Electrical Machines Research Articles

Enhancing induction machine fault detection through machine learning: Time and frequency analysis of vibration signals

The integration of machine learning algorithms into fault diagnosis is regarded as an advanced and effective method for detecting electrical system faults. Vibration signals are identified as valuable and easily accessible data for fault identification in electrical machines. In this study, experiments were conducted to assemble a dataset consisting of 525 × 3 instances, each containing 200 three-axis vibration signal measurements. These measurements were obtained from a laboratory test bench equipped with a customized winding three-phase induction machine. Instead of directly analyzing the vibration signals, a feature extraction approach was employed, calculating a comprehensive set of statistical, harmonic, and impulsive features from the time domain (e.g., ShapeFactor, SINAD, ClearanceFactor, ImpulseFactor, CrestFactor, Kurtosis, Skewness, THD, Std, RMS, Mean, PeakValue, SNR), along with spectral features from the frequency domain (e.g., Peak Frequencies, Amplitudes, BandPower). To improve model efficiency, Analysis of Variance (ANOVA) was applied to select only the most significant features, with F-statistics used to rank feature importance. Features with higher F-statistics were prioritized for their ability to explain more variance in motor fault classification. This selective approach reduced model complexity, helping to minimize overfitting and focus on features that had a substantial impact on predictive accuracy. By concentrating on these high-impact features, a balance between simplicity and performance was achieved. These selected features were then used to train five machine learning classifiers: Ensemble (Bagged Trees), Quadratic Support Vector Machine, Weighted K-Nearest Neighbors, Efficient Logistic Regression, and Neural Networks. The performance of these classifiers was evaluated using various metrics, with validation accuracy rates ranging from 78.3% to 98.8%. A comparative study with similar works in the literature was conducted, demonstrating that high performances were obtained with the proposed approach while maintaining computational efficiency.

Frequency domain solution method for electromagnetic influence analysis on torsional vibrations

In electric motor-driven machines, mechanical torsional dynamics are nonlinearly coupled with the electrical system through the electromagnetic torque and the counter-electromotive force. In this paper, an approach based on small-signal linearization is proposed for modeling the steady-state torsional dynamics of the coupled system. Using the linearized model, resonance interference diagrams, torsional response, and stability can be evaluated rapidly across numerous operating points, while accurately accounting for the electromagnetic effects. The model is validated with conventional time-stepping simulations of two induction machines. An example analysis of a 3 MW motor-driven compressor train displays the modification of torsional properties due to the electromagnetic coupling effect and explains the mechanism of torsional destabilization. Finally, previously published measurements of a 37 kW motor test bench are considered to validate the instability of the elastic torsional mode predicted by the model.

Development and Utilization of Synthetic Signals for Fault Diagnostics of Electrical Machines

Development and Utilization of Synthetic Signals for Fault Diagnostics of Electrical Machines

Influence of Eddy Current Losses in Nonsuperconducting Layers of HTS in Superconducting Electrical Machines

Influence of Eddy Current Losses in Nonsuperconducting Layers of HTS in Superconducting Electrical Machines

Bas KiTE PHEB Analysis Performance using MATLAB/SIMULINK

Abstract The performance analysis of Bas KiTE Plug-in Hybrid Electric Bus (PHEB) is crucial for assessing its effectiveness in power system applications to reduce fuel consumption and emission. In this study, MATLAB/SIMULINK is employed as a tool of modelling and simulation to evaluate the performance of Bas KiTE PHEB model. The research focuses on sizing of Bas KiTE PHEB and performance of Bas KiTE PHEB electric machine model, internal combustion engine model, and energy system storage model using MATLAB/SIMULINK in term power, fuel consumption and emission. Kuala Terengganu driving cycle were use as input to see the performance. Through this study, the preliminary of Bas KiTE PHEB model use MATLAB/SIMULINK can be used as reference to build the prototype of PHEB in Kuala Terengganu.

