Search Results (60)

This study presents a detailed investigation of the torque-speed characteristics of a WEG single-phase squirrel-cage induction motor (SPSCIM) of (1/2 hp), 110/220 V at 60 Hz. The primary objective was to derive the motor’s equivalent circuit and validate its performance curves through finite element analysis (FEA), simulation using MATLAB^®/Simulink^®, and experimental testing. Finite element simulations were conducted using the software FEMM (Finite Element Method Magnetics) to model the magnetic flux distribution within the motor’s stator and rotor. These simulations, based on the motor’s dimensions and nameplate data, provided essential insights into the electromagnetic behavior, including flux density and saturation effects, which are crucial for accurate torque-speed curve predictions. For experimental validation, tests were performed under open-circuit and locked-rotor conditions through a universal machine as a load emulator. The torque-speed characteristics were determined using the Suhr method and the classical approach, with the resulting curves compared to experimental measurements. Voltage and current were measured using AC PZEM-004T and DC PZEM-017 meters, while rotor speed was monitored with a Hall effect sensor (A3144). The results revealed strong agreement between the FEM simulations, Surh method, and experimental data, demonstrating the reliability and accuracy of the combined simulation and analytical methods for modeling the motor’s performance. The estimations using classical and Suhr methods, Simulink simulations, and FEMM yielded low error percentages, mostly below 2%. However, in the FEMM simulation, rotor resistance showed a higher error of around 20% due to unavailable data on the exact number of windings turns, a modifiable parameter that can be corrected through further adjustments in the simulation. The torque-speed curves obtained at different voltage levels showed an excellent correlation, confirming the effectiveness of the proposed approach in characterizing the motor’s operational behavior. Full article

Autophagy impairments have been implicated in various aging conditions. Previous studies in cervical motor neurons show an age-dependent increase in the key autophagy proteins LC3 and p62, reflecting autophagy impairment and autophagosome accumulation. Chloroquine is commonly used to inhibit autophagy by preventing autophagosome–lysosome fusion and may thus emulate the effects of aging on the neuromuscular system. Indeed, acute chloroquine administration in old mice decreases maximal transdiaphragmatic pressure generation, consistent with aging effects. We hypothesized that chloroquine alters diaphragm muscle neuromuscular junction (NMJ) morphology and increases denervation. Adult male and female C57BL/6 × 129J mice between 5 and 8 months of age were used to examine diaphragm muscle NMJ morphology and denervation following daily intraperitoneal injections of chloroquine (10 mg/kg/d) or vehicle for 7 days. The motor end-plates and pre-synaptic terminals were fluorescently labeled with α-bungarotoxin and anti-synaptophysin, respectively. Confocal microscopy was used to assess pre- and post-synaptic morphology and denervation. At diaphragm NMJs, chloroquine treatment decreased pre-synaptic volume by 12% compared to the vehicle (p < 0.05), with no change in post-synaptic volume. Chloroquine treatment increased the proportion of partially denervated NMJs by 2.7-fold compared to vehicle treatment (p < 0.05). The morphological changes observed were similar to those previously reported in the diaphragm muscles of 18-month-old mice. These findings highlight the importance of autophagy in the maintenance of the structural properties at adult NMJs in vivo. Full article

Maintenance strategies such as condition-based maintenance and predictive maintenance of machines have gained importance in industrial automation firms as key concepts in Industry 4.0. As a result, online condition monitoring of electromechanical systems has become a crucial task in many industrial applications. Motor current signature analysis (MCSA) is an interesting noninvasive alternative to vibration analysis for the condition monitoring and fault diagnosis of mechanical systems driven by electric motors. The MCSA approach is based on the premise that faults in the mechanical load driven by the motor manifest as changes in the motor’s current behavior. This paper presents a novel data-driven, MCSA-based CM approach that exploits autoregressive (AR) spectral estimation. A multiresolution analysis of the raw motor currents is first performed using the discrete wavelet transform with Daubechies filters, enabling the separation of noise, disturbances, and variable torque effects from the current signals. AR spectral estimation is then applied to selected wavelet details to extract relevant features for fault diagnosis. In particular, a reference AR power spectral density (PSD) is estimated using data collected under healthy conditions. The AR PSD is then continuously or periodically updated with new data frames and compared to the reference PSD through the Symmetric Itakura–Saito spectral distance (SISSD). The SISSD, which serves as the health indicator, has proven capable of detecting fault occurrences through changes in the AR spectrum. The proposed procedure is tested on real data from two different scenarios: (i) an experimental in-house setup where data are collected during the execution of electric cam motion tasks (imbalance faults are emulated); (ii) the Korea Advanced Institute of Science and Technology testbed, whose data set is publicly available (bearing faults are considered). The results demonstrate the effectiveness of the method in both fault detection and isolation. In particular, the proposed health indicator exhibits strong detection capabilities, as its values under fault conditions exceed those under healthy conditions by one order of magnitude. Full article

