Magnetic liquid double suspension bearing (MLDSB) combines electromagnetic suspension with hydrostatic support to achieve high load capacity and stability under medium-speed, heavy-load conditions. When the electromagnetic system fails, the hydrostatic unit temporarily levitates the rotor, but its performance is governed by oil viscosity. This study is aimed at quantifying how viscosity influences rub-impact dynamics in MLDSB. A three-degree-of-freedom nonlinear model was established and solved by a fourth-order Runge-Kutta scheme. Parametric bifurcation analyses were performed over speed ratio 0.8-1.05, eccentricity ratio 0.15-0.40, initial pressure 0.1-1.6 bar, and bias current 0.5-2.0 A. Key dynamic features including bifurcation characteristics, phase path, and Poincare mapping were validated on a purpose-built MLDSB test bench. Results reveal a critical viscosity window (mu approximate to 3-4 & times; 10-3 Pa & centerdot; s) below which the first period-doubling bifurcation occurs, followed by alternating multiperiodic and chaotic motion. Higher eccentricity or speed ratio broadens the bifurcation interval, whereas increased initial pressure mitigates chaos. These findings provide design guidelines for viscosity selection and control strategies to enhance the fault-tolerant performance of MLDSB under rub-impact conditions.
Cylindrical hydrodynamic bearings are essential components of rotating machines used in applications ranging from small motors to large turbo generators. They operate based on the principle of a thin oil film that separates the rotating shaft from the bearing, preventing mechanical contact between the surfaces. Studies on cylindrical bearings usually consider an adequate supply of lubricant. However, it is crucial to analyze the phenomenon of oil starvation to understand the influence of lubrication conditions on bearing performance. Researchers have developed various numerical models to represent the behavior of hydrodynamic bearings; these models are able to determine the supporting pressure by solving the Reynolds equation. The present work used the so-called p - theta formulation and energy equation to represent the dynamic behavior of a cylindrical bearing. A journal bearing was designed and manufactured for experimental purposes, capable of being instrumented to monitor variables that will be compared with simulation results. Sixteen thermocouple sensors, equally spaced along the circumferential length of the bearing, were used to monitor the oil film temperature. A similar scheme was adopted for pressure data acquisition, with 16 pressure transducers. The obtained results demonstrated that the resulting thermohydrodynamic model accurately represents the dynamic behavior of the cylindrical bearing.
This work presents and validates a direct analytical methodology for the parametric hydrodynamic design of Pelton rotors. The proposed approach combines a one-dimensional theoretical framework for defining global geometric rotor parameters with an inductive method for generating bucket geometry, formalized from a comprehensive mapping of a high-efficiency commercial reference bucket. The reliability of this integrated design methodology is assessed via high-fidelity unsteady Reynolds-averaged Navier-Stokes (URANS) computational fluid dynamics (CFD) simulations, coupled with the volume of fluid (VOF) multiphase model and the k - omega SST turbulence model on a rotating five-bucket domain. A theoretical efficiency of 93.4% was estimated, closely aligning with the 91.4% total efficiency obtained from the CFD model. Furthermore, simulations under part-load conditions demonstrated the designed rotor ' s high performance, with efficiency remaining above 85% in all off-design scenarios. Finally, the proposed methodology provides a practical, reproducible, and computationally efficient tool for generating high-performance geometries, serving as a robust foundation for integrating into future optimization workflows, fluid-structure interaction analyses, and advanced research in Pelton turbine design.
Advanced gas turbines play a crucial role in power generation, operating at turbine inlet temperatures exceeding 1200 degrees C to maximize efficiency and output. While nickel-based superalloys provide structural integrity, their performance is limited at temperatures above 1250 degrees C, necessitating the use of advanced cooling strategies. This study employs response surface methodology (RSM) to systematically analyze and optimize cooling hole configurations, enhancing thermal management in high-temperature environments. Existing configurations prove inadequate for next-generation gas turbines operating at 1400 degrees C with cooling air at 400 degrees C. Through predictive modeling, an optimal configuration is identified, characterized by a blade height of 300 mm, 45 holes with a diameter of 2.0 mm, and a spacing of 4.75 mm, which effectively reduces metal temperatures to below 1250 degrees C. This optimized design significantly improves cooling efficiency and mitigates thermal hotspots, offering a viable solution for high-temperature turbine applications. The findings from this study offer a practical and scalable solution for thermal management in high-performance gas turbines, supporting the advancement of turbine technology capable of operating under more demanding thermal conditions.
