Abstract:Void fraction as one of the basic parameters of gas-liquid two-phase flow is of great significance to the development and transportation of petroleum pipelines and the design of nuclear reactor cooling towers. This paper proposes a direct detection method of void fraction based on laser-induced fluorescence imaging technology and high-speed video recording system, which can effectively avoid optical distortion caused by pipe curvature and refractive index of the medium. Experiments were carried out on the multiphase flow circulation device of Hebei University, and 18 flow points were measured. The liquid flow measurement range is 10~ 35 L/ min and the gas flow measurement range is 2. 0~ 3. 0 L/ min. Using the idea of measurement comparison, the deviation of the void fractions obtained with two detection technologies is calculated and corrected, and the maximum deviation is only 0. 014 59. The results show that the void fraction values obtained with the two methods are in good agreement, which proves that the fluorescence imaging technology proposed in this paper is effective for the detection of gas-liquid two-phase stratified flow void fraction.

Abstract:Multi-direction pressure measurement in aero-engine inner flow field can provide important data support for aero-engine compression ratio calculation. In this paper, aiming at the problem that the existing porous probe combining with electrical pressure measurement method has the disadvantages of hysteresis and low measurement accuracy. Combining with the advantages of porous probe and optical fiber sensing technology, an integrated optical fiber probe for multi-direction pressure measurement of aero-engine internal flow is designed. The principle of multi-direction pressure conduction and pressure measurement of integrated probe is analyzed. FLUENT software is used to analyze the multi-direction airflow conduction performance and optimize the structural parameters of porous airflow conduction. A 5-hole integrated optical fiber probe was developed and the pressure measurement experiment was carried out. The results show that the average sensitivity of the multi-direction pressure sensors of the integrated probe is - 16. 267 μm/ MPa and the repeatability error is less than 2. 98% in the pressure range of 0. 7 ~ 1. 6 MPa. The maximum error of pressure measurement of various sensors is less than 1. 72% .

Abstract:Aiming at the measurement issue of dynamic parameters and behavior characteristics of particle clusters in circulating fluidized bed (CFB) , a multi-parameter simultaneous on-line measurement method of velocity, height and concentration of particle clusters based on light scattering principle is proposed. This method is used to measure the parameters of particle clusters in a twodimensional cold test bench of CFB. The light attenuation signal caused by particle clusters flowing through the measurement zone is obtained. The velocity of particle clusters is obtained from the cross-correlation analysis of the light intensity signals of the upper and lower measurement units. The signal is filtered with low-pass filter, and the height of particle clusters is obtained according to the time difference of the extreme points of the low-frequency signal and the measurement results of motion velocity. At the same time, the concentration is calculated according to the light intensity attenuation. Thus, the multi-parameter on-line measurement of velocity, height and concentration of particle clusters in CFB is realized based on light scattering. The measurement results show that under typical working condition, the velocity of particle clusters in the central zone of riser is quite stable, and the average velocity is 3. 81 m / s. The motion velocity of particle clusters in the near wall zone has large fluctuations, even is negative, and the average velocity is 0. 65 m / s. The concentration of particle clusters in the central zone is less than that in the near wall zone. The height of particle clusters in the central zone is mainly distributed in the range of 20 ~ 40 mm, and the height of particle clusters in the near wall zone is mainly distributed in the range of 30 ~ 60 mm. The results provide an effective measurement method for studying the formation, structure and motion of particle clusters in CFB.

Abstract:The real-time ocean wave simulation idea is introduced into the study of interface disturbance wave in annular flow to realize the accurate measurement of the liquid film thickness. A model of interface disturbance wave based on three-level ocean wave function is established based on the theoretical analysis and experimental research of the annular flow interface wave. A wavy liquid film thickness measurement sensor with adjustable insertion depth is designed based on direct measurement method. A dynamic calibration method of time-average liquid film thickness based on duty cycle algorithm is proposed. The source of measurement uncertainty of the sensor is analyzed and evaluated, and the system error compensation algorithm is proposed. The sensor is tested and verified on a double closedloop adjustable pressure medium pressure humidity experimental device. Results show that: under the conditions of arbitrary statistical time and arbitrary insertion depth increment, the relative measurement uncertainty of 94. 44% of the experimental points is within 2% when using the three-level ocean wave model. The liquid film thickness is reproduced using the direct measurement method and the measurement accuracy is improved using the system error compensation algorithm.

