Position Sensor Research Articles

Fault diagnosis and identification of rotating machinery based on one-dimensional convolutional neural network

The paper focuses on two kinds of rotating machinery, miniature table drilling machine and automobile engine, as the research object. Traditional machine learning has the need for manual feature extraction, and is very dependent on expert diagnostic experience and expertise, but also has the disadvantages of low accuracy, low timeliness, low efficiency, etc. For the traditional rotating machinery fault diagnosis method is more based on the traditional machine learning model, this paper puts forward a one-dimensional convolutional neural network-based fault identification method. According to the characteristics of the miniature table drilling machine and the automobile engine which are not detachable, the corresponding faults are set up respectively, Vibration signals of the attitude sensor are obtained by using the signal collector, and the collected data are preprocessed, then the CNN model is built for fault identification, and the network structure is constantly optimized to obtain the optimal network model with high accuracy (up to 100 %) and robustness. The results show that the one-dimensional convolutional neural network model improves the fault recognition accuracy and reduces the cost compared with the traditional machine learning SVM model when the original signal is used as the input signal.

Reaction Force-Based Position Sensing for Magnetic Levitation Platform with Exceptionally Large Hovering Distance

This work introduces a novel sensing concept based on reaction forces for determining the position of the levitating magnet (mover) for magnetic levitation platforms (MLPs). Besides being effective in conventional magnetic bearings, the applied approach enables operation in systems where the mover is completely isolated from the actuating electromagnets (EMs) of the stator (e.g., located inside a sealed process chamber) while levitating at an extreme levitation height. To achieve active position control of the levitating mover by properly controlling the stator’s EM currents, it is necessary to employ a dynamic model of the complete MLP, including the reaction force sensor, and implement an observer that extracts the position from the force-dependent signals, given that the position is not directly tied to the measured forces. Furthermore, two possible controller implementations are discussed in detail: a basic PID controller and a more sophisticated state-space controller that can be chosen depending on the characteristics of the MLP and the accuracy of the employed sensing method. To show the effectiveness of the proposed position-sensing and control concept, a hardware demonstrator employing a 207 mm outer-diameter (characteristic dimension, CD) stator with permanent magnets, a set of electromagnets, and a commercial multi-axis force sensor is built, where a 0.36 kg mover is stably levitated at an extreme air gap of 104 mm.

3D magnetic seed localization for augmented reality in surgery

PurposeFor tumor resection, surgeons need to localize the tumor. For this purpose, a magnetic seed can be inserted into the tumor by a radiologist and, during surgery, a magnetic detection probe informs the distance to the seed for localization. In this case, the surgeon still needs to mentally reconstruct the position of the tumor from the probe’s information. The purpose of this study is to develop and assess a method for 3D localization and visualization of the seed, facilitating the localization of the tumor.MethodsWe propose a method for 3D localization of the magnetic seed by extending the magnetic detection probe with a tracking-based localization. We attach a position sensor (QR-code or optical marker) to the probe in order to track its 3D pose (respectively, using a head-mounted display with a camera or optical tracker). Following an acquisition protocol, the 3D probe tip and seed position are subsequently obtained by solving a system of equations based on the distances and the 3D probe poses.ResultsThe method was evaluated with an optical tracking system. An experimental setup using QR-code tracking (resp. using an optical marker) achieves an average of 1.6 mm (resp. 0.8 mm) 3D distance between the localized seed and the ground truth. Using a breast phantom setup, the average 3D distance is 4.7 mm with a QR-code and 2.1 mm with an optical marker.ConclusionTracking the magnetic detection probe allows 3D localization of a magnetic seed, which opens doors for augmented reality target visualization during surgery. Such an approach should enhance the perception of the localized region of interest during the intervention, especially for breast tumor resection where magnetic seeds can already be used in the protocol.

Analysis and development of a sensor concept for multistable actuators with passive magnetic shape memory alloy

Abstract Based on the current state of research, the aim of this paper is to develop a position sensor for actuators using passive magnetic shape memory alloys. The presented sensor concept converts the strain dependent permeability of the magnetic shape memory alloy into an inductance change by using a sensor coil arrangement. The change in inductance of the sensor coil is converted into a change in the resonant frequency of a parallel oscillating circuit. A microcontroller detects the resonant frequency of the circuit and converts it into a position sensor signal. To evaluate the functionality of the sensor, a test rig is developed that deforms the magnetic shape memory element under realistic application conditions. Experimental measurements in a climate chamber are used to evaluate the performance and characteristics of the sensor. The characteristic, temperature drift and hysteresis behavior are analyzed. It is proven that the presented sensor concept can be used to determine the position of passive magnetic shape memory alloy actuators.

