Speed Estimation Method Research Articles

An Improved Rotor Position and Speed Estimation Method for PMSM with Hall Sensors

An Improved Rotor Position and Speed Estimation Method for PMSM with Hall Sensors

Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter

To compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5MW model. The first method for wind speed estimation involves a three-dimensional (3D) look-up table-based approach, constructed using drive train differential equations. The second method involves applying a continuous–discrete extended Kalman filter. To verify and compare the performance of the algorithms designed using these different methods, feed-forward control algorithms, available power estimation algorithms, and a linear quadratic regulator, based on fuzzy logic (LQRF) control algorithms, were selected and applied as verification means, using the estimated wind speed as the input. Based on the simulation results, the performance of the two methods was compared. The method using drive train differential equations demonstrated superior performance in terms of reductions in the standard deviations of rotor speed and electrical power, as well as in its prediction accuracy for the available power.

Non-invasive swimming speed estimation method based on tail-beat frequency determined from fish length measurement using stereo-cameras

Non-invasive swimming speed estimation method based on tail-beat frequency determined from fish length measurement using stereo-cameras

A Novel Estimating Algorithm of Critical Driving Parameters for Dual-Motor Electric Drive Tracked Vehicles Based on a Nonlinear Observer and an Adaptive Kalman Filter

High-speed dual-motor electric drive tracked vehicles (DDTVs) have emerged as a research hotspot in the field of tracked vehicles in recent years due to their advantages in fuel economy and the scalability of electrical equipment. The emergency braking of a DDTV at high speed can lead to slipping or even yawing (which is caused by a large deviation of forces at each track directly), posing significant challenges to the vehicle’s stability and safety. Therefore, the accurate real-time acquisition of critical driving parameters, such as the longitudinal force and vehicle speed, is crucial for the stability control of a DDTV. This paper developed a novel estimating algorithm of critical driving parameters for DDTVs equipped with conventional sensors such as rotary transformers at PMSMs and onboard accelerometers on the basis of their dynamics models. The algorithm includes a sensor signal preprocessing module, a longitudinal force estimation method based on a nonlinear observer, and a speed estimation method based on an adaptive Kalman filter. Through hardware-in-loop experiments based on a Speedgoat real-time target machine, the proposed algorithm is proven to estimate the longitudinal force of the track and vehicle speed accurately, whether the vehicle has stability control functions or not, providing a foundation for the further development of vehicle stability control algorithms.

A New Order Tracking Method for Fault Diagnosis of Gearbox under Non-Stationary Working Conditions Based on In Situ Gravity Acceleration Decomposition

Rotational speed measuring is important in order tracking under non-stational working conditions. However, sometimes, encoders or coded discs are not easy to mount due to the limited measurement environment. In this paper, a new in situ gravity acceleration decomposition method (GAD) is proposed for rotational speed estimation, and it is applied in the order tracking scene for fault diagnosis of a gearbox under non-stationary working conditions. In the proposed method, a MEMS accelerometer is locally embedded on the rotating shaft or disc in the tangential direction. The time-varying gravity acceleration component is sensed by the in situ accelerometer during the rotation of the shaft or disc. The GAD method is established to exploit the gravity acceleration component based on the linear-phase finite impulse response (FIR) filter and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. Then, the phase signal of time-varying gravity acceleration is derived for rotational speed estimations. A motor–shaft–disc experimental setup is established to verify the correctness and effectiveness of the proposed method in comparison to a mounted encoder. The results show that both the estimated average and instantaneous rotational speed agree well with the mounted encoder. Furthermore, both the proposed GAD method and the traditional vibration-based tacholess speed estimation methods are applied in the context of order tracking for fault diagnosis of a gearbox. The results demonstrate the superiority of the proposed method in the detection of tooth spalling faults under non-stationary working conditions.

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.

Бездатчиковая система управления высокочастотным синхронным двигателем с инкорпорированными магнитами на основе комбинированного способа оценки положения

The known methods to estimate the position in drives based on synchronous motors with incorporated magnets have significant limitations in accuracy and noise immunity at high frequencies of the main voltage harmonic. Due to the requirements to improve the weight, size, and cost indicators of electric drives, it is relevant to design a sensorless synchronous drive operating with frequencies from 0 to 1000 Hz and more in the presence of a wide 5…10:1 speed control zone with a constant nominal power. The results of the study are based on the analysis of theoretical and experimental data obtained by other authors, as well as computer modeling in the Delphi software environment. The initial information for the simulation is obtained from the technical description guide and data of a full-scale experiment on a real-life object. A vector control system has been developed for a high-frequency synchronous motor with incorporated magnets without a mechanical coordinate sensor based on a combination of speed and position estimation methods by means of high-frequency injection and EMF calculation. The study of the developed system is carried out by means of modeling, considering the influence of interference in the measuring channels and the effect of cross-saturation of the magnetic circuit. The proposed structure and control algorithms have provided operability and high-quality control indicators sufficient to design a traction drive with a maximum frequency of 800 Hz or more and an area of operation in a constant rated power mode of at least 8:1 with a signal-to-noise ratio characteristic of typical current sensors.