Abstract:Aiming at the development and evolution of the interface disturbance wave, this article uses a liquid film thickness measurement sensor based on near-infrared absorption and attenuation technology to conduct 154 sets of interface disturbance wave measurement experiments on an adjustable and medium pressure wet gas test facility. And the methods of recursive quantitative analysis are utilized to extract four characteristic parameters of the liquid film time sequence signal including the certainty. The wavelet analysis is combined to study the evolution law of the disturbance wave at the gas-liquid two-phase flow interface. Results show that with the increase of pipe pressure, the deterministic value of interface disturbance wave remains around 1, and the recursive entropy value decreases from 4. 5 to 3, which indicate that the order of interface wave motion is enhanced. The ratio value suddenly increases from 1 to 2. 5, and the recursive ratio value decreases from 0. 5 to 0. 3, which indicate that the periodicity of disturbance wave is significantly enhanced, and the transition from unsteady state to steady state. In fully annular flow, with the increase of pipe pressure, the two-phase interface is dominated by regular ripple waves. The certainty value of the interface wave decreases from 1 to 0. 6, the recursive entropy value decreasing from 3 to 2, the ratio value suddenly increases from 2. 5 to 15, and the recursion rate value dropping from 0. 3 to 0. 1.

Abstract:Liquid-solid two-phase flow is widely used in energy, petrochemical and other industrial processes. The pressure drop of twophase flow is an important flow parameter which is helpful for flow modeling and analysis. A liquid-solid two-phase pressure drop measurement model is formulated. An ultrasonic-based liquid-solid two-phase pressure drop measurement method is proposed, which combines the ultrasound Doppler and ultrasound transmission attenuation. A liquid-solid two-phase flow experiment platform is established to study the law of two-phase pressure drop. As the two-phase mixing flow rate and the solid phase volume fraction increase, the pressure drop of the liquid-solid two phases gradually increases. When the solid phase volume fraction is 0. 28% ~ 1. 37% and the two-phase mixing flow rate is 0. 9~ 1. 65 m/ s, the average relative errors between the two-phase pressure drop measured by the liquidsolid two-phase pressure drop measurement model and the Churchill model using the ultrasound method and the pressure drop measured by the differential pressure sensor are 4. 93% and 5. 10% , respectively. These results show the accuracy of the measurement model. The pressure drop measurement for the two-phase heterogeneous flow is carried out and it further broadens the application range of the pressure drop measurement model. This research work provides a basic method for non-invasive ultrasound measurement of liquid-solid two-phase pressure drop.

Abstract:Liquid-solid two-phase flow widely exists in industrial process and the online measurement of particle size distribution is important to the production optimization and control. As an undisturbed multiphase flow parameter measurement method, ultrasonic method's attenuation characteristics are closely related to the particle size and volume fraction of solid particles, and can be used to measure the particle size distribution online. Experimental devices for ultrasonic attenuation of liquid-solid two-phase medium were established. In the experiments silica sand is used as the solid particles and tap water is used as liquid phase. Chirp ultrasonic signal is used to excite and study the ultrasound attenuation characteristics of the liquid-solid two-phase medium. The experiment results show that with the increasing of the excitation frequency and solid volume fraction, the ultrasonic attenuation coefficient rises gradually. Twomey algorithm and genetic algorithm are used to inverse the particle size distribution of liquid-solid two-phase medium, the measurement results are compared with those of Malvern laser particle size analyzer, and the correlation coefficient is 0. 918.

