Abstract:A sensor array for multi-crack detection, consisting of complementary open-ended waveguide resonator rings in a microstrip configuration, has been designed to address, the challenges of simultaneous detection and low detection accuracy in the multi-form crack distribution of large metal structures, such as aircraft wings. The sensor array comprises complementary open-ended waveguide resonator rings of different sizes, which can detect characteristic parameters of straight cracks, pinholes, and star-shaped cracks. Experimental results demonstrate that the maximum detection sensitivity of the sensor array to parameter variations in the three types of cracks reached 150 MHz/mm, and the smallest detectable size of a straight crack is 10 mm×1 mm×0.1 mm. This sensor combines the advantages of strong radiation capability of complementary openended resonator rings, ease of surface conformability and substrate-integrated waveguide with low loss, high quality factor, and small size. It enables simultaneous detection of multi-form cracks on metallic materials, and offers advantages such as high sensitivity and wide detection range.

Abstract:Core saturation of traditional current transformers can cause distortion of the secondary current waveform, which may affect the accuracy of energy metering or cause malfunction of relay protection equipment, posing a threat to the power grid. A novel current transformer based on composite core (CCCT) is proposed to address such issue. The CCCT is mainly composed of a composite iron core, secondary winding, secondary resistance, magnetic sensor, and signal processing circuit. The composite iron core includes a complete inner iron core and an outer iron core with air gaps. The magnetic sensor is placed in the air gap of the outer iron core, and its output is used to compensate for the distorted secondary current. To verify the validity of the structure, this paper builds a finite element simulation model, makes a prototype, and carries out the measurement experiment including sinusoidal AC, sinusoidal half-wave current, short-circuit current and direct current. Results of the finite element simulation and experiment show that the composite error of the CCCT at rated current is less than 0.2%, the composite error under symmetrical steady short-circuit current is 2.04%, the peak value of instantaneous error under transient short-circuit current is 4.25%, and the goodness of fit between output and input under DC conditions is 0.999 9, which can simultaneously meet the accuracy requirements of standards for measuring current transformers and protective current transformers. Consequently, the CCCT has the potential to be applied to practical projects since it has good transient response characteristics and anti-DC characteristics while maintaining the high measurement accuracy of traditional current transformers.

Abstract:In response to the issue of decreased positioning accuracy in indoor Ultra-Wideband (UWB) localization due to non-line-of-sight (NLOS) interference, an adaptive Kalman filtering method based on robust estimation principles is proposed. This method combines weighted least squares estimation for distance measurements to derive positioning coordinates. In a line-of-sight scenario, distance measurements are conducted. The acquired data is utilized to compute the innovation vector and covariance. Based on this information, threshold criteria are established to identify measurement outliers resulting from non-line-of-sight (NLOS) conditions. Subsequently, the Sage-Husa filter is employed to estimate the system noise covariance. Weighted least squares estimation is applied to process distance measurements, resulting in the optimal estimation of tag coordinates. Verify the feasibility and effectiveness of the algorithm through MATLAB simulation and carry out distance measurement and positioning tests in indoor environments. Simulation and experimental results demonstrate that the adaptive Kalman filtering method based on robust estimation principles, combined with weighted least squares, effectively identifies NLOS errors and tracks sudden state changes during the localization process, the error in the x-direction is about 1 cm and in the y-direction is about 2 cm, thereby enhancing the accuracy of indoor UWB positioning.

