Motor Noise Research Articles

Model-free predictive current control based on improved sliding mode disturbance observer

The motor parameters of permanent magnet synchronous motor (PMSM) can be mismatched in operation due to temperature variations and magnetic saturation. The parameter mismatch leads to a significant decrease in the robustness of traditional deadbeat predictive current control (DPCC), which leads to issues such as current static error and motor vibration noise. In this paper, a model-free predictive current control (MFPCC) method based on an improved sliding mode disturbance observer (SMDO) was proposed. The method estimated the total disturbance of the system using the improved SMDO, which could effectively suppress sliding mode chattering and accelerate the convergence speed of current errors. Subsequently, the predicted control voltage was calculated using a hyper-local model and compensated for the time delay. The experimental results demonstrated that the improved MFPCC-SMDO method generated lower motor noise compared to the traditional DPCC method when the controller’s inductance parameters are mismatched.

Analysis of the Effect of Switching Frequency on Acoustic Noise in External Rotor Brushless DC Motors

Electric motors are actively used in various industries. Acoustic noise is crucial in electric motors, and specific standards are depending on their application areas. Despite better performance, including acoustic noise, than brushed motors, brushless motors still generate acoustic noise due to their mechanical, electrical, and electronic components. This study investigated the impact of varying the switching frequency through the driver on the acoustic noise of an external rotor brushless DC motor. Tests were conducted on a surface-mounted magnet brushless motor with different switching frequencies, and detailed information about the sections of the control board governing the brushless motor was provided. The study includes measurements of motor speed, current, switching frequency, phase signal, and acoustic noise measured at two different locations in decibels. It was observed that the acoustic noise increased at specific switching frequencies. Furthermore, the variation in switching frequency also affected the heating due to losses in the switching elements. Increasing the switching frequency in the 12-28 kHz range reduced motor speed and the measured acoustic noise, while temperature increases were observed in various frequency ranges.

Multisensory perception depends on the reliability of the type of judgment.

The brain engages the processes of multisensory integration and recalibration to deal with discrepant multisensory signals. These processes consider the reliability of each sensory input, with the more reliable modality receiving the stronger weight. Sensory reliability is typically assessed via the variability of participants' judgments, yet these can be shaped by factors both external and internal to the nervous system. For example, motor noise and participant's dexterity with the specific response method contribute to judgment variability, and different response methods applied to the same stimuli can result in different estimates of sensory reliabilities. Here we ask how such variations in reliability induced by variations in the response method affect multisensory integration and sensory recalibration, as well as motor adaptation, in a visuomotor paradigm. Participants performed center-out hand movements and were asked to judge the position of the hand or rotated visual feedback at the movement end points. We manipulated the variability, and thus the reliability, of repeated judgments by asking participants to respond using either a visual or a proprioceptive matching procedure. We find that the relative weights of visual and proprioceptive signals, and thus the asymmetry of multisensory integration and recalibration, depend on the reliability modulated by the judgment method. Motor adaptation, in contrast, was insensitive to this manipulation. Hence, the outcome of multisensory binding is shaped by the noise introduced by sensorimotor processing, in line with perception and action being intertwined.NEW & NOTEWORTHY Our brain tends to combine multisensory signals based on their respective reliability. This reliability depends on sensory noise in the environment, noise in the nervous system, and, as we show here, variability induced by the specific judgment procedure.

Thermodynamic precision of a chain of motors: the difference between phase and noise correlation

Inspired by recent experiments on fluctuations of flagellar beating in sperm and C. reinhardtii, we investigate the precision of phase fluctuations in a system of nearest-neighbor-coupled molecular motors. We model the system as a Kuramoto chain of oscillators with a coupling constant k and noisy driving. The precision p is a Fano-factor-like observable, which obeys the thermodynamic uncertainty relation (TUR), which is an upper bound related to dissipation. We first consider independent motor noises with diffusivity D: in this case, the precision goes as k/D , coherently with the behavior of spatial order. The minimum observed precision is that of the uncoupled oscillator punc ; the maximum observed precision is Npunc , saturating the TUR bound. Then we consider driving noises which are spatially correlated, as may happen in the presence of some direct coupling between adjacent motors. Such a spatial correlation in the noise does not evidently reduce the degree of spatial correlation in the chain, but sensibly reduces the maximum attainable precision p, coherently with experimental observations. The limiting behavior of the precision, in the two opposite cases of negligible interaction and strong interaction, is well reproduced by the precision of the single chain site punc and the precision of the center of mass of the chain Neffpunc with Neff<N : both do not depend on the degree of interaction in the chain, but Neff decreases with the correlation length of the motor noises.

