Speed Monitoring Research Articles

Monitoring and speed control of AC motor using PWM technique

This study focuses on how to monitor and regulate the speed of an AC motor using pulse width modulation (PWM) technology. PWM signals regulate motor voltage and enable continuous monitoring of voltage, current, and speed in addition to speed control. Comparing this technology to conventional techniques yields considerable advantages like enhanced power and speed control. PWM-based speed control can be implemented using circuits specifically designed for motor control or microcontrollers. It has been confirmed that PWM-based control can regulate the target motor under a variety of operating conditions and that it is reliable and efficient. To boost production and efficiency, this change management technique can be applied in a variety of industries, including robots, HVAC systems, and industrial automation. The study results show the significance of PWM technology for monitoring and controlling the speed of AC motors, providing productive and affordable solutions to a range of enterprises and sectors.

Turbulence correlation between moving trains and anemometer towers: Theoretical analysis, field measurements and simulation

A precise command of railway operations according to the measured instantaneous wind speed on an anemometer tower along a railway line is the development trend, whose challenges lie in the unknown transfer relation(s) between wind speed fluctuations on a moving train and an anemometer tower, i.e. the turbulence correlation between them. To address this issue, in the current work, the cross-correlation functions of wind speed fluctuations at the moving train and anemometer tower are derived, and an empirical formula of the coherence functions is obtained. The turbulence correlation is inversely related to the separation distance from the anemometer tower to the line, and there is little correlation when this distance is longer than double the longitudinal turbulence length scale. Field measurements of wind characteristics were carried out on an anemometer tower and a moving vehicle, and the turbulence correlation and its expression were validated. Three methods are proposed and compared to evaluate the instantaneous wind speed at the anemometer tower and moving train with this correlation. The methods based on the cross-spectral density and coherence function can accurately simulate the correlation, and the latter performance is slightly better (its simulation of the frequency domain correlation is 52.9% better than the former); the method based on solely independent and identically distributed random phases cannot fully simulate the correlation. From this, the effects of the correlation on train operations are studied and analysed in detail. Our analysis shows that neglecting this correlation leads to conservative estimates: wind speed differences between the anemometer tower and the moving train are at least 18.1% greater, and the safety and economic assessments of train operations in crosswinds are underestimated by at least 32.0%. Considering the correlation can reduce the (excess) safety risk/margin and is an inevitable development of adapting to the detailed assessment of the crosswind stability of vehicles. The quantitative description and simulation of the correlation presented in this work point to the critical importance of wind speed monitoring systems for the detailed crosswind assessment, and provide a theoretical basis for further research work on the crosswind stability of vehicles under true/realistic turbulent flow wind.

Nonlinear model predictive control with set terminal constraint for safe robot motion planning via speed and separation monitoring

This paper proposes a methodology for safely planning the motion of a robot manipulator sharing its workspace with a human operator. The motion of the robot is continuously re-planned via nonlinear model predictive control (NMPC), imposing the so-called speed and separation monitoring (SSM) condition to guarantee human safety. Contrary to previous works in the field, the NMPC algorithm is designed with an ellipsoidal terminal constraint, to enlarge the domain of attraction compared to the case in which a point terminal constraint was imposed. This is a very important aspect in real-world applications, allowing the robot to plan its motion from initial configurations that are relatively far from the goal point. Theoretical results are proved on recursive feasibility and closed-loop stability for both cases of NMPC with point and set terminal constraints, under the simplifying assumption of a static human. The effectiveness of the proposed approach is verified via numerical evaluation of the domain of attraction and with experiments on a UR5 manipulator.

Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring

This paper reviews the potential of low-power wireless networks to improve road safety. The authors characterized this type of network and its application in road transport. They also presented the available technologies, highlighting one that was considered the most promising for transport applications. The study includes an innovative and proprietary concept of area-based vehicle speed monitoring using this technology and describes its potential for enhancing road safety. Assumptions and a model for the deployment of network equipment within the planned implementation area were developed. Using radio coverage planning software, the authors conducted a series of simulations to assess the radio coverage of the proposed solution. The results were used to evaluate the feasibility of deployment and to select system operating parameters. It was also noted that the proposed solution could be applied to traffic monitoring. The main objective of this paper is to present a new solution for improving road safety and to assess its feasibility for practical implementation. To achieve this, the authors conducted and presented the results of a series of simulations using radio coverage planning software. The key contribution of this research is the authors′ proposal to implement simultaneous vehicle speed control across the entire monitored area, rather than limiting it to specific, designated points. The simulation results, primarily related to the deployment and selection of operating parameters for wireless sensor network devices, as well as the type and height of antenna placement, suggest that the practical implementation of the proposed solution is feasible. This approach has the potential to significantly improve road safety and alter drivers′ perceptions of speed control. Additionally, the positive outcomes of the research could serve as a foundation for changing the selection of speed control sites, focusing on areas with the highest road safety risk at any given time.

A Calculation Method of Bearing Balls Rotational Vectors Based on Binocular Vision Three-Dimensional Coordinates Measurement

The rotational speed vectors of the bearing balls affect their service life and running performance. Observing the actual rotational speed of the ball is a prerequisite for revealing its true motion law and conducting sliding behavior simulation analysis. To address the need for accuracy and real-time measurement of spin angular velocity, which is also under high-frequency and high-speed ball motion conditions, a new measurement method of ball rotation vectors based on a binocular vision system is proposed. Firstly, marker points are laid on the balls, and their three-dimensional (3D) coordinates in the camera coordinate system are calculated in real time using the triangulation principle. Secondly, based on the 3D coordinates before and after the movement of the marker point and the trajectory of the ball, the mathematical model of the spin motion of the ball was established. Finally, based on the ball spin motion model, the three-dimensional vision measurement technology was first applied to the measurement of the bearing ball rotation vector through formula derivation, achieving the analysis of bearing ball rolling and sliding characteristics. Experimental results demonstrate that the visual measurement system with the frame rate of 100 FPS (frames per second) yields a measurement error within ±0.2% over a speed range from 5 to 50 RPM (revolutions per minute), and the maximum measurement errors of spin angular velocity and linear velocity are 0.25°/s and 0.028 mm/s, respectively. The experimental results show that this method has good accuracy and stability in measuring the rotation vector of the ball, providing a reference for bearing balls' rotational speed monitoring and the analysis of the sliding behavior of bearing balls.

Non-contact measurement of conveyor belt speed based on fast point cloud registration of feature block

Abstract Non-contact, real-time measurement of conveyor belt speed is critical for energy-saving speed regulation and efficient development of coal mine conveyor systems. Existing speed measurement technologies for conveyor systems are often limited by the slippage and wear in contact measurement and complex environmental disturbance. This study introduces three-dimensional point cloud technology into coal flow information detection and innovatively presents a non-contact measurement method of conveyor belt speed based on fast point cloud registration of feature blocks. In the proposed method, a three-dimensional camera is used to capture point cloud data of the dynamically running conveyor belt, and the raw point cloud is preprocessed and segmented into blocks. Then, the C-MANV (curvature and mean angle of normal vectors) features of block point clouds are constructed based on the point cloud neighborhood curvature and the mean angle of neighborhood normal vectors. Finally, an improved block point cloud registration method based on C-MANV features is adopted to achieve the accurate measurement of the dynamic running speed of conveyor belt. Experimental results demonstrate that the proposed method achieves an average relative error of less than 1.8% in high-speed conveyor belt operation with an average processing time of less than 35 ms, which fulfils the accuracy and real-time requirements for conveyor belt speed detection in coal mines. This study provides an effective technical solution for the speed monitoring of coal mine conveyor systems.

