Search Results (391)

In recent years, distributed energy power supply systems have been widely used in remote areas and extreme environments. However, the intermittent and uncertain output power may cause power grid fluctuations, leading to higher harmonics in electromechanical equipment, especially motors. For permanent magnet synchronous motor (PMSM) systems, an electromagnetic (EM) vibration can cause problems such as energy loss and mechanical wear. Therefore, it is necessary to design control algorithms that can effectively suppress EM vibration. To this end, a vibration suppression algorithm for fractional-slot permanent magnet synchronous motors based on a d-axis current injection is proposed in this paper. Firstly, this paper analyzes the radial electromagnetic force of the fractional-slot PMSM to identify the main source of EM vibration in fractional-slot PMSMs. Based on this, the intrinsic relationship between the EM vibration of fractional-slot PMSMs and the d-axis and q-axis currents is explored, and a method for calculating the d-axis current to suppress the vibration is proposed. Experimental verification shows that the proposed algorithm can effectively suppress EM vibration. Full article

To address the challenges of extracting rolling bearing degradation information and the insufficient performance of conventional convolutional networks, this paper proposes a rolling bearing degradation state identification method based on the improved monopulse feature extraction and a one-dimensional dilated residual convolutional neural network (1D-DRCNN). First, the fault pulse envelope waveform features are extracted through phase scanning and synchronous averaging, and a two-stage grid search strategy is employed to achieve FCC calibration. Subsequently, a 1D-DRCNN model is constructed to identify rolling bearing degradation states under different working conditions. The experimental study collects the vibration signals of nine degradation states, including the different sizes of inner and outer ring local faults as well as normal conditions, to comparatively analyze the proposed method’s rapid calibration capability and feature extraction quality. Furthermore, t-SNE visualization is utilized to analyze the network response to bearing degradation features. Finally, the degradation state identification performance across different network architectures is compared in pattern recognition experiments. The results show that the proposed improved feature extraction method significantly reduces the iterative calibration computational burden while effectively extracting local fault degradation information and overcoming complex working condition influence. The established 1D-DRCNN model integrates the advantages of dilated convolution and residual connections and can deeply mine sensitive features and accurately identify different bearing degradation states. The overall recognition accuracy can reach 97.33%. Full article

This paper presents FWStab, a specialized video stabilization dataset tailored for flapping-wing platforms. The dataset encompasses five typical flight scenarios, featuring 48 video clips with intense dynamic jitter. The corresponding Inertial Measurement Unit (IMU) sensor data are synchronously collected, which jointly provide reliable support for multimodal modeling. Based on this, to address the issue of poor image acquisition quality due to severe vibrations in aerial vehicles, this paper proposes a multi-modal signal fusion video stabilization framework. This framework effectively integrates image features and inertial sensor features to predict smooth and stable camera poses. During the video stabilization process, the true camera motion originally estimated based on sensors is warped to the smooth trajectory predicted by the network, thereby optimizing the inter-frame stability. This approach maintains the global rigidity of scene motion, avoids visual artifacts caused by traditional dense optical flow-based spatiotemporal warping, and rectifies rolling shutter-induced distortions. Furthermore, the network is trained in an unsupervised manner by leveraging a joint loss function that integrates camera pose smoothness and optical flow residuals. When coupled with a multi-stage training strategy, this framework demonstrates remarkable stabilization adaptability across a wide range of scenarios. The entire framework employs Long Short-Term Memory (LSTM) to model the temporal characteristics of camera trajectories, enabling high-precision prediction of smooth trajectories. Full article

The precise measurement of crankshaft torsional vibration is critical for diesel engine reliability, yet it is often compromised by systematic errors from toothed disc runout. To address this challenge, this paper elucidates the dual mechanism of these errors, which manifest as micro-level voltage fluctuations in signal and macro-level time-domain deviations. Based on this understanding, a composite compensation method is proposed. First, a dual-line approximation method is presented for preprocessing the raw sensor signals, aiming to eliminate the distortion in rotational speed calculations caused by anomalous voltages. Second, a synchronous sampling scheme based on the differential measurement principle is developed. This scheme utilizes a symmetrically arranged dual-sensor structure to suppress runout errors and is combined with a time-domain feature reconstruction technique to restore the true rotational speed signal. Validation on a custom-built universal joint torsional vibration test rig demonstrates that the proposed method can effectively eliminate systematic deviations arising from toothed disc runout, thereby significantly enhancing the accuracy of torsional vibration measurements. The measurement method presented in this paper offers a valuable reference for the high-precision measurement of engine torsional vibration characteristics. Full article