Blade faults are widely regarded as one of the most frequent failures in gas turbines, and if left unchecked, they can result in catastrophic failure. To ensure the safe operation of these machines, it is essential to detect minor blade faults and monitor blade health regularly. In recent years, machine learning and artificial intelligence have been proposed as effective tools for machinery fault diagnosis. However, when features are manually extracted, some important and vital features may be overlooked, leading to unreliable results. This paper presents a novel unsupervised hybrid deep learning model based on convolutional neural network and transformer with cross-attention and autoencoder structure to address these challenges in blade fault detection. The developed method has adaptive learning capabilities for automatic feature extraction and signal processing with minimal human intervention and was found to be more efficient in terms of the accuracy of fault prediction. The study was conducted on a multistage rotor system designed and developed within our laboratory, and the results revealed that the developed model can achieve an impressive fault prediction accuracy ranging from 90% to 92% at a signal-to-noise ratio of 15 dB, which is a challenging level of noise for various stages and fault combinations indicating that the model is capable of accurately identifying minor blade faults, even in the presence of significant noise interference. This approach revolutionizes blade fault detection in complex rotor systems, offering superior reliability and efficiency, even in the absence of labeled data and noisy environments.
Focusing on the integrated multiphysics domain optimization design process for industrial permanent magnet motors, this paper presents the electromagnetic-thermal-rotor mechanical strength integrated design of a 355 kW, 1000 r/min industrial permanent magnet synchronous motor, yielding comprehensive design results that meet all motor performance requirements. A multi-objective optimization design method for permanent magnet synchronous motors is proposed based on a multiphysics domain collaborative optimization model, which effectively addresses the challenges of large computational workload and time consumption in the industrial motor optimization design process. Finally, finite element simulation validations of electromagnetic performance, temperature rise, and rotor mechanical structure strength were conducted, with the results matching the optimization outcomes, providing a reference for the development of industrial permanent magnet motors.
Fault diagnosis of rotating machines has undergone significant advancements through the use of deep learning models. However, the effectiveness of these models is often compromised by noisy raw vibration data collected from industrial machines, which can negatively impact accuracy rates. To address this challenge, we present an improved fault diagnosis method that combines a multiple parallel hidden layer denoising autoencoder (MPHLDAE) with a one-dimensional convolutional neural network (1DCNN). In this method, the MPHLDAE is used to effectively denoise the raw vibration signals, whereas the 1DCNN is responsible for diagnosing faults in the denoised data. Hyperparameter optimization was carried out through a grid search with k -fold cross-validation. Experimental evaluations were conducted using rolling bearing and gearbox vibration datasets intentionally mixed with varying levels of Gaussian noise. The results validate the superior denoising capability of the MPHLDAE compared with existing denoising techniques. The proposed method consistently achieves fault diagnosis accuracies exceeding 95.30%, even under low signal-to-noise ratio conditions. These findings prove the superiority of the proposed MPHLDAE-1DCNN method over other advanced methods for rotating machines.
Vibration signals are the most widely used condition monitoring data in deep learning-based fault diagnosis for rotating machines. However, relying solely on data from a single vibration sensor often limits the diagnostic accuracy of the diagnosis models. To overcome this challenge, researchers have explored multisensor data fusion techniques. Nevertheless, existing fusion approaches face challenges when dealing with variations in sampling frequencies and different sensor mounting orientations. In this paper, therefore, we propose a new data-level fusion method, compensated synchronized resampling and weighted averaging fusion (CSR-WAF), to enhance the accuracy of deep learning-based fault diagnosis in rotating machines. In this method, the CSR component first synchronizes the sampling frequencies of vibration data and compensates for sensor orientation. Subsequently, the WAF technique fuses the multisensor vibration data. The fused data are then processed using a one-dimensional convolutional neural network (1DCNN) for fault diagnosis. Experiments conducted using motor bearing vibration signals sampled at 12 and 48 kHz show that the proposed CSR-WAF-1DCNN method achieves an accuracy of 99.87%. Furthermore, the proposed method is applied to gearbox fault diagnosis, accounting for different sensor mounting directions, and achieves an accuracy of 97.91%. These results confirm the reliable performance and practical applicability of CSR-WAF-1DCNN across diverse data acquisition scenarios.
