Search Results (370)

The ongoing rise in global electricity demand highlights the need for advanced, efficient, and environmentally responsible energy conversion technologies. This research presents a comprehensive design, modeling, and experimental validation of a tubular permanent magnet linear alternator (PMLA) integrated with a free piston engine system. Linear alternators offer a direct conversion of linear motion to electricity, eliminating the complexity and losses associated with rotary generators and enabling higher efficiency and simplified system architecture. The study combines analytical modeling, finite element simulations, and a sensitivity-based design optimization to guide alternator and engine integration. Two prototype systems, designated as alpha and beta, were developed, modeled, and tested. The beta prototype achieved a maximum electrical output of 550 W at 57% efficiency using natural gas fuel, demonstrating reliable performance at elevated reciprocating frequencies. The design and optimization of specialized flexure springs were essential in achieving stable, high-frequency operation and improved power density. These results validate the effectiveness of the proposed design approach and highlight the scalability and adaptability of PMLA technology for sustainable power generation. Ultimately, this study demonstrates the potential of free piston linear generator systems as efficient, robust, and environmentally friendly alternatives to traditional rotary generators, with applications spanning hybrid electric vehicles, distributed energy systems, and combined heat and power. Full article

The effective part of the winding in a slotless motor varies across different axial sections of the motor, resulting in a three-dimensional structure. Therefore, it is not feasible to simply use the single-section simulation method of conventional radial field motors for motor simulation. Currently, the simulation of slotless motors primarily depends on three-dimensional electromagnetic fields, which present significant modeling challenges and require extensive simulation times, rendering them unsuitable for engineering applications. This paper introduces a method for analyzing slotless motors using a two-dimensional electromagnetic field, based on the electromagnetic field simulation software EasiMotor (R2025). The study elucidates the principle of employing a two-dimensional electromagnetic field to analyze slotless motors and applies this method to the design of a slotless motor with a diameter of 4.5 mm. Through the fabrication of prototypes and performance testing, experimental results validate the accuracy and efficiency of this method. Full article

This paper presents the application of a sliding mode observer for speed sensorless control of a six-phase induction machine. The use of nonlinear sliding mode techniques yields acceptable performance for both low- and high-speed motor operations over a wide speed range. The effectiveness and accuracy of the developed sensorless scheme are verified by experimental results, which demonstrate the system’s performance under various operating conditions. These results demonstrate the advantages of the proposal as a valid alternative to the conventional method, which uses a mechanical speed sensor for multiphase machines. Additionally, the sensorless approach can also serve as a redundant backup in the event of mechanical sensor failure, thereby increasing the reliability of the overall drive system. Full article

With the frequent occurrence of global floods, the demand for emergency rescue equipment has grown rapidly. The development and technological innovation of digital hydraulic drive systems (DHDSs) for emergency drainage pumps (EDPs) have become key to improving rescue efficiency. However, EDPs are prone to being affected by random and uncertain loads during operation. To achieve intelligent and efficient rescue operations, a DHDS suitable for EDPs was proposed. Firstly, the configuration and operation mode of the DHDS for EDPs were analyzed. Based on this, a multi-field coupling dynamic simulation platform for the DHDS was constructed. Secondly, the output characteristics of the system under alternating loads were simulated and analyzed. Finally, a test platform for the EDP DHDS was established, and the dynamic characteristics of the system under alternating loads were explored. The results show that as the load torque of the alternating loads increases, the amplitude of the pressure of the motor also increases, the output flow of the hydraulic-controlled proportional reversing valve (HCPRV) changes slightly, and the fluctuation range of the rotational speed of the motor increases. The fluctuation range of the pressure and the rotational speed of the motor are basically not affected by the frequency of alternating loads, but the fluctuation amplitude of the output flow of the HCPRV reduces with the increase in the frequency of alternating loads. This system can respond to changes in load relatively quickly under alternating loads and can return to a stable state in a short time. It has laudable anti-interference ability and output stability. Full article