Abstract:In order to study the formation principle of obstructing phenomenon and its influence on the results of wind speed calibration, this paper establishes the flow field simulation models of meteorological wind speed calibration for different blockage ratio. The influence of different blockage ratio conditions on the flow field of cylindrical and cuboid wind tunnel test section is simulated. The existing theoretical model and comparison method adopt a single correction coefficient to correct the wind speed indication value at all calibration points, which leads to deviation in the correction of low. With this in mind, a piecewise correction method is proposed to calculate the blocking correction coefficient independently for each calibration point. Combined with the anemometer calibration data of EC9-1, EL15-1A and ZQZ-TF, the influence of the piecewise correction method and the existing methods on the anemometer measurement characteristics of is compared. The experimental results indicate that the three obstruction correction methods can improve the measurement characteristics of anemometer, such as measurement error and frequency equation, which makes the indexes of slope, intercept, indicating value and frequency equation of anemometers are closer to the calibration data in standard wind tunnel. The piecewise correction method has the most significant effect on improving the overall deviation degree of the three types of anemometers. The deviation degree of the anemometer corrected by the piecewise correction method is closest to the value measured by the standard wind tunnel.

Abstract:The reliable operation of a switched reluctance motor (SRM) drive system requires the position sensor to provide reliable position signals. Therefore, given the problem that the Hall position sensor fails from time to time, which affects the normal operation of the motor, it is of great significance to study an effective position signal diagnosis method to improve the reliability of the system operation. In this paper, a fault diagnosis method combining time threshold and state prediction is proposed. Firstly, the type and location of position sensor faults are analyzed; secondly, the proposed method based on time threshold combined with state prediction is theoretically analyzed, which compares the actual state values of the three position signal combinations with the predicted state values in real time, and threshold constraints are applied in combination with the state value transition time to detect various kinds of faults and achieve fast and accurate results; finally, to validate the proposed method, a three-phase 12/8 structure is used to diagnose faults with a three-phase 12/8 structure. Finally, to verify the effectiveness of the proposed method, an experimental validation of the method is carried out with a three-phase 12/8 structure motor as the research object. The experimental results prove the feasibility and effectiveness of the method in fault detection. In addition, the method can be realized without complex calculations and additional hardware and can be extended to be used in drive systems such as brushless DC motors (BLDC).

Abstract:To address the issue of significant noise present in the acceleration and angular velocity output signals measured by MEMS sensors, a denoising method based on the pelican optimization algorithm (POA) of variational mode decomposition (VMD) and wavelet thresholding (WT) is proposed. Firstly, POA is used to optimally select the parameter combination of the VMD, then POA-VMD is applied to adaptively and non-recursively decompose the noisy signal into a series of intrinsic modal functions (IMF). Secondly, the IMFs are classified by calculating the cosine similarity of each IMF. Based on the result of the calculation, IMFs are classified into noise-dominant component and signal-dominated component. After classification, the noise-dominated component is subjected to improved wavelet threshold denoising, and finally the processed noise-dominated component is reconstructed with the signal-dominated component to obtain the noise-reduced MEMS sensor signal. The static and dynamic experimental results show that the signal-;to-noise ratio of the denoised signal is improved by 12 and 10 dB respectively, and the mean square error is reduced by 75.5% and 46.6% respectively, which is a significant denoising effect and can improve the accuracy of the MEMS sensor.

Abstract:In response to the challenging issue of distinguishing the types of internal defects of wind turbine blades in service, this paper proposes the theory and method of dynamic thermal imaging detection based on natural daylight excitation. Finite element simulation and experimental analyses are conducted to investigate the dynamic thermal imaging patterns of different internal defects of wind turbine blades under natural daylight excitation. Firstly, a finite element heat transfer simulation model of wind turbine blade slices is established, and the variation rule of thermal characteristics of two types of typical internal defects, namely, debonding and water, is revealed by numerical calculation under the daylight-excited heat conduction physical field. Secondly, blade slice is homogeneously processed, and a thermal imaging detection platform is constructed using unmanned aerial vehicles for daylight-induced thermal imaging. Finally, daylight-induced thermal imaging experiments are conducted during different times of day under natural daylight conditions. The simulation and experimental results indicate that the two types of typical defects of debonding and water inside the wind turbine blade will lead to different trends in the surface temperature field under daylight excitation, and debonding defects will lead to the dynamic evolution of hightemperature anomalies to low-temperature anomalies in the corresponding areas on the surface of the wind turbine blade, while water is the opposite, which will provide a new methodology for the intelligent operation and maintenance of in-service wind turbine blades.