Abstract:For the iterative image reconstruction algorithm of electrical capacitance tomography (ECT), linear forward problem solution is usually adopted to speed up image reconstruction. However, image reconstruction error is inevitably produced. In this paper, a nonlinear forward problem solution based on extreme learning machine (ELM) of ECT is proposed. The inputs and outputs of ELM network are permittivity distribution and predicted capacitance measurements, respectively. Image reconstruction is carried out based on the combination of the presented method and conventional Landweber iterative algorithm, which is named as ELM-Landweber iterative algorithm. In order to make the samples more representative, the distribution positions and sizes of objects in each phantom are randomly generated, and the corresponding normalized capacitance values are calculated as ELM network training and test samples. Simulation and static experiments are conducted for ELM-Landweber iterative algorithm and the reconstructed images are compared with those of conventional Landweber iterative algorithm. Experimental results show that the convergence speed of ELM-Landweber iterative algorithm is significantly enhanced, and the quality of the reconstructed image is obviously improved compared with conventional Landweber iterative algorithm. The average image relative error of training samples and test samples decreases from 0. 728 to 0. 504 and from 0. 596 to 0. 475, respectively.

Abstract:Convolutional neural network ( CNN) is applied in the image reconstruction of electrical capacitance tomography ( ECT) gradually due to its strong nonlinear fitting ability. Aiming at the hyperparameter regulation problem of CNN model, this paper investigates the effects of the model parameters on the image reconstruction results of ECT. Firstly, a dataset of “ capacitance matrixparticle concentration distribution” with 80 000 random flow patterns and 40 000 typical flow patterns is established with numerical method, then the CNN models with various hyperparameters are trained and validation through the training set in the dataset. The effects of the network hyperparameters, including the network initialization, grid density, number of the convolution kernels, number of the neurons in the fully connected layer and the structure of the hidden layers, on the image reconstruction accuracy are systematically studied. Further, a test dataset composed of 12 000 extra generated flow patterns is utilized to evaluate the performance of the CNN models. Static experiments were performed to compare and analyze the image reconstruction quality with various CNN models. Results demonstrate that the structure of network hidden layers has a relatively great effect on the image reconstruction accuracy, while the network initialization, grid density, number of the convolution kernels, number of the neurons in the fully connected layers have less effect on image reconstruction accuracy.

Abstract:Accurate identification of fluctuating vibration gas-liquid two-phase flow pattern is of great significance for the safe and stable operation of nuclear power platform under floating vibration. Through comparing the differential pressure signals and the corresponding spectrums in static and fluctuating vibration pipelines, it is found that the differential pressure signals in fluctuating vibration pipelines have larger fluctuation amplitude and contain more frequency components, and both flow patterns have dominant frequency, which is the fluctuating vibration frequency. Aiming at the complexity of the pressure difference signals of gas-liquid two-phase flow in fluctuating vibration state, complete ensemble empirical mode decomposition with adaptive noise ( CEEMDAN) and ensemble empirical mode decomposition (EEMD) are used to decompose the pressure difference signals after wavelet de-noising. It is found that CEEMDAN can reduce the mode components and obtain more effective components at the same time. Through calculating the Spearman correlation coefficient, the IMF components that have symbolic meaning are selected to perform Hilbert transform, and the energy is calculated and used as the eigenvalue. Probabilistic neural network is used to identify the flow pattern. The results show that using CEEMDAN to perform mode decomposition, combining with probabilistic neural network, the accuracy of the identification method is 95. 83% , and this method can be used to identify the flow pattern of gas-liquid two-phase flow under fluctuating vibration.

