Influence Of Measurement Noise Research Articles

Research on improved dynamic load identification method based on Kalman filter under noise interference

As a mathematical approach for solving inverse problems, the dynamic load identification method is particularly useful when directly measuring the load acting on a structure is challenging. The Kalman filter (KF) algorithm is commonly employed for dynamic load identification. However, in practice, measurement noise can significantly affect the accuracy of the KF algorithm's results. In this study, we investigate the impact of response noise on the accuracy of load identification results using the KF algorithm. To eliminate the influence of measurement noise on identification accuracy, we propose an improved dynamic load identification method. This method incorporates adaptive techniques to assess varying levels of response noise and improve the load identification accuracy. Notably, this method is applicable in both physical and modal domains. We conduct numerical simulations using a 5-DOF system under different noise conditions. Compared to traditional dynamic load identification method based on KF algorithm, the improved method consistently achieves superior accuracy in load identification across various noise levels. Moreover, an experiment is conducted to further validate the feasibility of the proposed method. The results showed that the proposed method is effective and can achieve high-precision identification of unknown loads.

Kinematic calibration of a 5-DOF double-driven parallel mechanism with sub-closed loop on limbs

Abstract This paper proposes a kinematic calibration method of a novel 5-degree-of-freedom double-driven parallel mechanism with the sub-closed loop on limbs. At first, considering the introduction of a sub-closed loop significantly increased the complexity and difficulty of kinematic error modeling, an equivalent transformation method is proposed for the limb with a sub-closed loop. Then kinematic error model of the parallel mechanism is established based on the closed-loop vector method and parasitic motion analysis, which is verified by virtual prototype technology. Because the full kinematic error model is generally redundant, error parameter identifiability analysis is carried out by QR decomposition of the identification Jacobian matrix, and the redundant parameters are removed. Additionally, the Sequence Forward Floating Search algorithm is utilized to optimize measurement configurations to reduce the influence of measurement noise. Finally, with a laser tracker as the measuring device, numerical simulations and experiments are implemented to verify the proposed kinematic calibration method. The experiment results show that average position and orientation errors are reduced from 2.778 mm and 1.115° to 0.263 mm and 0.176°, respectively, within the prescribed workspace.

Zastosowanie Metody Elementów Skończonych do analizy metody określania rozmiaru buta na podstawie pomiarów pola magnetycznego

Paper presents utilization of Finite Element Method (FEM) for analysis of usefulness of shoe size assessment method, based on magnetovision measurements. Measurement method based on disturbances of the uniform magnetic field (e.g. Earth’s magnetic field) is presented. Disturbances are caused by loose material of high magnetic permeability placed inside the tested shoe, which adapts to the insides of the shoe. The disturbed magnetic field is measured by the matrix of magnetometers. The values of magnetic field are an input into inverse transformation algorithm which fits the position of matrix sensors into the reference (obtained by FEM) distribution of magnetic field. Based on the fitted parameters of sensors matrix a ratio between the measured and reference shoe size was determined. A FEM-based forward transformation is presented, which allows for generation of three-dimensional array of magnetic field distribution. Initial tests of inverse transformation were conducted successfully. Next steps consisted of tests of inverse transformation method with an inclusion of the typical measurement noise. A FEM-based forward transformation was conducted of a modified shoe model (including simplified screw model). Inverse transformation was conducted but both influence of measurement noise, as well as the presence of additional high permeability material, resulted that the presented shoe size assessment method is not suitable for practical implementation.

Identification of fractional order time delay system with measurement noise using variable period integration operational matrix

This paper proposes a parameter identification method for fractional-order time-delay systems with measurement noise, based on the Legendre wavelet variable-period integration operational matrix. A variable-period integration operational matrix and a variable-period time-delay integration operational matrix are constructed by variable-period sampling principle. For the impulse noise, global Gaussian noise and local Gaussian noise present in the identification data, the variable-period sampling, operational matrix global coefficient decomposition and matrix local coefficient decomposition methods are used to reconstruct the variable-period integration operational matrix to separate the noise from the data; the fractional-order time-delay system is expressed as an integral equation by using the reconstructed variable-period operational matrix, this reduces the influence of measurement noise on system identification accuracy. To further improve the parameter identification accuracy, the integral equation is solved by the multi-innovation least squares algorithm. The proposed method’s effectiveness is verified through several simulations and an experimental study of a constant-temperature system.