Abstract:Aiming at the problems of RRT algorithm in narrow and long space, including slow convergence speed and rough planned path, the RSS_GN RRT algorithm was proposed. To enhance the algorithm’s convergence speed, a guide node-oriented strategy and a regional sampling strategy was proposed, greatly reducing the search for invalid regions. Next, the sampling angle constraint strategy was introduced to improve the planned path quality, and adopted the method of parent node expansion selection to effectively solve the problem of increased iteration times caused by angle constraint. Furthermore, the algorithm can dynamically reconstruct the map and plan obstacle avoidance path based on the perception information, enhancing its adaptability in the low-speed dynamic environment. The simulation results show that in a narrow and long channel environment, the RSS_GN RRT algorithm reduces path planning time by 77.3%, 51.9%, 84.7%, 98.8%, and 60.3% when compared to the RRT, Goal_bias RRT, RRT under angle constraint, Informed RRT*, and DR-RRT algorithms, respectively. It decreases the number of iterations by 95.9%, 92%, 98.3%, 98.3%, and 89.5% relative to above algorithms. The average curvature of the path is also reduced by 94.1%, 93.2%, 88.7%, 91%, and 92.9%, respectively. The simulation results prove that RSS_GN RRT algorithm’s significant advantages in enhancing planning speed and optimizing path quality. Simultaneously, this paper uses the Ackerman model car to actually measure the local obstacle avoidance ability of the algorithm. After testing, the car can reasonably avoid obstacles that appear during driving.

Abstract:One of the basic requirements for automatic parking of smart cars is to quickly and accurately detect unoccupied parking slots. To address this issue, an end-to-end train detection network that integrates the direction of the parking line with global features was designed. First, the coordinates of key parking spots and the direction of the corresponding parking line are extracted, and local features are extracted from the global features using the coordinates of the key spots. Integrate key point information, local features, and global features using the cross-attention mechanism, and use the entrance line discriminator to infer the composition relationship of key points’ parking slots. Based on the composition relationship of key points and the direction of the parking line, the regional image of the parking slot is cropped and sent to a customized parking slot occupancy classification network for classification, resulting in the occupancy information of the parking slot. The proposed method was tested on the public benchmark dataset PS2.0, where the detection accuracy of the method for rectangular parking slots was 99.65%, and for tilted parking slots was 99.04%. The detection rate of 80 frames per second was achieved on a single GPU. It has been verified that the proposed method can detect the location, direction, and occupancy of parking slots in real time with high accuracy.

Abstract:In the wireless power transfer (WPT) system of electric vehicles, it is a difficult problem to reduce the magnetic leakage and maintaining a high transmission efficiency through electromagnetic shielding technology. In this paper, an omni-directional wrap-around active magnetic shielding coil structure is proposed to reduce the magnetic leakage in the WPT system. First, the magnetic shielding principles and design rationale of this structure are analyzed, and the mathematical model of the structure is derived. Secondly, a method of coil optimization is proposed according to the high efficiency and low magnetic leakage characteristics of the coil structure, and the coil parameters to meet the design requirements are obtained. Finally, based on the obtained coil parameters, a WPT system based on the proposed structure is constructed, the reasonableness of the structure and method is verified by simulation and experiment. The results show that, the maximum magnetic leakage of the target surface is 3.76 μT at a transmission power of 4 kW when the proposed structure is offset by 0 cm, which represents a 43.63% reduction compared to an unshielded structure, and the transmission efficiency is as high as 95.58%. At the offset of 10 cm, the maximum magnetic leakage on target surface is 6.03 μT, which still complying with the magnetic leakage safety standards. And the transmission efficiency is 92.92%, which is higher than the same-sized passive shielded structures and traditional active shielded structures.

