved around the field. Data collected were wirelessly transferred to a receiver for data processing. This WDAQ system has been tested and evaluated in the field for two years. Test results indicated that the WDAQ system was able to record approximately 3,200 measurements from each sensor in one pivot circle (360°). The measurement error of the ultrasonic distance sensor was 0.2 to 3 cm in a measurement range of 14 to 209 cm, and the sensor-measured plant heights were strongly correlated with manually tape-measured plant heights in soybean and cotton crops (r2 = 0.97). Combined with the spatial information, measurements of plant height and crop canopy temperature were used to generate plant height and crop canopy temperature maps. Spatial variabilities of plant height and canopy temperature across the field could be identified from the maps and used in irrigation research. The WDAQ system has great potential for automatic creation of VRI prescription maps and plant-based irrigation scheduling. Keywords: Canopy temperature, Irrigation scheduling, Plant height, Sensors, Variable-rate irrigation.

Indoor SLAM application using geometric and ICP matching methods based on line features

This study presents an autonomous guided vehicle (AGV) with simultaneous localization and map building (SLAM) based on matching method, and extended Kalman filter SLAM. In general, the AGV is a large mobile robot that is used in transportation to carry cargoes, and it is guided using wired or wireless guidance systems. The guidance system based AGV accounts for a majority of robots in the mobile robot industry. However, in semiconductor factories, landmarks are unavailable; hence, the existing system has not been used in the mentioned environments. Therefore, the SLAM technology is applied in the environments, and can guide the AGV without landmarks. However, the accuracy of the SLAM can be low owing to measurement error of sensors and a cumulative calculation caused by localization sensors. Therefore, the accuracy is frequently assumed to be incorrect; moreover, the accuracy of the built map is low. In order to solve the problems, this study proposes the AGV with the SLAM based on matching methods; two matching method; geometric matching method and iterative closest point algorithm. The performance of the proposed method is compared with typical methods such as singular value decomposition / RIGID transformation based technologies using feature-point-based SLAM and is compared with the aforementioned two methods using the extended Kalman filter SLAM. The proposed method is more efficient than the typical methods used in the comparison.

Transverse sensitivity suppression using multi-axis surface encoder with parasitic error compensation

Transverse sensitivity that is mainly resulted from parasitic error motions can introduce undesired motion components and remarkably lower the manipulation qualities of most inertial sensors. This problem becomes even more apparent for multi-axial sensors as additional demands for multi-degree-of-freedom detection become higher. In this letter, a method to minify the transverse sensitivity of an inertial sensor by multi-degree-of-freedom optical sensing and measurement has been reported and tested. A multi-axis-surface-encoder-based biaxial optical accelerometer is fabricated for scheme validation. The surface encoder adopts multi-reading-unit arrangement, and it can not only detect small changes in displacement to calculate the applied acceleration along X- and Y-axes but also quantify the parasitic error motion caused by Z-twist. A suitable compensation strategy is also developed to reveal the concerned outputs without parasitic errors. Experimental results show that the configuration combined with the parasitic error compensation algorithm remarkably diminishes the sensor's transverse sensitivity and measurement error to 1.76% and 2.24%, respectively. Compared with the simple structure optimizations, the technique we proposed is more straightforward and effective. It is also applicable for transverse sensitivity suppression of other inertial sensors, allowing for a similar configuration, such as vibration sensors and inclinometers.

Safety monitoring of continuously welded pipeline subjected to ground deformation using wireless micro-electro-mechanical system inclinometers

Ground deformation, which is a common consequence of surface loads, construction activities, subterranean cavitations, soil subsidence, and so on, poses a serious threat on buried pipelines in the vicinity. Aiming at the influence of ground deformation on continuously welded underground pipelines, this article presents a real-time technique for pipeline health monitoring using wireless micro-electro-mechanical system inclinometers. Based on the multi-point inclination measurements, a strategy is proposed to achieve the two-level target: (1) detection and localization of the abnormal event and (2) estimation of pipeline flexural deflection in virtue of the elastic foundation beam model and cubic spline interpolation. Numerical studies are carried out to investigate the applicable conditions of the strategy and the influence of sensor placement and measurement error on the estimation accuracy. Field testing is finally conducted on a 24-m buried steel pipeline to validate the effectiveness of the proposed method for safety monitoring of the pipeline subjected to ground deformation.