A hybrid modeling approach integrating lumped mass and finite element methods is developed to establish the coupled dynamic model of a multispan flexible rotor system. The hollow shaft is discretized using Timoshenko beam elements to account for shear deformation, gyroscopic moments, and rotational inertia. A nonlinear dynamic ball bearing model incorporating time-varying stiffness and radial clearance effects is formulated based on Hertzian contact theory and piecewise Heaviside functions. Analytical derivations and finite element simulations determine the stiffness matrices of laminated couplings, with a 9.95% discrepancy observed between theoretical and numerical angular stiffness values. Viscoelastic rubber damping rings are characterized through a Mooney-Rivlin hyperelastic model combined with a Prony series formulation. Experimental validation on a multispan rotor test platform demonstrates critical speed predictions with less than 4.3% error relative to measured data. Following that, the numerical approach is used to examine how the ball bearing, laminated coupling, and rubber damping ring affect the system's dynamic behavior. The results demonstrate that the time-varying stiffness effect of the ball bearing is noticeable at low speeds and diminishes with increasing speed; the laminated coupling lessens the mutual dynamic effect between the system's rotors and makes each rotor comparatively independent. The natural frequency of the system could be changed by varying the angular stiffness of the laminated coupling since it is more susceptible to it. The rubber damping rings shift the system's critical speed and reduce resonance traversal amplitudes, while lowering bearing acceleration peaks to mitigate fatigue and extend operational lifespan. This work provides a theoretical framework for designing complex rotor systems with optimized dynamic performance.
The motor involves many factors such as electricity, magnetism, heat, and stress, and thus, electromagnetism, heat, and rotor mechanical structure must be comprehensively considered in motor designing. Here, an electromagnetic–thermal–mechanical structured collaborative optimized design method was proposed for permanent magnet synchronous motors (PMSMs) used in concrete stirring tankers in accordance with the requirements of large torque and low speed. With a water‐cooled PMSM with rated power of 47 kW as example, the stator and rotor structures of the PMSM for stirring tankers were optimized on finite element software. Then, the temperature field of the motor and the mechanical structural stress of the rotor were analyzed and designed. The finite element simulation showed the design solution can meet the electromagnetic performance, temperature field performance, and rotor mechanical structural strength required by the motor. Finally, a prototype was made and tested. The experimental results were consistent with the simulation results, which proves the effectiveness and correctness of the motor optimized design method proposed here.
The vibration signal of rolling beating is nonlinear and nonstationary, which makes feature extraction difficult for fault diagnosis. To improve the efficiency of feature extraction and fault diagnosis, a hybrid model based on optimized variational mode decomposition (VMD), fuzzy dispersion entropy (FDE), and a support vector machine (SVM) is proposed. Firstly, a parameter optimization method using the sparrow search algorithm (SSA) was applied to VMD to improve the decomposition ability. Subsequently, a feature vector based on the FDE was proposed as a fault-diagnosis feature. Finally, SVM was applied with the proposed feature vector for the fault diagnosis of rolling bearings. The simulation and experimental study results indicate that the proposed method can obtain useful features for fault diagnosis, particularly in short-length samples and noise conditions. The proposed method performed well in the fault diagnosis for different fault types and degrees of rolling bearings.
Literature research has shown that the high turbulent viscosity of original Reynolds-averaged Navier–Stokes (RANS) models in the wake reduces the cavitation instability, resulting in an unreasonable performance prediction of the cavity generation, shedding, and collapse. Hence, a density-corrected model (DCM) is utilized to improve the prediction capacity of the RNG k–ε model in the two-phase flow simulation. Selected results are provided for two cases, namely, a two-dimensional Clark-Y hydrofoil and a Venturi geometry associated with the unsteady cavitating flows. We compare hydrodynamic coefficients, cavity characteristics, and time-averaged velocity with available experimental results. This work is aimed at providing a better physical insight to describe the vortex dynamics and structures during the cavity evolution, explaining the mechanism of the transient cavitating flows by quantifying the force coefficients of the cavity and its surrounding turbulent structures, and revealing the promoting effect of the re-entrant jet on the cavitation transition. In addition, vorticity transport equations are conducted to analyze the interaction of vortex and cavitation. The results demonstrate that the DCM has sufficient robustness to predict the periodic cavity evolution of generation, breakup, shedding, and collapse. An unsteady cavitation process contains the development of complex vortex structures. Re-entrant jet near trailing of the attached cavity leads to distinct changes of velocity gradient, which has great influence on production and dissipation of vorticity. Intensive mass transfer between liquid and vapor phases may introduce the volume expansion or contraction as well as the baroclinic torque, which leads to unsteady distribution of vorticity.