Abstract:In order to solve the problem that visible light images suffer from performance degradation in low-light conditions, this paper proposes a semi-supervised domainadapted pedestrian detection algorithm. Firstly, the semi-supervised detection network is built by combining the mean teacher model and the YOLOv8 detector. Secondly, pseudo-images are generated for pseudo-cross-training using a combination of the image fusion algorithm and the style migration algorithm to reduce the problem of domain difference between images. Finally, the hybrid attention mechanism based on Transform is introduced into the backbone feature extraction network, which further improves the detection accuracy while enhancing the image resolution. The experimental results show that the detection accuracy of the algorithm reaches 89.3% and 66.8% on the LLVIP dataset and KAIST dataset, respectively, which is 7.6% and 19.8% higher compared to the SSDA-YOLO algorithm, 4% and 8.7% higher compared to the Efficient Teacher algorithm, and 1.8% and 17.9% compared to the fully supervised algorithm ICAFusion. Compared with previous algorithms, this algorithm has higher detection accuracy.

Abstract:In the production process of wide and thick plates, the rough rolling process requires the control of intersecting conical rollers to achieve the adjustment of slab length and width direction. This process heavily relies on manual operation, and the rhythm cannot be effectively controlled, which hinders the intelligent transformation process of the production line. The automatic steel transfer system for rough rolling slab, designed by combining visual inspection and automatic control technology, can effectively solve this problem. Video surveillance cameras are installed at the entrance and exit of the rough rolling mill to capture the status of the slab in the steel transfer roller area. The PIDNet model with an improved defogging module is used to extract the foreground contour of the slab, and the slab rotation angle is tracked in realtime through a combination angle measurement method. During the process, strategies such as safety limit, position optimization, speed regulation, and over rotation correction are integrated to jointly optimize the control of the steel conversion process, ensuring the stability and safety of the steel conversion. Autonomous learning of manual experience improves the efficiency of steel conversion. The application results show that the system can accurately measure the angle of the slab and achieve automatic steel conversion function, which can replace manual operation, save energy consumption, and achieve the goal of intelligent production.

Abstract:Aiming at the complicated problem of multi-parameter and mult-objective cooperative optimization of motor, a layered iterative optimization method based on nondominated sorting genetic algorithm is proposed. Firstly, the design flow and working principle of stator segment mixed excitation switched reluctance motor are introduced. Secondly, the parameters to be optimized and the optimization target of the motor are selected. After Pearson correlation coefficient is introduced to analyze the correlation between the motor parameters and the optimization target, the optimization parameters are stratified according to the correlation results. The nonlinear model of each layer optimization parameter and optimization objective is established, and the nonlinear objective model is introduced into the multi-objective optimization algorithm. Finally, the optimal individual is selected in Pareto front, the hierarchical iterative optimization of motor structure parameters and control parameters is completed, the optimal structure parameters and control parameters of the motor are determined, and the finite element analysis software is used to verify. Compared with the initial model, the efficiency of the optimized motor is slightly improved, the average torque is increased by 12.44% and the torque ripple is reduced by 64.96%. The experimental prototype is manufactured according to the optimal parameters, and the experimental results verify the effectiveness and superiority of the optimal design.

Abstract:Due to energy conservation and user needs, the set temperature and air volume of HVAC often change, which can lead to operating mode change and thus increase the difficulty of fault diagnosis. In this paper, research on fault diagnosis methods for HVAC under variable operating mode is carried out. Firstly, in order to accurately simulate the variable operating mode and typical faults of the HVAC system, a dedicated building energy simulator software TRNSYS is used for modeling of HVAC in various operating modes. Secondly, considering that the traditional PCA algorithm model, once established, could not be updated online thus cannot deal with the changes in system operating modes and generally leads to a large number of false alarms, a recursive principal component analysis (RPCA) method is developed for fault diagnosis of HVAC in varying operating modes to reduce false alarm by updating key parameters including mean and variance online. Finally, the effectiveness and superiority of the proposed method are verified by the joint simulation of TRNSYS and MATLAB.

