Search Results (833)

Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article

Aiming at the problems of low thermal analysis efficiency and high computational cost of traditional computational fluid dynamics (CFD) methods for modular fault-tolerant permanent magnet synchronous motors (MFT-PMSMs) under complex working conditions, this paper proposes a fast modeling and calculation method of motor temperature field based on a high-efficiency and high-precision network algorithm. In this method, the physical structure of the motor is equivalent to a parameterized network model, and the computational efficiency is significantly improved by model partitioning and Fourth-order Runge Kutta method. The temperature change of the cooling medium is further considered, and the temperature rise change of the motor at different spatial positions is effectively considered. Based on the finite element method (FEM), the space loss distribution under rated, single-phase open circuit and overload conditions is obtained and mapped to the thermal network nodes. Through the transient thermal network solution, the rapid calculation of the temperature rise law of key components such as windings and permanent magnets is realized. The accuracy of the thermal network model was verified by using fluid-structure coupling simulation and prototype test for temperature analysis. This method provides an efficient tool for thermal safety assessment and optimization in the motor fault-tolerant design stage, especially for heat capacity check under extreme conditions and fault modes. Full article

Electric submersible pumps (ESPs) are critical for artificial lift operations; however, they are prone to frequent failures, often resulting in high operational costs and production downtime. Traditional ESP failure classifications are limited by lack of standardization and the conflation of failure modes with root causes. To address these limitations, this study proposes a new two-step integrated failure modes and root cause (IFMRC) classification system. The new framework clearly distinguishes between failure modes and root causes, providing a systematic, structured approach that enhances fault diagnosis and failure analysis and can lead to better failure prevention strategies. This methodology was validated using a case study of over 4000 ESP installations. The data came from Egypt’s Western Desert, covering a decade of operational data. The sources included ESP databases, workover records, and detailed failure investigation (DIFA) reports. The failure modes were categorized into electrical, mechanical, hydraulic, chemical, and operational types, while root causes were linked to environmental, design, operational, and equipment factors. Statistical analysis, in this case study, revealed that motor short circuits, low flow conditions, and cable short circuits were the most frequent failure modes, with excessive heat, scale deposition, and electrical grounding faults being the dominant root causes. This study underscores the importance of accurate root cause failure classification, robust data acquisition, and expanded failure diagnostics to improve ESP reliability. The proposed IFMRC framework addresses limitations in conventional taxonomies and facilitates ongoing enhancement of ESP design, operation, and maintenance in complex field conditions. Full article

This article presents a non-linear MEC for a linear PM motor, and its experimental validation. In the MEC model, winding flux leakage and iron saturation are considered. In addition, two different linear PM motor models (bread-loaf and surface-type) are examined for linear PM motors. An iterative method is used to predict the magnetic behavior of saturated magnetic steel. The proposed MEC for linear PM motors is compared with finite element analysis (FEA) to determine its accuracy and suitability. FEA is widely regarded as a highly accurate and reliable tool for analyzing linear PM motors. However, its primary limitation lies in its considerable computational time requirement. This disadvantage becomes particularly problematic during the early stages of the design process. Therefore, the proposed model addresses this limitation. Also, experimental results validate the practicality of the MEC. Finally, the proposed model can be a tool for different slot/pole combinations. Thus, the model can be considered suitable for both bread-loaf and surface-type PM motors. Full article

In the context of increasing the complexity and intelligence of modern power systems, traditional maintenance approaches for circuit breakers have shown limitations in meeting both reliability and economic requirements. This paper proposes a multi-sensor data fusion fault detection method based on the RF-Adaboost algorithm for spring-operated circuit breakers. By integrating pressure, speed, coil current, and energy storage motor sensors into the mechanism, multi-source operational data are acquired and processed via denoising and feature extraction techniques. A fault detection model is then constructed using the RF-Adaboost classifier. The experimental results demonstrate that the proposed method achieves over 96% accuracy in identifying typical fault states such as coil voltage deviation, reset spring fatigue, and closing spring degradation, outperforming conventional approaches. These results validate the model’s effectiveness and robustness in diagnosing complex mechanical failures in circuit breakers. Full article