Humans mostly perceive tactile sensations in daily life as a combination of warmth, vibration, and pressure. To understand the complex tactile perception and cognitive processes, in this study, we aimed to develop a multimodal stimulator and investigate changes in neuronal activity. An actuator that can display warmth (W), vibration (V), and pressure (P) on the distal region of the index finger has been developed. Preliminary experiments were conducted with nine subjects. Electroencephalograms were measured for six tactile stimuli—three single stimuli (W, V, and P) and three combination stimuli (W + V, V + P, and W + V + P)—and event-related desynchronization/synchronization (ERD/S) analysis were performed. The actuator can present all kinds of stimuli in the same location and control stimulation parameters quantitatively. For all experiments, there was an ERD in the α and β bands about 0.5 s after stimulation followed by ERS was observed in the C3 area. The change in the peak-to-peak value was the largest for warmth and the smallest for pressure. In contrast, in the duration of the ERD, W was the shortest and P was the longest. As stimulus presented simultaneously, the ERD became longer in both the alpha and beta bands. In the beta band, the peak of ERD became larger. The developed system was confirmed to be capable of providing valid tactile stimulation, inducing appropriate neuronal activation, and enabling multimodal tactile research. Full article

Torque and velocity fluctuations in internal combustion engines (ICEs), particularly during idle and low-speed operation, can reduce efficiency, increase vibration, and impose mechanical stress on coupled systems. This work presents the design, simulation, and experimental validation of a passive balancing cam mechanism developed to mitigate fluctuations in single-cylinder internal combustion engines (ICEs). The system consists of a cam and a spring-loaded follower that synchronizes with the engine cycle to store and release energy, generating a compensatory torque that stabilizes rotational speed. The mechanism was implemented on a single-cylinder Honda^® engine and evaluated through simulations and laboratory tests under idle conditions. Results demonstrate a reduction in torque ripple amplitude of approximately 54% and standard deviation of 50%, as well as a decrease in angular speed fluctuation amplitude of about 43% and standard deviation of 42%, resulting in significantly smoother engine behavior. These improvements also address longstanding limitations in traditional powertrains, which often rely on heavy flywheels or electronically controlled dampers to manage rotational irregularities. Such solutions increase system complexity, weight, and energy losses. In contrast, the proposed passive mechanism offers a simpler, more efficient alternative, requiring no external control or energy input. Its effectiveness in stabilizing engine output makes it especially suited for integration into hybrid electric systems, where consistent generator performance and low mechanical noise are critical for efficient battery charging and protection of sensitive electronic components. Full article

To address the high vibration and noise in fractional-slot concentrated-winding permanent magnet synchronous machines for electric vehicles, this study focuses on a 30-pole, 36-slot fractional-slot concentrated-winding permanent magnet synchronous machine. These issues are mainly caused by the modulation of high-order radial electromagnetic forces into low-order radial electromagnetic forces, known as the teeth modulation effect. The characteristics of radial electromagnetic forces are analyzed using the Maxwell stress tensor method, and the modulation process is examined. A novel unequal-teeth stator structure is proposed to reduce vibration and noise. Finite element simulations are performed to investigate how this structure affects the amplitude of modulated low-order radial electromagnetic forces. The optimal ratio of the unequal-teeth design is identified to effectively suppress the modulation effect. Simulation results indicate that an appropriately chosen unequal-teeth proportion leads to significant improvements in the machine’s vibration and noise performance across various operating conditions, providing a preliminary validation of the feasibility and effectiveness of the proposed unequal-teeth design methodology. Full article

This research presents an IoT-empowered adaptive mobility framework that integrates high-dimensional gesture recognition with edge-cloud orchestration for safety-critical human–machine interaction. The system architecture establishes a three-tier IoT network: a perception layer with 60 GHz FMCW radar and TOF infrared arrays (12-node mesh topology, 15 cm baseline spacing) for real-time motion tracking; an edge intelligence layer deploying a time-aware neural network via NVIDIA Jetson Nano to achieve up to 99.1% recognition accuracy with latency as low as 48 ms under optimal conditions (typical performance: 97.8% ± 1.4% accuracy, 68.7 ms ± 15.3 ms latency); and a federated cloud layer enabling distributed model synchronization across 32 edge nodes via LoRaWAN-optimized protocols (κ = 0.912 consensus). A reconfigurable chassis with three operational modes (standing, seated, balance) employs IoT-driven kinematic optimization for enhanced adaptability and user safety. Using both radar and infrared sensors together reduces false detections to 0.08% even under high-vibration conditions (80 km/h), while distributed learning across multiple devices maintains consistent accuracy (variance < 5%) in different environments. Experimental results demonstrate 93% reliability improvement over HMM baselines and 3.8% accuracy gain over state-of-the-art LSTM models, while achieving 33% faster inference (48.3 ms vs. 72.1 ms). The system maintains industrial-grade safety certification with energy-efficient computation. Bridging adaptive mechanics with edge intelligence, this research pioneers a sustainable IoT-edge paradigm for smart mobility, harmonizing real-time responsiveness, ecological sustainability, and scalable deployment in complex urban ecosystems. Full article