Design of Electronic Filter for Noise and Vibration Reduction in Brushed DC Motor

With the recent conversion of internal combustion engines to electric vehicles, new noise issues have arisen, and among them, the noise generated by internal vehicle auxiliary systems is being considered. This study introduces an electronic filter designed with a motor model featuring vibration components, aiming to minimize the noise and vibrations generated by a Brushed DC (BDC) motor commonly employed in vehicle internal systems. It introduces a method to identify the connectors and internal parameters used in the motor for the matching of the model and experimental motor, and to measure and estimate these parameters. The model is separated and executed to ensure convergence, and it is validated by comparing the analysis results with the measured values. A filter is designed using the model to reduce current oscillations in the motor, confirming a subsequent reduction in noise and vibration. This research suggests the potential to attenuate noise and vibration in already produced motors by attaching only a filter without modifying the internal motor structure. Moreover, it is anticipated that a filter can be designed to predict and mitigate the noise and vibration components of the motor based on changes in load.

Optimization of PMSM for EV based on Vibration and Noise Suppression

The key to the suppression of vibration and noise for PMSM is the optimization of electromagnetic excitation force. The method of motor body optimization can effectively reduce the radial excitation force of the motor, so as to suppress the vibration and noise of the motor. Firstly, the stator structure of the motor is optimized with V-shape skew slot based on the analytical modeling of the radial electromagnetic excitation force of the motor. Then, the structural parameters of the motor that affect the electromagnetic excitation force of the motor are determined, and the average torque, torque ripple and radial electromagnetic excitation force generated by tangential electromagnetic excitation force are taken as the optimization objectives. The sensitivity analysis and classification of the structural parameters of the motor are carried out. The multi-objective genetic algorithm and response surface method are combined to optimize the structural parameters of the motor. Finally, the finite element analysis, modal analysis, multi-speed vibration and noise analysis of the optimized motor are done. The performance comparisons before and after optimization have proved that the peak of equivalent sound power level have decreased by 8.65% after the optimization of V-shaped skewed slot structure. After the optimization of structural parameters, the power level of permanent magnet synchronous motor has been reduced by 9.22%. For the vibration noise caused by resonance and the main frequency of vibration noise harmonics, the suppression effects are also better than those of V-shape skewed slots optimization, and the ERPL values are reduced by 9.22% and 10.12%, respectively, in two cases. The results show that the vibration and noise of permanent magnet synchronous motor are effectively suppressed.

Comparison of cuff inflation and cuff deflation brachial sphygmomanometry with intra-arterial blood pressure as reference.

Conventional sphygmomanometry with cuff deflation is used to calibrate all noninvasive BP (NIBP) instruments and the International Standard makes no mention of calibrating methods specifically for NIBP instruments, which estimate systolic and diastolic pressure during cuff inflation rather than cuff deflation. There is however increasing interest in inflation-based NIBP (iNIBP) instruments on the basis of shorter measurement time, reduction in maximal inflation pressure and improvement in patient comfort and outcomes. However, we have previously demonstrated that SBP estimates based on the occurrence of the first K1 Korotkoff sounds during cuff deflation can underestimate intra-arterial SBP (IA-SBP) by an average of 14 ± 10 mmHg. In this study, we compare the dynamics of intra-arterial blood pressure (IABP) measurements with sequential measurement of Korotkoff sounds during both cuff inflation and cuff deflation in the same individual. In 40 individuals aged 64.1 ± 9.6 years (range 36-86 years), the overall dynamic responses below the cuff were similar, but the underestimation error was significantly larger during inflation than deflation, increasing from 14 ± 10 to 19 ± 12 mmHg ( P < 0.0001). No statistical models were found which could compensate for this error as were found for cuff deflation. The statistically significant BP differences between inflation and deflation protocols reported in this study suggest different behaviour of the arterial and venous vasculature between arterial opening and closing which warrant further investigation, particularly for iNIBP devices reporting estimates during cuff inflation. In addition, measuring Korotkoff sounds during cuff inflation represents significant technical difficulties because of increasing pump motor noise.

