最新刊期
卷 53 ， 期 6 ， 2019
- 摘要：An inverse calculation method of correction parameters for machine settings based on the design for 6σ is proposed to focus on the problem that the coupling among parameters for machine settings is difficult to control in the process of correction for spiral bevel gears and the resulting correction parameters have no practical significance. The method considers the coordination optimization between the precise tooth surface geometry and the tooth contact performance, and establishes a multi-objective optimization(MOO)model of collaborative manufacturing considering both geometric and physical performance for spiral bevel gears. The sensitivity coefficient of the tooth surface error at contact points to the machining settings is calculated, and then the sensitivity coefficient is used to obtain inverse corrections for the machining parameters that have great influence on the tooth surface error. Finally, the inverse corrections for machining parameters are used to obtain the loaded tooth contact performance through the loaded tooth contact analysis(LTCA), and the influence of unnecessary coupling among machine settings on the tooth contact performance is reduced. The calculation results and a comparison with the correction method of machine tool settings without considering the sensitivity coefficient show that both the maximum value and the minimum value of the ease-off obtained by the proposed method are reduced by at least 2 μm, and the loaded tooth contact performance items meet given requirements, which verifies the reliability and practicability of the proposed method.关键词：spiral bevel gears;collaborative manufacturing;multi-objective modification optimization365|5|2更新时间：2025-07-09
- 摘要：A design approach of multi-objective tooth optimization for hypoid gears with ease-off topological modification is proposed to improve comprehensive meshing performances of automobile drive axle. Firstly, the modified pinion is represented by a sum of two vector functions that determine the conjugate tooth of gear and the pinion normal ease-off surfaces, respectively, and are expressed by both predesigned transmission error function and tooth profile modification curves. Secondly, based on tooth contact analysis(TCA), loaded tooth contact analysis(LTCA)and the latest friction theory of gear, the bearing deformations, sliding velocity, distribution of loads and local friction coefficient of the discrete points on contact line of gears are obtained, and the instantaneous meshing efficiency and Block flash temperature are further determined. Thirdly, the best ease-off surfaces are obtained by solving a multi-objective optimization problem with minimal amplitude of loaded transmission error(ALTE), minimal instantaneous flash temperature and maximal average meshing efficiency. Moreover, the distribution of sliding velocity, the radius of curvature on tooth, and the influences of contact ratio on meshing performances are explored. Numerical results show that the best ease-off modification has enough parabola transmission errors at the meshing start and the meshing end, which can effectively reduce the sensitivity of installation error and ALTE. In addition, profile modification and small inclination of contact path contribute to a greater contact ratio, and the loads at top and root concentrate near the pitch line, while the sliding speed near the pitch line is smaller, as a result the meshing efficiency increases and the flash temperature decreases. However, when the tooth surface mismatch is too large, the loads and friction power consumption will increase, and the meshing efficiency will decrease.关键词：hypoid gear;ease-off topological modification;tooth flash temperature;meshing efficiency;loaded transmission error;loaded tooth contact analysis507|5|14更新时间：2025-07-09
- 摘要：A novel diagnostic approach for bearing faults using regularized locally maintaining projections(En-LPP)is proposed to solve the problems of high dimension and strong relevance caused by feature fusion in bearing fault detection domains. Firstly, the entropy normalization method is used to combine a similarity matrix into the optimization function of the traditional locality preserving projections(LPP)algorithm, and to solve it along with the projection vector to obtain a regularized LPP algorithm with reduced dimension. Then, the original bearing vibration signal is decomposed into ten signal components through wavelet transform and empirical mode decomposition, and 12-dimensional statistical features for each component are extracted. Final high-dimensional feature vectors are generated by combining and normalizing these statistical features of all components. These feature vectors are then inputted into the proposed algorithm, and iteration is performed to obtain the similarity matrix and the projection vector that satisfy termination conditions. Finally, the reduced dimension feature set is used to train an extreme learning machine model for determining the final working state of a bearing to achieve fault detection. Experimental results and comparisons with the traditional LPP algorithm and other dimension reduction algorithms show that the proposed En-LPP algorithm has better performance for bearing fault diagnosis. The classification accuracy is improved by more than 7% on average under the 72-dimensional feature set of the wavelet transform and 48-dimensional feature set of empirical mode decomposition, and the averaged classification accuracy of the regularized LPP algorithm with combinations of 4 classifiers is about 17% higher than those of other dimension reduction algorithms. The proposed approach has better ability to distinguish dimension-reduced features, which makes its fault diagnosis performance robust under different combinations of conditions.关键词：fault diagnosis;locality preserving projections algorithm;dimension reduction;entropy regularization340|6|2更新时间：2025-07-09