The rotary vector (RV) reducer, as a highly precise transmission mechanism, can lead to a series of consequential hazards when experiencing local faults. Conducting corresponding research on performance monitoring and fault diagnosis holds significant importance. In order to achieve higher diagnostic accuracy, better classification, prediction effect, and less use of sensors, this paper proposes a local fault diagnosis method of RV reducer based on Motor current signature analysis (MCSA). Firstly, the centre gear local fault electromechanical coupling model is created by combining the working principle of the servo motor, the working characteristics of the RV reducer, and the influencing factors of motor current. Then, according to the actual operating conditions of industrial robots (IRs), the corresponding experimental platform is designed and constructed, and current signals in four different modes are collected. Fast Fourier transform (FFT) is performed on the current signals to obtain frequency domain features, and the correctness of the local fault coupling model of the centre gear is experimentally verified. Finally, the time-domain statistical features, time–frequency domain features, and CNN features of servo-feedback current signals under different rotational speeds are extracted and used in the implementation of local fault diagnosis for the RV reducer, respectively. In this paper, it is confirmed that MCSA can be used for localized fault diagnosis of the RV reducer, and combined with a deep learning network, it can effectively predict the fault modes with an average accuracy of more than 96%.
In industrial robot welding heat exchanger plate route planning, there are drawbacks to using the traditional ant colony algorithm (ACA), including poor search efficiency and a propensity to trend toward the local optimum. To address the above issues, first of all, the article introduces the improved pheromone volatilization factor, which is adjustable based on iteration times in the ACA. Secondly, it combines the new perturbation strategy and the cross-mutation operation, proposes an improved genetic algorithm, and fuses it into the ACA, which increases the diversity of the ACA’s path searching and improves the ability of the local and global search. Finally, the improved ACA mechanism is verified by experiments and compared with six existing path planning algorithms (including three ACA variant algorithms and three mainstream algorithms). The experimental results show that the IACAG algorithm has excellent performance in welding heat exchanger path planning and can achieve lower calculation time and iteration times under the conditions of optimal solution and zero deviation, showing comprehensive advantages in solution quality, stability, and efficiency.
To give timely and accurate diagnosis in the early stage of demagnetization failure for effective control and treatment, based on wavelet packet analysis, principal component analysis (PCA) dimensionality reduction, and least squares support vector machine(LSSVM), the extraction of features and the classification of demagnetization faults are completed. Since it is difficult to collect real data sets of demagnetization faults in practice, a two-dimensional finite element simulation model of permanent magnet synchronous motor (PMSM) under uniform demagnetization and partial demagnetization faults is established based on the Maxwell simulation platform. The wavelet packet analysis is used to extract the demagnetization feature of the A-phase current of the PMSM. Based on PCA dimensionality reduction, the dimensionality reduction of fault features is realized. The LSSVM is used to identify the fault and complete the fault classification. The simulation results show that the method has a high classification accuracy rate for demagnetization faults.
The flow capacity of the intake port has a great influence on the charging efficiency of the internal combustion engine, affecting the engine’s performance, so it is critical to improve the intake port flow capacity. In this paper, the intake port numerical model was established, and the influence of the inlet and outlet area ratio on intake port flow capacity was studied. The results show that, under the same condition of relative pressure difference, the intake port discharge coefficient increases sharply and then slowly with an increase in area ratio. Under the same condition of area ratio, the discharge coefficient is only determined by relative pressure difference and decreases with an increase of relative pressure difference when the inlet and outlet area ratio>1. The optimal area ratio decreases and then converges with an increase in relative pressure difference. When the intake port is designed, the optimal area ratio under the working condition of a smaller relative pressure difference should be applied in order to ensure the discharge coefficient is optimal under all working conditions.
