Motor Efficiency Research Articles

Health monitoring and fault analysis of induction motors: a review

Abstract Induction motors are in high demand for industrial applications due to their reliable and efficient performance compared to other electrical motors. Therefore, the implementation of a predictive maintenance scheme is necessary to minimise financial losses in terms of revenue and maintenance costs. This scheme involves health monitoring system, aimed to enhance motor reliability, extend their useful lifespan and reduce maintenance expenses. Furthermore, traditional health monitoring systems often involve human intervention, which can slow down the diagnosis process. Therefore, a self-sufficient automated system has evolved to improve motor performance and reliability. This paper presents a comprehensive study of induction motor failures and the various methods used for their diagnosis. Additionally, it also discussed the importance of predictive maintenance and the pivotal role played by automated systems in ensuring the sustained efficiency of induction motors in industrial settings.

Flagellum pumping efficiency in shear-thinning viscoelastic fluids

Microorganism motility often takes place within complex, viscoelastic fluid environments, e.g. sperm in cervicovaginal mucus and bacteria in biofilms. In such complex fluids, strains and stresses generated by the microorganism are stored and relax across a spectrum of length and time scales and the complex fluid can be driven out of its linear response regime. Phenomena not possible in viscous media thereby arise from feedback between the swimmer and the complex fluid, making swimming efficiency co-dependent on the propulsion mechanism and fluid properties. Here, we parameterize a flagellar motor and filament properties together with elastic relaxation and nonlinear shear-thinning properties of the fluid in a computational immersed boundary model. We then explore swimming efficiency, defined as a particular flow rate divided by the torque required to spin the motor, over this parameter space. Our findings indicate that motor efficiency (measured by the volumetric flow rate) can be boosted or degraded by relatively moderate or strong shear thinning of the viscoelastic environment.

Bearing Electrical Erosion Signal Detection Technique Based on Current Signal Analysis for Electric Motors

Abstract In modern industries, enhancing the efficiency and performance of electric motors is a critical requirement. Pulse width modulation (PWM) inverters utilized to enhance the energy efficiency of electric motors generate complex shaft voltages and bearing currents, leading to bearing electrical erosion. This study proposes a new method for detecting high-frequency (HF) circulating current and electric discharge machining (EDM) current signals in bearings for electric motors. The proposed method utilizes common mode voltage (CMV) and bearing current data, analyzing the relationship between these signals. Subsequently, it applies filtering techniques and differentiation for signal preprocessing. The Interquartile Range (IQR) method is used to detect outliers, classify HF circulating current and EDM current signals, and perform time-series data clustering to determine the occurrence frequency and timing of EDM signals. Finally, the implementation outcomes of the proposed method are validated, and its classification efficacy is evaluated and benchmarked against established methodologies through a comprehensive performance analysis. The proposed technique is anticipated to be applicable to the maintenance and prediction of modern electric motors in future developments, contributing to enhanced durability and reliability.

A multi-objective optimal control approach to motor strategy changes in older people with mild cognitive impairment during obstacle crossing.

Mild cognitive impairment (MCI) may lead to difficulty maintaining postural stability and balance during locomotion. This heightened susceptibility to falls is particularly evident during tasks such as obstacle negotiation, which demands efficient motor planning and reallocation of attentional resources. This study proposed a multi-objective optimal control (MOOC) technique to assess the changes in motor control strategies during obstacle negotiation in older people affected by amnestic MCI. Motion data from 12 older adults with MCI and 12 controls when crossing obstacles were measured using a motion capture system, and used to obtain the control strategy of obstacle-crossing as the best compromise between the conflicting objectives of the MOOC problem, i.e., minimising mechanical energy expenditure and maximising foot-obstacle clearance. Comparisons of the weighting sets between groups and obstacle heights were performed using a two-way analysis of variance with a significance level of 0.05. Compared to the controls, the MCI group showed significantly lower best-compromise weightings for mechanical energy expenditure but greater best-compromise weightings for both heel- and toe-obstacle clearances. This altered strategy involved a trade-off, prioritising maximising foot-obstacle clearance at the expense of increased mechanical energy expenditure. The MCI group could successfully navigate obstacles with a normal foot-obstacle clearance but at the cost of higher mechanical energy expenditure. MCI alters the best-compromise strategy between minimising mechanical energy expenditure and maximising foot-obstacle clearances for obstacle-crossing in older people. These findings provide valuable insights into how MCI impacts motor tasks and offer potential strategies for mitigating fall risks in individuals with MCI. Moreover, this approach could serve as an assessment tool for early diagnosis and a more precise evaluation of disease progression. It may also have applications for individuals with impairments in other cognitive domains.