Abstract:To improve the spatial resolution of ECT sensors in a concentric annulus measurement area, different number of combined electrodes ECT sensors are designed. For four types of concentric annulus area with inner and outer diameter ratios of 0. 2, 0. 3, 0. 4 and 0. 5, traditional 12 external electrodes (EE) and 12-4, 12-6, 12-8 internal-external electrodes (IEE) are considered. The IEE sensors are implemented with external-opposite-internal ( EOI) and external-opposite ( EO) measuring strategies. The capacitance between different electrode pairs is calculated for typical permittivity distributions by numerical simulation approach. The obtained capacitances are used to reconstruct images using LBP, Tikhonov, and Landweber algorithms, respectively. Based on numerical simulation and experimental investigation, the main results are given as follows. As the ratio of the inner and outer diameters of the concentric annulus area increases, there is an optimal number of inner electrodes which the quality of reconstructed images with the 12-4 electrodes structure are higher in the measurement. When LBP algorithm is used, the EOI excitation strategy has a slight advantage over the EO excitation strategy in imaging accuracy. When the Tikhonov algorithm and Landweber iterative algorithm are used, the EO excitation strategy can effectively reduce the influence of abnormal capacitance, and effectively improve the quality of reconstructed images when combined with IEE sensors in the measurement of concentric annulus areas with different inner and outer diameter ratios.

Abstract:Perfluoroalkoxy alkane (PFA) tubes are widely used in the transportation of chemical liquids in semiconductor industry due to its good corrosion resistance and stability. In order to precisely control the dosage and time period of various liquid medicines, noncontact flow measurement of PFA tubes with small diameters is needed urgently. In this paper, ultrasonic array probe is used to excite the ultrasonic guided wave on the PFA thin tube and the flow is measured by the guided wave. Firstly, the difference of mode selection principle between ultrasonic wedge probe and array probe is compared, and the frequency selection characteristic of array probe is explained. Then, the width and thickness of array probe element are designed, and three kinds of array probe with different element spacing are prepared. The exciting and receiving characteristics of the array probe are obtained through frequency scanning. The performance of mode selection of the array probe is compared and analyzed, and the flow measurement experiment is carried out. Results show that the array probe can enhance the received signal′s magnitude and improve the measurement sensitivity. Different guided wave modes can be selected by changing the array spacing. When the L(0,5) guided wave mode is selected on the 3 mm PFA tube, the error limit of the measurement results in the flow rate range of 0~ 6. 70 m/ s is ±0. 22 m/ s.

Abstract:The transient process of gas-liquid two-phase CO2 flow can occur in carbon capture and storage pipelines. Large measurement errors exist when Coriolis mass flowmeters are used to measure the mass flowrate of CO2 under such conditions. To solve this problem, a method for mass flowrate correction based on a gated recurrent unit (GRU) neural network is proposed. Since the GRU is suitable for dynamic process prediction, the GRU model is trained by using the collected datasets from a CO2 gas-liquid two-phase flow rig and optimized by using a grid search method combined with the K-fold cross-validation. The optimized GRU model is evaluated in terms of measurement accuracy and generalization capability by using eight groups of datasets under typical experimental conditions. The GRU model is compared with the least squares support vector machine (LS-SVM) model. Experimental results show that the GRU model could achieve better results than the LS-SVM model. The output of the GRU model can follow the change of CO2 mass flowrate in the steady state after the transient process, and relative error is within ±5% .

Abstract:The traditional wide-area synchronous measurement technology based on GPS / Beidou is vulnerable to hostile cyber-attacks, and its reliability is difficult to guarantee. In order to solve this problem, this paper utilizes the pulsar signal high accuracy characteristics of periodic pulse and immunity to existing cyber-attacks to establish a new adaptive synchronous sampling method. Firstly, polyphase filtering,de-dispersion and period folding are performed on the pulsar signal received by the radio telescope to obtain pulse profile. Then, the local crystal oscillator frequency is monitored with the least square method, and the time control interval is dynamically adjusted and controlled to realize adaptive synchronized sampling. To verify the effectiveness of the algorithm, this paper uses the observation data of the pulsar J0437- 4715 to generate synchronization signals and realize the synchronized measurement of the power grid signal. The recursive discrete Fourier transform algorithm is applied to calculate the phase angle and frequency of the sampling data, and the obtained average errors of phase angle and frequency are -9. 75×10 -5 ° and 9. 83×10 -7 Hz, respectively, which verifies the effectiveness of the proposed adaptive synchronized sampling method.