Optimization of Material Removal Rate and Wear Rate in EDM Machining of Inconel 625 Using a Novel Nano Al-Ni Composite Electrode: A Response Surface Methodology Approach

The electric discharge machine is a novel machining technique. When the tool and the work item do not communicate. Components composed of difficult-to-machine hard materials. In the current experimental inquiry, the process factors that influence the machining concert process and its effectiveness are explored. The response table for each set of machining limitations is used in conjunction with the response surface methodology in the research to determine the appropriate levels of machining factors. The work's machining input parameters are optimized for maximum material removal rate (MRR) and reduce the electrode wear rate (EWR) when electro-discharge machining Inconel 625 using the tool Nano Al-Ni composite electrode. Another analysis of variance is to identify the factors causing the different answers mentioned above. EDAX was also used to investigate the material composition of the workpiece. Machined workpiece surfaces were evaluated using scanning electron microscopy to assess surface accuracy and microstructural characteristics (SEM). The following conclusions may be drawn from this work. The MRR parameter that is most affected is pulse off time. As pulse-off time lengthens, MRR increases. According to the response graph, the greatest MRR value that can be attained under the ideal parameters of Ip = 8A, T-ON = 50μs, and T-OFF= 100μs is 0.0115 g/min. The magnitudes of MRR range from 0.0091 to 0.044 g/min. Impact of T ON and T OFF: Higher EWR is 0.0022 g/min is seen in runs with T ON = 70 μs and T OFF = 100 μs.

Design Micro-Hydro Power Plant Water Resources System for Small Medium Enterprise Rice Mill in Nagari Kamang Hilia Agam

Processing rice products into rice requires further processing, where the grain harvested from farmers is processed into rice. The Nagari Binaan Community Service team from Padang State University will later develop this process. Farmer groups say that the availability of continuous and cheap electricity is their obstacle in producing good and cheap rice products. Counseling and assistance in making civil buildings capture turbulence water micro-hydro power plants is a solution to partner problems offered by the community service team of the Nagari program assisted by Padang State University. The output of the Nagari Fostered Program will be used to build a 5000-watt capacity power plant, supporting the need for free and environmentally friendly electricity. This aligns with the government's renewable energy program for 2030, which aims to produce 25% of the national electricity from renewable sources. This program is planned for 3 (three) stages, where the first year is mapping and detailed engineering design in the form of reports and design drawings. The second year is in the form of civil construction, and the third year's target is the installation of electrical machines and panels for commissioning. There will be training for partners, such as farmers in Kanagarian Kamang Hilia, Agam.

Результаты испытаний макетного образца передачи с бесконтактным зацеплением на основе постоянных магнитов

Purpose: the paper studies the properties of a coaxial magnetic transmission with permanent magnets and magnetizing windings. The dependences of the efficiency on the rotation frequency and load power are revealed, as well as the dependence of the maximum torque preceding the engagement failure on the magnetizing current and the rotation frequency. Methods: experimental study on a stand by loading the drive motor through the test transmission on a generator loading on a resistor. The process of studying the stand is also described, including determining the losses in test electric machines by the method of individual losses, as well as establishing the correspondence between the frequency of the tachogenerator voltage and the rotation frequency of the drive motor. Results: it was found that the magnetic transmission is capable of operating both in the reducer mode and in the multiplier mode, while the efficiency in the reducer mode is generally higher and depends on the rotation frequency of the transmission to a greater extent than in the multiplier mode. It can also be said that the use of magnetization allows increasing the maximum torque given to the load, but reduces the efficiency due to the increase in losses in the steel. Practical significance: the results of the work can be applied in mechanical engineering when designing loaded magnetic transmissions operating in the mode of uneven loads in machine drives.

A study of powertrain parameter optimization based on performance requirements of a vehicle platform

Powertrain top down design is a vital process for vehicle platformization and performance development. To obtain the optimal component size of a plug-in hybrid electric vehicle (PHEV) platform, this study proposes an optimization framework with the goal of minimizing performance redundancy. First, we analyze the characteristics of the powertrain architecture, and propose the indicators through performance gradient planning of the vehicle platform. Then we establish the mathematical model of the powertrain, and formulate the optimization problem. Particle Swarm Optimization (PSO) is used to solve the problem and a platform optimization problem with eight vehicle models is performed. Optimization results show that compared to traditional single vehicle powertrain optimization, platform based powertrain optimization can greatly reduce the number of components and save development costs through the sharing of components. With only three electric machines, two engines, two generators, and two sets of transmissions, the performance requirements of the entire platform can be met, and the maximum performance redundancy of different vehicle models is only 0.205. It can be seen that platform based powertrain optimization can greatly reduce vehicle costs and improve profit margins.