Abstract:In order to enrich the theory of space electro-optical technique, break through technical bottleneck of laser communication ranging integration in medium region, this paper proposes a laser triangulation ranging model with dynamic scale. Firstly, by analyzing main technical constraints of different high-precision laser ranging in medium region, defeats and shortages in terms of measurement accuracy of traditional triangulation method are showed and demonstrated. On this basis, based on the electric vacuum semiconductor composite detector model system, it is proposed that a dynamic scale mechanism can effectively extend the measurement range of the laser triangulation ranging in medium region, then make up for the vacancy of the above integration system in medium region. The correctness and effectiveness of the model are verified by theoretical analysis, parameter calculation, system design, simulation and experiments, the measurement resolution of the triangulation ranging model with dynamic scale proposed by this paper can reach one thousandth of measuring range, which can fully satisfy the needs of subsequent engineering and systematic design.

Abstract:Satellite navigation signal simulation and testing are the necessary links in the development and construction of global navigation satellite system and navigation receiver manufacturers. The progress of related technologies reflects the level of a country’s satellite navigation system construction and application. The application of BOC signals in the BeiDou new modulation has put forward new requirements and challenges. Aiming to generate the baseband signal of BOC B1C BeiDou new modulation based on the pseudo range iteration and time-delay filtering technology, the open-source software defined receiver is used to capture, track, demodulate and locate the self-generated baseband signal. The positioning results show that the 3D positioning error variance is better than 0.74 m and the 2D positioning error variance is better than 0.43 m, which meet the simulation and test requirements.

Abstract:By combining wireless ultraviolet (UV) communication and radio frequency (RF) communication, leveraging their respective advantages, the relay cooperation scheme for hybrid links was investigated though modeling and numerical computation method. By utilizing the unique features of the wireless UV non-line-of-sight scattering channel and the RF wireless fading channel, a hybrid UV/RF cooperation and relay model was established, along with three distinct transmission strategies, in typical channel conditions. The probability of system outage and the bit error rate (BER) were theoretically investigated along with their analytical expressions. Finally, the system performance was numerically calculated under different channel parameters for hybrid UV/RF links. The numerical results demonstrate that the hybrid UV/RF relay cooperation scheme can achieve significantly lower outage probability across varying link distances and channel conditions in comparison to a single UV communication link. When BER is equal to 1×10-6, the proposed strategies can achieve a performance gain ranging from 0.4 to 6.1 dB across various UV channel turbulence strengths and RF channel Ricean factors.

Abstract:Permanent magnetic synchronous generator (PMSG), as the main equipment of offshore wind power generation, is an important development direction of wind power technology. However, the transient stability of PMSG decreases when it is disturbed by large disturbances. Therefore, in view of the difficulty in determining the threshold of transient stability of PMSG fans under large disturbance, this paper established a simplified mixed potential function model of PMSG fan system based on the theory of mixed potential function, and derived the criterion of transient stability of PMSG fans after large disturbance. The accuracy of the transient stability criterion was verified by the simulated grid-side current waveform and grid-side power waveform, and the correlation between the transient stability of PMSG fans and fan parameters, fault depth, fault time and wind speed were analyzed by adjusting the parameter values.

Abstract:The debonding defect is a crucial indicator affecting the physical safety performance of packing materials, and computed tomography (CT) stands as an effective method for its detection. However, due to the debonding defects’ close to the outer contour and small area, low contrast, they are susceptible to interfere from other information during segmentation, making traditional active contour models less suitable. In order to segment the packing materials debonding defects, SoftMax and regular term ChanVese (SPCV) model based on Chan-Vese is proposed by introducing SoftMax and regular term in this paper. Differences in segmentation effects between the SRCV model and various active contour models were systematically compared. Segmentation results on different images are utilized to demonstrate anti-interference capabilities and balance global and local information capabilities of SRCV model. The depth of debonding was measured using Euclidean distance. When the SRCV model is used to segment the debonding defects of packing materials, the segmentation curves are closer to the debonding edges and smoother. The accuracy and the Dice coefficient were 99.56% and 99.82%, respectively. And the error of the debonding depth was not more than 6%. The results show that the SRCV model is particularly suitable for the segmentation of tiny debonding defects with a large amount of interfering information, and has obvious advantages over other active contour models.