The Electric Vehicle: Solving the Silent Problem

Electric Vehicles (EV) are key stakeholders for a Green House Gas (GHG) emission free future. However, the integration of these vehicles in our society can create some collateral damage. The lack of noise of electric motors compared to Internal Combustion Engines (ICE) can lead to more accidents, as the acoustic signals from vehicles raise awareness of its presence. Pedestrians, Cyclists and very especially blind and partially sighted people are the groups at risk Thus, sound systems for EVs have to be designed and for that a study on the noise intensity in the frequency spectrum has to be made. This paper presents an experimental research on how to approach this problem.

Simulation Analysis and Optimization of Electromagnetic Vibration and Noise of Permanent Magnet Motor

Simulation Analysis and Optimization of Electromagnetic Vibration and Noise of Permanent Magnet Motor

Effect of juggling expertise on pointing performance in peripheral vision.

Juggling is a very complex activity requiring motor, visual and coordination skills. Expert jugglers experience a "third eye" monitoring leftward and rightward ball zenith positions alternately, in the upper visual fields, while maintaining their gaze straight-ahead. This "third eye" reduces their motor noise (improved body stability and decrease in hand movement variability) as it avoids the numerous head and eye movements that add noise into the system and make trajectories more uncertain. Neuroimaging studies have shown that learning to juggle induces white and grey matter hypertrophy at the posterior intraparietal sulcus. Damage to this brain region leads to optic ataxia, a clinical condition characterised by peripheral pointing bias toward gaze position. We predicted that expert jugglers would, conversely, present better accuracy in a peripheral pointing task. The mean pointing accuracy of expert jugglers was better for peripheral pointing within the upper visual field, compatible with their subjective experience of the "third eye". Further analyses showed that experts exhibited much less between-subject variability than beginners, reinforcing the interpretation of a vertically asymmetrical calibration of peripheral space, characteristic of juggling and homogenous in the expert group. On the contrary, individual pointing variability did not differ between groups neither globally nor in any sector of space, showing that the reduced motor noise of experts in juggling did not transfer to pointing. It is concluded that the plasticity of the posterior intraparietal sulcus related to juggling expertise does not consist of globally improved visual-to-motor ability. It rather consists of peripheral space calibration by practicing horizontal covert shifts of the attentional spotlight within the upper visual field, between left and right ball zenith positions.

Dynamic Eccentricity Impact on Electromagnetic Vibration and Acoustic Noise of Interior Permanent Magnet Synchronous Motors With Different Numbers of Parallel Branches

Dynamic Eccentricity Impact on Electromagnetic Vibration and Acoustic Noise of Interior Permanent Magnet Synchronous Motors With Different Numbers of Parallel Branches

Understanding EMG PDF Changes With Motor Unit Potential Amplitudes, Firing Rates, and Noise Level Through EMG Filling Curve Analysis.

EMG filling curve characterizes the EMG filling process and EMG probability density function (PDF) shape change for the entire force range of a muscle. We aim to understand the relation between the physiological and recording variables, and the resulting EMG filling curves. We thereby present an analytical and simulation study to explain how the filling curve patterns relate to specific changes in the motor unit potential (MUP) waveforms and motor unit (MU) firing rates, the two main factors affecting the EMG PDF, but also to recording conditions in terms of noise level. We compare the analytical results with simulated cases verifying a perfect agreement with the analytical model. Finally, we present a set of real EMG filling curves with distinct patterns to explain the information about MUP amplitudes, MU firing rates, and noise level that these patterns provide in the light of the analytical study. Our findings reflect that the filling factor increases when firing rate increases or when newly recruited motor unit have potentials of smaller or equal amplitude than the former ones. On the other hand, the filling factor decreases when newly recruited potentials are larger in amplitude than the previous potentials. Filling curves are shown to be consistent under changes of the MUP waveform, and stretched under MUP amplitude scaling. Our findings also show how additive noise affects the filling curve and can even impede to obtain reliable information from the EMG PDF statistics.

