Search Results (666)

To improve the control accuracy and address the parameter disturbance issues of joint-driven permanent magnet synchronous motors in intelligent manufacturing, this paper proposes an improved deadbeat predictive current predictive control (DPCC) scheme that eliminates dead zones. This scheme establishes a multi-parameter identification model based on the error equation of the d-q axis predicted current, which improves the problem of not being able to identify all parameters caused by insufficient input signals. It also implements decoupling compensation for the coupling between the d-q axis inductance, stator resistance, and magnetic flux linkage. To meet the anticipated control objectives and account for external disturbances, a low-complexity specified performance tracking controller (LCSPC) based on output target error signals has been designed. This mitigates output delay issues arising from nonlinear components during motor operation. Finally, simulation analysis and experimental testing demonstrate that the proposed control scheme achieves high identification accuracy with minimal delay, thus meeting the transient control performance requirements for motors in intelligent manufacturing processes. Full article

Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) is a receptor found in microglia within the central nervous system (CNS) as well as in several other cell types throughout the body. TREM2 has been highlighted as a “double-edged sword” due to its contribution to anti- or pro-inflammatory signaling responses in a spatial, temporal, and disease-specific fashion. Many of the functions of TREM2 in relation to neurological disease have been elucidated in a variety of CNS pathologies, including neurodegenerative, traumatic, and vascular injuries, as well as autoimmune diseases. Less is known about the function of TREM2 in motoneurons and sensory neurons, whose cell bodies and axons span both the CNS and peripheral nervous system (PNS) and are exposed to a variety of TREM2-expressing cells and mechanisms. In this review, we provide a brief overview of TREM2 and then highlight the literature detailing the involvement of TREM2 along the spinal cord, peripheral nerves and muscles, and sensory, motor, and autonomic functions in health, aging, disease, and injury. We further discuss the current feasibility of TREM2 as a potential therapeutic target to ameliorate damage in the sensorimotor circuits of the spinal cord. Full article

The accurate estimation of parameters in dynamical systems stands for an open key research issue in modeling, control, and fault diagnosis. The presence of noise in input and output signals poses a serious challenge for accurate real-time dynamical system parameter estimation. This paper proposes a new robust algebraic parameter estimation methodology for integer-order dynamical systems that explicitly incorporates the signal filtering dynamics within the estimator structure and enhances noise attenuation through fractional differentiation in frequency domain. The introduced estimation methodology is valid for Liouville-type fractional derivatives and can be applied to estimate online the parameters of differentially flat, oscillating or vibrating systems of multiple degrees of freedom. The parametric estimation can be thus implemented for a wide class of oscillating or vibrating, nth-order dynamical systems under noise influence in measurement and control signals. Positive values are considered for the inertia, stiffness, and viscous damping parameters of vibrating systems. Parameter identification can be also used for development of actuators and control technology. In this sense, validation of the algebraic parameter estimation is performed to identify parameters of a differentially flat, permanent-magnet direct-current motor actuator. Parameter estimation for both open-loop and closed-loop control scenarios using experimental data is examined. Experimental results demonstrate that the new parameter estimation methodology combining signal filtering dynamics and fractional calculus outperforms other conventional methods under presence of significant noise in measurements. Full article

Induction motors (IM) play essential tasks in distinct production sectors because of their low cost and robustness. Considering that most of the energy demand in industry is allocated for powering up IM, recent research has focused on detecting and predicting faults to avoid severe disturbances. Broken rotor bars (BRB) in IM cause a significant deficit of energy, above all in those applications where constant changes in speed are required, increasing the probability of a catastrophic failure. Variable speed drives (VSD) introduce harmonic components to the power supply current controlling the IM rotating speed, which make it difficult to identify BRB. Therefore, in this work, an innovative methodology is proposed for detecting BRB in VSD-fed IM with a wide rotating-speed bandwidth during their start-up transient. The introduced procedure performs a statistical analysis for computing the mean, median, mode, variance, skewness, and kurtosis, to identify slight changes on the acquired current signal. These values are fed into an artificial neural network (ANN), which carries out the IM operational condition classification as healthy (HLT) or with BRB. Experimentally obtained results corroborate the effectiveness of the proposed approach to detecting BRB even for dynamically varying rotating speed, reaching a high accuracy of 99%, similar to recently reported techniques. Full article