SENSORLESS SPEED ESTIMATION OF THREE PHASE INDUCTION MOTORS BASED ON DYNAMIC MODEL

In speed control systems of induction motor electrical drives, real-time speed monitoring is necessary. Speed monitoring can be done using the direct method, which uses a mechanical sensor mounted on the motor shaft, or the indirect method, which is based on estimation, mainly from the dynamic model of the motor. Speed estimation based on the dynamic model of the motor in orthogonal coordinates is the most widespread method in sensorless speed control systems of three-phase squirrel cage induction motor electrical drives, especially those of high accuracy. This paper presents the open-loop speed estimator used for speed estimation in three-phase induction motors. The proposed speed estimators are based on the orthogonal coordinate’s dynamic model of an induction motor in a stator reference frame. This technical solution is simple and has a low cost. The currents and voltages of the two motor phases are the input variables of the estimator, while the output variable is the induction motor speed. The speed estimator model is built using LabVIEW software. The dynamics and accuracy of the estimator proposed in this paper have been tested experimentally. The speed measured by the industrial incremental encoder is compared with that of a speed estimator modeled in LabVIEW software. The obtained experimental results show a good match between the measured and estimated speeds under the step torque load changes of the induction motor.

Diesel Engine Turbocharger Monitoring by Processing Accelerometric Signals through Empirical Mode Decomposition and Independent Component Analysis

In this study, a method for the monitoring of internal combustion engine operation by vibration signals is proposed. The work falls within the context of the increasingly stringent standards relating to the environmental impact of engines and the development of monitoring and control techniques to ensure increased engine performance as well as fuel saving and reduction of pollutant emissions. Experimentation was performed on a turbocharged light-duty compression ignition direct-injection engine. Two monoaxial accelerometers were installed on the engine compressor case, the speed of which has been demonstrated to be closely related to the engine operation. Vibration measurements of the engine compressor case have been processed by combining the Empirical Mode Decomposition technique with Independent Component Analysis and Short Time Fourier Transform to indirectly estimate the turbocharger speed. The obtained traces have been compared to the direct turbocharger velocity measures during the stationary running of the engine (speed and load conditions varied in the complete engine’s range of operation). The results point out the potentiality of the methodology in algorithms devoted to identifying modifications of the combustion development regarding regular operation via indirect turbocharger speed monitoring.

Wireless Online Rotation Monitoring System for UAV Motors Based on a Soft-Contact Triboelectric Nanogenerator.

Real-time monitoring of UAV motor speed is crucial for enhancing control performance and ensuring flight safety. However, this task faces challenges such as difficult sensor installation and high costs. This study introduces a wireless rotational speed sensing system based on a UAV-rotary triboelectric nanogenerator (UR-TENG). By employing a carefully designed structure with soft contact and a freestanding-triboelectric-layer mode, UR-TENG exhibits characteristics like low friction, affordability, ease of production, and self-powering capability. This eliminates the need for an external power source and addresses the complexity of installation in the limited space of UAVs. Experimental findings demonstrate that UR-TENG possesses high sensitivity and stability, maintaining the structural integrity of the UAV. The goodness of fit is notably high at 0.99959, with a maximum error rate of only 0.014 within a range of 6270 rpm. Moreover, UR-TENG integrates with a microcontroller unit (MCU) and external circuitry to form a monitoring system. This system transmits electrical signals to a PC via a Wi-Fi module, facilitating real-time rotational speed sensing and anomaly detection. Finally, a practical application demonstration on a UAV validates the adaptability of UR-TENG to complex operational environments. This study presents a promising approach for online rotation monitoring of UAV motors, with potential for commercialization, and introduces new avenues for TENG application in UAV technology.

AI support for colonoscopy quality control using CNN and transformer architectures

BackgroundConstruct deep learning models for colonoscopy quality control using different architectures and explore their decision-making mechanisms.MethodsA total of 4,189 colonoscopy images were collected from two medical centers, covering different levels of bowel cleanliness, the presence of polyps, and the cecum. Using these data, eight pre-trained models based on CNN and Transformer architectures underwent transfer learning and fine-tuning. The models’ performance was evaluated using metrics such as AUC, Precision, and F1 score. Perceptual hash functions were employed to detect image changes, enabling real-time monitoring of colonoscopy withdrawal speed. Model interpretability was analyzed using techniques such as Grad-CAM and SHAP. Finally, the best-performing model was converted to ONNX format and deployed on device terminals.ResultsThe EfficientNetB2 model outperformed other architectures on the validation set, achieving an accuracy of 0.992. It surpassed models based on other CNN and Transformer architectures. The model’s precision, recall, and F1 score were 0.991, 0.989, and 0.990, respectively. On the test set, the EfficientNetB2 model achieved an average AUC of 0.996, with a precision of 0.948 and a recall of 0.952. Interpretability analysis showed the specific image regions the model used for decision-making. The model was converted to ONNX format and deployed on device terminals, achieving an average inference speed of over 60 frames per second.ConclusionsThe AI-assisted quality system, based on the EfficientNetB2 model, integrates four key quality control indicators for colonoscopy. This integration enables medical institutions to comprehensively manage and enhance these indicators using a single model, showcasing promising potential for clinical applications.