High-Efficiency e-Powertrain Topology by Integrating Open-End Winding and Winding Changeover for Improving Fuel Economy of Electric Vehicles

The fuel economy of electric vehicles (EVs) is an important factor in determining the competitiveness of EVs. Since the fuel economy is affected by the efficiency of an e-powertrain composed of a motor and inverter, it is necessary to select a high-efficiency topology for the e-powertrain. In this paper, a novel topology of e-powertrains to improve the fuel economy of EVs is proposed. The proposed topology aims to improve the system efficiency by integrating open-end winding (OEW) and winding changeover (WC). The proposed OEW-PMSM with WC enables to drive a permanent magnet synchronous motor (PMSM) in four different modes. Each mode can increase inverter efficiency and motor efficiency by changing motor parameters and maximum modulation index. In this paper, the system efficiency of the proposed topology was evaluated using electromagnetic finite element analysis and a loss model of power semiconductors. In addition, the vehicle simulations were performed to evaluate the fuel economy of the proposed topology, thereby proving the superiority of the proposed topology compared with the conventional PMSM.

Optimization of Steam Boiler Airflow Control System in a Typical Oil Refinery Power Plant

This paper presents optimization of Steam Boiler Airflow Control System in a Typical Oil Refinery Power Plant. The power plant is equipped with four steam boilers using a damper-louvers airflow control system. Analysis has revealed significant energy wastage due to inefficiency and over-air supply, with the system requiring 59,904,436 kWh annually at a financial cost of N1, 093,255,884. The existing system's inefficiency results in an annual electrical energy waste of 8,502,302 kWh. This inefficiency is attributed to the Force Draft Fan (FDF) motor operating at maximum speed continuously, regardless of the steam profile or airflow requirements. This study aimed at optimizing the steam boiler airflow control system in a typical oil refinery power plant for improved power efficiency, energy conservation, and cost savings citing Port Harcourt Refinery Company (PHRC) at Alesa-Eleme, Rivers State, Nigeria. To address this, the damper-louvers system was replaced with a Variable Frequency Drive (VFD), which would adjust the motor speed to match the required airflow, thereby reducing energy wastage. The process was simulated and VFD/FDF motor analyzed in MATLAB Simulink, determining the optimal speed, airflow, and power requirements. Results obtained show that the VFD control system improves the FDF motor's power efficiency from 47.7% to 85%, with an annual energy saving of 8,502,302 kWh (8.5 GWh). This results in a total cost benefit of N478.18 million annually, representing a 43.8% cost saving. The implementation cost of the VFD infrastructure was estimated at N111, 440,000, with a payback period of approximately eleven months. The findings suggest deployment of higher FDF motor speed ratings to enhance speed control and energy efficiency in Oil refinery power plant.