Abstract:Aiming at the problem that the actual environmental noise in engineering applications is mainly manifested as Gaussian colored noise and the algorithms for processing Gaussian white noise fail, a fourth-order cumulant ESPRIT algorithm is proposed for the estimation of the frequency and attenuation factor of multicomponent attenuated sinusoidal signals in Gaussian colored noise environments. First, the relationship between the fourth-order cumulants and the autocorrelation and intercorrelation matrices in the observed samples is derived to find their fourth-order cumulant matrices. Second, the generalized eigenvalue decomposition of the fourth-order cumulants is performed, and the signal attenuation factor and frequency estimates can be obtained based on the generalized eigenvalues. Finally, the proposed algorithm is validated by simulation experiments. The average estimation errors of the proposed algorithm for the angular frequency and the attenuation factor of the multicomponent fading sinusoidal signal are 0.002 0π rad and 0.002 0 at the hybrid signal-to-noise ratio of 0 dB. Compared with ESPRIT and Prony algorithms, the proposed algorithm has stronger noise suppression ability and higher parameter estimation accuracy in Gaussian white noise and Gaussian colored noise backgrounds.

Abstract:As an important facility for offshore oil development, offshore platforms are prone to damage in harsh environments, such as seawater immersion and impact, which affects their safety. As a high stress concentration area, tubular joints are more prone to be damaged in offshore platforms. Therefore, the detection of tubular joints is of great significance. In order to adapt to the damage detection of the tubular joints that are the special structures on offshore platforms, using eddy current thermography, the structure of the tubular joints and the distribution of eddy currents induced on the surface of the tubular joints are considered, three forms of coil were designed: Round table coil, delta coil and flat double coil, and the corresponding simulation was completed by SolidWorks and COMSOL. In order to meet laboratory requirements, the actual models of coils and the workpiece to be measured were manufactured. The accuracy of the simulation and the effectiveness of the designed coils were verified through experiments and MATLAB data processing. The results show that the flat double coil has better heating effect on the tubular joints of offshore platforms, and can be used to detect the damage defects.

Abstract:Aiming at the problem that traditional gait recognition methods based on silhouette are limited by the ability to extract input features and model features, which leads to low recognition accuracy, an improved GaitSet method for gait recognition via fusion of silhouette enhancement and attention mechanism is proposed. Firstly, the outline of the pedestrian is obtained by preprocessing, and its average value is obtained. Then the GEI energy diagram is synthesized, which is used as the input feature of the neural network model to enhance the representation of human appearance. Secondly, the attention mechanism is introduced in the process of feature extraction to enhance the feature extraction ability of the model, so as to improve the accuracy of gait recognition. Finally, experiments are carried out on the CASIA-B and OU-MVLP benchmark data sets, and the average Rank-1 accuracy of the proposed method is 87.7% and 88.1%, respectively. Especially under the most complex walking conditions with overcoat, compared with GaitSetv2 algorithm, the accuracy is improved by 6.7%, indicating that the proposed method has stronger accuracy. Notably, the proposed innovative method adds almost no additional parameter number, computational complexity, and inference time, which proves the rapidity of its individual modules.

Abstract:To improve the inspection efficiency of transmission lines and ensure the segmentation accuracy and speed of transmission lines, this paper proposes GU-Net, a lightweight network based on improved U-Net. Firstly, based on the U-Net network, the lightweight trunk extraction network Ghost-Net is introduced in the encoder part; then a bilinear interpolation method to complete the up-sampling and use the depth-separable convolution to replace part of the ordinary convolution; finally, introduce multiple loss functions in the training process to solve the imbalance between the transmission line and the background pixel occupancy, and train the model with a migration learning strategy. Tested on the E-Wire transmission line dataset, the MIoU and F1-score of the GU-Net network are 80.04% and 87.77%, respectively, which are 4.26% and 2.96% better than Wire-Detection, an existing semantic segmentation network for lightweight transmission lines, with almost no loss in the segmentation speed, and the number of references is about 20% of it. The experimental results show that the algorithm proposed in this paper can achieve fast, efficient and lightweight segmentation of transmission lines in complex images.