This paper proposes an anti-disturbance model predictive fault-tolerance control strategy for open-circuit faults of five-phase flux intensifying fault-tolerant interior permanent magnet (FIFT-IPM) motors. This strategy is applicable to electric agricultural equipment that has an open winding failure. Due to the rich third-harmonic back electromotive force (EMF) content of five-phase FIFT-IPM motors, the existing model predictive current fault-tolerant control algorithms fail to effectively track fundamental and third-harmonic currents. This results in high harmonic distortion in the phase current. Hence, this paper innovatively proposes a multi-plane virtual vector model predictive fault-tolerant control strategy that can achieve rapid and effective control of both the fundamental and harmonic planes while ensuring good dynamic stability performance. Additionally, considering that electric agricultural equipment is usually in a multi-disturbance working environment, this paper introduces an adaptive gain sliding-mode disturbance observer. This observer estimates complex disturbances and feeds them back into the control system, which possesses good resistance to complex disturbances. Finally, the feasibility and effectiveness of the proposed control strategy are verified by experimental results. Full article

The substantia nigra (SN) has historically been regarded as a pivotal element of the brain’s motor circuits, notably within the context of the nigrostriatal pathway and Parkinson’s disease. However, recent advancements in neuroimaging techniques, particularly tractography, have facilitated the delineation of its anatomical projections. These techniques have revealed the involvement of the SN in a more extensive array of functional networks encompassing cognitive, emotional, and motivational domains. This paper reviews the current knowledge on the structural connectivity of the SN in humans based on diffusion tensor imaging and tractography. It summarizes the main projection pathways, including classical and newly described connections, such as the direct SN pars compacta connections to the thalamus, cortico–neural inputs, and connections to limbic regions and the hippocampus. Furthermore, the text delves into the distinctions between the SN pars compacta and SN pars reticulata subregions, exploring their parcellation based on connectivity. The paper demonstrates that the SN is a functionally diversified nucleus, the implications of which are significant for the understanding of both motor and neuropsychiatric disorders. The present study addresses the paucity of comprehensive treatment in this area and provides a framework for further research on dopaminergic circuits. Full article

Introduction: Huntington’s disease (HD) disrupts cortico-striato-thalamocortical circuits decades before clinical onset. Electroencephalography (EEG) offers millisecond temporal resolution, low cost, and broad accessibility, yet its mechanistic and biomarker potential in HD remains underexplored. We conducted a mechanistic review to synthesize half a century of EEG findings, identify reproducible electrophysiological signatures, and outline translational next steps. Methods: Two independent reviewers searched PubMed, Scopus, Google Scholar, ResearchGate, and the Cochrane Library (January 1970–April 2025) using the terms “EEG” OR “electroencephalography” AND “Huntington’s disease”. Clinical trials published in English that reported raw EEG (not ERP-only) in human HD gene carriers were eligible. Abstract/title screening, full-text appraisal, and cross-reference mining yielded 22 studies (~700 HD recordings, ~600 controls). We extracted sample characteristics, acquisition protocols, spectral/connectivity metrics, and neuroclinical correlations. Results: Across diverse platforms, a consistent spectral trajectory emerged: (i) presymptomatic carriers show a focal 7–9 Hz (low-alpha) power loss that scales with CAG repeat length; (ii) early-manifest patients exhibit widespread alpha attenuation, delta–theta excess, and a flattened anterior-posterior gradient; (iii) advanced disease is characterized by global slow-wave dominance and low-voltage tracings. Source-resolved studies reveal early alpha hypocoherence and progressive delta/high-beta hypersynchrony, microstate shifts (A/B ↑, C/D ↓), and rising omega complexity. These electrophysiological changes correlate with motor burden, cognitive slowing, sleep fragmentation, and neurovascular uncoupling, and achieve 80–90% diagnostic accuracy in shallow machine-learning pipelines. Conclusions: EEG offers a coherent, stage-sensitive window on HD pathophysiology—from early thalamocortical disinhibition to late network fragmentation—and fulfills key biomarker criteria. Translation now depends on large, longitudinal, multi-center cohorts with harmonized high-density protocols, rigorous artifact control, and linkage to clinical milestones. Such infrastructure will enable the qualification of alpha-band restoration, delta-band hypersynchrony, and neurovascular coupling as pharmacodynamic readouts, fostering precision monitoring and network-targeted therapy in Huntington’s disease. Full article

