Speed Estimation Research Articles

Iterative improvement in tacholess speed estimation using instantaneous error estimation for machine condition monitoring in variable speed

Knowing the instantaneous angular speed (IAS) is crucial for monitoring the condition of variable speed rotating machinery. Thanks to advantages such as cost-saving, simplicity, and reduced installation difficulties, tacholess speed estimation (TSE) methods, based on the vibration signal itself, have attracted increasing attention in recent years. The major problem limiting the use of TSE methods in industry is that the accuracy of the IAS estimates is usually unknown. A less accurate IAS estimate implies errors in the extracted phase-time map. When this map is used for order tracking, it leads to a smeared order spectrum, thereby affecting subsequent vibration analysis. However, how the smearing of the order spectrum, introduced by phase modulation from the IAS error, can be used to further improve the accuracy of the estimated IAS has not been fully studied. This study first investigates the influence of the IAS error on the order tracked signal. It is realized that the instantaneous error of an estimated IAS can be estimated from the order tracked vibration signal. The estimated IAS can therefore be calibrated iteratively using the estimated instantaneous error in the IAS. The proposed method for iteratively improving accuracy of the estimated IAS based on the estimated instantaneous IAS error is validated using industrial data from wind turbines. While the method does add significant computational complexity, little additional human input is required beyond that of established methods. The results show the effectiveness of the method in producing IAS estimates that are several times more accurate than those given by existing methods.

Dynamic Performance Evaluation of a Brushless AC Motor Drive Using Different Sensorless Schemes

The presented study concerns with evaluating the dynamic performance of an isotropic sinusoidal brushless motor drive while utilizing different sensorless schemes. Three estimation algorithms are considered: the first depends on extracting the speed and position via comparing two values of motor's voltage in two co-ordinate systems; the second extracts the speed and position signal via comparing two different values of motor's current defined in two co-ordinates; while the third depends on estimating the motor's flux and use it to get the speed and position. The vector control is adopted to manage the drive dynamics. The detailed mathematical derivations for all system components are presented to facilitate the performance analysis. The theoretical base of each sensorless scheme is also described in detail. The target of the provided comparative analysis is to outline the weakness and strength points of each adopted sensorless schemes while estimating the speed and rotor position for a wide operating speed range. The judgment is measured in terms of the speed and rotor position estimation errors and the dynamic response as well. The performance evaluation process is carried out using MATLAB/Simulink software in which all system parts are simulated using their mathematical models. The findings from the study state that when it comes to dynamic speed behaviour, the voltage-based sensorless technique dominates, while the current-based sensorless approach gives stability in speed estimate priority. Alternatively, the third adopted sensorless scheme offers an acceptable high-speed performance and respectable performance at lower speeds. Statistically, it is found that the voltage-based estimation technique gives respectively lower speed and position estimation errors with percentages of 35% and 10% lower than their values under the current-based estimation technique, and with percentages of 35% and 30% lower than their values under the third adopted scheme.

THE PHYSIOLOGICAL VALIDITY OF CRITICAL SPEED DERIVED FROM A 3-MINUTE ALL-OUT SWIM TEST

INTRODUCTION AND AIMS The boundary between heavy and severe exercise domains is one of the most consistent predictors of swimming performance. The gold standard approach to assessing this physiological threshold (numerous 30-minute constant-load trials) is complex and time-consuming, leading practitioners to explore other methodologies, which has left coaches questioning the validity of testing outcomes and reducing testing implementation. The recent proposal of a modified 3-minute all-out swim test has produced reliable estimates of critical speed (CS), however the estimates have not been validated against physiological measures. The aim of this study was to establish the physiological validity of CS derived from the modified 3-minute all-out swim test in elite swimmers. METHODS 16 elite swimmers completed a 3-minute test consisting of 12 maximal 25-m efforts separated by 5s of rest. CS was calculated as the average speed of the slowest two 25-m efforts from the last four completed. To identify MLSS, swimmers completed a 30-minute trial broken into 6 x 5-minute intervals. Blood lactate concentration collected at each interval determined the subsequent interval speed. Over two additional visits, participants completed a 30-minute constant speed trial – at 2% above and 2% below CS to determine the physiology surrounding CS. Statistical analyses were conducted to compare CS and MLSS and determine their association, and to compare physiological measures within the two constant load trials. RESULTS MLSS and CS were not statistically different (p>0.05) and had a strong positive correlation (r=0.98). A physiological steady state was verified in the below CS trial (p>0.05). A physiological steady state was not obtained in the above CS trial (p<0.05). CONCLUSIONS These findings suggest the modified 3-minute all-out swim test as a valid methodology to assess the CS of elite swimmers. Implementation of this methodology could enable critical insight into how athletes are adapting to training and contribute significantly to swimming talent identification.