A highly integrated shape memory alloy actuator and precision self-sensing tracking control based on position compensation

Shape memory alloy (SMA) actuators are widely used in various actuators due to their advantages of simplicity, high strength, and flexibility. The mapping relationship between the resistance change and displacement during the phase transition process of SMA enables its self-sensing control without external displacement sensors. However, the limited accuracy and high complexity of existing self-sensing models are two crucial factors restricting the wide application of self-sensing control. Due to the high nonlinearity of the SMA wire’s phase transition process, it is challenging to achieve accurate position estimation using a simple self-sensing model. Thus, this paper proposes a novel approach by utilizing the simplest linear model for position estimation. The inevitable modeling errors are directly compensated in the final control results, rather than compensating for estimation errors. This significantly reduces the implementation difficulty of SMA self-sensing control and effectively improves control accuracy. Furthermore, a highly integrated prototype of SMA actuator is designed in this paper, where the additional position sensor and self-resetting loading device are integrated, greatly reducing the size of the SMA actuator. A series of self-sensing tracking experiments are conducted on this prototype, validating that the compensated self-sensing control can achieve comparable performance to the control method with external sensors.

Condition Monitoring of Pneumatic Drive Systems Based on the AI Method Feed-Forward Backpropagation Neural Network.

Machine condition monitoring is used in a variety of industries as a very efficient strategy for equipment maintenance. This paper presents a study on monitoring a pneumatic system using a feed-forward backpropagation neural network as a classifier and compares the results obtained with different sensor signals and associated extracted features as input for classification. The vibrations of the body of a pneumatic cylinder are acquired using both common industrial sensors and low-cost sensors integrated into an Arduino board. Pressure sensors for both chambers and a position sensor are also used. Power spectral density (PSD) is used to extract features from the acceleration signals, as well as statistical indices. Statistical indices are considered for pressure and position sensors. The results, which are based on experimental data obtained on a test bench, show that a feed-forward neural network makes it possible to identify the operating states with a good degree of reliability. Even with low-cost instrumentation, it is possible to realize reliable condition monitoring based on vibrations. This last result is particularly important as it can help to further increase the uptake of this maintenance approach in the industrial environment.

ERROR ANALYSIS OF VISUAL ODOMETRY FOR A SMALL SIZE UNMANNED AERIAL VEHICLE

Abstract. Usage of small size Unmanned Aerial Vehicle (UAV) with autonomous flight capability became widespread at both civil and military applications because of its low costs and ease of use. However, inertial or GPS based positioning and orientation sensors can be disabled at battlefields and the UAV losses its path in a short time. Visual odometry becomes a solution for the aforementioned hassle. Real-time detection of ground control points, matching the same control points at the stereo pair images and correcting the position and orientation data with least square adjustment are the challenges of visual odometry. In this study, error analysis of position and orientation of a UAV flying through mountainous region which cannot obtain any augmenting data for position and orientation is conducted by error propagation law. The flight of UAV is simulated with exact initial position and orientation data. Optic images of ground obtained by nadir directed camera are georeferenced using the digital elevation model of the region, interior and exterior camera parameters. The digital elevation model is used to correct the relief displacement. The stereo-pair image is obtained when the UAV flies 200 m and image coordinates of the common control points are detected by colinearity equations. Position and orientation of UAV is corrected by least square adjustment and the process is repeated for each obtained image acquired at 200 m interval. The analysis revealed that the precisions of horizontal and vertical positioning becomes 75 and 200 meter, respectively. The precision of roll angle becomes worse than 0.5 degree, while the precisions of pitch and yaw angle are estimate to be better that 0.1 degree.