The traditional approach that separates remaining useful life prediction from maintenance strategy design often fails to support efficient decision-making. Effective maintenance requires a comprehensive consideration of prediction accuracy, cost control, and equipment safety. To address this issue, this paper proposes a multi-objective maintenance optimization method based on stochastic modeling. First, a multi-sensor data fusion technique is developed, which maps multidimensional degradation signals into a composite degradation state indicator using evaluation metrics such as monotonicity, tendency, and robustness. Then, a linear Wiener process model is established to characterize the degradation trajectory of equipment, and a closed-form analytical solution of its reliability function is derived. On this basis, a multi-objective optimization model is constructed, aiming to maximize equipment safety and minimize maintenance cost. The proposed method is validated using the NASA aircraft engine degradation dataset. The experimental results demonstrate that, while ensuring system reliability, the proposed approach significantly reduces maintenance costs compared to traditional periodic maintenance strategies, confirming its effectiveness and practical value. Full article

Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional methods in adaptive parameter optimization and weak feature enhancement, this paper proposes an innovative diagnostic framework integrating Improved Goose optimized Variational Mode Decomposition (IGOOSE-VMD), RobustICA, and CYCBD. First, to mitigate modal aliasing issues caused by empirical parameter dependency in VMD, we fuse a refraction-guided reverse learning mechanism with a dynamic mutation strategy to develop the IGOOSE. By employing an energy-feature-driven fitness function, this approach achieves synergistic optimization of the mode number and penalty factor. Subsequently, a multi-channel observation model is constructed based on optimal component selection. Noise interference is suppressed through the robust separation capabilities of RobustICA, while CYCBD introduces cyclostationarity-based prior constraints to formulate a blind deconvolution operator with periodic impact enhancement properties. This significantly improves the temporal sparsity of fault-induced impact components. Experimental results demonstrate that, compared to traditional time–frequency analysis techniques (e.g., EMD, EEMD, LMD, ITD) and deconvolution methods (including MCKD, MED, OMEDA), the proposed approach exhibits superior noise immunity and higher fault feature extraction accuracy under high background noise conditions. Full article

To address the problems of feature extraction, cost of obtaining labeled samples, and large differences in domain distribution in bearing fault diagnosis on variable operating conditions, an unsupervised domain-adaptive bearing fault diagnosis method based on migration learning using MSACNN-IJMMD-DANN (multi-scale and attention-based convolutional neural network, MSACNN, improved joint maximum mean discrepancy, IJMMD, domain adversarial neural network, DANN) is proposed. Firstly, in order to extract fault-type features from the source domain and target domain, this paper establishes a MSACNN based on multi-scale and attention mechanisms. Secondly, to reduce the feature distribution difference between the source and target domains and address the issue of domain distribution differences, the joint maximum mean discrepancy and correlation alignment approaches are used to create the metric criterion. Then, the adversarial loss mechanism in DANN is introduced to reduce the interference of weakly correlated domain features for better fault diagnosis and identification. Finally, the method is validated using bearing datasets from Case Western Reserve University, Jiangnan University, and our laboratory. The experimental results demonstrated that the method achieved higher accuracy across different migration tasks, providing an effective solution for bearing fault diagnosis in industrial environments with varying operating conditions. Full article

DC link power supply systems that integrate power electronic converters are increasingly being adopted. In particular, emerging “source–load” systems, in which the DC link interfaces with converters, have attracted increasing research interest due to concerns about power quality and system stability. This paper addresses mid- and low-frequency oscillation issues in DC link voltage supplied induction motor drives (IMDs). It begins by constructing a multiple-input multiple-output (MIMO) state-space model of the induction motor. For the first time, the dq-axis control system is represented as an equivalent admittance model that forms two single-input single-output (SISO) loops. The PI controller and induction motor are integrated into the inverter’s input impedance model; Furthermore, the effectiveness and accuracy of the derived impedance model are experimentally validated under various operating conditions of the induction motor using a custom-built test platform. The experimental results offer a practical reference for system enhancement and stability evaluation. Full article