Gas transportation through the pipeline network requires high pressure; otherwise, movement will be impossible. Due to friction with the walls of the pipes, the gas loses both speed and pressure. Therefore, it is necessary to increase the pressure in the main pipeline, which can extend for thousands of kilometers. For this purpose, compressor stations are located along the main network, facilitating the gas transport process by raising it to the required pressure. Compressor stations mainly consume the transported gas, with approximately 5% expended on gas compression. This article presents a study on the fuel cost for compressor stations, using mathematical models to optimize the operation of gas compressor units. The objective is to minimize gas consumption as a fuel and provide an optimization method with control over blower revolutions and valve installations to ensure safe transportation. Mathematical modeling of the described process detected the technically unjustified fuel gas consumption during the operation of gas compressor units at a specific compressor station under consideration. The paper proposes a method for minimizing costs, which can serve as the basis for providing recommendations to engineers-dispatchers. Compressor stations of the type under study lack measuring instruments for mass flow; therefore, the methods such as PID controllers, MPC, or neural networks are not applicable here.
The rotary vector (RV) reducer is one of the widely used mechanical components in industrial systems, specifically in robots. The stability of the transmission performance of the RV reducer is crucial for the efficient operation of industrial equipment. The manufacturing and assembly errors of various components of the RV reducer during the production process are important factors that affect the transmission performance. However, in previous research work, the coupling effect of multiple errors on the transmission accuracy of RV reducer has not been fully considered. Furthermore, a vague relationship between system transmission errors and various errors also has not been thoroughly discussed, which presents a challenge to analyze and optimize the errors of components using the simulation technology of virtual prototype. Therefore, we propose a novel approach to use the response surface method (RSM) to investigate the transmission accuracy of RV reducer. Firstly, based on the constructed virtual prototype of RV reducer, the individual effects of different original errors on the overall transmission error are analyzed. Secondly, a response surface approximation model using RSM is constructed to analyze the effect of multiple error interactions on the transmission accuracy of the RV reducer, and the potential functional relationship between multiple error factors and the overall transmission error is also explored. Finally, the authenticity of the proposed approach is verified by setting up some comparative experiments. This study provides a reference for the efficient analysis and optimization of the transmission accuracy of RV reducers.
In order to study the elastic contact fatigue problems of composite cylindrical roller bearing, through the three stages of contact fatigue crack initiation, propagation, and ablating of cylindrical roller bearing theoretically analyzed, the subsurface stress is one of the factors of contact fatigue damage. By finite element method and theoretical analytic method with solid cylindrical roller bearing contact surface, the size and distribution of shear stress are analyzed, and comparing the calculation results of two methods, the comparison results show that the finite element method to calculate the bearing contact problem is scientific and reasonable. Through the finite element method of cylindrical roller bearing and elastic composite cylindrical roller bearing subsurface shear stress and equivalent stress on the surface of numerical analysis, the calculation results show that the subsurface stress value of elastic composite cylindrical roller bearings was 31.65% smaller than that of ordinary cylindrical roller bearings, and the distribution of the maximum subsurface stress value of elastic composite cylindrical roller bearings was shallower than that of cylindrical roller bearings. The elastic composite cylindrical roller bearings have significant advantages over cylindrical roller bearings in terms of subsurface stress and have stronger resistance to contact fatigue damage. The finite element method is used to analyze the subsurface stress of elastic composite cylindrical roller bearings with different filling degrees. The results show that the subsurface shear stress and equivalent stress values of elastic composite cylindrical roller bearings with filling degrees of 55% to 65% are maintained at a relatively low level, and the depth of the maximum stress is minimal, which is basically distributed on the surface of the rolling body. The magnitude and distribution of subsurface stresses in elastic composite cylindrical roller bearings provide a reference for more reasonable structural design.
This study focuses on a differential wheeled robot’s (DWR) prescribed-time fractional order position control. Firstly, based on the kinematic model of DWR, a distance-related orientation error is designed using the inverse sine function. Based on this, an improved linear velocity constraint function is proposed. Compared with existing methods, while ensuring the correlation between velocity and orientation error, the multibalance point risk caused by large angle errors is avoided. Then, a prescribed-time fractional order position controller based on a time-varying scaling function is proposed to stabilize the kinematic system of DWR in the prescribed time. This controller can stabilize the position control system of the DWR in a prescribed time by adjusting the prescribed-time parameter, avoiding the infinite gain risk in traditional prescribed-time controllers. Finally, through numerical simulation, we verify that the proposed control law can converge the system status of DWR to the bounded interval in the prescribed time.