The mechanical connection between a transmission system and an electric motor gives rise to a strong interaction between their respective dynamics. In particular, the coupling between an electric motor and a nonlinear spur gear transmission significantly influences the overall dynamic behavior of the integrated system. This study presents a detailed investigation into the electromechanical coupling effects between a permanent magnet synchronous machine (PMSM) and a nonlinear spur gear transmission. To focus on these effects, three configurations are analyzed: (i) a standalone gear pair model without motor interaction, (ii) a combined gear–motor system without dynamic coupling, and (iii) a fully coupled electromechanical system where the mechanical feedback influences motor control. The dynamic interaction between the motor’s torsional vibrations and the gear transmission is captured using the derivative of the transmission error as a feedback signal, enabling a closed-loop electromechanical model. Numerical simulations highlight the critical role of this coupling in shaping system dynamics, offering insights into the stability and performance of electric drive–gear transmission systems under different operating conditions. It also underscores the limitations of traditional modeling approaches that neglect feedback effects from the mechanical subsystem. The findings contribute to a more accurate and comprehensive understanding of coupled motor–gear dynamics, which is essential for the design and control of advanced electromechanical transmission systems in high-performance applications. Full article

In the continuous drilling process of oil wells, to achieve the efficient screening of drilling fluids by the vibrating screen while ensuring the safety of the screening operation, an anti-resonance system driven by two exciters with triple-frequency (denoted as 3:1 frequency ratio) is proposed. Initially, differential motion equations are formulated utilizing Lagrange’s equation, followed by the definition of vibration isolation coefficients adopting ratios. Triple-frequency synchronization and stability criterion between two eccentric blocks are subsequently elucidated via the asymptotic method and Routh–Hurwitz criterion. Concurrently, the effects of structural parameters on vibration isolation capacity, steady-state trajectory, and the triple-frequency synchronization phase are investigated through numerical computation. Ultimately, the reliability of the theoretical study is corroborated by simulation analysis. Results indicate that under the allowable system parameters for the practical project, the amplitude of the vibration body can exceed three times that of the isolation body; the two solutions of the stable phase difference (SPD) are different by π, one of which is stable and the other is unstable, and the stability of phase difference is determined by the sign of the stability coefficient. This work is useful for developing new vibrating screens and other multi-frequency vibration machines. Full article

This study addresses the challenge of ride height control in linear motor-based active suspension systems by proposing a control strategy based on linear active disturbance rejection control (LADRC). The effectiveness of the proposed approach is experimentally validated using a high-precision test platform built on the NI cRIO-9014 real-time controller. The platform integrates a permanent magnet synchronous linear motor, a motor driver, acceleration sensors, and a vibration control system to realize closed-loop control of vehicle body height. Experimental results demonstrate that, compared with conventional PID control, LADRC achieves superior performance in height regulation accuracy, dynamic responsiveness, vertical acceleration suppression, and steady-state stability. In step response experiments, LADRC reduces the regulation time by 53.8% (from 1.3 s to 0.6 s) and lowers the steady-state error from 0.502 mm to 0.05 mm. In sinusoidal trajectory tracking tests, the LADRC approach reduces peak and RMS tracking errors by 81.5% and 80.3%, respectively. Moreover, under random road excitation, LADRC effectively attenuates high-frequency body vibrations, with reductions of 29.58% in peak vertical acceleration and 12.23% in RMS acceleration. Full article

With the increasing popularity of low-cost, clean, and environmentally friendly new energy sources, the proportion of grid-connected new energy units has increased significantly. However, since these units are frequency decoupled from the grid through a power electronic interface, they are unable to provide inertia support during active power perturbations, which leads to a decrease in system inertia and reduced frequency stability. In this study, the urgent need to accurately assess inertia is addressed by developing an energy-function-based inertia identification technique that eliminates the effect of damping terms. By integrating vibration mechanics, the proposed method calculates the inertia value after a perturbation using port measurements (active power, voltage phase, and frequency). Simulation results of the Western System Coordinating Council (WSCC) 9-bus system show that the inertia estimation error of the method is less than 1%, which is superior to conventional methods such as rate-of-change-of-frequency (RoCoF) and least squares methods. Notably, the technique accurately evaluates the inertia of synchronous generators and doubly fed induction generators (DFIGs) under virtual inertia control, providing a robust inertia evaluation framework for low-inertia power systems with high renewable energy penetration. This research deepens the understanding of inertial dynamics and contributes to practical applications in grid stability analysis and control strategy optimalization. Full article