Influence of different cleaning methods on the concentration of airborne endotoxins and microbial aerosols in the oral clinical environment

AimThis study aims to evaluate the effectiveness of various cleaning methods in reducing airborne endotoxin and microbial aerosols during oral cleaning procedures.MethodForty patients undergoing oral cleaning procedures were randomly assigned to one of four groups (n = 10 per group). Group A received strong suction alone; Group B received strong suction combined with an air disinfection machine; Group C received strong suction combined with a dental electric suction machine; Group D received strong suction in conjunction with both an air disinfection machine and a dental electric suction machine. Airborne aerosol concentrations were assessed at four-time points: before treatment, 30 min into treatment, immediately after treatment, and 60 min after treatment ended. Samples were collected at distances of 20 cm, 60 cm, and 1 m from the patient’s oral cavity using the natural sedimentation method. T-test was used to evaluate the difference among tested groups.ResultsAirborne endotoxins and microbial aerosols levels increased significantly during treatment, with the highest levels observed at 20 cm from the patient’s mouth. During treatment, groups with additional cleaning methods (Groups B, C, and D) exhibited higher levels of airborne endotoxins and microbial aerosols compared to Group A (strong suction alone). However, post-treatment analysis revealed that Group D demonstrated the lowest level of airborne endotoxins and microbial aerosols, while Group A exhibited the highest.ConclusionsImplementing effective aerosol management strategies can significantly reduce aerosol dispersion in the oral clinical environment. Continuous monitoring aerosol concentrations and the application of appropriate control measures are essential for minimizing infection risks for both patients and healthcare providers during oral cleaning procedures.

Dynamic resistance of a REBCO tape carrying direct current under a mixture magnetic field of AC field with DC-biased field

Abstract When the REBCO coated conductor tape carries a direct current(DC) transport current whilst exposed to the alternating current(AC) magnetic field, a DC electrical resistance can be observed, which is called “dynamic resistance”. The High Temperature Superconducting(HTS) magnet wound by the REBCO tapes is located in the HTS electrical machine as field winding carrying DC transport current. The operational environment of the HTS electrical machine involves a complex magnetic field, encompassing both AC and DC components. The interaction between the AC magnetic field and the DC transport current induces dynamic resistance and dynamic loss in the REBCO tape which is a distinctive trait of REBCO tape. Additionally, the presence of an extra DC-biased magnetic field can decrease the critical current of the REBCO tape, thereby altering its electromagnetic properties and potentially compromising its safety. As the basis of superconducting magnets, it is particularly important to study the dynamic resistance and loss distribution of the single REBCO tape under a mixture of magnetic field backgrounds, according to the real working environments in various applications. In this paper, the electromagnetic-thermal coupling model of multilayer REBCO tape based on H-formulation is built in COMSOL Multiphysics. The electromagnetic characteristics and dynamic resistance of the tape are presented when the tape is applied AC magnetic field and carries DC transport current. The effects of perpendicular and parallel DC-biased magnetic fields on the dynamic resistance and loss distribution of the REBCO tape are investigated in the paper. And the DC transport current safety margin will be observed in different applied DC-biased magnetic fields. This study comprehensively demonstrates the variation of dynamic resistance and loss distribution under a complex background magnetic field, which is significant for exploring the electromagnetic characteristics and calculating the loss of the HTS magnets in the HTS electrical machine.

Implementation of machine learning methods in predicting failures in electrical submersible pump machines

The Electric Submersible Pump (ESP) is one of the artificial lift tools widely used in oil and gas wells. Approximately 25% of oil and gas wells have been equipped with ESP. The ESP unit excels in lifting fluid rates much larger than most other types of artificial lift. The purpose of this research is to develop a machine learning model that can predict the likelihood of ESP installation failure. By making predictions at the early stage, necessary actions can be prepared to prevent and anticipate these issues. Thus, the reduction in the amount of produced fluid can be minimized. The model in this study uses 8 input parameters, namely, motor Ampere, Frequency, Pump Intake Pressure, Temperature Motor, Output Volt, Pump Discharge Pressure, Input Voltage, and Motor Horse Power (HP). There are also 8 failure classifications for which the model will be created. The initial stage involves collecting and cleaning raw data and processing it to detect anomalies. Once the data is processed, it proceeds to the model formation and model validation stage. The method used for the model is based on the comparison of two methods, namely, decision tree and k-nearest neighbor. The output of the research is a dashboard that can display visualizations of data and the prediction results from the created model. Millions of data points are used to create a database consisting of 77 wells over the past 2 years. The model developed generates several types of failures that can be read. These failures have their own characteristic parameters. The model obtained has an accuracy rate above 90%. The output of this model includes the most likely failure prediction based on the latest input data, thus effectively addressing well problems, warning of impending failures, reducing failures, and assisting in scheduling ESP repairs and maintenance.