With the ongoing advancement of triboelectric nanogenerator (TENG) technology, a novel internal integrated monitoring sensor has been introduced for traditional industrial equipment. A multilayer triboelectric material deep groove ball triboelectric nanogenerator (DGTG) device has been proposed to monitor the rotational speed and slip state of the rolling elements. The DGTG utilizes a copper inner ring charge supplementation mechanism to maintain the maximum charge density on the rolling element, thereby ensuring a strong electrical signal output. The deviation between the output frequency of the electrical signal and the theoretical value allows for effective monitoring of the slip state during bearing operation. Experimental results demonstrate that when the inner ring speed ranges from 100 to 2000 rpm, the open-circuit voltage generally remains above 30 V. The short-circuit current signal exhibits a fitting coefficient of R² = 0.99997 with respect to the roller’s rotational speed frequency and motor speed, while the open-circuit voltage signal shows a fitting coefficient of R² = 0.99984, indicating a strong linear relationship and a good response to varying speeds. Compared to the traditional photoelectric sensors commonly used in industry, the measurement difference between the three signals is consistently less than 5.5%, and real-time monitoring of the slip rate is possible when compared to the theoretical value. The DGTG developed in this study occupies minimal space, offers high reliability, and fully leverages the bearing structure, enabling real-time monitoring of bearing speed and slip. Full article

Electric drive systems (EDSs) are vital for automotive and industrial applications but remain highly vulnerable to cyber and physical anomalies (CPAs), such as inverter open-circuit faults, sensor failures, and malicious cyberattacks. Ensuring reliable EDS operation requires the controller to receive accurate and uncompromised feedback and reference signals continuously. However, many existing data-driven detection and mitigation strategies rely on large training datasets, impose significant computational overhead, and often lose effectiveness under various abnormal operating conditions. To overcome these limitations, this paper introduces a trust evaluation framework that continuously assesses the reliability of all incoming signals to the EDS controller by combining behavioral analysis with historical reliability records. The proposed scheme offers a lightweight and model-independent approach, enabling reliable, adaptive decision-making by leveraging both current and historical signal behavior. To this end, this paper further integrates the resulting trust values into a torque-split optimization algorithm, enabling adaptive load optimization by dynamically reducing the torque contribution of motors operating under abnormal or low-trust conditions, thereby demonstrating clear applicability for automotive drive systems. The framework is validated in a real-time OPAL-RT environment across multiple CPA scenarios, demonstrating accurate anomaly detection and adaptive torque redistribution. Owing to its simplicity and versatility, the proposed method can be readily extended to other safety-critical drive applications. Full article

Traditional fault detection methods, often designed for centralized or cloud-based systems, are ill-suited for the edge. The deployment of predictive maintenance solutions on ultra-low-cost embedded platforms remains a significant challenge due to strict limitations in memory, processing capacity, and energy availability. To address these challenges, vibration and motor current signals were analyzed using an ultra-low-cost RP2040 microcontroller. For fault detection, this study uses statistical time-domain features and principal component analysis (PCA), followed by classification with eXtreme Gradient Boosting (XGBoost) models distilled for resource-constrained deployment. Experimental evaluation demonstrated that vibration-based features achieved a diagnostic accuracy of 94.1%, while current-based representations obtained 95.5% accuracy when using principal components, compared to 83.2% with handcrafted statistical features. Model distillation reduced memory footprint by up to 2.5× (from 0.42 MB to 0.15 MB) without compromising diagnostic fidelity, enabling deployment within the 264 KB RAM and 2 MB Flash constraints of the RP2040 microcontroller. This study proposes a modular framework that systematically evaluates statistical features, dimensionality reduction, sensor synchronization, and model distillation, thereby identifying the most cost-efficient combination of techniques that balances diagnostic accuracy with strict memory and processing constraints. The findings establish that accurate fault detection can be realized directly on severely resource-limited hardware, thereby extending the practical applicability of condition monitoring to cost-sensitive industrial environments. Full article

Induction motors are a common component of electromechanical equipment in mining operations, yet they are susceptible to failures resulting from frequent start–stops, overloading, wear and tear, and component failure. It is evident that such failures can result in severe ramifications, encompassing industrial accidents and economic losses. The present paper proposes a detailed study of engine fault diagnosis technology. It has been demonstrated that prevailing intelligent engine diagnosis algorithms exhibit a limited diagnostic efficacy under variable operating conditions, and the reliability of diagnostic outcomes based on individual signals is questionable. The present paper puts forward the proposition of an investigation into a fault diagnosis algorithm for induction motors. This investigation utilized a range of analytical methods, including signal analysis, deep learning, transfer learning, and information fusion. Currently, the methods employed for fault diagnosis based on traditional machine learning are reliant on the selection of statistical features by those with expertise in the field, resulting in outcomes that are significantly influenced by human factors. This paper is the first to integrate a multi-branch ResNet strategy combining three-phase and single-phase currents. A range of three-phase current input strategies were developed, and a deep learning-based motor fault diagnosis model with adaptive feature extraction was established. This enables the deep residual network to extract fault depth features from the motor current signal more effectively. The experimental findings demonstrate that deep learning possesses the capacity to automatically extract depth features, thereby exceeding the capabilities of conventional machine learning algorithms with regard to the accuracy of motor fault diagnosis. Full article