Maximum Entropy-Based Auto Drift Correction Using High- and Low-Precision Sensors

With the advancement in the Internet of Things (IoT) technologies, variety of sensors including inexpensive, low-precision sensors with sufficient computing and communication capabilities are increasingly deployed for monitoring large geographical areas. One of the problems with the use of inexpensive sensors is that they often suffer from random or systematic errors such as drift. The sensor drift is the result of slow changes that occur in the measurement driven by aging, loss of calibration, and changes in the phenomena being monitored over a time period. These drifting sensors need to be calibrated automatically for continuous and reliable monitoring. Existing methods for drift detection and correction do not consider the measurement errors or uncertainties present in those inexpensive low-precision sensors, hence, resulting in unreliable drift estimates. In this article, we propose a novel framework to automatically detect and correct the drifts by employing Bayesian Maximum Entropy (BME) and Kalman filtering (KF) techniques. The BME method is a spatiotemporal estimation method that incorporates the measurement errors of low-precision sensors as interval quantities along with the high-precision sensor measurements in their computations. Our scheme can be implemented in a centralized as well as in a distributed manner to detect and correct the drift generated in the sensors. For the centralized scheme, we compare several Kriging-based estimation techniques in combination with KF, and show the superiority of our proposed BME-based method in detecting and correcting the drift. We also propose a multivariate BME framework for drift detection, in which multiple features can be used to improve the drift estimates. To demonstrate the applicability of our distributed approach on a real-world application scenario, we implemented our algorithm on each wireless sensor node in order to perform in-network drift detection. The evaluation on real IoT datasets gathered from an indoor and an outdoor deployments reveal the superiority of our method in correctly identifying and correcting the drifts that develop in the sensors, in real time, compared to the existing approaches in the literature.

Dynamic measurement errors prediction for sensors based on firefly algorithm optimize support vector machine

The accuracy of dynamic measurement error predicting has the great significant influence on the precision and stability of the sensors. In order to solve the problem of the low accuracy of the model in traditional dynamic measurement error prediction, support vector machine (SVM) is applied to predicting the dynamic measurement error of sensors. However, in prediction tasks, a proper set of design parameters are essential for the performance and efficiency of SVM model. Hence, parameters selection for SVM by firefly algorithm (FA) is proposed in this study to avoid the local minimum value which occurs in the traditional method of parameter optimization. Two sensors of dynamic measurement error data are considered for modeling. Root mean squared error (RMSE) and mean absolute percentage error (MAPE) are employed to evaluate the performances of the models. These results are also compared with the results obtained from the grid search SVM (GS-SVM)and the particle swarm optimization SVM (PSO-SVM). Experimental results show that the model of SVM based on FA algorithm predicts more accurately and effectively on dynamic measurement errors prediction for sensors than the other models.

Magnetostrictive strain monitoring of cement-based magnetoelectric composites in a variable magnetic field by fiber Bragg grating

In this paper, a cement-based magnetoelectric (ME) laminated composite is fabricated by bonding the piezoelectric material lead zirconate titanate (PZT) between two C-M layers made of the mixture of cement and magnetostrictive material to form a sandwich structure. As well known, inherent measurement errors of traditional electric sensors are definitely induced by magnetic interference in magnetic field. Thus, it is impossible to measure the magnetostrictive strain due to a variable magnetic field. In this paper, the fiber Bragg grating (FBG) sensors were used instead of the traditional strain gauges to measure the strain of the cement-based magnetostrictive layers in a variable bias magnetic field. It can be concluded that the FBG sensors is a reliable choice as its tiny size, high sensitivity and immunity to magnetic interference.