Abstract:Insulators are an important part of transmission lines, and their discharge problem is one of the main causes of transmission line faults, so there is a need for algorithms that can accurately and quickly assess the severity of insulator discharge and can be monitored in real time at the edge. In this paper, in order to address the above problems, the YOLOv8 target detection algorithm is firstly lightweighted and improved. Firstly, Mosaic-9 data enhancement method is introduced to improve the input, which improves the robustness and generalization ability of the algorithm; then GhostNet network is introduced to replace the backbone network, which realizes the lightweighting of the model; then the GeLU activation function is introduced to replace the ReLU activation function, which improves the convergence speed and detection accuracy of the algorithm; then the GELU activation function is introduced to replace the ReLU activation function. The GeLU activation function is introduced to replace the RELU activation function to improve the convergence speed and detection accuracy of the algorithm; finally, the SIoU loss function is introduced to optimize the network, and the UDD-YOLO edge-end insulator discharge severity assessment algorithm is finally formed. Experimentally verified, it achieves 87.6% mAP and 58 frames/s inference speed in the edge-end device, which meets the requirement of evaluating the severity of insulator discharge in the edge-end, and the effectiveness and superiority of the algorithm proposed in this paper is proved by ablation and comparison tests.

Abstract:In the process of grinding, chatter is the most important reason for the surface of the roll to produce vibration lines, which seriously affects the surface quality of the workpiece. In order to avoid the adverse effects of chatter, an online chatter monitoring method based on time-varying filtered empirical mode decomposition (TVFEMD) and instantaneous energy ratio (IER) is proposed. The method uses reliable indicators to detect the occurrence of chatter in advance, and solves the problem that the chatter characteristics of roll grinding machine are weak in early stage and difficult to identify quickly under background noise. Firstly, the collected vibration signals are processed in real time by sections. Secondly, the time-varying filtering empirical mode decomposition is carried out for signals within each grinding wheel rotation cycle to improve the signal-to-noise ratio. Then, the chatter sensitive frequency band is selected by using the instantaneous frequency and instantaneous energy ratio, and the instantaneous energy ratio of the chatter sensitive frequency band is taken as the chatter characteristic. Finally, the chatter monitoring threshold is determined based on the instantaneous energy ratio rise, and the current machining state is judged. The results show that the proposed method can detect chatter in the transition stage under different processing conditions of roller grinder, and the early warning of chatter can be achieved faster. Compared with traditional timefrequency analysis methods such as EMD, it has obvious advantages in early chatter monitoring.

Abstract:Arc fault is an important cause of electric vehicle fire. The complex driving conditions of electric vehicles, the high voltage and current in the motor and its drive system, and the great randomness and concealment of the arc fault, make it difficult to conduct the real vehicle experiment. Therefore, a method using a fractional horsepower motor and load system is proposed in order to simulate faults and carry out a large number of experiments to research arc fault characteristics. Firstly, based on the load torque calculation and equivalent scaling, a simulation experiment platform is built to collect the line series arc fault current of a three-phase permanent magnet synchronous motor. Secondly, a driving motor and load simulation system of electric vehicle controlled by space vector pulse width modulation is built via MATLAB software, and through introducing and improving Cassie arc fault models, the line series arc fault of the electric vehicle threephase permanent magnet synchronous motor is simulated and analyzed. Finally, a feature extraction method based on the proportion of flat shoulder width and the proportion of wavelet packet decomposition energy is used to quantitatively evaluate the results by comparing the simulation and the measured data. The results show that the relative average error of the proposed Gaussian arc fault composite model is only 7.6%, the smallest one among all models. The constructed simulation system can effectively simulate the actual line arc fault, which is of great significance for the prevention and control of electric fire in electric vehicles.