Wind turbine emulator using three-phase IM controlled through an adaptive reactive power estimator fed by soft-VSI topology

PurposeThe purpose of this paper aims to design a robust wind turbine emulator (WTE) based on a three-phase induction motor (3PIM).Design/methodology/approachThe 3PIM is driven by a soft voltage source inverter (VSI) controlled by a specific space vector modulation. By adjusting the appropriate vector sequence selection, the desired VSI output voltage allows a real wind turbine speed emulation in the laboratory, taking into account the wind profile, static and dynamic behaviors and parametric variations for theoretical and then experimental analysis. A Mexican hat profile and a sinusoidal profile are therefore used as the wind speed system input to highlight the electrical, mechanical and electromagnetic system response.FindingsThe simulation results, based on relative error data, show that the proposed reactive power control method effectively estimates the flux and the rotor time constant, thus ensuring an accurate trajectory tracking of the wind speed for the wind emulation application.Originality/valueThe proposed architecture achieves its results through the use of mathematical theory and WTE topology combine with an online adaptive estimator and Lyapunov stability adaptation control methods. These approaches are particularly relevant for low-cost or low-power alternative current (AC) motor drives in the field of renewable energy emulation. It has the advantage of eliminating the need for expensive and unreliable position transducers, thereby increasing the emulator drive life. A comparative analysis was also carried out to highlight the online adaptive estimator fast response time and accuracy.

Reliability and validity of free‐weight countermovement jumps to characterize force‐velocity‐power profiles

AbstractThe aim of this study was to determine test‐retest reliability and concurrent validity of vertical force‐velocity‐power (FVP) profiles using Smith machine and free‐weight countermovement jumps (CMJs). A repeated‐measure cross‐over design with randomized load order and counterbalanced trials was employed. Sixteen resistance‐trained males (age: 26.4 ± 3.9 years, height: 179.6 ± 8.1 cm, body mass: 84.5 ± 10.8 kg) performed maximal loaded CMJs with 4–50 kg on six occasions, with three trials utilizing a Smith machine and three utilizing free‐weights. Jump height was estimated with a linear‐position transducer, and the Samozino computation method estimated theoretical maximal jump parameters. Reliability and concurrent validity were determined for jump height for each jump load and estimated theoretical maximal jump parameters using estimates of bias (mean difference, 95% limits of agreement) and agreement (intraclass correlation coefficients, ICCs). The jump height and maximum theoretical power demonstrated good‐to‐excellent reliability between sessions for both methods (ICC: 0.872–0.947) and concurrent validity between methods (ICC: 0.885–0.969). However, reliability for theoretical maximal force, velocity, and force‐velocity gradient was not as high using either method (ICC = 0.320–0.615) and concurrent validity was poor (ICC: 0.122–0.340). In summary, using both jump methods, a linear‐position transducer provides reliable jump height and theoretical maximal power values. However, our data do not support the reliability or validity of FV relationships using linear position transducers.

Low-Cost Throttle-by-Wire-System Architecture for Two-Wheeler Vehicles

<div>This article investigates the performance of a low-cost throttle-by-wire-system (TbWS) for two-wheeler applications. Mopeds/scooters are still restricted as environmentally harmful. TbWSs can contribute to environmental protection by replacing conventional restrictors. Its consisting of an anisotropic magnetoresistance (AMR) throttle position sensor and a position-controlled stepper motor-driven throttle valve actuator. The decentralized throttle position sensor is operating contactless and acquires redundant data. Throttle valve actuation is realized through a position-controlled stepper motor, sensing its position feedback by Hall effect. Using a PI controller, the stepper motor position is precisely set. Both units transmit and receive data by a CAN bus. Furthermore, fail-safe functions, plausibility checks, calibration algorithms, and energy-saving modes have been implemented. Both modules have been evaluated through hardware-in-the-loop testing in terms of reliability and measuring/positioning performance before the system was integrated into a <i>Peugeot Kisbee 50 4T</i> (Euro 5/injected). Finally, the sensor unit comes with a measurement deviation of less than 0.16%, whereas the actuator unit can approach throttle valve positions with a deviation of less than 0.37%. The actuators’ settling time does not exceed 0.13 s in the case of stable, step loss free, and noiseless operation.</div>

Automatic Control of a Wheeled Robot

This work aims to automatically control a wheeled robot from a specific location A to another B. We assume the robot’s trajectory is straight (the line (AB)). To solve this problem, we need the coordinates of point A and point B, i.e., the position sensor's state. A microcontroller must then process this information. Next, the variation in coordinates mean displacement, and this variation depends on speed, so we also interest in speed here. Finally, for the robot to arrive at point B, its direction of movement must be the same as the direction of (AB). The direction depends on the orientation angle of the moving robot.