This paper details the development of a full turbine model and ensuing aero-servo-elastic analysis of the International Energy Agency’s 15MW Reference Wind Turbine. This model provides the means to obtain realistic turbine performance data, of which normal and tangential blade loads are extracted and applied to a simplified drivetrain model developed expressly to quantify the shaft eccentricities caused by aerodynamic loading, thus determining the impact of aerodynamic loading on the generator structure. During this process, a method to determine main bearing stiffness values is presented, and values for the IEA-15MW-RWT obtained. It was found that wind speeds in the region of turbine cut-out induce shaft eccentricities as high as 56%, and that tangential loading has a significant contribution to shaft eccentricities, increasing deflection at the generator area by as much as 106% at high windspeeds, necessitating its inclusion. During a subsequent generator structure optimisation, the shaft eccentricities caused by the loading scenarios examined in this paper were found to increase the necessary mass of the rotor structure by 40%, to meet the reduced airgap clearance. Full article

Due to the scarcity of labeled samples, the practical engineering application of deep learning-based hydraulic pump fault diagnosis methods is extremely challenging. This study proposes a semi-supervised learning method based on data augmented consistency regularization (DACR) to address the issue of lack of labeled data in diagnostic models. It utilizes augmented data obtained from the improved symplectic geometry modal decomposition method as additional perturbations, expanding the feature space of limited labeled samples under different operating conditions of the pump. A high-confidence label prediction process is formulated through a threshold determination strategy to estimate the potential label distribution of unlabeled samples. Consistent regularization loss is introduced in labeled and unlabeled data, respectively, to regularize model training, reducing the sensitivity of the classifier to additional perturbations. The supervised loss term ensures that the predictions of the augmented labeled samples are consistent with the true labels. Meanwhile, the unsupervised loss term can be used to minimize the difference between the distributions of unlabeled samples for different augmented versions. Finally, the proposed method is combined with Kolmogorov–Arnold Network (KAN). Comparative experiments based on data from two models of hydraulic pumps verify the superior recognition performance of this method under low label rate. Full article

In this study, an axial flux double airgap permanent magnet-assisted synchronous reluctance motor (AF-Pma-SynRM) was designed for electric vehicles (EVs). The AF-Pma-SynRM model employs a forced liquid cooling method (cooling jacket) for a high current density. The model was tested using multi-objective optimization and multi-physics analysis. The AF-Pma-SynRM design has achieved 95.6 Nm of torque, 30 kW of power, and 93.8% efficiency at a 3000 rpm rated speed. The motor exhibits a maximum speed of 10,000 rpm, 253.1 Nm of torque, and 65 kW of output power. This study employs a novel barrier structure for axial motors characterized by fixed outer and inner dimensions, and is suitable for mass production. In the final stage, the motor was cooled using the cooling jacket method, and the average temperature of the winding was measured as 73.83 °C, and the average magnet temperature was 66.44 °C at a nominal power of 30 kW. Also to show variable speed performance, an efficiency map of the AF-Pma-SynRM is presented. Full article

Elevators are a vital part of urban infrastructure, playing a key role in smart cities where increasing population density has driven the rise in taller buildings. As an essential means of vertical transportation, elevators have become an integral part of daily life, making their design, construction, and maintenance crucial to ensuring safety and compliance with evolving industry standards. The safety of elevator systems depends on the continuous monitoring and fault-free operation of Permanent Magnet Synchronous Motor (PMSM) drives, which are critical to their performance. Furthermore, the fault-free operation of PMSM drives reduces operating costs, increases service life, and improves reliability. The PMSM drive components may be susceptible to electrical, mechanical, and thermal faults that, if undetected, can lead to operational disruptions or safety risks. The integration of artificial intelligence and Internet of Things (IoT) technologies can enhance fault prediction, reducing downtime and improving efficiency. Ongoing challenges such as managing machine thermal load and developing more durable materials for PMSMs require the development of suitable models that are adapted to existing drive systems. The proposed framework for fault prediction is validated on a real residential elevator equipped with a PMSM drive. Multimodal signal data is processed through a Generative Adversarial Network (GAN)-enhanced Positive Unlabeled (PU) classifier and a Reinforcement Learning (RL)-based adaptive decision engine, enabling robust and scalable fault prediction in a non-intrusive fashion. Full article