Robust speed estimation in induction motors (IM) is essential for control systems (especially in sensorless drive applications) and condition monitoring. Traditional model-based techniques for inverter-fed IM provide a high accuracy but rely heavily on precise motor parameter identification, requiring multiple sensors to monitor the necessary variables. In contrast, model-independent methods that use rotor slot harmonics (RSH) in the stator current spectrum offer a better adaptability to various motor types and conditions. However, many of these techniques are dependent on full-band processing, which reduces noise immunity and increases computational cost. This paper introduces a novel subband signal processing approach for rotor speed estimation focused on RSH tracking under both steady and non-steady states. By limiting spectral analysis to relevant content, the method significantly reduces computational demand. The technique employs an advanced time-frequency analysis for high-resolution frequency identification, even in noisy settings. Simulations and experiments show that the proposed approach outperforms conventional RSH-based estimators, offering a robust and cost-effective solution for integrated speed monitoring in practical applications. Full article

In order to analyze and solve the problem of excessive commutation spark of DC motor in ship electric propulsion system, which leads to a decrease in output power and low torque, this paper first establishes a mathematical model of the ship DC motor, builds its simulation model based on the mathematical model, and conducts simulation verification. Secondly, the Cassie arc model is introduced to model the commutation spark, and the Cassie arc model is connected in series in the armature winding of the DC motor to achieve virtual injection of excessive spark fault of the DC motor. Finally, the Fourier transform and wavelet analysis are used to process the data of the armature winding current and excitation current of the DC motor. The simulation results show that when an arc fault occurs in the DC motor, the ripple coefficient of the armature current and excitation current will increase, and the high-frequency component will increase. DB8 is an adopted wavelet function that decomposes the armature current and excitation current six times, and calculates the energy changes before and after the fault of each decomposed signal layer. It is found that without considering the approximate components, the D4 layer wavelet energy of the armature current and excitation current has the largest proportion in the detail components. The D1, D2, and D3 layers’ wavelet decomposition signals of the armature current and excitation current have significant energy changes; that is, the energy increase in the middle and high frequency parts exceeds 20%, and the D3 layer wavelet decomposition signal has the largest energy change, exceeding 40%. This can be used as a fault characteristic quantity to determine whether the DC motor has a large spark fault. This study can provide reference and guidance for online detection technology of excessive sparks in ship DC motors. Full article

This project presents the development of an open-source educational platform based on an automotive Electrically Excited Synchronous Machine (EESM) repurposed from a KIA Sportage mild-hybrid vehicle. The introduction provides an overview of hybrid drive systems and the primary configurations employed in automotive applications, including classifications based on power flow and the placement of electric motors. The focus is placed on the parallel hybrid configuration, where a belt-driven starter-generator assists the internal combustion engine (ICE). Due to the proprietary nature of the original control system, the unit was disassembled, and a custom control board was designed using a Texas Instruments C2000 Digital Signal Processor (DSP). The motor features a six-phase dual three-phase stator, offering improved torque smoothness, fault tolerance, and reduced current per phase. A compact Anisotropic Magneto Resistive (AMR) position sensor was implemented for position and speed measurements. Current sensing was achieved using both direct and magnetic field-based methods. The control algorithm was verified on a modified six-phase inverter under simulated vehicle conditions utilizing a dynamometer. Results confirmed reliable operation and validated the control approach. Future work will involve complete hardware testing with the new control board to finalize the platform as a flexible, open-source tool for research and education in hybrid drive technologies. Full article

Abrupt speed variations and motor start-ups have been pointed out as critical challenges in the framework of fault diagnostics in induction motor drives, namely inter-turn short circuit faults. Generally, abrupt accelerations influence the typical symptoms of the fault, and consequently, the fault detection becomes ambiguous, impacting prompt and effective decision-making. To overcome this issue, this study proposes an inter-turn short-circuit fault diagnostic technique for asymmetrical six-phase induction motor drives operating under both smooth and abrupt motor accelerations. A time–frequency domain spectrogram of the AC component extracted from the q-axis reference current signal serves as a reliable fault indicator. This technique stands out for the compromise between robustness and computational effort using only one control variable accessible in the model predictive control algorithm, thus discarding both voltage and current signals. Experimental tests involving various load torques and fault severities, in transient regimes, were performed to validate the proposed methodology’s effectiveness thoroughly. Full article