Performance Optimization Algorithm for Motor Design with Adaptive Weights Based on GNN Representation

Motor design involves multiple complex parameters, and traditional methods rely on experience and experimentation, which are inefficient and difficult to optimize. With the rapid development of emerging industries such as electric vehicles and intelligent manufacturing, the performance requirements for motors are constantly increasing. Optimizing design under multi-objective and multi-constraint conditions has become a key challenge. To address this, this paper proposes a motor design performance optimization algorithm based on Graph Neural Networks (GNN) representation and adaptive weighting. GNN, as a deep learning model capable of handling complex structured data, can model the multi-parameter relationships in motor design and automatically extract key features through its feature propagation mechanism, thus overcoming the difficulty of capturing parameter dependencies with traditional methods. At the same time, Mixed-Integer Linear Programming (MILP) provides a powerful global optimization tool that can find the global optimal solution when dealing with complex decision variables and constraints, overcoming the shortcomings of traditional optimization algorithms in terms of global convergence. Moreover, the adaptive weighting mechanism allows the optimization algorithm to dynamically adjust the weights according to the influence of parameters on motor performance, ensuring the accuracy and adaptability of optimization results in different scenarios. Through the organic combination of these three methods, this paper aims to solve the problems of low efficiency, poor global convergence, and inability to dynamically adjust the importance of design parameters in traditional motor design optimization. By introducing advanced machine learning models and optimization algorithms, this work constructs an efficient motor design performance optimization framework.

Prediction Of Brushless DC Motor And Propeller Efficiency Using An Artificial Neural Network Model

This study focuses on an artificial neural network model that allows users of brushless motor and propeller test rigs to compare the accuracy of data received in the interface software during testing. Brushless motors are widely used in modern aviation and industrial applications. Therefore, it is essential to analyze the factors affecting motor efficiency and accurately predict this data. This study involves the creation of an artificial neural network model from data to predict the percentage of motor efficiency for the brushless motors and propellers used in the test, whose length is measured in inches. Within the scope of this research, a useful tool is provided for users to flexibly test motor and propeller configurations and accurately analyze test results. The developed artificial neural network model has the ability to make reliable and accurate predictions for various motor-propeller configurations. Furthermore, the model is easy to use and offers expandable features. This study aims to create a valuable reference source for users of brushless motor and propeller test rigs to effectively analyze test data.

How can phase change materials improve vehicle cooling systems for better motor efficiency and thermal management?

Phase change materials, or PCMs, have become an attractive option for improving thermal management in the vehicle industry. With the increasing demand for efficient cooling systems and improved driving ranges in electric vehicles, PCMs offer an innovative approach to managing heat dissipation and improving performance. While PCMs are already applied in industries such as construction, their potential to revolutionize automotive thermal management could lead to advancements in all aspects of the field. They are a perfect option for increasing energy efficiency because of their capacity to collect and release thermal energy. This study explores the application of PCMs in vehicles and assesses their effectiveness in improving cooling systems.

Optimized design of a fault-tolerant 12-slot/10-pole six-phase surface permanent magnet motor with asymmetrical winding configuration for electric vehicles

This paper presents a comprehensive methodology for optimizing the design of a 12-slot/10-pole permanent magnet (PM) motor with a six-phase winding configuration tailored for electric vehicles (EVs). The design aims to enhance motor performance under both healthy and fault conditions. While the single neutral configuration offers superior torque during faults, it also introduces zero sequence currents and additional space harmonics, which can lead to increased torque ripple that is difficult to control. This study addresses these challenges through innovative machine design optimization. The optimization process begins with sizing equations to establish an initial design. K-means clustering techniques are then employed to identify distinct loading points that accurately represent the full EV driving cycle, effectively minimizing computational power requirements. Following this, the Full Range Minimum Loss (FRML) strategy is applied to determine optimal current profiles across these loading points, significantly reducing copper losses. Finally, a multi-objective optimization approach is utilized to minimize torque ripple, enhance average torque, and optimize machine losses. The results demonstrate substantial improvements in torque and reduced ripple, validated through experiments conducted with a 2 kW lab-scale motor. This integrated approach not only ensures a robust and efficient motor design but also enhances fault tolerance, making it well-suited for advanced EV applications.