Using the Microsoft Kinect™ to assess 3-D shoulder kinematics during computer use

Shoulder joint kinematics has been used as a representative indicator to investigate musculoskeletal symptoms among computer users for office ergonomics studies. The traditional measurement of shoulder kinematics normally requires a laboratory-based motion tracking system which limits the field studies. In the current study, a portable, low cost, and marker-less Microsoft Kinect™ sensor was examined for its feasibility on shoulder kinematics measurement during computer tasks. Eleven healthy participants performed a standardized computer task, and their shoulder kinematics data were measured by a Kinect sensor and a motion tracking system concurrently. The results indicated that placing the Kinect sensor in front of the participants would yielded a more accurate shoulder kinematics measurements then placing the Kinect sensor 15° or 30° to one side. The results also showed that the Kinect sensor had a better estimate on shoulder flexion/extension, compared with shoulder adduction/abduction and shoulder axial rotation. The RMSE of front-placed Kinect sensor on shoulder flexion/extension was less than 10° for both the right and the left shoulder. The measurement error of the front-placed Kinect sensor on the shoulder adduction/abduction was approximately 10° to 15°, and the magnitude of error is proportional to the magnitude of that joint angle. After the calibration, the RMSE on shoulder adduction/abduction were less than 10° based on an independent dataset of 5 additional participants. For shoulder axial rotation, the RMSE of front-placed Kinect sensor ranged between approximately 15° to 30°. The results of the study suggest that the Kinect sensor can provide some insight on shoulder kinematics for improving office ergonomics.

Simulation Model of Hall Current Sensor Based on Simscape Environment

Focus on the problems of current sensor model based on MATLAB/Simulink/Simscape, such as the internal structure of the model is simple and not reflect the principle and structure itself, the measurement results is ideal and not reflect the influence of noises. Therefore, a model is designed, which can reflect the principle, internal mechanism and the measurement error of hall current sensor in the simscape modeling environment. The model reflects the closed-loop principle, includes the current noise and other interference factors, and the model has no load effect on the measuring circuit. Test results show that the model system can better reflect the actual phenomenon, and make a prominent contribution for the research of circuit.

3D multi-directional sensor with pyramid mirror and structured light

Structured-light-based 3D multi-directional sensors are widely used due to their flexible field of view (FOV), which is a significant benefit. Nevertheless, existing 3D multi-directional sensors based on linear lasers are only capable of measuring a slice of a 3D scene at low resolution. In addition, other 3D multi-directional sensors are capable of measuring 3D scenes by shooting 2D structured light patterns in different directions through curved mirrors. However, the projected and captured structured light patterns are blurred due to the optical aberration property of curved mirrors, resulting in inaccurate 3D reconstruction. This paper proposes a 3D, high-resolution, high-accuracy, real-time multi-directional sensor that utilizes pyramid mirrors to decompose the FOVs into different directions and reduce the optical aberration phenomenon. The mathematical model of the 3D sensor is presented, and the FOV is analyzed. A dual-frequency phase-shifting fringe pattern is used for 3D reconstruction in real time. Moreover, the sensor is calibrated using a planar circle calibration gauge and the Householder reflection constraint. Finally, the experimental results demonstrate that the measurement error of the 3D multi-directional sensor is less than 1mm and 0.03rad, thus verifying the method's feasibility.