Automatic Control of a Wheeled Robot

This work aims to automatically control a wheeled robot from a specific location A to another B. We assume the robot’s trajectory is straight (the line (AB)). To solve this problem, we need the coordinates of point A and point B, i.e., the position sensor's state. A microcontroller must then process this information. Next, the variation in coordinates mean displacement, and this variation depends on speed, so we also interest in speed here. Finally, for the robot to arrive at point B, its direction of movement must be the same as the direction of (AB). The direction depends on the orientation angle of the moving robot.

Combined navigation system for substation inspection robot based on DGPS/DR multi-sensor fusion

To realize the routine inspection work of the capacitor tower inspection operation robot on the capacitor tower in the substation, and to guarantee the accuracy and safety of the robot’s movement in the substation, a multi-sensor combination navigation system and the corresponding Kalman fusion filter are designed. Combined with the overall structure of the substation and the working environment of the inspection robot, the navigation and positioning requirements of the robot are analyzed and compared, and the multi-sensor combination positioning technology using a combination of DGPS, attitude sensors and velocity sensors is determined. The Kalman fusion filter based on the constant velocity model is used to fuse the positioning information of multiple sensors, and more accurate and reliable navigation information is obtained compared with the single-sensor positioning system. According to the working environment of the substation, an outdoor planar robot mobile navigation experiment is carried out to collect the positioning information of multiple sensors and carry out data fusion processing to verify the accuracy and robustness of multi-sensor combination navigation and positioning.

The effect of a single cycle of ischemia on bar velocity during bench press exercise

Introduction: The main goal of this study was to investigate the effects of a single cycle of ischemia applied before the bench press exercise on acute bar velocity changes. Materials and Methods: Twelve physically active males participated in the study. The experiment followed a randomized, cross-over design under two testing conditions. The experimental condition involved the application of a single cycle of ischemia (5 minutes; 80% arterial occlusion pressure [AOP]) before the first set of bench press exercise (2 sets at 60% 1RM). The control condition did not use ischemia. The peak and mean bar velocities were measured using a linear position transducer. Results: Two-way repeated measures ANOVA revealed no statistically significant differences in the peak or mean bar velocity between the ischemia and control conditions both during the first and the second set of the bench press exercise. Conclusions: The results of this study indicate that a single cycle of ischemia applied before the bench press exercise did not influence changes in peak or mean bar velocity.

The Validity of Perceptual Recovery Status on Monitoring Recovery During a High-Intensity Back-Squat Session.

The aim of this study was to assess the validity of PRS on monitoring recovery during a high-intensity back-squat session. Ten healthy men participated in the 2-session study (separated by at least 48h). Session 1 included anthropometrics, PRS familiarization, and a 1-repetition-maximum back squat. Session 2 included a high-intensity protocol (5 sets of 5 repetitions; 5-min interset recovery; 85% of 1-repetition maximum). PRS was obtained before the first set and during the last 30seconds of each 5-minute recovery; rating of perceived exertion (RPE) was also collected. A linear position transducer collected mean barbell velocity (MBV). Repeated-measures correlations assessed the common intraindividual relationships of PRS scores to intraset MBV and RPE, respectively. A very large, positive correlation appeared between PRS and MBV (r [95% CI] = .778 [.613 to .878]; P < .0001). A large, negative correlation emerged between PRS and RPE (r [95% CI] = -.549 [-.737 to -.282]; P < .001). Results indicate that PRS can be a means for practitioners to monitor individualized recovery. PRS tracked well with RPE, strengthening its utility in a practitioner-based setting. Findings provide insight into the practicality of PRS for recovery monitoring. It could be used alongside other measures (eg,MBV and countermovement jump) to individually program and maintain performance.