Predictive Maintenance of Induction Motor in the Cutting Section of Paper Industry

Induction Motors are vital to the paper industry because they power a variety of equipment needed for pulp processing, drying and cutting among other tasks. Ensuring the consistent manufacture of these motors and satisfying market demands depends heavily on their dependability. However, the extreme humidity, dust and fluctuating loads that occur in the paper sector present serious obstacles to the efficiency and durability of induction motors. To maintain continuous production and satisfy consumer demand, the paper industry significantly depends on the effective operation of a variety of machinery including induction motors in the cutting area. Unexpected malfunctions in these vital parts however might result in expensive downtime and lost output. By using data-driven strategies that anticipate and avoid breakdowns before they happen, predictive maintenance techniques provide a proactive way to reduce such risks. This report provides an extensive analysis of the application of predictive maintenance techniques designed especially for induction motors in the paper industry’s cutting division. The predictive maintenance techniques includes Random Forest Tree, Linear Regression and Support Vector Machines algorithm with these algorithm the Induction Motor’s variations in temperature, vibration, sound, and speed parameters were collected and trained for predicting the failure. Among these algorithms Support Vector Machines shows greater advantage in predicting the failure with the accuracy of 84% whereas Random Forest Tree with 75% and Linear regression with 72%. As well as the real time data’s were collected and stored in the database. The performance of the Induction Motor were efficiently improved and monitored under normal and fault condition by Machine Learning techniques.

Heat transfer and economic characteristics of direct oil cooling for electric traction motor under driving cycles

Currently, the energy density of electric traction motor is continuously increasing to match the performance and reliability of electric vehicles with IC engine vehicles. The commercially employed water jacket cooling fails to achieve the desired efficiency of high energy density electric traction motor. Direct oil cooling is one of potential cooling strategies to replace the conventional cooling method for achieving the desired thermal management of next generation electric traction motors. However, the database to commercialize the direct oil cooling based on thermal management and economic aspects is not enriched sufficiently and thus extensive research studies need to be conducted in this field. Therefore, the heat transfer and economic characteristics of direct oil cooling for electric traction motor are investigated considering different cooling oils and driving duty cycles in the present work. The integrated electromagnetic-thermal numerical model is developed using ANSYS Motor-Cad commercial software to simulate the heat transfer and economic characteristics. The reliability of coupled numerical model is assured by validating the simulated results with experimental and reference data in error range of ± 5 %. The winding temperature, heat transfer coefficient, expected insulation lifetime, levelized cost of energy (LCOE), and net cumulative savings are evaluated as heat transfer and economic characteristics. Three driving duty cycles namely, UDDS (Urban Dynamometer Driving Schedule), NEDC (New European Driving Cycle) and US06 (Highway Driving Cycle) and transmission oil, shell thermal oil and Novec dielectric oil are investigated. The performance of electric traction motor with direct oil cooling is superior compared to that with water jacket cooling. The direct cooling with Novec dielectric oil shows improved heat transfer and economic characteristics compared to transmission oil and shell thermal oil. The direct cooling with Novec dielectric oil restricts the maximum winding temperature within 80.21 °C, 67.65 °C and 116.82 °C under driving cycles of UDDS, NEDC and US06, respectively. In addition, the enhanced insulation lifetime, heat transfer coefficient, LCOE and net cumulative savings of 7.91 × 105h, 23036.73 W/m2-K, 0.170 $/kWh and 47151.13 $, respectively are reflected for direct cooling with Novec dielectric oil.

Optimal powertrain system design and V2V and V2I based hierarchical control strategy of FRIDEV under vehicle-following scenarios

In this paper, the front- and rear-independent-drive electric vehicle (FRIDEV) is selected as the target vehicle for its good dynamic and economy performance. Based on the analysis of the cost, efficiency, and loss characteristics of different motors, the interior permanent magnet synchronous motor (IPMSM) and induction motor (IM) are applied in the target powertrain system. Then, the proposed powertrain system is optimized based on the analysis of driving cycles. The optimal control strategy for the target powertrain system is developed, which consists of the driving mode switching algorithm and the driving torque distribution based on adaptive particle swarm optimization (APSO). Furthermore, in order to achieve the efficient autonomous driving of the target vehicle under the vehicle-following scenarios, a hierarchical control strategy is proposed with the combination of upper-level control of the speed planning strategy and the lower-level control of powertrain system control strategy. The upper-level control of speed planning strategy is designed, which can determine and track the target vehicle speed based on the information from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Finally, simulation analysis in MATLAB/Simulink and virtual driving experiments are conducted to verify the superiority of the proposed powertrain system and hierarchical control strategy.