Flow measurement of CO2 in a binary gaseous mixture using an averaging Pitot Tube and Coriolis mass flowmeters

To combat the growing emissions of CO2 from industrial processes, Carbon Capture and Storage (CCS) and Carbon Capture and Utilization technologies (CCU) have been accepted worldwide to address these pressing concerns. So as to efficiently manage material and financial losses across the entire stream, accurate accounting and monitoring through fiscal metering of CO2 in CCS transportation pipelines are core and required features for the CCS technologies. Moreover, these technical requirements are part of the legal compliance schemes and guidelines from various regulatory bodies. The CO2 transportation pipelines will likely have multiple inputs from different capture plants, each with varying composition of CO2 and thus introducing impurities into the CO2 stream. The presence of other ordinary or hydrocarbon gases in the CO2 gas stream could affect the functionality of metering instruments by introducing additional errors, particularly in the case of volumetric flowmeters. In this study, volumetric and direct mass measurement methods for the flow measurement of CO2 mixtures using two totally different metering principles are experimentally evaluated. An Averaging Pitot Tube with Flow Conditioning Wing (APT-FCW) and Coriolis mass flowmeters (CMF) are used to assess the flow metering of CO2 in a binary gaseous mixture. Different gases (nitrogen, air, oxygen, argon and propane) are diluted as contaminants into the pure CO2 gas flow for various mass fractions to produce an adulterated mixture of the CO2 gas. Comparative analysis of the measurement results under these flow conditions relative to that of pure CO2 gas show that the measurement error of the APT-FCW sensor increases with the mass fraction of the diluent component, and gases with density closer to that of CO2 have a much lesser effect on the performance of the APT-FCW flow sensor for smaller mass fractions. The CMF proved to be very reliable in the gas combination processes and as a reference meter for the APT-FCW sensor. Further analytical observations are discussed in detail.

Cardinality Balanced Multi-Target Multi-Bernoulli Filter with Error Compensation.

The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter developed recently has been proved an effective multi-target tracking (MTT) algorithm based on the random finite set (RFS) theory, and it can jointly estimate the number of targets and their states from a sequence of sensor measurement sets. However, because of the existence of systematic errors in sensor measurements, the CBMeMBer filter can easily produce different levels of performance degradation. In this paper, an extended CBMeMBer filter, in which the joint probability density function of target state and systematic error is recursively estimated, is proposed to address the MTT problem based on the sensor measurements with systematic errors. In addition, an analytic implementation of the extended CBMeMBer filter is also presented for linear Gaussian models. Simulation results confirm that the proposed algorithm can track multiple targets with better performance.

Model Study of the Influence of Ambient Temperature and Installation Types on Surface Temperature Measurement by Using a Fiber Bragg Grating Sensor.

Surface temperature is an important parameter in clinical diagnosis, equipment state control, and environmental monitoring fields. The Fiber Bragg Grating (FBG) temperature sensor possesses numerous significant advantages over conventional electrical sensors, thus it is an ideal choice to achieve high-accuracy surface temperature measurements. However, the effects of the ambient temperature and installation types on the measurement of surface temperature are often overlooked. A theoretical analysis is implemented and a thermal transfer model of a surface FBG sensor is established. The theoretical and simulated analysis shows that both substrate strain and the temperature difference between the fiber core and hot surface are the most important factors which affect measurement accuracy. A surface-type temperature standard setup is proposed to study the measurement error of the FBG temperature sensor. Experimental results show that there are two effects influencing measurement results. One is the “gradient effect”. This results in a positive linear error with increasing surface temperature. Another is the “substrate effect”. This results in a negative non-linear error with increasing surface temperature. The measurement error of the FBG sensor with single-ended fixation are determined by the gradient effect and is a linear error. It is not influenced by substrate expansion. Thus, it can be compensated easily. The measurement errors of the FBG sensor with double-ended fixation are determined by the two effects and the substrate effect is dominant. The measurement error change trend of the FBG sensor with fully-adhered fixation is similar to that with double-ended fixation. The adhesive layer can reduce the two effects and measurement error. The fully-adhered fixation has lower error, however, it is easily affected by substrate strain. Due to its linear error and strain-resistant characteristics, the single-ended fixation will play an important role in the FBG sensor encapsulation design field in the near future.