Abstract:The magnetic flux leakage testing (MFL) is an effective defect detection method which is widely used in the on-line detection of ferromagnetic materials. How to use the magnetic flux leakage signal to reconstruct the three-dimensional irregular defect profile is a key problem in magnetic flux leakage detection. However, the finite element model of three-dimensional magnetic flux leakage detection of irregular defects requires a large amount of calculation. Therefore, it is difficult to obtain accurate magnetic flux leakage signals quickly. Moreover, due to the inadequacy of defect reconstruction, it is difficult to achieve accurate profiles of irregular defects in the study. In this paper, a unit magnetic dipole band superposition model is proposed for computing magnetic flux leakage signals of threedimensional irregular defects. The effectiveness of the forward model for magnetic flux leakage calculation is verified. For the highdimensional optimization problem of three-dimensional defect profile reconstruction, a trust region-based projection Levenberg-Marquart algorithm with boundary constraints is proposed. The contour reconstruction of three-dimensional irregular defects is realized. Experimental results show that this method does not need a lot of MFL detection data. Compared with the swarm intelligence algorithm, the reconstruction error is reduced by 90. 1%, the maximum depth error is reduced by 53. 9%, and the time consumption is reduced by 96. 1%, thus realizing the high-precision defect reconstruction.

Abstract:To solve the problem of vibration detection of double-section post porcelain insulators, this study takes 220 kV series post porcelain insulators as the research object. The SolidWorks software is utilized to model and import them into ANSYS software to set the model parameter calculation and analysis. The detection feasibility of porcelain insulators with series double-section posts by vibroacoustic is verified. Meanwhile, the influence of the vibration-picking distance and the specific location of the vibration pickup point at the bottom of the lower flange on the detection accuracy of the insulators is studied. The general rules are obtained. The achieved simulation rules are verified by building a 220 kV insulator test bench. The final conclusion shows that the excitation-pickup distance has an impact on the vibration detection results of porcelain pillar insulator. For double-section post insulators, when the excitation point and the pick-up point are on the same straight line passing through the center of the lower flange, the frequency spectrum characteristics of the signals collected by the two symmetric pick-up points on the blue center do not change with the change of the excitation pick-up distance. According to the experimental results, it is concluded that the model B03. 8-1 220 kV double-section porcelain pillar insulator is the best. The optimal excitation-the pick-up distance is 35 mm.

Abstract:The weak-stiffness ball-end-milling-cutter is widely used in the milling of deep cavity die parts. The chatter is easy to occur in the machining process. How to determine machining stability region is an important way to enable stable milling. However, the milling system has a characteristic of variable time delay. It is difficult to analyze the milling stability, which restricts the improvement of machining quality. Therefore, a milling stability analysis method for the ball-end-milling-cutter with weak stiffness is proposed. Firstly, the dynamic equation of the cutter system with weak stiffness is established. Then, the time delay of the selected point of the cutter tooth is solved by using Newton-Raphson. Finally, based on the full-discretization method, a stability analysis method considering the regeneration effect with variable delay is proposed, and the critical cutting depth corresponding to different rotational speeds is obtained by the Floquet theorem. The milling stability lobe diagram is constructed. Experimental results show that there are chatter frequencies in the milling force when milling in the unstable region of the lobe diagram. Compared with those of the milled surface in the stable region, the Sy and Sa of the milled surface are increased by 35% and 42% . Results show that the analytical method is reliable, which can provide a basis for the selection and optimization of the cutting parameters.