Improved spectral amplitude modulation for tacholess estimation of rotation speed

Rotation speed is key information for rotating machinery, whether it’s a negative feedback control method based on speed or equipment status monitoring and fault diagnosis. In order to accurately and efficiently estimate the rotation speed under tacholess condition, a novel method named improved spectral amplitude modulation (ISAM) is proposed in this paper. In ISAM, the vibration signal is first filtered through a band-pass filter to eliminate interference from irrelevant frequency components. Subsequently, an appropriate weight, which modifies the amplitude spectrum of the time–frequency representation (TFR) to decompose different components based on their powers, is selected to enhance the instantaneous frequency (IF) of rotation speed. The modified signal is then reconstructed based on the edited amplitude and original phase. Finally, the IF can be extracted from the TFR of the modified signal with the multiple time–frequency curve extraction (MTFCE) technique. Additionally, entropy is utilized to guide the selection of weight. The effectiveness of the proposed method is evaluated through simulation analysis and case studies. The results show that the ISAM can accurately estimate rotation speed, with the mean absolute error (MAE) at 0.13. Compared to an existing IF estimation method, ISAM can reduce computation time by 95%.

Stable 3D Deep Convolutional Autoencoder Method for Ultrasonic Testing of Defects in Polymer Composites.

Ultrasonic testing is widely used for defect detection in polymer composites owing to advantages such as fast processing speed, simple operation, high reliability, and real-time monitoring. However, defect information in ultrasound images is not easily detectable because of the influence of ultrasound echoes and noise. In this study, a stable three-dimensional deep convolutional autoencoder (3D-DCA) was developed to identify defects in polymer composites. Through 3D convolutional operations, it can synchronously learn the spatiotemporal properties of the data volume. Subsequently, the depth receptive field (RF) of the hidden layer in the autoencoder maps the defect information to the original depth location, thereby mitigating the effects of the defect surface and bottom echoes. In addition, a dual-layer encoder was designed to improve the hidden layer visualization results. Consequently, the size, shape, and depth of the defects can be accurately determined. The feasibility of the method was demonstrated through its application to defect detection in carbon-fiber-reinforced polymers.

Research on fault detection and remote monitoring system of variable speed constant frequency wind turbine based on Internet of Things

In order to study the operating characteristics of variable speed constant frequency wind turbine under different working conditions and the monitoring system of wind turbine. In this paper, the simulation model of each component system of wind turbine is established by MATLAB/Simulink module, and the influence law of different wind speed and ground fault types on the output power of wind turbine is studied. The active power of wind turbines under different short-circuit fault types is compared. At the same time, in order to realize real-time monitoring of wind turbine speed and output power, an online monitoring system for wind turbine operation based on industrial Internet of Things is proposed, and the composition and operation characteristics of this remote monitoring system are given. The practical application shows that the on-line monitoring system can accurately and remotely monitor the running status of the wind turbine and avoid the unstable running of the wind turbine. The research conclusions of this paper can provide reference for the design and construction of wind turbines and the operation of connecting to the power grid.

Smart traffic management of vehicles using faster R-CNN based deep learning method

With constant growth of civilization and modernization of cities all across the world since past few centuries smart traffic management of vehicles is one of the most sorted after problem by research community. Smart traffic management basically involves segmentation of vehicles, estimation of traffic density and tracking of vehicles. The vehicle segmentation from videos helps realization of niche applications such as monitoring of speed and estimation of traffic. When occlusions, background with clutters and traffic with density variations, this problem becomes more intractable in nature. Keeping this motivation in this research work, we investigate Faster R-CNN based deep learning method towards segmentation of vehicles. This problem is addressed in four steps viz minimization with adaptive background model, Faster R-CNN based subnet operation, Faster R-CNN initial refinement and result optimization with extended topological active nets. The computational framework uses adaptive background modeling. It also addresses shadow and illumination issues. Higher segmentation accuracy is achieved through topological active net deformable models. The topological and extended topological active nets help to achieve stated deformations. Mesh deformation is achieved with minimization of energy. The segmentation accuracy is improved with modified version of extended topological active net. The experimental results demonstrate superiority of this framework with respect to other methods.