A data and physical model dual-driven based trajectory estimator for long-term navigation

Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation (DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory (Bi-LSTM) based quasi-static magnetic field (QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.

Numerical Analysis of Free Play-Induced Aeroelastic Phenomena: A Numerical Approach With Adaptive Step Size Control

This study presents a detailed numerical analysis of nonlinear aeroelastic behavior in a two degree of freedom (DOF) model, focusing on plunge and pitch motions and employing the continuation method (CM) with an adaptive step size control algorithm. The research incorporates free-play nonlinearity at the plunge hinge, a common structural nonlinearity in aeronautics that can induce detrimental limit cycle oscillations (LCOs) during flight. By examining three scenarios—linear response, unhindered plunge motion, and nonlinear stiffness behavior—the study assesses the effects of free play on flutter and LCO phenomena, including discontinuity-induced bifurcations like grazing bifurcation. Additionally, the study explores parameter variation for nonlinear flutter analysis, revealing the dynamics of grazing bifurcation and its impact on LCO behavior. The research also demonstrates the method’s superior accuracy in flutter speed estimation and mode-switching identification, despite higher computational demands. The findings underscore the diminishing influence of nonlinear free-play behavior on LCO amplitude, providing insights with significant implications for aeroelastic design and aircraft safety.

Sensorless Speed Estimation of Induction Motors through Signal Analysis Based on Chaos Using Density of Maxima.

Three-phase induction motors are widely used in various industrial sectors and are responsible for a significant portion of the total electrical energy consumed. To ensure their efficient operation, it is necessary to apply control systems with specific algorithms able to estimate rotation speed accurately and with an adequate response time. However, the angular speed sensors used in induction motors are generally expensive and unreliable, and they may be unsuitable for use in hostile environments. This paper presents an algorithm for speed estimation in three-phase induction motors using the chaotic variable of maximum density. The technique used in this work analyzes the current signals from the motor power supply without invasive sensors on its structure. The results show that speed estimation is achieved with a response time lower than that obtained by classical techniques based on the Fourier Transform. This technique allows for the provision of motor shaft speed values when operated under variable load.

An Efficient and Interpretable Stacked Model for Wind Speed Estimation Based on Ensemble Learning Algorithms

Wind energy has gained tremendous attention recently due to its abundance potential and to obviate the adverse impacts of fossil fuel consumption. However, the efficient development and operation of wind energy systems require precise and accurate wind speed prediction. To fulfill this aim, machine learning algorithms have shown its prowess in accurately predicting the wind speed and this study proposes an interpretable stacked ensemble with ridge regressor utilizing ML algorithms such as catboost (CB), gradient boost (GB), multilayer perceptron (MLP), and random forest (RF) as a base model. The base models’ estimations are integrated by applying a ridge regressor to enhance the accuracy of wind speed estimation. The dataset consists of 87 600 samples from which 80% samples are used for training. The proposed approach provides interpretability of the prediction results through Shapley additive explanations and feature importance analysis. The result of the proposed method is calculated by comparing the estimation outcomes with each model such as CB, GB, MLP, and RF in terms of error metrics such as root mean squared error (RMSE) and mean absolute error (MAE). The proposed model outperforms the others due to a reduction of error in RMSE by 19.89% and MAE by 24% respectively.

Numerical Investigation of Some Reductions for the Gatenby–Gawlinski Model

Two (consecutive) reductions of the complete Gatenby–Gawlinski model for cancer invasion are proposed in order to investigate the mathematical framework, mainly from a computational perspective. After a brief overview of the full model, we proceed by examining the case of a two-equations-based and one-equation-based reduction, both obtained by means of a quasi-steady-state assumption. We focus on invasion fronts, exploiting a numerical strategy based on a finite volume approximation, and perform corresponding computational simulations to study the sharpness/smoothness of the traveling waves. Then, we employ a space-averaged wave speed estimate—referred to as the LeVeque–Yee formula—to quantitatively approach the propagation phenomenon. Concerning the one-equation-based model, we propose a scalar degenerate reaction-diffusion equation, which proves to be effective in order to qualitatively recover the typical trends arising from the Gatenby–Gawlinski model. Finally, we carry out some numerical tests in a specific case where the analytical solution is available.