Abstract:Aiming at the problem that existing convolutional neural network models are large in size and high in computation, which results in not being able to consider both detection rate and accuracy of power inspection UAVs, an ED-YOLO network based on model compression is proposed to achieve the target detection algorithm for UAV obstacle avoidance. The target detection algorithm is based on YOLOv4, which firstly adds a channel attention mechanism to the backbone network to improve detection accuracy without increasing the amount of computation. Secondly, the depth separable convolution is used to replace the traditional convolution in the feature pyramid part to reduce the amount of convolutional computation. Finally, the model compression strategy is used to trim the redundant channels in the network to reduce the model size and improve the model detection speed. Tests were conducted on the dataset independently constructed with 9 600 flight obstacles of power inspection UAV, the obstacle target average detection accuracy for ED-YOLO is reduced only by 1. 4% compared with that for YOLOv4, while the model size is reduced by 94. 9% , the amount of floating point operations is reduced by 82. 1% and the prediction speed is increased by 2. 3 times. Experiment results show that compared with various other existing methods, the ED-YOLO target detection algorithm based on model compression proposed in this paper has the advantages of high accuracy, small size and fast detection speed, and meets the requirements of obstacle avoidance detection for power inspection UAVs.

Abstract:The existing PCB defect detection methods has problems of low efficiency, high false detection rate, low generality and poor real-time performance. To address these issues, a PCB defect detection method based on the improved you only look once (YOLO) v4 algorithm is proposed. Anchor frames are determined by the improved dichotomous K-means clustering combined with intersection over union (IoU) loss function. In this way, the problem that the pre-defined anchor frames are not applicable to PCB small target defect detection is solved. MobileNetV3 is introduced as a feature extraction network to enhance the detection performance of small target defects on PCB, while facilitating deployment in the field on lightweight mobile terminals. Inceptionv3 is introduced as the detection network, which utilizes multiple convolutional kernels for operations to meet the requirements of PCB defect detection in multiple categories. The PCB_DATASET dataset is used as the test object. The proposed method is compared with Faster R-CNN, YOLOv4 and MobileNetV3-YOLOv4 for evaluation experiments. Results show that the mean average precision ( mAP) of the proposed method is 99. 10% , the model size is 53. 2 MB, and the detection speed is 43. 01 FPS. The detection mAP is improved by 4. 88% , 0. 05% , and 2. 01% , respectively. The model size is reduced by 0, 203. 2, and 3. 3 MB, respectively. And the detection speed is improved by 29. 93 FPS. The speed is increased by 29. 93, 6. 37, and 0. 79 FPS, which meets requirements of high inspection accuracy and inspection speed in PCB industrial production sites.

Abstract:The existing wired structured light 3D imaging system has the problems of tedious calibration steps and low robustness. To address these issues, this paper proposes a 3D imaging method based on the single plane calibration of linear structured light, which could achieve high accuracy and high robustness of 3D imaging. The 3D imaging method simplifies the calibration steps of 3D space and improves the robustness of the system by constructing a single plane mapping model from the structured light plane to the imaging plane. In this model, the coordinates of the corners in the checkerboard image are used as the control points for the calibration of the line structured light plane, which improves the number of control points and reduces the accidental error. The model does not need to estimate the internal and external parameter matrix of the imaging system, which can simplify the intermediate steps of calibration and improve the repeatability and accuracy of calibration. In addition, the model does not need to calculate the center point of the light stripe in the line structured light image, which avoids the influence of image algorithm errors on the calibration results. Experimental results show that the calibration procedure of the 3D imaging method is simple and has many calibration control points. The robustness of the calibration is high, the calibration average distance residual is 0. 158 mm, and the repeatability measurement accuracy of single point is better than ±1 μm.

Abstract:The outdoor images collected by monocular camera are easily affected by the light intensity. The scale of images is ambiguous. In addition, the pose estimation of convolution neural networks (CNNs) is not accurate. To address these issues, a monocular visioninertial odometry using CNN and the extended Kalman filter (EKF) is proposed. The CNN is used to replace the conventional odometry of the front-end vision based geometric constraints. The output of the monocular camera is used as the EKF measurement to correct the estimated pose of CNN. The error covariance of EKF is optimized by the CNN. The monocular camera pose data and the inertial measurement unit (IMU) data are fused in EKF to estimate the motion pose. The monocular scale informations and the cumulative errors of the IMU are compensated. Experimental results show that the proposed algorithm performs more precise pose estimation. The accuracy and feasibility of the algorithm are verified. Compared with the Depth-VO-Feat algorithm that relies on monocular images, the proposed algorithm combines monocular image and IMU data for pose estimation. The translation and rotation errors of the 09 sequence in KITTI dataset are reduced by 45. 4% and 47. 8% , respectively. The translation and rotation errors of 10 sequences are reduced by 68. 1% and 43. 4% , respectively.