Studying the interactions between biological organisms and their environment provides engineers with valuable insights for developing complex mechanical systems and fostering the creation of novel technological innovations. In this study, we introduce a novel bio-inspired three degrees of freedom (DOF) spherical robotic manipulator (SRM), designed to emulate the biomechanical properties observed in nature. The design utilizes the transformation of spherical Complex Spatial Kinematic Pairs (CSKPs) to synthesize bio-inspired robotic manipulators. Additionally, the use of screw theory and the Levenberg–Marquardt algorithm for kinematic parameter computation supports further advancements in human–robot interactions and simplifies control processes. The platform directly transmits motion from the motors to replicate the ball-and-socket mobility of biological joints, minimizing mechanical losses, and optimizing energy efficiency for superior spatial mobility. The proposed 3DOF SRM provides advantages including an expanded workspace, enhanced dexterity, and a lightweight, compact design. Experimental validation, conducted through SolidWorks, MATLAB, Python, and Arduino, demonstrates the versatility and broad application potential of the novel bio-inspired 3DOF SRM, positioning it as a robust solution for a wide range of robotic applications. Full article

In contrast to the conventional topology, wherein the Device Under Test (DUT) controller and the electric motor emulator (EME) are powered by the DC (Direct Current) voltage source independently, the common DC bus topology necessitates a single power supply. This reduces the cost and complexity of the motor emulator system, making it more favorable for large-scale industrial applications. However, this topology introduces significant circulating current issues in the system. A common DC bus circulating current suppression method is proposed in this paper for the motor emulator. First, the mechanism of zero-sequence circulating current generation in the common DC bus topology is analyzed and the expression for the system’s zero-sequence voltage difference is derived. Then, a control method based on a Hybrid PWM (Pulse Width Modulation) strategy that unifies SPWM (SIN Pulse Width Modulation) and SVPWM (Space Vector Pulse Width Modulation) is proposed, which has been shown to be effective in suppressing the zero-sequence circulating current in a motor emulator system with a common DC bus topology. The proposed control method has been experimentally validated using a motor emulator system. Full article

Abnormal locomotor patterns may occur in case of either motor damages or neurological conditions, thus potentially jeopardizing an individual’s safety. Pathological gait recognition (PGR) is a research field that aims to discriminate among different walking patterns. A PGR-oriented system may benefit from the simulation of gait disorders by healthy subjects, since the acquisition of actual pathological gaits would require either a higher experimental time or a larger sample size. Only a few works have exploited abnormal walking patterns, emulated by unimpaired individuals, to perform PGR with Deep Learning-based models. In this article, the authors present a workflow based on convolutional neural networks to recognize normal and pathological locomotor behaviors by means of inertial data related to nineteen healthy subjects. Although this is a preliminary feasibility study, its promising performance in terms of accuracy and computational time pave the way for a more realistic validation on actual pathological data. In light of this, classification outcomes could support clinicians in the early detection of gait disorders and the tracking of rehabilitation advances in real time. Full article

Background: The involvement of the circadian system in the etiology and treatment of Parkinson’s disease (PD) is becoming an increasingly important topic. The prodromal symptoms of PD include insomnia, fatigue, depression and sleep disturbance which herald the onset of the primary symptoms of bradykinesia, tremor and rigidity while robbing patients of their quality of life. Light treatment (LT) has been implemented for modifying circadian function in PD but few studies have examined its use in a protracted term that characterizes PD itself. Methods: The present exploratory study monitors the effect of LT over a 10 year course of PD in the context of ongoing circadian function. Results: Improvement in circadian based symptoms were seen soon after LT commenced and continued for the duration of the study. Improvement in motor function was more subtle and was not distinguishable until 1.2 years after commencing treatment. Improvement in most motor and prodromal symptoms remained in steady state for the duration of the study as long as patients were compliant with daily use. Conclusions: The sequence of improvement in prodromal symptoms and motor function seen here parallels the slow, incremental repair process mimicking the protracted degenerative sequelae of PD that extends over decades. This process also emulates the slow incremental improvement characterizing the reparative course seen with circadian symptoms in other disorders that improve with LT. Recent findings from epidemiological work suggest that early disruption of circadian rhythmicity is associated with increased risk of PD and the present findings are consistent with that hypothesis. It is concluded that intervening in circadian function with LT presents a minimally invasive method that is compatible with internal timing that slows the degenerative process of PD. Full article