Robust sensing matrix design for the Orthogonal Matching Pursuit algorithm in compressive sensing

In compressive sensing, Orthogonal Matching Pursuit (OMP) is a greedy algorithm used for recovering sparse signals from their incomplete linear measurements. Conventionally, the OMP algorithm relies on both the measurement matrix and the measurement signal to reconstruct sparse signals. A sensing matrix can be designed to have a small mutual coherence with respect to (w.r.t.) the measurement matrix, which is used to boost the performance of the OMP algorithm in sparse signal reconstruction. Nevertheless, sensing matrices designed by current methods are vulnerable to measurement noises. In this paper, we begin by examining the underlying cause of the non-robustness to measurement noises exhibited by these sensing matrices. Subsequently, we propose a novel approach to design a robust sensing matrix capable of withstanding the influence of measurement noises. Finally, we conduct numerical simulations to demonstrate the effectiveness and robustness of the sensing matrix designed by the proposed method.

Moving horizon estimation and control of a binary simulated moving bed chromatographic processes with Langmuir isotherms

Simulated moving bed chromatographic (SMB) chromatographic processes are widely used for separations in pharmaceutical and biotechnological industries. In the present work, the control of such processes is proposed based on a semi-centralized control scheme that utilizes a combination of model predictive control (MPC) and classical PID control. The necessary information from the process, i.e. the internal concentration profiles, for the MPC is obtained by a moving horizon estimator (MHE) in real-time from the available limited measurements (the cycle-averaged concentrations of the extract and raffinate product streams and the dimensionless retention times of the concentration waves in the regeneration zones). As a test case study, the separation of a hypothetical system governed by the Langmuir isotherms in the nonlinear concentration range of the isotherms is considered. First, the stand-alone MHE is validated in open loop mode with no plant-model mismatch under deterministic and stochastic conditions. In the latter case, the true measurements are subject to random normally distributed noise. To evaluate the performance of the proposed control strategy, a reference tracking (change of the requirements for both the purities and the retention times) scenario is simulated. The investigated scenarios are: (i) no plant-model mismatch, (ii) MHE with ideal noiseless measurements and finally (iii) MHE under the influence of measurement noise. Results show that the controller is able to follow the change of the references from reduced purity to complete separation and vice versa closely.

Physics-guided deep learning based on modal sensitivity for structural damage identification with unseen damage patterns

Recently, wide attention has been paid to develop deep learning-based models for structural damage identification. However, in most of the studies, the pure deep learning-based structural damage identification methods lack physical interpretability and scientific consistency for generalization. Therefore, although such methods can achieve good damage identification results when data with similar damage patterns used for training and testing the network model, they usually give poor predictions in the unseen domain, i.e., the network’s extrapolation ability is limited. In this research, a physics-guided deep learning neural network (PGDLNN) is proposed by incorporating the physical loss constructed by structural modal parameters sensitivity analysis into the original Convolutional Neural Network (CNN) for structural damage identification. The model’s physical interpretability is improved, which can lead to more accurate damage identification results, especially in the domain with unseen damage patterns in training the network model. Numerical and experimental studies on simply supported beams are carried out to demonstrate the feasibility and effectiveness of the proposed method. The influences of measurement noise and incomplete measurements are also investigated. The results show that the incorporation of physical knowledge to deep learning model can enhance the generalization of the network, and hence improve the accuracy of damage severity quantification. This study not only performs damage localization and quantification simultaneously using the physics-guided deep learning neural network, but also demonstrates the superiority of incorporating physical knowledge into data-driven deep learning models for structural health monitoring.

A novel method to enhance the accuracy of parameter identification in elasto-geometrical calibration for industrial robots

Elasto-geometrical calibration is crucial for enhancing the absolute accuracy of robots in machining operations through the identification and compensation of parameter errors. However, the presence of inconsistent measurement units and improper selection of measuring poses can result in the ill-conditioned identification matrix (ICIM) issue, consequently impacting the accuracy of parameter identification. This paper introduces a novel method to tackle this challenge. Initially, an elasto-geometrical error model is developed based on the orientation-independent measurements (OIM), efficiently reducing the impact of mismatched positions and orientations on the ICIM problem. Subsequently, a PSO-SFFS algorithm is proposed to optimize the measurement configurations and minimize the influence of measurement noise. Furthermore, the incorporation of screw theory and the consideration of parallelogram mechanisms enhance the precision and comprehensiveness of the error model. Subsequent to the development of the error model, calibration comparison experiments are conducted on an industrial robot. Both simulation and experimental results validate the effectiveness of the proposed method in improving parameter identification accuracy.