Rotor speed estimation for half-broken bar detection in induction motors using Kalman filtering

This study addresses the early detection of half-broken bars in induction motors (IMs). Most research on fault detection focuses on current signature analysis because it can provide online and remote monitoring at a low cost. However, errors caused by spectral leakage around the fundamental frequency supply reduce its reliability for fault diagnosis at the incipient stages. The proposed detection technique is based on a spectral analysis of the motor speed. To detect a half-broken bar, measurements of stator voltages and currents are processed by an adaptive extended Kalman filter for system state estimation. Then, a spectral analysis is applied to the estimated motor speed to obtain its power spectral distribution. Finally, the fault harmonic amplitude at 2sfs is used as fault indicator for fault diagnosis. The proposed approach has three main advantages: (1) speed estimation is unaltered by spectral leakage around the main frequency supply, (2) the cost of speed sensors is avoided, and (3) the absence of connecting shaft couplings prevents corruption of speed data by mechanical interference. Numerical simulations and experimental results were carried out for the squirrel cage IM to provide encouraging validation of the proposed early fault detection technique.

A novel three-module real-time adjustable accuracy speed estimator for active magnetic bearing system

This research aims to investigate the rotational speed estimation in active magnetic bearing (AMB) system. Rotational speed sensors are commonly employed for speed acquisition. Nevertheless, with the continuous advancement of AMB system, the inherent weaknesses of the speed sensors are becoming increasingly prominent in practical applications. Therefore, achieving online speed estimation can effectively address the engineering challenges arising from deficiencies in speed sensors. However, in terms of robustness, accuracy, and tracking performance, the existing speed estimation algorithms are insufficient to simultaneously meet the requirements of the AMB system. Moreover, present techniques are challenging to address prevalent issues in the AMB system, such as noise, harmonic interference, amplitude variations, and misalignment of the rotor axis trajectory. To tackle these problems, this work designs a novel three-module real-time adjustable accuracy speed estimator for AMB system. The algorithm employs displacement signals to estimate the rotational speed. And, it can flexibly adjust its noise resistance and accuracy through parameter tuning. Additionally, it can significantly diminish the influence of noise, harmonic interference, amplitude variations, and misalignment of the rotor axis trajectory. Finally, the simulation and experimental results demonstrate the effectiveness of this algorithm and validate its superior performance in robustness, accuracy, and tracking performance.

MHD Stagnation Point Radiative Flow of Hybrid Casson Nanofluid across a Stretching Surface

This paper aims to investigate the hybrid stagnancy point in casson nanofluid on a transient elongation plane in accordance with impact of thermal heat. Resistive heating effect also considered in this study. Nano particles of assumed Cu and Al are utilized. Hence derived partial differential equations are broken down into nonlinear differential equations using suitable Transformations. Then succeeding results are worked out by employing Bvp4c Method. The obtained values for the analysis are indicated by indicating velocity and temperature graphs for distinct constants like radiation constant (Rd), Casson constant (β), magnetic variable (M), Prandtl constant (Pr), unsteady parameter (s). As well, the confined parameter, coefficient of skin friction is analysed. For continuing estimates of Casson parameter speed of liquid flow will be lessen. On intensifying Prandtl number of liquid shrink gradually. It is observed that for rapidly rised values of both Nano molecules velocity of the liquid lessens whereas reverse tendency is identified for temperature profile. Utilisation of hybrid nanoflows has a benifit of heat convertion improvement. The outcomes of current investigation are liquid is better within the congruence with comparision of subsist work.

Channel Estimation for Underwater Acoustic Communications in Impulsive Noise Environments: A Sparse, Robust, and Efficient Alternating Direction Method of Multipliers-Based Approach

Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an l1−l1 optimization problem via the Alternating Direction Method of Multipliers (ADMM), effectively exploiting channel sparsity and addressing impulsive noise outliers. A non-monotone backtracking line search strategy is also developed to improve the convergence behavior. The proposed algorithm is low in complexity and has robust performance. Simulation results show that it exhibits a small performance deterioration of less than 1 dB for Channel Impulse Response (CIR) estimation in impulsive noise environments, nearly matching its performance under Additive White Gaussian Noise (AWGN) conditions. For Delay-Doppler (DD) doubly spread channel estimation, it maintains Bit Error Rate (BER) performance comparable to using ground truth channel information in both AWGN and impulsive noise environments. At-sea experimental validations for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems further underscore the fast convergence speed and high estimation accuracy of the proposed method.