Rapid manufacturing of copper-graphene composite EDM electrode for improved machining performance

ABSTRACT Recently, graphene has emerged as reinforcing material for copper-based electric discharge machine (EDM) tools because of its superior electrical, thermal and high melting properties. 3D printing methods to fabricate electrodes are convenient and faster compared to conventional methods. Therefore, this study investigated the fabrication of copper-graphene composite electrodes for EDM using rapid tooling and pressureless microwave sintering. The influence of graphene content on the machining performance was examined, revealing enhanced electrical conductivity and machining efficiency. Electrical conductivity of the composite electrodes having 0.25 and 0.5 wt% of graphene was higher than pure copper. Additionally, upto 89% improvement in material removal rate and upto 14% reduction in electrode wear rate was obtained. Optical study showed an improvement of upto 28% in surface finish of the workpiece machined using composite electrode having 0.25 wt% graphene. A complex shaped composite tool was successfully fabricated and machined cavity was obtained.

Optimizing High-Performance Predictive Modeling of the Medium-Speed WEDM Processing of Inconel 718

The purpose of this research was to create a predictive model for a medium-speed wire electrical discharge machine (WEDM) utilizing an artificial neural network (ANN). Medium-speed WEDM experiments were developed based on the I-optimal mixture design for machining, the Inconel 718 superalloy. During the experiment, the input parameters were the spark ontime, spark offtime, wire feed, and current, with the material removal rate (MRR) and surface roughness (Ra) selected as performance indicators. The ANN model was trained on experimental data and built using a feed-forward backpropagation neural network with a (4-8-2) structure and the Bayesian regularization (BR) learning approach. The model correctly predicted the relationship between the medium-speed WEDM’s primary process parameters and machining performance. An integrated ANN model and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) were used to determine the ideal parameters for the MRR and Ra, resulting in a set of Pareto-optimal solutions. The confirmation experiment revealed that the mean prediction error between the experimental and ideal solutions had a maximum error percentage of 1% for the MRR and 2% for the Ra, which are within acceptable ranges. This showed that the best process–parameter combinations were better for the MRR and Ra.

The Si-doped BCC-based high-entropy alloy to overcome soft magnetic–mechanical properties trade-off via coherent B2 nanoprecipitates

The trade-off between magnetic property and mechanical property usually occurs in traditional soft magnetic materials (SMMs) because the strengthening strategy (e.g. precipitation hardening) can worsen soft magnetic properties through the hindrance of magnetic domain wall motion. This dilemma is overcome in current work by developing the FeCoNiAlSi0.01 (Fe24.94Co24.94Ni24.94Al24.94Si0.24 in at.%) high-entropy alloy (HEA) (termed as Si0.24 HEA) with excellent soft magnetic performance and attractive mechanical property through coherent B2 nanoprecipitates (6 nm) distributed in body-centered-cubic (BCC) matrix. The atom probe tomography (APT) result shows that the B2 nanoprecipitates have similar composition to the BCC matrix. The Si0.24 HEA shows small width of domain branching and low anisotropy constant leading to the optimum alternating current (AC) soft magnetic properties. The respective total loss (AC Ps), the coercivity (AC Hc), and the eddy current loss (Pe) at 950 Hz of the Si0.24 HEA are 21.20 W/kg, 230 A/m, and 16.25 W/kg, which is reduced by 45 %, 44 %, and 48 % compared with the FeCoNiAl (Si-free HEA). The Si0.24 HEA shows good mechanical property with the yield strength of 987 MPa and engineering strain of 30 %, which is 12 % and 1.3 times higher than that of the Si-free HEA. Moreover, the current studied HEAs exhibit high saturation magnetization (Ms = 108–115 Am2/kg) and Curie temperature (TC = 1053–1097 K, larger than TC of Fe (1043 K)), which indicates their perspective high-temperature applications as novel SMMs for the need of modern power electronics and electrical machines.