When connecting a renewable energy source to a medium-voltage grid, it has to fulfil grid codes and be able to work in a medium-voltage range (>10 kV). Multilevel converters (MLCs) are recognized for their low total harmonic distortion (THD) and ability to work at high voltage compared to other converter types, making them ideal for applications connected to medium-voltage grids whilst being compliant with grid codes and voltage ratings. Cascaded H-bridge multilevel converters (CHBs-MLC) are a type of MLC topology, and they does not need any capacitors or diodes for clamping like other MLC topologies. One of the problems in these types of converters involves the double-frequency harmonics in the DC linking voltage and power, which can increase the size of the capacitors and converters. The use of line frequency transformers for isolation is another factor that increases the system’s size. This paper proposes an isolated CHBs-MLC topology that effectively overcomes double-line frequency harmonics and offers isolation. In the proposed topology, each DC source (renewable energy source) supplies a three-phase load rather than a single-phase load that is seen in conventional MLCs. This is achieved by employing a multi-winding high-frequency transformer (HFT). The primary winding consists of a winding connected to the DC sources. The secondary windings consist of three windings, each supplying one phase of the load. This configuration reduces the DC voltage link ripples, thus improving the power quality. Photovoltaic (PV) renewable energy sources are considered as the DC sources. A case study of a 1.0 MW and 13.8 kV photovoltaic (PV) system is presented, considering two scenarios: variations in solar irradiation and 25% partial panel shedding. The simulations and design results show the benefits of the proposed topology, including a seven-fold reduction in capacitor volume, a 2.7-fold reduction in transformer core volume, a 50% decrease in the current THD, and a 30% reduction in the voltage THD compared to conventional MLCs. The main challenge of the proposed topology is the use of more switches compared to conventional MLCs. However, with advancing technology, the cost is expected to decrease over time. Full article

Three-phase induction motors are widely adopted in industrial systems due to their robustness, ease of maintenance, and simple operation. However, they are prone to various types of faults, notably stator winding faults. Previous research indicates that 20–40% of three-phase induction motor failures are stator-related, with inter-turn short circuits as a leading cause. These faults can pose significant risks to both the motor and connected equipment. Therefore, the early detection of inter-turn short circuit (ITSC) faults is essential to prevent system breakdowns and improve the safety and reliability of industrial operations. This paper presents a comparative investigation of two distinct diagnostic methodologies for the detection of ITSC faults in induction motors. The first methodology is based on a Motor Current Signature Analysis (MCSA) utilizing the short-time Fourier transform (STFT) for the real-time monitoring of fault-related harmonics. The second methodology is centered around the monitoring of the zero-sequence voltage (ZSV). The findings from several experimental tests performed on a 1.1 kW three-phase induction motor across a range of operating conditions highlight the superior performance of the ZSV method with respect to the MCSA-based STFT method in terms of reliability, rapidity, and precision for the diagnosis of ITSC faults. Full article

The propulsion system is a critical component of medium–low-speed maglev trains and the single-sided linear induction motor (SLIM) has been adopted to generate thrust. However, the SLIM operates periodically in maglev trains. The temperature of the secondary plate of the SLIM rises significantly due to eddy currents when the train enters and leaves the station, where large slip occurs. Subsequently, the temperature decreases through natural cooling during the shift interval time. This periodic operating condition is rarely addressed in the existing literature and warrants attention, as the temperature accumulates over successive periods, potentially resulting in thermal damage and thrust variation. Furthermore, the conductivity of plate varies significantly in the process, which affects the losses and thrust, requiring a coupled analysis. To investigate the temperature variation patterns, this paper proposes a coupled model integrating the lumped parameter thermal network (LPTN) and the equivalent circuit (EC) of the SLIM. Given the unique structure of the F-shaped rail, the LPTN mesh is well designed to account for the skin effect. Three experiments and a finite element method (FEM)-based analysis were conducted to validate the proposed model. Finally, optimizations were performed with respect to different shift interval time, plate materials, and carriage numbers. The impact of temperature on thrust is also discussed. The results indicate that the minimum shift interval time and maximum carriage number are 70.7 s and 9, respectively, with thrust increasing by 22.0% and 22.0%. Furthermore, the use of copper as the plate material can reduce the maximum temperature by 22.01% while decreasing propulsion thrust by 26.1%. Full article