Examination of rapid adjustment system based on screen score obtained using continuous shear wave elastography.

Continuous shear wave elastography (C-SWE) can be expected to be applied to portable muscle elasticity diagnosis. To establish diagnostic technology, it will be necessary to improve measurement techniques and quantitative measurement accuracy. In this study, we investigated two screen scores: the quality index (Q-index), which determines whether the intensity of a power Doppler image is appropriate, and the shear wave propagation direction index (SWDI), which determines the uniformity of shear wave propagation. First, we performed numerical simulations with white noise and found that the coefficient of variation of shear wave velocity estimation was less than 5% when the normalized Q-index was greater than 0.27. Furthermore, regarding the SWDI, we clarified the relationship between the standard deviation in shear wave propagation direction and the SWDI. Next, the relationship between the Q-index and coefficient of variation of estimated shear wave velocity was evaluated through experiments using a tissue-mimicking phantom. The results showed that there was a negative correlation between the Q-index and the coefficient of variation, and the fluctuation of the propagation velocity could be inferred from the Q-index. Finally, we showed the results of applying the screen scores to muscle relaxation monitoring and confirmed its usefulness in clinical applications. By applying the screen scores, we showed improved stability in speed estimation in C-SWE, and demonstrated the possibility of clinical applicability.

Bayesian estimation of dissipation and sound speed in tube measurements using a transfer-function model.

This study discusses acoustic dissipation, which contributes to inaccuracies in impedance tube measurements. To improve the accuracy of these measurements, this paper introduces a transfer function model that integrates diverse dissipation prediction models. Bayesian inference is used to estimate the important parameters included in these models, describing dissipation originating from various mechanisms, sound speed, and microphone positions. By using experimental measurements and considering a hypothetical air layer in front of a rigid termination as the material under test, Bayesian parameter estimation allows a substantial enhancement in characterization accuracy by incorporating the dissipation and sound speed estimates. This approach effectively minimizes residual absorption coefficients attributed to both boundary-layer effects and air medium relaxation. The incorporation of dissipation models leads to a substantial reduction (to 1%) in residual absorption coefficients. Moreover, the use of accurately estimated parameters further enhances the accuracy of actual tube measurements of materials using the two-microphone transfer function method.

Non-Adaptive Speed and Position Estimation of Doubly-Fed Induction Generator in Grid-Connected Operations

Non-Adaptive Speed and Position Estimation of Doubly-Fed Induction Generator in Grid-Connected Operations

Machine Learning Estimation of Maximum Vertical Velocity from Radar

Abstract The quantification of storm updrafts remains unavailable for operational forecasting despite their inherent importance to convection and its associated severe weather hazards. Updraft proxies, like overshooting top area from satellite images, have been linked to severe weather hazards but only relate to a limited portion of the total storm updraft. This study investigates if a machine learning model, namely, U-Nets, can skillfully retrieve maximum vertical velocity and its areal extent from three-dimensional gridded radar reflectivity alone. The machine learning model is trained using simulated radar reflectivity and vertical velocity from the National Severe Storm Laboratory’s convection permitting Warn-on-Forecast System (WoFS). A parametric regression technique using the sinh–arcsinh–normal distribution is adapted to run with U-Nets, allowing for both deterministic and probabilistic predictions of maximum vertical velocity. The best models after hyperparameter search provided less than 50% root mean squared error, a coefficient of determination greater than 0.65, and an intersection over union (IoU) of more than 0.45 on the independent test set composed of WoFS data. Beyond the WoFS analysis, a case study was conducted using real radar data and corresponding dual-Doppler analyses of vertical velocity within a supercell. The U-Net consistently underestimates the dual-Doppler updraft speed estimates by 50%. Meanwhile, the area of the 5 and 10 m s−1 updraft cores shows an IoU of 0.25. While the above statistics are not exceptional, the machine learning model enables quick distillation of 3D radar data that is related to the maximum vertical velocity, which could be useful in assessing a storm’s severe potential. Significance Statement All convective storm hazards (tornadoes, hail, heavy rain, straight line winds) can be related to a storm’s updraft. Yet, there is no direct measurement of updraft speed or area available for forecasters to make their warning decisions from. This paper addresses the lack of observational data by providing a machine learning solution that skillfully estimates the maximum updraft speed within storms from only the radar reflectivity 3D structure. After further vetting the machine learning solutions on additional real-world examples, the estimated storm updrafts will hopefully provide forecasters with an added tool to help diagnose a storm’s hazard potential more accurately.