Motor Fault Diagnosis and Detection with Convolutional Autoencoder (CAE) Based on Analysis of Electrical Energy Data

This study develops a Convolutional Autoencoder (CAE) and deep neural network (DNN)-based model optimized for real-time signal processing and high accuracy in motor fault diagnosis. This model learns complex patterns from voltage and current data and precisely analyzes them in combination with DNN through latent space representation. Traditional diagnostic methods relied on vibration and current sensors, empirical knowledge, or harmonic and threshold-based monitoring, but they had limitations in recognizing complex patterns and providing accurate diagnoses. Our model significantly enhances the accuracy of power data analysis and fault diagnosis by mapping each phase (R, S, and T) of the electrical system to the red, green, and blue (RGB) channels of image processing and applying various signal processing techniques. Optimized for real-time data streaming, this model demonstrated high practicality and effectiveness in an actual industrial environment, achieving 99.9% accuracy, 99.8% recall, and 99.9% precision. Specifically, it was able to more accurately diagnose motor efficiency and fault risks by utilizing power system analysis indicators such as phase voltage, total harmonic distortion (THD), and voltage unbalance. This integrated approach significantly enhances the real-time applicability of electric motor fault diagnosis and is expected to provide a crucial foundation for various industrial applications in the future.

Orthographic character complexity modulates dynamic neural activity in skilled handwriting.

Handwriting is an outstanding case of a highly complex and efficient fine motor skill. However, little is known about its neural underpinnings during continuous handwriting production. In the present study, we examined the effects of orthographic character complexity (i.e. the stroke number of a Chinese character) on both neural and behavioural activities during an EEG-based naturalistic fluent sentence-handwriting task from 102 adult Chinese native speakers. For each written character, the interval between finishing the preceding character and its onset (inter-character interval) as well as the amplitudes of the onset-synchronized event-related potential (ERP) in pre- and post-onset time windows was defined as dependent variables. The effects of character complexity and other confounding factors were analysed with linear mixed models. Character complexity increased the inter-character interval and significantly affected ERP amplitudes in both pre- and post-onset time windows. The ERP pattern in the pre-event time window exhibited a dipole-like activation in the left motor cortex, and its amplitude increased with character complexity in line with the documented relationship between the lateralized readiness potential and motor complexity. This study demonstrates the feasibility of studying neurocognitive processes in complex naturalistic motor tasks and extends our knowledge about the dynamic pattern of handwriting-related neural activities.

Analysis of the influence of the parameters of a new low-loss magnetic material of the stator core on the efficiency of a high speed electric motor

Analysis of the influence of the parameters of a new low-loss magnetic material of the stator core on the efficiency of a high speed electric motor

Augmenting microgrid control with GHNO for efficient BLDC motor integration and renewable energy utilization

Augmenting microgrid control with GHNO for efficient BLDC motor integration and renewable energy utilization

New method for determining iron power losses in switched reluctance motors with laminated and composite magnetic cores