High-frequency injection (HFI) is widely adopted for the sensorless control of permanent magnet synchronous motors (PMSMs) at low speeds. However, conventional HFI strategies relying on fixed-frequency carrier modulation and square-wave injection concentrate current harmonic energy within narrow spectral bands, thereby inducing pronounced high-frequency motor vibrations and noise. To mitigate this issue, this paper proposes a noise suppression strategy based on synchronized periodic frequency modulation (PFM) of both the carrier and high-frequency square-wave signals. By innovatively synchronizing the periodic modulation of the triangular carrier in space vector pulse width modulation (SVPWM) with the injected high-frequency square wave, harmonic energy dispersion and noise reduction are achieved, substantially lowering peak acoustic emissions. First, the harmonic characteristics of the voltage-source inverter output under symmetric triangular carrier SVPWM are analyzed within a sawtooth-wave PFM framework. Concurrently, a harmonic current model is developed for the high-frequency square-wave injection method, enabling the precise derivation of harmonic components. A frequency-synchronized modulation strategy between the carrier and injection signals is proposed, with a rigorous analysis of its harmonic suppression mechanism. The rotor position is then estimated via high-frequency signal extraction and a normalized phase-locked loop (PLL). Comparative simulations and experiments confirm significant noise peak attenuation compared to conventional methods, while position estimation accuracy remains unaffected. This work provides both theoretical and practical advancements for noise-sensitive sensorless motor control applications. Full article

To optimize the modulated vibration generated by the integer-slot interior permanent magnet synchronous motor (IPMSM), a stepwise segmented skewed pole method was proposed, using an 8-pole 48-slot IPMSM as an example. First, the vibration characteristics of the motor were studied, and the theoretical mechanisms of the magnetic field modulation effect and radial force modulation effect were explained. The study showed that high-order radial forces can excite larger low-order vibrations under the influence of radial force modulation. Then, in response to the axial spacing in the linear skewed pole structure when canceling the 48th-order radial force, a stepwise skewed pole structure was proposed. The suppression mechanism of this skewed pole structure on the motor’s modulated vibration was analyzed, and the optimization effect of different segment numbers on the motor’s vibration acceleration at $12f_e$ was discussed. Finally, models for the motor’s magnetic field, structural field, and acoustic field before and after skewing were established, and simulations were conducted to compare the magnitudes of the radial forces at each order and their vibration noise performance. The results showed that after stepwise skewed pole optimization, the radial force that excites the modulated vibration was reduced by 68%, the maximum vibration acceleration on the casing surface was reduced by 84%, and the overall noise was reduced by 7.491 dB, effectively suppressing electromagnetic vibration noise. Full article

A PCB stator axial flux permanent magnet (AFPM) motor is presented that overcomes the manufacturing challenges associated with the complex geometry of conventional stators by employing a PCB substrate. Traditionally, AFPM motors are produced by winding coils around the stator teeth, a process that requires specialized winding machinery and is both labor intensive and time consuming, ultimately incurring considerable manufacturing costs and delays. In contrast, PCB substrates offer significant advantages in manufacturability and mass production, effectively resolving these issues. Furthermore, the primary material used in PCB substrates, FR-4, exhibits a permeability similar to that of air, resulting in negligible electromagnetic cogging torque. Cogging torque arises from the attraction between permanent magnets and stator teeth, creating forces that interfere with motor rotation and generate unwanted vibration, noise, and potential mechanical collisions between the rotor and stator. In the PCB stator design, the conventional PCB circuit pattern is replaced by the motor’s coil configuration, and the absence of stator teeth eliminates these interference issues. Consequently, a slotless motor configuration with minimal vibration and noise is achieved. The PCB AFPM motor has been applied to a vehicle-mounted electric water pump (EWP), where mass production and space efficiency are critical. In an EWP, which integrates the impeller with the motor, it is essential that vibrations are minimized since excessive vibration could compromise impeller operation and, due to fluid resistance, require high power input. Moreover, the AFPM configuration facilitates higher torque generation compared to a conventional radial flux permanent magnet synchronous motor (RFPM). In a slotless AFPM motor, the absence of stator teeth prevents core flux saturation, thereby further enhancing torque performance. AC losses occur in the conductors as a result of the magnetic flux produced by the permanent magnets, and similar losses arise within the PCB circuits. Therefore, an optimized PCB circuit design is essential to reduce these losses. The Constant Trace Conductor (CTC) PCB circuit design process is proposed as a viable solution to mitigate AC losses. A 3D finite element analysis (3D FEA) model was developed, analyzed, fabricated, and validated to verify the proposed solution. Full article