Spinal cord injury (SCI) is a severe neurological condition that typically results in irreversible loss of motor and sensory function. Emerging evidence indicates that neuroplasticity, the ability of the nervous system to reorganize by forming new neural connections, plays a pivotal role in structural and functional recovery post-injury. This insight lays the groundwork for the development of rehabilitation and therapeutic strategies designed to leverage neuroplasticity. In this review, we offer an exhaustive overview of the neuroplastic alterations and mechanisms that occur following an SCI. We examine the role of neuroplasticity in functional recovery and outline therapeutic approaches designed to augment neuroplasticity post-SCI. The process of neuroplasticity post-SCI involves several physiological processes, such as neurogenesis, synaptic remodeling, dendritic spine formation, and axonal sprouting. Together, these processes contribute to the reestablishment of neural circuits and functional restoration. Enhancing neuroplasticity is a promising strategy for improving functional outcomes post-SCI; however, its effectiveness is influenced by numerous factors, including age, injury severity, time since the injury, and the specific therapeutic interventions employed. A variety of strategies have been suggested to promote neuroplasticity and expedite recovery, including pharmacological treatments, biomaterial-based therapies, gene editing, stem cell transplantation, and rehabilitative training. The combination of personalized rehabilitation programs with innovative therapeutic techniques holds considerable potential for maximizing the benefits of neuroplasticity and enhancing clinical outcomes in SCI management. Full article

Lampreys are primitive fish that rely significantly on olfactory cues throughout their complex life cycle. The olfactory system of the sea lamprey (Petromyzon marinus) is among the best characterized in vertebrates. In recent decades, tremendous advances have been made by isolating individual compounds from sea lampreys that can replicate natural behavior when artificially applied in the wild. In no other aquatic vertebrate has the olfactory ecology been described in such extensive detail. In the first section, we provide a comprehensive review of olfactory behaviors induced by specific, individual odorants during every major developmental stage of the sea lamprey in behavioral contexts such as feeding, predator avoidance, and reproduction. Moreover, pheromonal inputs have been shown to induce neuroendocrine responses through the hypothalamic-pituitary-gonadal axis, triggering remarkable developmental and physiological effects, such as gametogenesis and increased pheromone release. In the second section of this review, we describe a hypothetical endocrine signaling pathway through which reproductive fitness is increased following pheromone detection. In the final section of this review, we focus on the neuronal circuits that transform olfactory inputs into motor output. We describe specific brain signaling pathways that underlie odor-evoked locomotion. Furthermore, we consider possible modulatory inputs to these pathways that may induce plasticity in olfactory behavior following changes in the external or internal environment. As a whole, this review synthesizes previous and recent progress in understanding the behavioral, endocrine, and neuronal responses of lampreys to chemosensory signals. Full article

Electric vehicle applications frequently use brushless direct-current (BLDC) motors due to their high torque and efficiency. However, coil damage may result from their use at high rotating speeds and extremely high temperatures, requiring preventative maintenance. This study describes the creation of a better online monitoring platform that is coupled with an improved fault detection and protection system for small electric vehicles. Designing a fault detection system with real-time analysis to identify open-circuit problems is part of the process. The results indicate that the reliability and operating efficiency of electric vehicle applications have been greatly enhanced by the development of a potential fault-monitoring and protection solution. Full article

In order to solve the zoom delay issue for high-magnification zoom optical systems, a voice coil motor (VCM) is used to achieve rapid zooming. In this paper, the structural design of VCMs is systematically analyzed through magnetic field numerical computations. Firstly, finite element method (FEM) is used to analyze magnetic field of single magnets, and simulations correspond to experimental results. Both FEM and equivalent magnetic charge (EMC) results confirm that increasing magnet thickness while reducing its lateral dimensions will contribute to magnetic enhancement. Furthermore, the influence of structural parameters VCM is analyzed, validating the yoke’s critical role in suppressing edge effects and optimizing magnetic circuit efficiency, and optimal yoke thickness and magnet width range are determined. Moreover, a simple EMC calculation method is proposed for rapid and accurate determination of the magnetic field distribution in the VCM air gap. Optimal structural parameters of VCM are determined for a 40× rapid zoom lens with cost and space limitations. Driving force F_drive = 5.58 N is about 5 times the demand force F_d = 1.06 N, and the prototype fabrication of the rapid zoom lens is successfully accomplished. Moving group reaches 35.4 mm destination within 0.18 s, and photographs confirm that the rapid zoom system achieves 100-ms-level short/long-focus transition. Rapid zoom lens shows great potential in applications including security surveillance, industrial visual inspection, and intelligent logistics management. Full article