Actuator multiplicative and additive simultaneous faults estimation using a qLPV proportional integral unknown input observer

AbstractThis paper introduces a technique for simultaneous estimation of additive and multiplicative faults in the actuators of nonlinear systems represented by quasi‐linear parameter varying (qLPV) models based on a proportional‐integral unknown input observer. The qLPV model, structured with a tensor product, allows for optimized flexibility of the observer gain. A distinguishing aspect of our method is the novel approach to nonlinearity, which is not only recast as a convex sum but also in the input vector. The study comprehensively analyses the robustness and convergence conditions through Lyapunov stability evaluation. A robust performance criterion is incorporated to minimize the influence of measurement noise and disturbances. As a result, a set of linear matrix inequalities are obtained. Two examples are examined to demonstrate the practical applicability and efficacy of the proposed method, highlighting the observer's performance under the actuator faults.

Model-free adaptive predictive control with unscented Kalman filter for hovercraft speed and heading stabilization

This paper develops a novel unscented Kalman filter-model free adaptive predictive control (UKF-MFAPC) method for an underactuated hovercraft speed and heading stabilization under the influence of measurement noise. First, the proposed method is based on the compact form dynamic linearization (CFLD) technique, which involves an analysis of the tight format dynamic linearization method. This approach specifically addresses the unique non-real-time scalability of the heading control subsystem, rendering the CFDL method unsuitable. Through redefining the linear combination of lateral velocity and heading as output to fulfill all assumptions of CFDL methods, this scheme effectively mitigates the lateral velocity vibration problem of the hovercraft. Secondly, this controller introduces the measurement noise during the data collection process into the optimization function and combines the unscented Kalman filtering method with data-driven models, the influence of error suppression noise on the controller is suppressed, ensuring that the output can still be controlled within a certain range of expected values. This paper also delves into the convergence of tracking errors and examines the stability of bounded inputs and outputs. Finally, the noise suppression effect and stability of the UKF-MFAPC scheme are demonstrated through a comparative analysis of simulation results with proposed model-free methods.

Uncertain damage identification methods based on residual force vector under the influence of measurement noise

This paper proposes probabilistic and interval methods, grounded in the residual force vector technique, to quantify measurement uncertainties and effectively integrate them into the identification process. In the probabilistic method, the measurement noise is assumed to be a zero-mean normal distribution, while the stiffness parameters of structural elements are assumed to follow a normal distribution, represented by mean values and standard deviations. In the interval method, the measurement noise is modelled as interval numbers, and the interval perturbation technique is employed to derive interval bounds for elemental stiffness parameters. A two-step model updating strategy is implemented, separately addressing pristine and damaged structures, to mitigate modeling errors. By using the results of probabilistic damage identification, a novel damage index termed damage expectation capable of capturing both the extent and probability of damage is introduced. Furthermore, an interval damage index is introduced to quantify the damage based on interval damage identification results. The impact of noise levels on the statistical properties of uncertain damage indices is also investigated. Numerical examples reveal that deterministic indexes exhibit a monotonic increase in average values with increasing noise levels. Particularly, as noise levels rise, the standard deviation of the combination index initially surpasses the deterministic damage index but eventually diminishes. Through the proposed methodology, structural damage can be identified even in the presence of uncertainties. The efficacy of the proposed damage identification technique is also validated by experimental data.

Linear Convergence of distributed estimation with constraints and communication delays

Motivated by the multi-sensor estimation problem, we consider a distributed constraint parameter estimation problem on a directed graph. Our goal is to design a fast distributed algorithm to handle constraint parameter estimation problems, especially considering the communication delays when sensors interact on a directed graph. Based on the gradient-tracking algorithm and indirect projection method, the Projected Distributed Parameter Estimation Algorithm (PDPEA) is proposed, which can deal with the closed convex set constraint and use the historical information stored by the sensors to achieve fast and accurate mean gradient estimation. The analysis shows that the PDPEA converges to a global and consensual minimizer even under the influence of measurement noise, arbitrarily bounded communication delays, and directed information topologies. Under the assumption that the local cost function is strongly convex and smooth, we show that the algorithm converges at a linear rate for an appropriate choice of stepsize. Lastly, we demonstrate the effectiveness of the proposed algorithm by setting up a multi-sensor parameter estimation network.