A wearable closed-loop transcutaneous electrical nerve stimulation (TENS) platform has been developed to address the limitations of conventional open-loop neuromodulation systems. Unlike existing systems such as CLoSES—which targets intracranial stimulation—and electromyography-triggered functional electrical stimulation (EMG-FES) platforms primarily used for motor rehabilitation, the proposed device uniquely integrates low-latency surface electromyography (sEMG)-driven control with six-channel current stimulation in a fully wearable, non-invasive format aimed at ambulatory pain modulation. The system combines real-time sEMG acquisition, adaptive signal processing, a programmable multi-channel stimulation engine, and a high-voltage, boost-regulated power supply within a compact, battery-powered architecture. Bench-top evaluations demonstrate rapid response to EMG events and stable biphasic output (±22 mA) across all channels with high electrical isolation. A human-subject protocol using the Cold Pressor Test (CPT), heart rate variability (HRV), and galvanic skin response (GSR) has been designed to evaluate analgesic efficacy. While institutional review board (IRB) approval is pending, the system establishes a robust foundation for future personalized, mobile neuromodulation therapies. Full article

Background: Freezing of gait (FoG) is one of the most debilitating motor symptoms in Parkinson’s disease (PD). It often leads to falls and reduces quality of life due to the risk of injury and loss of independence. Several types of wearable sensors have emerged as promising tools for the detection of FoG in clinical and real-life settings. Objective: The main objective of this systematic review was to critically evaluate the current usability of wearable sensor technologies for FoG detection in PD patients. The focus of the study is on sensor types, sensor combinations, placement on the body and the applications of such detection systems in a naturalistic environment. Methods: PubMed, IEEE Explore and ACM digital library were searched using a search string of Boolean operators that yielded 328 results, which were screened by title and abstract. After the screening process, 43 articles were included in the review. In addition to the year of publication, authorship and demographic data, sensor types and combinations, sensor locations, ON/OFF medication states of patients, gait tasks, performance metrics and algorithms used to process the data were extracted and analyzed. Results: The number of patients in the reviewed studies ranged from a single PD patient to 205 PD patients, and just over 65% of studies have solely focused on FoG + PD patients. The accelerometer was identified as the most frequently utilized wearable sensor, appearing in more than 90% of studies, often in combination with gyroscopes (25.5%) or gyroscopes and magnetometers (20.9%). The best overall sensor configuration reported was the accelerometer and gyroscope setup, achieving nearly 100% sensitivity and specificity for FoG detection. The most common sensor placement sites on the body were the waist, ankles, shanks and feet, but the current literature lacks the overall standardization of optimum sensor locations. Real-life context for FoG detection was the focus of only nine studies that reported promising results but much less consistent performance due to increased signal noise and unexpected patient activity. Conclusions: Current accelerometer-based FoG detection systems along with adaptive machine learning algorithms can reliably and consistently detect FoG in PD patients in controlled laboratory environments. The transition of detection systems towards a natural environment, however, remains a challenge to be explored. The development of standardized sensor placement guidelines along with robust and adaptive FoG detection systems that can maintain accuracy in a real-life environment would significantly improve the usefulness of these systems. Full article

High-performance musical instrument training is a demanding discipline that engages cognitive, neurological, and physical skills. Professional musicians invest substantial time and effort into mastering their repertoire and developing the muscle memory and reflexes required to perform complex works in high-stakes settings. While existing surveys have explored the use of music in therapeutic and general training contexts, there is a notable lack of work focused specifically on the needs of professional musicians and advanced instrumental practice. This topical review explores the potential of EEG-based brain–computer interface (BCI) technologies to integrate real-time feedback of biomechanic and cognitive features in advanced musical practice. Building on a conceptual framework of technology-enhanced musical practice (TEMP), we review empirical studies of broad contexts, addressing the EEG signal decoding of biomechanic and cognitive tasks that closely relates to the specified TEMP features (movement and muscle activity, posture and balance, fine motor movements and dexterity, breathing control, head and facial movement, movement intention, tempo processing, ptich recognition, and cognitive engagement), assessing their feasibility and limitations. Our analysis highlights current gaps and provides a foundation for future development of BCI-supported musical training systems to support high-performance instrumental practice. Full article