This paper presents the design and performance evaluation of an experimental platform that emulates the static and dynamic behavior of a 3 kW Wind Energy Conversion System (WECS). The platform includes a wind turbine emulator (WTE) using a separately excited DC motor (SEDCM) as the prime mover, coupled with a grid-connected doubly-fed induction generator (DFIG). This setup enables comprehensive laboratory studies of a WECS without the need for large-scale field installations. A novel inertia compensation strategy is implemented to ensure the SEDCM accurately replicates the power and torque characteristics of a real wind turbine across various wind profiles. The DFIG was chosen for its high efficiency at variable wind speeds and its reduced power converter requirements compared to other generators. The control strategy for the DFIG is detailed, highlighting its performance and seamless integration within the system. Unlike most studies focusing on generators connected to simple loads, this research considers a grid-connected system, which introduces additional challenges and requirements. This study thoroughly investigates the grid-connected converter, addressing specific demands for grid connection and ensuring compliance with grid standards. Experimental results validate the effectiveness of the emulator, demonstrating its potential as a key tool for optimizing wind turbine control strategies and real-time algorithm validation, and enhancing the performance and reliability of renewable energy systems. Full article

This article focuses on the analysis and non-invasive online diagnostics of the operating condition of bearings integrated into three-phase squirrel cage induction motors, an electric machine that, due to its construction and operational characteristics, has a significant presence in the industry. The proposed signal-processing analysis tool is based on the non-invasive monitoring of stator electrical currents. To improve robustness in the diagnosis of bearing faults beyond the state-of-the-art, a hybrid approach was employed. The Short-Time Fourier Transform (STFT) and Park’s Vector Approach (PVA) were combined and applied to the stator currents. This hybridization allowed the benefits of both methods to be combined: (i) proper evaluation of time-varying phenomena and (ii) the ability to distinguish the type of fault affecting the bearing. To demonstrate the feasibility of the approach, comparisons were made between the proposed hybrid technique and both the STFT and the Extended Park’s Vector Approach (EPVA), which have been previously considered in the diagnosis of these and other induction motor faults. The validation of the proposed solution was conducted through computational simulations and laboratory tests, ultimately aiming to generate a database of results to inform future research in this area. To emulate bearing failures in an experimental context, artificial damage to bearing components was introduced. Full article

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions. Full article

Grid-forming inverters (GFMIs) have been identified as critical assets in ensuring modern power system reliability. Their ability to synthesize an internal voltage reference while emulating synthetic inertia has sparked extensive research. These characteristics have recently piqued interest in their capacity to provide blackstart ancillary services. The blackstart of a bulk power system poses significant challenges, namely the large transients from the energization of unloaded transformers, rotational motor loads, and long transmission cables, which have been effectively studied using conventional synchronous generators (SGs). The concept of an inverter-based resource (IBR)-based blackstart continues to be an open research area necessitating further investigations due to the known limitations of IBRs such as low short-circuit current capabilities. This paper presents a blackstart case study of a bulk power system investigating the performances of a conventional SG to a GFMI when utilizing hard switching methods. The paper qualitatively investigates the transient inrush currents from the transformer and rotational load energization sequences. Additional examinations into the significance of the GFMI’s current-limiting schemes and voltage control loop compensator gains are presented. Furthermore, the harmonic distortions from the transformer energization sequence are also evaluated. Finally, a full network energization case is presented to demonstrate how both sources can provide blackstart provisioning services. The models are developed in EMTDC/PSCAD using real-world transmission planning data. Full article