Expendable Conductivity–Temperature–Depth-Assisted Fast Underwater Sound Speed Estimation by Convolutional Neural Network with Reduced Fully Connected Layers

Obtaining accurate sound speed profiles (SSPs) in near-real-time is of great significance for ocean exploration, underwater communication and improving the performance of sonar systems. In response to the problem that traditional sound speed estimation methods cannot obtain real-time sound speed distribution or rely too much on sonar observation data, we propose an SSP estimation method based on a convolutional neural network with reduced fully connected layers (RFC-CNN) in this paper. This method utilizes neural networks to extract the complex nonlinear features of various types of data. With the help of the historical SSPs and shallow seawater sound speed and temperature data obtained by expendable conductivity–temperature–depth probes (XCTDs), a more accurate estimation of the regional sound speed distribution can be realized quickly. This approach can save the observation cost and significantly improve the real-time performance of SSP estimation.

A fast method for robust estimation of gearbox instantaneous speed

Under non-stationary condition, estimating the instantaneous frequency (IF) of shaft from vibration signal is an ideal way for gearbox fault monitoring without tachometer. Among them, the method based on harmonic phase demodulation has always occupied a dominant position. In the low signal-to-noise ratio (SNR) condition, the noisy harmonic components may cause greater IF estimation error. In this work, an improved approach for estimating the IF of gearbox reference shaft is proposed based on the short time Fourier transform spectrogram. The theoretical derivation proves that the high-order meshing harmonic can reduce the IF estimation error of gearbox reference shaft. By constructing the spectrogram weight factor, an improved maximum tracking algorithm is proposed to redefine the IF estimation range. On this basis, the robust IF estimation of high-order meshing harmonic with low SNR is realized. Finally, it is equivalent to the estimated IF of gearbox reference shaft. The proposed strategy is compared with the two advanced IF estimation methods. The civil aircraft engine dataset and the wind turbine gearbox dataset provided in the Safran contest demonstrate that the proposed strategy works. In the case of heavy load and large speed change, the proposed method has extremely low consumption time while maintains good estimation accuracy.

Fault feature extraction and diagnosis method for gearbox under variable operating conditions

In this paper, a fault feature extraction method based on the combination of computational order tracking (COT) and variational mode decomposition (VMD) is proposed to solve the problem of fault feature extraction in a gearbox under variable speed conditions. First, a speed estimation method based on a forward-backward greedy algorithm and Fourier fitting is proposed to solve the speed estimation problem under variable speed conditions. Then, a feature extraction method based on COT, VMD, and fast kurtometry is proposed. COT is used to calculate the order. After the signal is decomposed by VMD, a fast kurtogram is used to filter the modal components with obvious fault features so as to extract fault features. Finally, combined with the analysis of actual equipment examples, the experimental results show that steady-state filtering can effectively filter the background noise and improve the signal-to-noise ratio. The rotation speed estimated by the forward–backward greedy algorithm and Fourier fitting is very close to the actual speed, which verifies that the method proposed in this paper can effectively solve the problem of fault feature extraction of a gearbox under variable speed conditions.

Accurate and Efficient Instantaneous Angular Speed Estimation Method for Rolling Bearing Under Time-Varying Speed

Accurate and Efficient Instantaneous Angular Speed Estimation Method for Rolling Bearing Under Time-Varying Speed

Analysis and simulation of a novel speed estimation method based on oversampling and noise shaping techniques

Angle measurement is widely used in the servo positioning and speed control of electrical drives, where optical encoders are employed as sensors and coupled to microcontrollers with complex peripherals to provide effective position and speed measurement methods. These methods can achieve high precision and fast response, but are not particularly efficient with computer resources and require high computational costs. Therefore, fast timers, encoder signal oversampling, and a complex post-processing algorithm are used together in a recent research work to achieve a high-precision speed estimation method. Our work will prove that encoder outputs share the oversampling and noise shaping properties typical of sigma-delta modulated signals if they are sampled regularly. Based on this, a new simple and accurate speed estimation method is proposed. Here, encoder outputs are synchronously sampled, differenced, and low-pass filtered. As a result, the speed estimation circuit and its interface with the control unit are greatly simplified, while high accuracy can be achieved. A detailed mathematical analysis of the proposed technique is also performed, and the results obtained are validated by means of a simulation environment using Matlab&Simulink.