Power losses from motors and drives can decrease their efficiency. The development of devices with high efficiency is a necessity nowadays, with a view to reducing CO2 emissions. The application of new magnetic materials is one method for increasing the efficiency of motors, although prediction and calculation of the losses in the magnetic circuits of motors are also very important. To reduce the iron losses, iron powder composites are used here instead of Fe-Si laminated steel sheets, in a low-power switched reluctance motor (SRM), and a new method of determining the iron loss in the magnetic circuit is developed. Through the use of new materials in the motor, we can achieve a 19% decrease in the iron loss in comparison to a commercial motor. A comparison of the measurement results for our new method of iron loss prediction showed that the difference is approximately 20-26%, depending on the type of magnetic circuit used for the motor. The usage of soft magnetic composites rather than electrical steel sheets can decrease the loss in magnetic circuits. The results from our new method of determination of iron loss are in good agreement with results of measurements, meaning that it can be applied in a detailed analysis of SRMs. The application of soft magnetic composites in electromagnetic and electromechanical converters is a new method for lowering iron losses in new devices. The determination of such losses in SRMs is still challenging, and the proposed method broadens the state of the art in this area. The method developed here is based on motor current measurements, FEM calculations, and measurements of the magnetisation curves and iron loss for samples according to IEC standards. The results obtained in this work can be used by designers for the development of new kinds of SRMs.

Design and Application of Parameter Self-tuning Regulator for DC Motor based on Neural Network

This study introduces a cutting-edge approach to regulating DC motors, featuring a unique combination of Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks. This innovative system capitalizes on the adaptive learning capabilities of ANNs to dynamically fine-tune the control parameters of DC motors. This adaptability ensures optimal motor performance across diverse operational conditions, addressing the challenges posed by fluctuating loads and varying speed requirements. The integration of LSTM networks into this framework adds a layer of predictive functionality, allowing the system to anticipate future motor states. Such foresight enables the regulator to make proactive adjustments, significantly enhancing its responsiveness to changes in operational demands. The dual application of ANN’s adaptive control mechanisms and LSTM’s predictive capabilities is particularly effective in overcoming the non-linearity and variability that are typical challenges in DC motor control. This synergy ensures that the motor operates efficiently, stably, and with a quick response time, even under varying and unpredictable conditions. The practical application of this advanced regulator in real-world scenarios has shown marked improvements in motor performance. These enhancements are evident in the increased efficiency, stability, and responsiveness of the motors, making them more suitable for a wide range of industrial applications. This study marks a notable progression in the field of DC motor control technology. By integrating advanced machine learning techniques, it offers a solution that is not only more efficient and reliable but also adaptable to the evolving demands of industrial environments. The innovative combination of ANN and LSTM networks in this regulator design paves the way for smarter, more responsive, and efficient motor control systems, potentially transforming how motors are managed in various industrial applications.

Efficient DC motor speed control using a novel multi-stage FOPD(1 + PI) controller optimized by the Pelican optimization algorithm.

This paper introduces a novel multi-stage FOPD(1 + PI) controller for DC motor speed control, optimized using the Pelican Optimization Algorithm (POA). Traditional PID controllers often fall short in handling the complex dynamics of DC motors, leading to suboptimal performance. Our proposed controller integrates fractional-order proportional-derivative (FOPD) and proportional-integral (PI) control actions, optimized via POA to achieve superior control performance. The effectiveness of the proposed controller is validated through rigorous simulations and experimental evaluations. Comparative analysis is conducted against conventional PID and fractional-order PID (FOPID) controllers, fine-tuned using metaheuristic algorithms such as atom search optimization (ASO), stochastic fractal search (SFS), grey wolf optimization (GWO), and sine-cosine algorithm (SCA). Quantitative results demonstrate that the FOPD(1 + PI) controller optimized by POA significantly enhances the dynamic response and stability of the DC motor. Key performance metrics show a reduction in rise time by 28%, settling time by 35%, and overshoot by 22%, while the steady-state error is minimized to 0.3%. The comparative analysis highlights the superior performance, faster response time, high accuracy, and robustness of the proposed controller in various operating conditions, consistently outperforming the PID and FOPID controllers optimized by other metaheuristic algorithms. In conclusion, the POA-optimized multi-stage FOPD(1 + PI) controller presents a significant advancement in DC motor speed control, offering a robust and efficient solution with substantial improvements in performance metrics. This innovative approach has the potential to enhance the efficiency and reliability of DC motor applications in industrial and automotive sectors.