- 摘要：A precise state space model of regenerative suspension system including inertial mass is established to study the influences of the damper's inertial mass on the vibration insulation performance and energy recovery performance of the vehicle regenerative suspension system and to optimize the electromagnetic damper selection. The damper's inertial mass is derived from the dynamic equation of the vehicle suspension system, and the acceleration of the suspension system, the suspension travel and the wheel's dynamic deformation are used as outputs of the system. The influence of the inertial mass level on the main performance of the suspension system is analyzed by using the frequency domain transmission characteristics of the outputs of the state space model system. Simulation results show that the average energy recovery power of the suspension system decreases with the increase of the equivalent inertial mass of the damper. Although the amplitude-frequency transmission characteristics of the main performance indicators in the low frequency band are slightly improved, the transmission characteristics in the middle frequency band deteriorate seriously, and excessive inertial mass of the damper will cause the overall performance of the suspension system to deteriorate. The simulation results are verified on a 1/4 suspension system bench test. Results show that under the same excitation condition, up to 44% attenuation of energy recovery power is caused by the inertial mass of the electromagnetic damper and that higher-level inertial mass causes the transmission performance of the key performance indicators of the suspension system to produce different degrees of deterioration in the middle frequency band, the resonance frequency is slightly shifted forward, and the overall performance of the suspension system deteriorates. The experimental results verify the correctness of the simulation results.关键词：vehicle suspension;electromagnetic damper;inertial mass;regenerative suspension;frequency domain transmission characteristic460|6|6更新时间：2025-07-09
- 摘要：A target tracking algorithm based on combination of adaptive feature representation and a fern classifier is proposed to solve the problem that the linear interpolation update strategy used in the tracking algorithms based on kernel correlation filter architecture cannot deal with sudden changes in target's appearance. Firstly, dimension reduction through principal component analysis is carried out for extracted feature layers of the target to obtain good feature layers. Secondly, the target location is determined by combination of the passive-aggressive filters, where the aggressive filter is updated with a fixed update rate in each frame and the passive filter is updated only when a threshold condition is met. The aggressive filter is used to predict the next frame position of the target, and the passive filter is used to calculate the reliability of the predicted position generated by the aggressive filter and detector. When the prediction of the aggressive filter is unreliable, the detector predicts a position of the target. The best prediction of the target position is finally determined by comparing the reliability of both predicted positions. Experiment results show that the proposed method effectively addresses the model corruption and tracking failure caused by target mutation. Results on the OTB2015 dataset show that the proposed method significantly outperforms the discriminative correlation filter with channel and spatial reliability algorithm by 2.78% and 4.26% in precision and area under curve metrics, respectively.关键词：target tracking;correlation filter;adaptive feature selection;detector378|5|0更新时间：2025-07-09
- 摘要：Quadrature Kalman Gaussian mixture(QK-GM)implementation for δ-generalized labeled multi-Bernoulli(δ-GLMB)filter is proposed to solve the problem that the sequential Monte Carlo(SMC)implementation for δ-GLMB filter in nonlinear model is too complex to realize fast and accurate filtering. The algorithm is based on the Gauss-Hermite quadrature rule to obtain a set of weighted quadrature points, and these points are then utilized to calculate mean values and covariance matrixes of the multiobjective density functions. The proposed QK-GM implementation is compared with the existing algorithms, including extended Kalman Gaussian mixture(EK-GM), unscented Kalman Gaussian mixture(UK-GM)and SMC implementation, in detail in terms of tracking accuracy and time consumption for multi-target tracking in the presence of different clutter densities and detection probabilities. Simulation results and a comparison with SMC method show that the QK-GM implementation of δ-GLMB filter improves the multi-target tracking accuracy by more than 10% at the expense of a completely acceptable time cost. The proposed algorithm may provide a new method for applications of δ-GLMB filter in nonlinear scenes.关键词：δ-generalized labeled multi-Bernoulli;quadrature Kalman;Gaussian mixture;multi-target tracking298|5|1更新时间：2025-07-09