Prolonged Blue Light Exposure Does Not Elicit Adverse Changes to Physical Activity in Male Mice

Color plays a role in human and animal circadian rhythm patterns, behaviors, and emotional responses. Environments containing blue colors improve information storage in humans and reproductive success in animals; however, environments containing blue light influence behavioral and emotional responses in rodents and humans in adverse ways. Exposure to blue light alters sleeping patterns, induces the stress response, and elicits depressive behaviors. These initial responses then affect awake time functionality and productivity. Under normal physiological conditions, mice, when provided access to an in-cage running wheel, exhibit high levels of nocturnal physical activity behavior. It is unknown if this nocturnal behavior changes after prolonged blue light exposure. Therefore, this project's hypothesis is wheel running utilization decreases after exposure to blue light during the diurnal restful period in the normally nocturnal mouse. The purpose of this study was to expose mice to overhead blue LED lights during the daytime rest period and to examine nighttime physical activity patterns. Daily wheel running distance, duration, and speed monitoring in C57BL/6j male mice (n=12) occurred for 21 days. During the second week of the study, an experimental group (n=6) underwent blue light-emitting diode (LED) light exposure, and a control group (n=6) experienced white LED light exposure at the same intensity. The lights were on a 12-hour light/dark cycle at full brightness during the exposure period. Differences in wheel running distance, duration, and speed following exposure to blue LED light were assessed via individual two-way (group by phase of study) ANOVAs. Blue LED light exposure did not appear to affect average daily wheel running distance [F=0.42, p=0.57], duration [F=0.79, p=0.42], or speed [F=0.08, p=0.87]. Following exposure to blue LED light during the restful diurnal period, wheel running remained stable. This lack of effect indicates that mechanisms may exist to protect mice from the adverse effects associated with blue light exposure, especially as it pertains to participating in routine physical activity. These postulated mechanisms may allow robust participation in physical activity-related behaviors in these animals and represent a potential biological regulator that could aid in mitigating the adverse effects of blue light exposure experienced in humans. All funding for this research was provided by the Union University Department of Biology. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Experimental Study on the Distribution Law of Wind Speed in Rectangular Section Tunnels of Coal Mines

Ventilation is the cornerstone of coal mine safety production, and accurate monitoring of ventilation parameters is the fundamental guarantee for ventilation technology decision-making. A calculation model for the average wind speed line position in rectangular section tunnels of coal mines was established based on the Boussinesk theory and Prandtl turbulence theory to address the issue of difficulty in meeting decision-making needs in wind speed monitoring accuracy. The airflow field of rectangular section tunnels under different wind speed conditions was measured at the coal mine site, and the average position of the measured average wind speed line was obtained. The comparison and verification with the theoretical calculation results were carried out, with an absolute error of less than 5%.

Study of the necessity of a speed monitoring display at the returning chicane on a freeway bypass