Vehicle Speed and Position Estimation considering Microscopic Heterogeneous Car-Following Characteristics in Connected Vehicle Environments

This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates.

VEHICLE TRACKING AND SPEED ESTIMATION FROM UNMANNED AERIAL VEHICLES USING SEGMENTATION-INITIALISED TRACKERS

Abstract. We propose an effective vehicle tracker and speed estimation method from Unmanned Aerial Vehicles (UAVs) videos that can be deployed on UAV-embedded edge devices. Our tracker uses segmentation-derived vehicle regions to initialise a MOSSE tracker. This enables road operators to make multipurpose use of segmentation outputs while still being able to track the vehicles across frames. The vehicle speed is estimated using flight parameters derived from the UAV's flight computer and the vehicle displacement across frames. We trained CABiNet on the UAVid urban segmentation benchmark dataset and finetuned it on a dataset collected at our study site. A mean Intersection over Union (mIoU) of 0.73 was obtained for the vehicle class. Our segmentation-initialised MOSSE tracker was evaluated on the VisDrone Multi-Object Tracking (MOT) benchmark dataset and compared against traditional methods that utilise object regions for tracker initialisation. Our approach yielded a Multi-Object Tracking Precision (MOTP) of 0.872 compared to 0.830 when using YOLOv4. Our vehicle speed estimations approach was evaluated using a privately collected ground truth vehicle speed dataset. Our approach yielded a Root Mean Square Error (RMSE) between 3.42 and 16.12 km/hr across different flight configurations. Finally, our approach was deployed on an NVIDIA Jetson Xavier NX edge device and could be executed at 8 Frames Per Second (FPS). The results indicate that our approach is a simple yet fast alternative to traditional tracking methods while producing multipurpose segmentation information.

Sensorless HSPMSM Control of an Improved SMC and EKF Based on Immune PSO

Here, for controlling a high-speed flywheel permanent magnet synchronous motor (HSPMSM), a position sensorless control method for estimation of motor rotor position and speed is proposed to address the problems faced by mechanical position sensors of high cost, large size, and poor interference immunity. The extended Kalman filter (EKF) has difficulty obtaining the optimal covariance matrix when performing state estimation. Therefore, the particle swarm algorithm (PSO) with an immune mechanism is used to optimize the covariance matrix of the EKF. However, the EKF algorithm makes the system less robust due to its delay effect. Based on the traditional sliding mode control rate, the exponential convergence law is improved, and the continuous function sat(s) is used instead of the symbolic function sgn(s). This improves the convergence law and proves the asymptotic stability of the designed sliding mode variable structure controller based on Lyapunov’s stability theorem. Then, the novel control law is applied to the sliding mode surface (SMS). An ordinary sliding mode controller (OSMC) using a linear sliding mode controller (LSMC), a global sliding mode controller (GSMC) using a global sliding mode surface (GSMS), and an integral sliding mode controller (ISMC) using an integral sliding mode surface (ISMS) are designed for improving control. Joint simulation in MATLAB and Simulink verifies that the optimized EKF based on the immune PSO can improve precision and accuracy for controlling the electronic rotor position and speed. Comparing the new sliding mode controller with a traditional PI controller reveals that the proposed system has stronger resistance to load disturbance and better robustness.

Estimating omnidirectional urban vertical wind speed with direction-dependent building morphologies

A main external factor that limits the energy assessment of high-rise buildings in urban areas is the vertical wind conditions. Conventional buildings' energy assessment tools apply a universal vertical wind profile estimation equation in all directions and only consider four typical terrain types. However, the various shape and configurations of buildings in cities can induce the unique wind speed in each direction, and the vertical wind speeds in the same terrain type can be different even in the same city. To get more realistic vertical wind conditions, this study developed an omnidirectional urban vertical wind speed estimation method with direction-dependent building morphologies. A pie-shaped segment method is applied to calculate building morphological features in approaching wind direction. Machine learning methods are developed to calculate parameters of the modified direction-dependent power law urban wind function to convert the Typical Meteorological Year (TMY) wind speed to urban vertical wind speeds. To validate the proposed method, wind tunnel data in Hong Kong are collected for a case study. The results indicate that the proposed method with morphological inputs can effectively estimate the vertical wind speed at different wind directions and heights, and deep neural networks and support vector regression have better performance.