Parameter Identification of Permanent Magnet Synchronous Motor with Dynamic Forgetting Factor Based on H∞ Filtering Algorithm

To address system parameter changes during permanent magnet synchronous motor (PMSM) operation, an H∞ filtering algorithm with a dynamic forgetting factor is proposed for online identification of motor resistance and inductance. First, a standard linear discrete PMSM parameter identification model is established; then, the discrete H∞ filtering algorithm is derived using game theory reducing state and measurement noise influence. A cost function is defined, solving extremes values of different terms. A dynamic forgetting factor is introduced to the weighted combination of initial and current measurement noise covariance matrices, eliminating identification issues from different initial values. On this basis, a dynamic forgetting factor is added to weigh the combination of the initial measurement noise covariance matrix and the current measurement noise covariance matrix, which eliminates the influence of the discrimination error caused by the different initial values. Finally, the identification model is built in MATLAB/Simulink for simulation analysis to verify the feasibility of the proposed algorithm. The simulation results show the proposed H∞ filtering algorithm rapidly and accurately identifies resistance and inductance values with significantly improved robustness. The forgetting factor enables quick stable recognition even with poor initial values, enhancing PMSM control performance.

Optimizing measurement schemes to improve indoor airflow and temperature CFD–EnKF joint simulation

An ensemble Kalman filter (EnKF) effectively rectifies simulation inaccuracies arising from uncertain flow field boundary conditions. In this methodology, measurement data plays a pivotal and indispensable role. This study investigated the requirements for observation data in joint EnKF and computational fluid dynamics (CFD) simulation models to correct errors. It established guiding principles for measurement schemes and validated them using numerical experiments on the airflow and temperature fields in a small chamber. The optimized scheme significantly reduced simulation errors, enhancing assimilation accuracy. In addition, the influence of measurement noise was considered, revealing a positive correlation between assimilation and measurement errors, thus highlighting the significance of high-precision instruments. Additionally, this study confirmed that the optimal number of measurement points corresponds to the number of uncertain boundary condition variables. Fewer measurement points hinder accurate estimation, whereas increasing their number does not significantly improve the data assimilation accuracy, regardless of measurement noise. Overall, this study provides guidelines for implementing EnKF data assimilation to ensure reliable and precise results.

State-observer-based asymptotic tracking control for electro-hydraulic actuator systems with uncertainties and unmeasurable velocity

In this paper, a velocity estimation-based adaptive dynamic surface asymptotic tracking control strategy for electro-hydraulic actuator (EHA) systems subjected to uncertainties and unmeasurable velocity is proposed. Firstly, an accurate velocity signal is estimated by constructing a new state observer, reducing the influence of measurement noise, system cost, and installation complexity. Moreover, the observer's robust gain is updated by an adaptive law, reducing the conservativeness of its selection. Then, a continuous friction model based on the desired velocity signal is used for feedforward compensation to alleviate the negative effect of estimation noise. Moreover, the adaptive dynamic surface control (ADSC) technique is incorporated to avoid “complexity explosion” and eliminate filtering errors via adaptive terms. Finally, the asymptotical stability of the closed-loop system is theoretically proved via Lyapunov analysis, while the efficacy of the proposed control strategy is experimentally validated and numerically verified.