Ultrasound channel attention residual network for medical plane wave echo data-based average sound speed estimation

The average sound speed estimation is crucial for ultrasound imaging quality and diagnostic. In this article, the deep learning techniques were utilized and an innovative Ultrasound Channel Attention Residual Network (UCA-ResNet) was proposed. The UCA-ResNet incorporated a specially designed Ultrasound Channel Attention (UCA) block, which effectively enhanced relevant ultrasound channel features and convolutional channel features. For evaluation, the simulation, phantom, and in vivo experiments were conducted. In the simulation experiment, UCA-ResNet achieved remarkable results, with a mean absolute error (MAE) of 0.40 m/s, root mean square error (RMSE) of 1.25 m/s, standard deviation of error (SDE) of 1.25 m/s, and a one-time estimation time of 3.67 ms. Moreover, the phantom and in vivo experiments further validated the high accuracy and low computational cost of UCA-ResNet. The UCA-ResNet can accurately estimate the average sound speed using a single plane wave echo data while maintaining low computational cost. It has potential in enhancing medical ultrasound imaging quality and providing novel diagnostic insights. (Code and data will be available upon acceptance of the manuscript.)

Robust speed estimation for a moving harmonic acoustic source with a single stationary sensor.

A noise-insensitive cost function was developed for estimating the speed of harmonic acoustic sources in uniform linear motion. This function weighs and integrates the energy distribution of received tones in the time-frequency plane to enhance the robustness of parameter estimation under low signal-to-noise ratio conditions, where weight values are intentionally combined with the law of observed instantaneous frequency. As the cost function is differentiable, the procedure of parameter estimations also has high computing efficiency. Processing data of SWellEx-96 experiments with real ocean noise confirmed the anti-noise capabilities of this cost function to conventional processing methods.

Exploring the potential of smartphone MEMS sensors for cost-effective rotating machinery speed estimation

PurposeThe purpose of this study is to explore the use of smartphone-embedded microelectro-mechanical sensors (MEMS) for accurately estimating rotating machinery speed, crucial for various condition monitoring tasks. Rotating machinery (RM) serves a crucial role in diverse applications, necessitating accurate speed estimation essential for condition monitoring (CM) tasks such as vibration analysis, efficiency evaluation and predictive assessment.Design/methodology/approachThis research explores the utilization of MEMS embedded in smartphones to economically estimate RM speed. A series of experiments were conducted across three test setups, comparing smartphone-based speed estimation to traditional methods. Rigorous testing spanned various dimensions, including scenarios of limited data availability, diverse speed applications and different smartphone placements on RM surfaces.FindingsThe methodology demonstrated exceptional performance across low and high-speed contexts. Smartphones-MEMS accurately estimated speed regardless of their placement on surfaces like metal and fiber, presenting promising outcomes with a mere 6 RPM maximum error. Statistical analysis, using a two-sample t-test, compared smartphone-derived speed outcomes with those from a tachometer and high-quality (HQ) data acquisition system.Research limitations/implicationsThe research limitations include the need for further investigation into smartphone sensor calibration and accuracy in extremely high-speed scenarios. Future research could focus on refining these aspects.Social implicationsThe societal impact is substantial, offering cost-effective CM across various industries and encouraging further exploration of MEMS-based vibration monitoring.Originality/valueThis research showcases an innovative approach using smartphone-embedded MEMS for RM speed estimation. The study’s multidimensional testing highlights its originality in addressing scenarios with limited data and varied speed applications.

Testing the Relationship of Linguistic Complexity to Second Language Learners’ Comparative Judgment on Text Difficulty

AbstractThis study examined the relationship of linguistic complexity, captured using a set of lexical richness, syntactic complexity, and discoursal complexity indices, to second language (L2) learners’ perception of text difficulty, captured using L2 raters’ comparative judgment on text comprehensibility and reading speed. Testing materials were 180 texts abridged from college English coursebooks, and raters were 90 advanced Chinese learners of L2 English. Forty‐five raters read paired texts and determined which text was harder to understand in each pair, and another 45 raters read paired texts and determined which text they read faster in each pair. Two stepwise linear regression models containing lexical, syntactic, and discoursal features explained 48.1% and 54.6% of the variance in L2 learners’ estimates of text comprehensibility and reading speed, respectively, outperforming four commonly used language readability models. These findings contribute useful insights into the relationship between linguistic complexity and L2 learners’ perception of text difficulty.