A novel algorithm for robust estimation of ants' speed on convoluted trajectories derived from their gait pattern

AbstractAccurate measurements of travel distance and the corresponding speed are crucial for the analysis of animal movements. Particularly, the trajectories of ants were used in numerous behavioral studies. However, measurements of travel distance involve the dilemma of setting the proper time window: Estimates from a short time window are vulnerable to spatial errors in observation, while estimates from a long time window lead to an underestimation of the travel distance. To overcome these difficulties, we propose a novel algorithm that successively interpolates two consecutive points of an ant's trajectory for a given time window by embracing the alternating tripod gait and other gait patterns of ants. We demonstrate that this algorithm is more reliable compared with the conventional method of travel distance estimation based on the sum of the consecutive straight‐line displacement (SLD). After obtaining speed estimates for a range of sampling time windows, we applied a fitting method that can estimate the actual speed without prior knowledge of spatial error distribution. We compared results from several methods of speed estimation extracted from the empirical and simulated data of ant trajectories. The accuracy of our algorithm was comparable with or much higher than the accuracy of the sum over the consecutive SLD with optimal window length. Hence, subjective selection of the sampling time window can be avoided by using the proposed algorithm. We provide software that enables empirical scientists to utilize the proposed methods rather than the conventional SLD method of distance and speed estimation.

Mag-ODO: Motion speed estimation for indoor robots based on dual magnetometers

In indoor environments, accurate speed estimation is crucial for providing continuous and reliable position of mobile robots. However, conventional odometry methods may suffer from performance degradation, particularly in scenarios involving wheel odometry slipping or visual odometry blurring. In this study, we propose Mag-ODO, a novel motion speed estimation method based on dual magnetometers aimed at enhancing the robustness of indoor robot positioning systems. The dual magnetometers are mounted at the front and back of the robot, and the speed is estimated by matching the magnetic-filed waveforms sampled from the magnetometers. We employ the dynamic time warping (DTW) algorithm to implement waveform matching and use both the magnetic field strengths and the changing trend of the magnetic field strengths as the matching cost function, effectively reducing the matching error. Mag-ODO has two key advantages: immunity to magnetometer bias and transient disappearance of magnetic field gradients within a matching time window does not affect speed estimation accuracy. Test results show that Mag-ODO performs similarly to Wheel-ODO in magnetic-rich environments (RMSE < 0.06 m/s) and comparable dead reckoning (DR) performance with inertial navigation systems (INS) in both straight and curved environments.

Tacholess Instantaneous Speed Estimation Considering the Characteristics of Vibration Harmonics

Knowledge of instantaneous shaft speed is vital for non- stationary condition monitoring of rotating machinery in real applications. To avoid installing expensive and inconvenient encoders, many researchers have developed instantaneous speed estimation methods by extracting the shaft speed from vibration signals. However, previous methods show limitations due to challenges in vibration signals. Therefore, we propose a novel instantaneous speed estimation method considering the characteristics of vibration harmonics to overcome the difficulties. Multiple harmonic components and their characteristics are utilized to obtain an accurate ridge in the time-frequency representation (TFR). The proposed method is validated and compared with the previous methods using a gear vibration simulated signal and civil aircraft engine dataset. The results show the accuracy and robustness of the proposed method.

MVCV-Traffic: multiview road traffic state estimation via cross-view learning

Fine-grained urban traffic data are often incomplete owing to limitations in sensor technology and economic cost. However, data-driven traffic analysis methods in intelligent transportation systems (ITSs) heavily rely on the quality of input data. Thus, accurately estimating missing traffic observations is an essential data engineering task in ITSs. The complexity of underlying node-wise correlation structures and various missing scenarios presents a significant challenge in achieving high-precision estimation. This study proposes a novel multiview neural network termed MVCV-Traffic, equipped with a cross-view learning mechanism, to improve traffic estimation. The contributions of this model can be summarized into two parts: multiview learning and cross-view fusing. For multiview learning, several specialized neural networks are adopted to fit diverse correlation structures from different views. For cross-view fusing, a new information fusion strategy merges multiview messages at both feature and output levels to enhance the learning of joint correlations. Experiments on two real-world datasets demonstrate that the proposed model significantly outperforms existing traffic speed estimation methods for different types and rates of missing data.