Combined parameter and shape optimization of electric machines with isogeometric analysis

Combined parameter and shape optimization of electric machines with isogeometric analysis

Sliding mode control based dynamic voltage restorer for voltage sag compensation

The reliability and efficiency of electrical power systems are critical for modern industries, which increasingly rely on electric machines and devices. However, power electronic converters add non-linear demands to the electric power distribution systems (EPDS), that can cause power quality (PQ) problems, which could harm electric devices. As a result, EPDS are pointed for the most effective and economical techniques to enhance PQ. Therefore, Dynamic Voltage Restorer (DVR) is employed to maintain voltage stability in modern EPDS. This study focuses on the application of Sliding Mode Control (SMC) in DVRs to protect against voltage dips, enhancing power system sustainability. It details the construction, operation, and control methods of DVRs, comparing the conventional PI-controller with SMC. The arctic puffin optimizer is utilized to get the optimum gains of the PI regulator, and at the same time, it is used to specify the optimal sliding surface required for SMC. A distribution system contains DVR is suggested and implemented utilizing Simulink/MATLAB. The simulation results of the DVR employing SMC and the conventional PI-controller demonstrate a marked improvement in voltage regulation and power quality. Specifically, the SMC-based DVR exhibited a reduction in voltage sag duration and magnitude by approximately 30 % compared to the PI-controller. Additionally, the total harmonic distortion (THD) in the load voltage was reduced by 25 %, indicating a significant enhancement in power quality. These improvements of the enhanced performance of the SMC-based DVR supports the seamless integration of renewable energy sources, which often introduce variability into the power grid.

Impact of close proximity pulse width modulation switching events on electric machine terminal voltages

AbstractElectric machines form an essential part of a wide range of modern systems. When speed control is required, the use of pulse width modulation‐based inverters is generally the solution of choice. It is also usual to connect the machine to the inverter using a cable. The combination of these three elements produces the potential for voltages which exceed the dc link voltage to occur at the machine terminals. Methods for predicting the terminal voltage exist; however, these methods assume that the pulses applied to the system can be considered as isolated, discrete events. The authors highlight an issue with this assumption. When a switching event occurs, it will cause a voltage disturbance in the unswitched phases of the system due to the mutual coupling between the phases. If a second switching event occurs within a short time of this event the resultant voltage will interact with the previous switching event resulting in a higher terminal voltage than would be the case for an isolated event. This effect can be problematic for insulation design if it is not considered. This issue is demonstrated, with the worst‐case scenarios identified and potential methods of reducing terminal voltage being proposed.

Experimental Determination of Influences of Static Eccentricities on the Structural Dynamic Behavior of a Permanent Magnet Synchronous Machine

In electrified vehicles, the masking noise behavior of internal combustion engines is absent, making the tonal excitation of the electric machine particularly noticeable in vehicle acoustics, which is perceived as disturbing by consumers. Due to manufacturing tolerances, the tonal NVH characteristics of the electric machine are significantly influenced at wide frequency ranges. This paper presents a systematic exploration of the influence of static eccentricity as one manufacturing tolerance on the NVH behavior of Permanent Magnet Synchronous Machines (PMSMs). The study utilizes a novel test bench setup enabling isolated variations in static eccentricity of up to 0.2 mm in one PMSM. Comparative analysis of acceleration signals reveals significant variations in the dominance of excitation orders with different eccentricity states, impacting critical operating points and dominant frequency rages of the electric machine. Despite experimentation, no linear correlation is observed between increased eccentricity and changes in acceleration behavior. Manufacturing eccentricity and deviations in rotor magnetization are discussed as potential contributors to the observed effects. The findings emphasize static eccentricity as a critical parameter in NVH optimization, particularly in electrified powertrains. However, the results indicate that further investigations are needed to explore the influence of eccentricities and magnetization deviations on NVH behavior comprehensively.

A Review of State-of-the-Art Multiphase and Hybrid Electric Machines

A Review of State-of-the-Art Multiphase and Hybrid Electric Machines