Abstract:Aiming at the problem that it is difficult to detect the small defects with low contrast existing on the surface of lithium battery electrode plate, a new lithium battery electrode plate surface defect detection method based on improved Canny operator is proposed. Firstly, bilateral filtering is used to solve the image edge blur problem caused by Gaussian filtering when reducing noise, and on this basis, multi-scale detail enhancement algorithm is introduced to enhance the low contrast image. Secondly, the 3×3 gradient template of Sobel operator is used to calculate the gradient amplitude and gradient direction of electrode plate images. Finally, based on the maximum entropy and Otsu algorithm, the high and low thresholds of the image are automatically obtained. The detection results of the two algorithms after threshold segmentation are edge-fused with logic and operation, and the discontinuous edges are repaired with morphological closed operation and thinning algorithm to obtain the final detection edge. The experiment results show that the traditional Canny operator and Otsu-Canny algorithm are difficult to effectively detect the defects, including different types of dark spots, exposed foil and scratches. However, the algorithm in this paper can achieve good detection results for these defects, which can effectively reduce the same color background noise, while highlight the target defect area. The correct detection rate reaches 98% , the propose algorithm has a certain practical value.

Abstract:Environment perception is one of the key technologies of the intelligent vehicle. However, there are limitations in object detection and object positioning by visual sensor or lidar only. Based on the image and lidar object detection, a fusion algorithm of vehicle object shape-position using stereo vision and lidar is proposed in this paper. Firstly, the deep learning methods are used for object detection on image and point cloud. Then, the shape-position of object is determined by the object shape-position estimation method based on 3D points and object types. Finally, the image object and point cloud object are fused simultaneously after data association and the shape-position of the object are acquired. The proposed algorithm is evaluated on the KITTI data set and actual road scenarios. Experimental results show that the detection accuracy of the proposed method is 5. 72% and 1. 8% higher than those of the YOLOv3 network and the Point-GNN network, respectively. In addition, the average error of object shape and position within 20 m is 4. 34% and 4. 52% , respectively.

Abstract:Laser ultrasonic detection technology has the characteristics of non-contact, wide bandwidth and high resolution. The conventional ultrasonic is not sensitive to the small defects of nuclear damage inside the rail head, which is difficult to locate and quantitatively detect. In this study, the calculation model of the defect location quantitative detection based on the flight time of the modal conversion reflected wave and diffracted wave is proposed, which is based on the principles of laser ultrasonic body wave scattering and diffraction. By using COMSOL simulation, a two-dimensional finite element model of the interaction between the ultrasonic body wave and the internal defect is formulated. The model conversion state of the ultrasonic body wave at the internal defect of the rail is analyzed, and the feasibility of the positioning and quantitative method is verified. Secondly, a fixed scanning laser ultrasonic experiment detection system is established to implement B-scan experiments on hole defects of different buried depths and diameters. Experimental results show that the laser ultrasonic detection technology can effectively detect the micro hole defects in the rail. Based on the calculation model and the proposed detection method, the positioning relative error of the detection of the internal defects of the rail is within 6% , and the quantitative relative error is within 9% .