KSPGD algorithm to restrain the influence of measurement noise in adaptive fiber coupling

KSPGD algorithm to restrain the influence of measurement noise in adaptive fiber coupling

Semi-supervised local manifold regularization model based on dual representation for industrial soft sensor development

Data-driven soft sensors are crucial for predicting key quality variables in the process industry. In most processes, the dynamic characteristics are obvious, and the relationship between the primary and secondary variables is strongly nonlinear. Moreover, the worst thing is that labeled samples are usually scarce due to certain technical difficulties or measurement costs. To deal with these issues, this paper first proposes a semi-supervised manifold regularization model based on dual representation (SsMRM-DR). Then, a semi-supervised local manifold regularization model based on dual representation (SsLMRM-DR) is further proposed from the viewpoint of local manifold regularization. In the SsLMRM-DR, a space–time weighted similarity calculation method is designed to reduce the influence of measurement noise on similarity calculation. At the same time, a local manifold regularization method is developed to use unlabeled samples more efficiently and to improve the prediction accuracy and computational efficiency of the SsLMRM-DR. A numerical example and a real industrial process are used to evaluate the performance of the proposed schemes. The results show that SsLMRM-DR is effective and it has a good application prospect of soft sensors in the industry.

A Calibration Algorithm of 3-D Laser Scanner Based on KMPE Loss Function

Aiming at solving the estimation problem of measurement model parameters of 3D laser scanner, a kernel mean p-power error (KMPE) loss function-based calibration algorithm is proposed in this paper. The KMPE loss function is robust to measurement noise and outliers. Thus, a KMPE loss function-based nonlinear optimization model of measurement model parameters of 3D laser scanner is firstly established according to the distance constraint between each laser scanning point on the calibration sphere and the center of the calibration sphere. Then, to conduct calibration of the model parameters, the optimization model is solved by combining the success-history based parameter adaptation for differential evolution (SHADE) algorithm with the Levenberg-Marquardt (LM) algorithm. Experimental results show that the proposed algorithm can achieve high calibration accuracy of the measurement model parameters of the 3D laser scanner by effectively suppressing the influence of measurement noise and outliers.

Fundamental limits in structured principal component analysis and how to reach them

How do statistical dependencies in measurement noise influence high-dimensional inference? To answer this, we study the paradigmatic spiked matrix model of principal components analysis (PCA), where a rank-one matrix is corrupted by additive noise. We go beyond the usual independence assumption on the noise entries, by drawing the noise from a low-order polynomial orthogonal matrix ensemble. The resulting noise correlations make the setting relevant for applications but analytically challenging. We provide characterization of the Bayes optimal limits of inference in this model. If the spike is rotation invariant, we show that standard spectral PCA is optimal. However, for more general priors, both PCA and the existing approximate message-passing algorithm (AMP) fall short of achieving the information-theoretic limits, which we compute using the replica method from statistical physics. We thus propose an AMP, inspired by the theory of adaptive Thouless-Anderson-Palmer equations, which is empirically observed to saturate the conjectured theoretical limit. This AMP comes with a rigorous state evolution analysis tracking its performance. Although we focus on specific noise distributions, our methodology can be generalized to a wide class of trace matrix ensembles at the cost of more involved expressions. Finally, despite the seemingly strong assumption of rotation-invariant noise, our theory empirically predicts algorithmic performance on real data, pointing at strong universality properties.

UWB NLOS Recognition Based on Improved Convolutional Neural Network Assisted by Wavelet Analysis and Gramian Angular Field

In the ultra-wide band (UWB)-based indoor wireless positioning scene, non-line-of-sight (NLOS) is a common phenomenon due to the existence of the obstacles and it may result in the significant positioning errors. So it is an important research issue to recognize the UWB NLOS signals from the line-of-sight (LOS) signals. As an emerging deep learning technology, convolutional neural network (CNN) has been applied successfully to handle this issue. However, the traditional CNN omits the influence of measurement noises and does not consider the mining of the signal’s temporal relationship. To solve this problem, a UWB NLOS recognition method, called wavelet Gramian CNN (WGCNN), is proposed by integrating the wavelet analysis and the Gramian angular field (GAF). Firstly, the discrete wavelet transform is used to extract the trend curve of the channel pulse response (CIR) signals by eliminating the measurement noises. Further, two GAFs, consisting of Gramian angular summation field (GASF) and Gramian angular difference field (GADF), are introduced to transform the one-dimensional CIR signal into the two-dimensional images, which describe the temporal correlation of the signals. Moreover, a GAF image re-organization strategy is designed to capture the key sub-images to eliminate the influence of invalid image parts. Finally, the re-organized sub-images are fed into the CNN model to build a LOS/NLOS classifier. The experiments are performed on a benchmark data set provided by the EU "Horizon 2020" programme, and the results shows that the proposed WGCNN method has higher recognition accuracy than the traditional CNN method.