- 摘要：An ELU-CNN image denoising model based on convolution neural network(CNN)is proposed to realize automatic detection of the defects on the shaft lining. It is a 28-layer full-convolution denoising network model and consists of five feature extraction modules(FEMs)and skip connections. The skip connections combine the output features of the first convolution layer with the output features of each FEM to ensure the full extraction of image features. The residual learning is used in the ELU-CNN model to relieve the gradient disappearance problem, to improve the convergence speed, and to ensure that the learned nonlinear mapping from a noisy image by the denoising model after training is a noise image. ELU is used as an activation function. It has soft saturation performance, and the average of its outputs is close to zero. These properties can accelerate the convergence of the model and enhance the robustness of the model to the input noises. The denoising effect of ELU-CNN is verified on the standard test sets BSD68 and set12 as well as actual shaft wall images, and is compared with those of some advanced methods. The experimental results on BSD68 and set12 with noise concentration σ=(15, 25, 35, 50, 75)and comparisons with FFDNet model show that the average of peak signal to noise ratios of ELU-CNN increases(0.17, 0.11, 0.08, 0.05, 0.03)dB and(0.18, 0.16, 0.08, 0.06, 0.07)dB, respectively. ELU-CNN can better preserve the texture information of fractures when removing blind noise from shaft wall images.关键词：image denoising;convolution neural network;shaft lining image;deep vertical shaft375|5|1更新时间：2025-07-09
- 摘要：A UAV cooperative formation tracking controller is designed to address collisions between unmanned aerial vehicles(UAVs)due to the inconsistent information of attitude and position when they track a moving target, and the flight-stability of the designed controller is analyzed. The path planning of a single UAV and the orbit of a multi-UAV cooperative formation tracking a moving target are studied. A feedback control is used to ensure heading convergence for the path planning of a single UAV. Moreover, a guidance vector field method is proposed to achieve heading convergence, and it is able to analyze and solve the collision problem between the UAVs. Further, a multiple variables controller is used to maintain the cooperative UAV formation in a circular orbit during flight, and adaptive estimations for unknown wind conditions and moving targets are used to determine the flight stability of the formation in the circular orbit. Simulation results show that a formation of three UAVs rapidly get close to a target with the same attitude and position while remaining relatively stable, and the relative distance error between any two UAVs rapidly converges to the stable value in the case of unknown wind conditions and moving targets. This study provides an effective way for closely and cooperatively tracking target, and facilitates multi-UAV distributed controller design.关键词：unmanned aerial vehicles cooperative formation;information architecture;vector potential field;multiple variables controller;stability analysis462|5|0更新时间：2025-07-09
- 摘要：An online monitoring method of capacitance value of sub-module for flexible high voltage direct current(HVDC)transmission system using adaptive filter is proposed to solve the problem that the sub-module capacitors of modular multi-level converter(MMC)in the flexible HVDC system often age and fail, threatening the safety of the system, and to monitor the capacitor state. Firstly, the capacitor's capacitance value is calculated in real time based on the relationship among the capacitor's voltage, current and capacitance value after the discretization process, in which the information of the sub-module voltage and bridge arm current displayed in the control system in real time is used. Then an adaptive filtering technology is introduced to filter out the noises in calculation results to obtain the final capacitance value. The proposed calculation method is verified by using the measured data from a Simulink platform and a valve section test platform. The verification results show that the final capacitance values calculated are always stable around the true value. When the uncertainty of voltage and current measurement is less than 1%, the errors of calculation results are guaranteed to be less than 0.5%, which meets the requirement of sub-module capacitor condition monitoring.关键词：flexible high voltage direct current transmission;modular multi-level converter;sub-module capacitor;adaptive filter;condition monitoring385|5|6更新时间：2025-07-09
- 摘要：Detailed formation mechanisms of vibration band gaps in locally resonant periodic structures are investigated based on a two-dimensional locally resonant periodic structure. Firstly, based on the dynamic theory a basic theoretical hypothesis of the formation mechanism for the band gap of locally resonant periodic structure is proposed following the modal superposition principle. Secondly, the detailed formation mechanism of vibration band gaps is further clarified according to the formation mechanism model of band gaps. Finally, the formation mechanism of vibration band gaps is verified by the interaction between the elastic wave and the structure during the formation of vibration band gaps in a typical two-dimensional locally resonant periodic structure. The result shows that there exist six types of generalized propagation modes of waves in the locally resonant periodic structure, while these generalized propagation modes are formed by the mutual transformation of the main modes and these main modes are generated by corresponding 12 modes based on the superposition principle. The vibration mode dominated by the main mode of the vibrator determines the formation of a vibration band gap by suppressing or releasing the generalized propagation mode. When the oscillator's main mode suppresses the generalized propagation mode of the wave, a generalized sub-band gap that can only suppress the corresponding propagation mode is formed. The influencing mechanism of the band gap is further studied and an active design method of the band gap is proposed. This research may perfect the basic theory of periodic structure, lay a theoretical foundation for the study of band gap theory of periodic structure and the active design of band gap characteristics, and provide a new method for vibration reduction of engineering structures.关键词：periodic structure;vibration band gap;vibration reduction;local resonance617|5|7更新时间：2025-07-09
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