Induction motors (IMs) must meet high reliability and safety standards in mission-critical applications, such as electric vehicles (EVs), where sensorless control strategies are fundamental. However, sensorless rotor speed estimation demands improvements to overcome filtering distortions, tuning complexities, and sensitivity to IM model mismatch. Algebraic methods offer inherent filtering capabilities and design flexibility to address these challenges without introducing additional dynamics into the control system. The objective of this paper is to provide an algebraic estimation strategy that yields an accurate rotor speed estimate for sensorless IM control. The strategy includes an algebraic estimator with single-parameter tuning and inherent filtering action. We propose an EV case study to experimentally evaluate and compare its performance with a typical drive cycle and a dynamic torque load that emulates a small-scale EV power train. The algebraic estimator exhibited a signal-to-noise ratio (SNR) of 43 dB. The closed-loop experiment for the EV case study showed average tracking errors below 1 rad/s and similar performance compared to a well-known sensorless strategy. Our results show that the proposed algebraic estimation strategy works effectively in a nominal speed range for a practical IM sensorless application. The algebraic estimator only requires single-parameter tuning and potentially facilitates IM model updates using a resetting scheme. Full article

Electro-hydraulic actuators have witnessed significant development over recent years due to their remarkable abilities to perform complex and dynamic movements. Integrating such an actuator in humanoids is highly beneficial, leading to a humanoid capable of performing complex tasks requiring high force. This highlights the importance of safety, especially since high power output and safe interaction seem to be contradictory; the greater the robot’s ability to generate high dynamic movements, the more difficult it is to achieve safety, as this requires managing a large amount of motor energy before, during, and after the collision. No matter what technology or algorithm is used to achieve safety, none can be implemented without a stable control system. Hence, one of the main parameters remains the quality and reliability of the robot’s control architecture through handling a huge amount of data without system failure. This paper addresses the development of a stable control architecture that ensures, in later stages, that the safety algorithm is implemented correctly. The optimum control architecture to utilize and ensure the maximum benefit of electro-hydraulic actuators in humanoid robots is one of the important subjects in this field. For a stable and safe functioning of the humanoid, the development of the control architecture and the communication between the different components should adhere to some requirements such as stability, robustness, speed, and reduced complexity, ensuring the easy addition of numerous components. This paper presents the developed control architecture for an underdeveloped electro-hydraulic actuated humanoid. The proposed solution has the advantage of being a distributed, real-time, open-source, modular, and adaptable control architecture, enabling simple integration of numerous sensors and actuators to emulate human actions and safely interact with them. The contribution of this paper is an enhancement of the updated rate compared to other humanoids by 20% and by 40 % in the latency of the master. The results demonstrate the potential of using EtherCAT fieldbus and open-source software to develop a stable robot control architecture capable of integrating safety and security algorithms in later stages. Full article

This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural network model has been developed to control joint trajectories in multi-fingered robotic hands. The proposed neural network simulates cortical and spinal areas, as well as the connectivity between them, during the execution of voluntary movements similar to those performed by humans or monkeys. Furthermore, this neural connection allows for the interpretation of functional roles in the motor areas of the brain. The proposed neural control system is tested on the fingers of a robotic hand, which is driven by agonist–antagonist tendons and actuators designed to accurately emulate complex muscular functionality. The experimental results show that the corticospinal controller produces key properties of biological movement control, such as bell-shaped asymmetric velocity profiles and the ability to compensate for disturbances. Movements are dynamically compensated for through sensory feedback. Based on the experimental results, it is concluded that the proposed biologically inspired adaptive neural control system is robust, reliable, and adaptable to robotic platforms with diverse biomechanics and degrees of freedom. The corticospinal network successfully integrates biological concepts with engineering control theory for the generation of functional movement. This research significantly contributes to improving our understanding of neuromotor control in both animals and humans, thus paving the way towards a new frontier in the field of neurobiological control of anthropomorphic robotic systems. Full article

This paper introduces a new real-time method based on a combination of kernel density estimators and pyramid histogram of oriented gradients for identifying a point of interest along the stem of seedlings suitable for stem–stake coupling, also known as the ‘clipping point’. The recognition of a clipping point is a required step for automating the stem–stake coupling task, also known as the clipping task, using the robotic system under development. At present, the completion of this task depends on the expertise of skilled individuals that perform manual clipping. The robotic stem–stake coupling system is designed to emulate human perception (in vision and cognition) for identifying the clipping points and to replicate human motor skills (in dexterity of manipulation) for attaching the clip to the stem at the identified clipping point. The system is expected to clip various types of vegetables, namely peppers, tomatoes, and cucumbers. Our proposed methodology will serve as a framework for automatic analysis and the understanding of the images of seedlings for identifying a suitable clipping point. The proposed algorithm is evaluated using real-world image data from propagation facilities and greenhouses, and the results are verified by expert farmers indicating satisfactory performance. The precise outcomes obtained through this identification method facilitate the execution of other autonomous functions essential in precision agriculture and horticulture. Full article