Self‐Powered Underwater Pressing and Position Sensing and Autonomous Object Grasping with a Porous Thermoplastic Polyurethane Film Sensor

AbstractMost flexible ionic tactile sensors can hardly be used in deep sea due to their poor antiswelling and anticompression properties under high hydrostatic pressure. To achieve pressure and position sensing under high hydrostatic pressure, a self‐powered underwater tactile sensor made of a porous thermoplastic polyurethane (TPU) film is presented in this paper. The sensor works by generating an electric current due to the different moving velocities of ions in the porous film under pressing. Experimental results show that the magnitude of the generated current signal increases with the applied pressure, the contacting area, and ion concentration of the solution. The direction and magnitude of the current signal depend on the pressing position of the film. The signal magnitude decreased with the closer to the center of the film. The maximum pressure sensitivity and positioning resolution are 0.62 kPa−1 and 1.31 mm respectively. Response time (0.19 s), 0.67 s recovery time, and 50–600 kPa pressure detection range are achieved. In addition, the signal magnitude is decreased only by 15.53% when the sensor is placed underwater at a simulated depth of 100 m. Proof of concept demonstration of underwater autonomously grasping objects of different weights with this sensor is successfully achieved.

Detection of incipient rotor unbalance fault based on the RIME-VMD and modified-WKN

Due to the high incidence and inconspicuous initial characteristics of rotor unbalance faults, the detection of incipient unbalance faults is becoming a very challenging problem. In this paper, a new method of small rotor unbalance fault diagnosis based on RIME-VMD and modified wavelet kernel network (modified-WKN) is proposed. Firstly, in order to extract the small unbalance fault information from the vibration signals with low signal-to-noise ratio (SNR) more efficiently, the RIME algorithm is used to search for the optimal location of the penalty factor and decomposition layer in the variable mode decomposition (VMD). Secondly, the most relevant decomposition components to the small unbalance fault information are selected by using Pearson Correlation Coefficients and utilized to reconstruct the signal. Finally, the modified-WKN diagnostic model that is used for multi-sensor data fusion is constructed. The model can acquire features of vibration signals from multiple position sensors, which enhances the ability of the modified WKN diagnostic model to deal with incipient fault modes. Based on the experimental analysis of rotor unbalance fault datasets with different SNRs, it is verified that the detection performance of the proposed method is better than the traditional WKN and VMD-WKN methods. Specifically, the proposed method is more sensitive to the initial unbalance faults.

Enhanced indoor positioning through human-robot collaboration

AbstractIndoor positioning is a critical component for numerous applications and services. However, GNSS systems face challenges in delivering accurate positioning information in indoor environments. Current indoor positioning research primarily concentrates on enhancing the positioning performance of individual terminals through various techniques. As we transition into the Internet of Things (IoT) era, former indoor positioning methods need refinement. In this paper, we propose a novel indoor positioning method that leverages robots as mobile base stations to mitigate the problem of inadequate fixed base stations and aims to enhance positioning accuracy by incorporating pedestrian inertial navigation data. The process involves several steps. First, the mobile robots accurately determine their positions and performing coordinate transformations to ensure consistency with pedestrian coordinate systems. Then, pedestrians use the ranging information from these robots along with their smartphones’ sensors for multi-source fusion positioning. Finally, an Extended Kalman Filter (EKF) is applied to fuse the multiple sources of data, considering various sources of errors, to provide enhanced positioning performance. Experimental results demonstrate the effectiveness of this approach in addressing indoor positioning challenges. This method could benefit numerous scenarios involving robots, enhancing pedestrian positioning accuracy and overall system robustness. The paper provides a comprehensive exploration of this proposed method, its implications, and potential directions for future advancements.

Circuit equation in power superimposition for position sensor using single‐phase transformer on PMSM power lines

AbstractPermanent magnet synchronous motors (PMSM) are very popular in the industrial market. Recently, robots are installed to compensate for the shortage of labor. Due to the increase in motors, the number of power lines and position sensor cable wiring are increased; this increase in complicated wiring is a problem. As a method to solve these problems, power superimposition technology in the power line of a PMSM is expected to reduce the distribution cables. In this paper, the power superposition technology method, and circuit equations of mounting transformers in power lines are presented and are then verified by experiment, simulation, and linear analysis. The circuit equation is proposed as a three‐phase circuit equation, which is transformed into a dq‐circuit equation by a transformation matrix. The characteristics of the electric angular frequency versus the dq‐axis voltage demonstrated satisfactory results, with errors within 1% in linear analysis and simulation. The linear analysis and experiment are shown to be similar, although there are some errors. The validity of the circuit equations could be demonstrated by the verification results.

3-D-Printing and Reliability Evaluation of an Easy-to-Fabricate Position Sensing System for Printed Functional Wearable Assistive Devices

3-D-Printing and Reliability Evaluation of an Easy-to-Fabricate Position Sensing System for Printed Functional Wearable Assistive Devices