Objective Roadwork zones represent a hazardous place within the highway system, with higher crash rates and injury severity, often due to excessive speed and noncompliance with speed limits. On freeways, a common layout is to close a full roadway and to divert traffic onto the opposite roadway, with one lane closed to accommodate the redirected traffic, by driving through the median strip. In this scenario, the second chicane can be a hazardous point if it is not correctly signaled. This paper examines the need to install a speed monitoring display (SMD) before the returning chicane on a bypass. Methods A two-phase study was conducted on a Spanish freeway where a roadway was temporarily closed. Two measurement points were established on the bypass, one in the middle and one at the end, prior to the return chicane through the median strip. During Phase 1, the portable SMD was installed and during Phase 2, it was removed. The average and the 85th percentile of the speed distribution at each point during both phases were compared. Additionally, mean difference tests were conducted and a speed prediction model was developed. Results With the SMD, drivers reduced their speed from the midpoint to the returning chicane, within the range of 7-10 km/h. Conversely, upon removal of the SMD, motorists increased their speed while driving through the bypass, resulting in excessive speed at the most hazardous point, the chicane leading back to the original roadway. The difference in mean speed between the two phases was 18 km/h at the returning chicane. Conclusions In addition to the traffic calming measures implemented prior to entering roadwork zones on freeways, which are conveniently established in the standards; it is necessary to evaluate potentially dangerous areas of the layout and implement additional measures where required. Specifically, in the case of final chicanes of bypasses with reduced radii, it is recommended that a speed monitoring display be installed as a mandatory element in order to inform drivers of this challenging segment. Highway administrations around the world should maintain a SMD at the returning chicane of a bypass while roadworks last.

A Compact-Sized Fully Self-Powered Wireless Flowmeter Based on Triboelectric Discharge.

Flow sensing exhibits significant potential for monitoring, controlling, and optimizing processes in industries, resource management, and environmental protection. However, achieving wireless real-time and omnidirectional sensing of gas/liquid flow on a simple, self-contained device without external power support has remained a formidable challenge. In this study, a compact-sized, fully self-powered wireless sensing flowmeter (CSWF) is introduced with a small size diameter of down to less than 50mm, which can transmit real-time and omnidirectional wireless signals, as driven by a rotating triboelectric nanogenerator (R-TENG). The R-TENG triggers the breakdown discharge of a gas discharge tube (GDT), which enables flow rate wireless sensing through emitted electromagnetic waves. Importantly, the performance of the CSWF is not affected by the R-TENG's varied output, while the transmission distance is greater than 10m. Real-time wireless remote monitoring of wind speed and water flow rate is successfully demonstrated. This research introduces an approach to achieve a wireless, self-powered environmental monitoring system with a diverse range of potential applications, including prolonged meteorological observations, marine environment monitoring, early warning systems for natural disasters, and remote ecosystem monitoring.

Research on Road Geological Hazard Risk Assessment Model Based on Computational Deep Learning Algorithm Integrated With GIS

This paper studies the geological disaster emergency command and management system based on geographic information system. This system is for the province, city, county land resources and other aspects of the comprehensive application, the realization of real-time information display, meteorological early warning, group measurement and mass prevention, monitoring and early warning, major disaster management, prevention plan, quick report processing, emergency investigation, emergency disposal, emergency consultation and other functions. Through the GSM network, the monitoring data is sent to the monitoring center through the mobile phone, and then the monitoring center processes, stores, displays and alarms the monitoring data. Finally, B/S structure is used to build a remote Web data service publishing platform, so that it can carry out real-time monitoring at any place, and improve the speed, efficiency and intuition of high-speed slope disaster. It is proved that the method has the advantages of stable operation, accurate data, fast transmission speed and high monitoring accuracy through the practical application of a highway project foundation pit slope monitoring.

Electrochemical Deposition and Etching of Quasi-Two-Dimensional Periodic Membrane Structure.

In this paper, two experimental procedures are reported, namely electro-deposition in the ultrathin liquid layer and chemical micro-etching. Firstly, a large area quasi-two-dimensional periodic membrane with adjustable density is deposited on a Si substrate driven by half-sinusoidal voltage, which is composed of raised ridges and a membrane between the ridges. The smaller the voltage frequency is, the larger the ridge distance is. The height of a raised ridge changes synchronously with the amplitude. The grain density distribution of membrane and raised ridge is uneven; the two structures change alternately, which is closely related to the change of growth voltage and copper ion concentration during deposition. The structural characteristics of membrane provide favorable conditions for micro-etching; stable etching speed and microscope real-time monitoring are the keys to achieve accurate etching. In the chemical micro-etching process, the membrane between ridges is removed, retaining the raised ridges, thus a large scale ordered micro-nano wires array with lateral growth was obtained. This method is simple and controllable, can be applied to a variety of substrates, and is the best choice for designing and preparing new functional materials. This experiment provides a basis for the extension of this method.