Adopting mobile robots for high voltage (HV) live-line operations can mitigate personnel casualties and enhance operational efficiency. However, conventional mechanical arms cannot inspect concealed spaces in the power cable tunnel because their joint integrates metallic motors or hydraulic serial-drive mechanisms, which limit the arm’s length and insulation performance. Therefore, this study proposes a 7-degree-of-freedom (7-DOF) bionic mechanical arm with rigid-flexible coupling, mimicking human arm joints (shoulder, elbow, and wrist) designed for HV live-line operations in concealed cable tunnels. The arm employs a tendon-driven mechanism to remotely actuate joints, analogous to human musculoskeletal dynamics, thereby physically isolating conductive components (e.g., motors) from the mechanical arm. The arm’s structure utilizes dielectric materials and insulation-optimized geometries to reduce peak electric field intensity and increase creepage distance, achieving intrinsic self-insulation. Furthermore, the mechanical design addresses challenges posed by concealed spaces (e.g., shield tunnels and multi-circuit cable layouts) through the analysis of joint kinematics, drive mechanisms, and dielectric performance. The workspace of the proposed arm is an oblate ellipsoid with minor and major axes measuring 1.25 m and 1.65 m, respectively, covering the concealed space in the cable tunnel, while the arm’s quality is 4.7 kg. The maximum electric field intensity is 74.3 kV/m under 220 kV operating voltage. The field value is less than the air breakdown threshold. The proposed mechanical arm design significantly improves spatial adaptability, operational efficiency, and reliability in HV live-line inspection, offering theoretical and practical advancements for intelligent maintenance in cable tunnel environments. Full article

This paper analyzes the electromagnetic characteristics of an interior permanent magnet synchronous motor (IPMSM) for electric vehicle traction under various control imbalance conditions in a twin inverter system, assuming that one of the inverters fails to operate properly. The imbalance conditions are first investigated through dynamometer experiments and then applied to finite element method (FEM) simulations to evaluate their electromagnetic effects. Since the focus is on scenarios where a single inverter malfunctions, a stator winding configuration is first redefined to ensure stable operation in a single inverter system by preventing voltage and current imbalances within the circuit. When the stator winding is configured with eight parallel paths, the dynamometer test results show a phase voltage imbalance. However, when the number of parallel circuits is reduced to four, this voltage imbalance disappears. Using this configuration, a twin inverter system is constructed, and various imbalance conditions are applied to intuitively examine the electromagnetic characteristics when one inverter fails to accurately control current magnitude or phase angle. The simulation results showed that applying unbalanced conditions to the current and current phase angle led to a decrease in torque and an increase in torque ripple. In addition, when one of the inverters was completely disconnected, the motor performance analysis showed that it operated with approximately half of its original performance. Based on dynamometer experiments and finite element method (FEM) simulations, the electromagnetic characteristics under inverter fault conditions and appropriate stator winding configurations were analyzed. When an optimal number of parallel circuits is applied to the stator winding and a twin inverter system is employed, the load on each individual inverter is reduced, enabling accurate control. This makes the application to high-voltage and high-current systems feasible, allowing higher performance. Moreover, even if one inverter fails, the system can still operate at approximately half its capacity, ensuring high operational reliability. Full article

This study investigates the low-signal behavior of electric motors in automotive applications, emphasizing impedance measurement, evaluation, and the definition of a simplified dummy load. A comprehensive experimental analysis was conducted on two induction motors with different power ratings (300 W and 45 kW), exploring the influence of winding topology, rotor position, and excitation amplitude on the impedance response. A simplified equivalent circuit model (ECM), derived solely from terminal impedance measurements, was developed and validated to construct a practical dummy load. This model facilitates realistic simulations without requiring detailed internal motor specifications. Experimental results confirm that the dummy load accurately replicates the measured impedance characteristics in the low-to-mid frequency range, demonstrating its effectiveness for electromagnetic interference (EMI) prediction and system-level simulations in automotive electric drive system. Full article