Experimental Study on No-Load Loss Characteristics of an Alternating Current Excitation Motor

An AC excitation power supply will produce a series of harmonic currents in motors compared to the conventional power supply, increase the time harmonic component, and then generate a harmonic magnetic field; harmonics will cause motor vibrations and noise in the motor and will produce a corresponding additional loss, increasing the motor temperature rise; these key technical problems need to be solved in domestic pumped storage AC excitation motor engineering applications. Herein, the no-load loss characteristic test of a 3 MW alternating current excited motor was mainly carried out using a test prototype of the 3 MW alternating current excitation motor. Further, with the aid of the finite element method, the numerical study of the no-load loss characteristics of a 3 MW alternating current excited motor was performed. The relationship between the key factors such as the no-load characteristics, no-load loss characteristics, and constant loss and voltage per unit value is analyzed, and the variation law of the no-load core loss of motors under different loads is explored. The results demonstrate that, under no-load conditions, when the applied voltage was less than the rated voltage, the voltage was proportional to the current. When the applied voltage was more significant than the rated voltage, the current increased as the voltage increased, but the relationship between the two was no longer proportional. The constant loss of the motor maintained a linear relationship with the square of the unit value of the voltage scale. When the square of the unit value of the voltage scale was zero, the loss was equivalent to the wind friction loss under no load. The core loss increased with the increase in load, and the greater the load, the faster the increase rate of the motor iron loss. The comparison deviation between the test and simulation results was less than 10%. The simulation and experimental results verified the effectiveness of finite element modeling and the finite element calculation method.

Bench tests of noise-insulating materials

A group of employees of the Scientific, Educational and Production Laboratory "Outboard Boat Motors" of the Department of Water Transport Operation of the Federal State Budgetary Educational Institution "AGTU" achieved certain results in the fight against noise, reflected in a number of patents of noise-insulating hoods of various modifications and publications on the analysis of the frequency spectrum of outboard boat motor noise, evaluation of the effectiveness of outboard motor hoods of small-sized Based on the accumulated scientific research, several proto-types of multilayer means recommended for sound insulation of outboard boat motors have been created. In the conditions of a large range of directions, there is a need to apply single-layer or multi-layer structures to the properties of sound insulation without forming a physical model of the hood, casing. This is due to the fact that the production of a hood model entails a complex technological process, while creating a sample in the form of a plate from the same material is much more profitable in terms of financial, labor and time costs. Based on the needs of the scientific educational and production laboratory of outboard boat motors in the study of noise generated by outboard boat motors and means of combating them, it was decided to create a stand to assess the effectiveness of noise-reducing materials, which will allow modeling the conditions of natural operation of a noise-insulating material without the use of full-scale tests with outboard by motor. The created stand allows testing in two directions. The first direction is the testing of physical models of casings for noise insulation using audio files reproducing the noise of the required equipment. The second direction is the study of materials for their noise-insulating properties when generating waves of different frequencies.

Comparative Analysis of Vibration and Noise of Axial Flux Motor with Different Pole and Slot Combinations

Comparative Analysis of Vibration and Noise of Axial Flux Motor with Different Pole and Slot Combinations

Impact of the Stator Coil Pitch on Acoustic Noise and Vibration of Squirrel Cage Induction Motors

In this article, the rate of acceleration, speed, and displacement of electric motor structure particles in response to radial electromagnetic forces is studied in detail. For this purpose, the vibrations and acoustic noise of three similar induction motors are investigated by changing the stator winding pitch through finite element modeling (FEM) by conducting electromagnetic, structural, and frequency analyses. Three similar induction motors with 24 slots in the stator are selected, two motors are wound with a shorted pitch by 2 and 1 slot, respectively, and the third motor's winding is considered full pitch winding. The simulation results are validated by experimental measurements of the amount of acoustic noise and vibrations of three motors. The results of the simulations and experimental measurements are in good agreement and indicate less noise and vibrations in the first induction motor with two short slots in the stator winding pitch.