Abstract:To accurately predict the location and size of defects in the pipeline, a synthetic aperture guided wave imaging algorithm based on frequency wavenumber domain is proposed. And the principle of explosive reflection imaging is also utilized. The T ( 0,1) mode guided wave is excited in the pipeline by magnetostriction. The collected echo signal is subjected to two-dimensional Fourier transform and angular spectrum operation to reconstruct the sound field in the frequency domain. Finally, the focus imaging in the target area is realized by using the inverse Fourier transform. The imaging results are verified by experiments and compared with the original B-scan results. Results show that the side lobe effect is effectively controlled by the proposed algorithm. The imaging resolution is improved about 30% and the quantitative error is reduced by 26. 1% . The affection of the axial position, the depth and the inclination angle of defects are also studied. By using the algorithm to reconstruct the defect image, the detection accuracy is only affected by the absolute size of the circumferential defects and the detection of circumferential surface defects has a high sensitivity.

Abstract:This article proposes a new probe design with concentrated windings that can be easily opened and closed. The external inspection of pipes is realized, which is based on the principle of rotating electromagnetic field eddy current. Through the finite element (FE) model constructed in COMSOL, the characteristics of the rotating magnetic field generated by the concentrated winding and the influence of various parameters of the receiving coil on the detection effect are studied. Simulation results show that the concentrated winding can generate a rotating electromagnetic field that can be used to achieve defect detection on the outer wall of the pipeline. The pick-up of the distorted signal can be realized in a better way by utilizing the circular coil which has many turns and is close to the outer wall of the pipe and at the end of the excitation coil. An encircling rotating field eddy current (RoFEC) non-destructive evaluation system with concentrated excitation windings is established to achieve the detection of 0. 5 mm wide circumferential and axial cracks. The ability of the system for identifying cracks at different depths is tested. Cracks located at different positions on the outer wall of the pipeline from 0 to 90° are used to verify the ability of the detection system to determine the circumferential location of the defect. Results show that the developed system can detect crack of arbitrary direction and locate its circumferential location, which provides a new approach for the external detection of tubular components such as coiled tubes.

Abstract:Sediments detection on the grading electrodes installed in the inner cooling system is the necessary part to ensure safe operation of the high voltage direct current (HVDC) converter valve. At present, the sediments detection of the grading electrodes mainly depends on manual screening, which has great blindness and is easy to cause system leakage and other faults. To address these issues, an online sediments detection method based on the echoing characteristics of ultrasonic guided wave is proposed. The L(0,2) mode guided wave with low dispersion and long transmission distance is selected as the excitation signal. Sediments of electrodes are considered as porous medium and the characteristic parameters are calculated. Then, a fluid-solid-acoustic multi-physical field simulation model of sediments detection is formulated, and the optimum excitation frequency is determined. The reflection and absorption characteristics of 0. 2 ~ 1. 0 mm thick sediments on acoustic signals are simulated. The mode conversion processes in the interaction between the longitudinal L(0,2) mode and sediments are analyzed. Finally, the experimental system is established to verify the actual detection accuracy in the sediments thickness of 0. 1~ 0. 8 mm. Experimental results show that the accuracy of online sediments detection method based on the echoing characteristics of ultrasonic guided wave is up to 0. 1 mm, which demonstrates the online sediments detection method is effective for sediments detection with grading electrodes of the HVDC converter valve cooling system.

Abstract:To improve the quality of ultrasonic coherent plane wave compounding ( CPWC) imaging, a weighting algorithm based on circular statistics vector (CSV) is proposed. In this algorithm, the phase of delayed signal is taken as the circular statistical samples and the coherence factor reflecting the consistency of phase distribution is established through the sample average resultant vector. Furthermore, according to the different number of beamforming and coherence factor construction, the weighting algorithm of total circular statistics vector (tCSV) is proposed. Compared with CPWC, results show that the scattering target resolution and cyst contrast radio of CSV and tCSV are increased by at least 23. 67% and 27. 69% , respectively. And the CNR value is decreased by up to 39. 37% . Compared with the coherence factor (CF) and the sign coherence factor (SCF), the maximum resolution and contrast of CSV and tCSV algorithms are reduced by about 12. 83% and 88. 31% , respectively. However, the ability to suppress background noise and retain the amplitude of target echo wave is better. The CNR value is improved by about 20% , and the image quality is more robust.