A novel forward computational modal analysis method of the motor stator assembly considering core lamination and winding stacking

The forward computational modal analysis (CMA) of the motor stator assembly is gaining attention as a technical challenge in the area of motor noise. There is still no effective method to obtain accurate anisotropic material parameters (AMPs) required for CMA without prototype motors. In this paper, a novel forward calculation method (FCM) for calculating AMPs of the motor stator assembly is proposed, in considering structural discontinuities and composite material properties due to core lamination and winding stacking. The method is based on multi-scale theory. Firstly, the multi-scale equations coupling the macroscopic model and the mesoscopic model of the stator core and winding are established. By decoupling the multi-scale equations, the equivalence and equivalence preconditions of AMPs of the two scale models are described. Accordingly, the finite element method (FEM) for calculating AMPs of the stator core and winding based on the mesoscopic model is proposed. Then, the AMPs of the stator core and winding are calculated by the FCM, and the influencing factors are further studied, respectively. Finally, the effectiveness of this method is verified by modal experiments. Compared with experimental results, the maximum relative error of natural frequencies of the stator assembly is within 1.5%. This method can achieve accurate CMA without prototype motors and modal experiments, which contributes to the accurate prediction and control of electromagnetic noise in the initial motor design stage.

Validation of Electrical Noise of a DC Motor through Controlled Varistor Cracking: An Experimental Study

The varistor is an electronic component that protects the DC motor’s circuitry from electrical noise or transients that can cause damage. It works as a voltage-dependent resistor that can change its resistance according to the applied voltage. Once the voltage surpasses a specific threshold, the varistor conducts and directs the excess voltage away from the motor’s circuitry. In small DC motor manufacturing, ring varistors are vital for reducing electrical noise, minimizing spark-induced damage to the commutator and brush, and extending the motor’s lifespan. Additionally, they prevent damage to electronic parts in the customer’s mechanism set. The objective of this study is to investigate the impact of varistor cracks or chips that may occur during the soldering process of varistors to the commutator. To confirm the occurrence of cracks or chips, intentional damage will be inflicted on the varistors. The study aims to determine how the presence of cracked or chipped varistors affects the electrical noise produced by a DC motor during its operation. The resulting spark was observed through an oscilloscope, and it was found that the effect could be substantial, up to 5 to 10 times the rated voltage supplied to the motor. In the next phase of this study, further tests will be conducted on motors without varistors to provide a comparison.

Lightweight soundproofing meta-panel for two separate broadband noises

We propose a lightweight soundproofing meta-panel, with a multi-scale lattice structure sandwiched between thin membranes, effectively suppressing two separate broadband noises. By adjusting the geometrical parameters, the meta-panel is designed to induce negative effective mass density and negative effective bulk modulus for two distinct target frequency bands: 100–500 Hz and 1000–1500 Hz, thereby achieving high transmission loss that outperforms the mass density law. A meta-panel sample is fabricated via 3D printing technology and we evaluate its sound insulation performance by measuring transmission loss and insertion loss in an impedance tube and an anechoic chamber. Experimental measurements show that the meta-panel can exhibit excellent insulation performance while being significantly lighter in weight (about 5%) compared to a steel plate, and furthermore, it effectively suppresses low-frequency road noise and high-frequency motor noise from an actual electric car.

Machine learning predictive model for motor moise by expanding dataset using electromagnetic force characteristics

To understand the variations of electric vehicle motor noise, the electromagnetic force change due to the tilting and the eccentricity between the rotor and the stator on the noise should be studied. However, it takes enormous computation time to consider all the various conditions, so machine learning (ML) model that can reduce the analysis time of electromagnetic force and noise was developed. The developed ML model showed very high accuracy prediction performance with an R2 value higher than 0.99 even for data that was not used at all for training. The speed increase was more than 100 times of FE analysis. The authors doubt that the high accuracy of the developed model implies the potential for electromagnetic force data structure to be much simpler than it appears. Therefore, various analyzes were conducted on the force and uneven magnetic pull. The change in the temporal-spatial main frequency component was not large, and only the change in the side order was meaningful. It was confirmed that to create a predictive model using a smaller dataset for not only the UMP but also all the individual teeth force is possible. This new way of creating a training dataset for an ML model helps to increase the training efficiency. Based on the generated prediction model, it can be used to identify changes in noise characteristics in major rotational orders and derive suitable improvements.