The existing unbalance identification algorithm without trial weight adopted an optimization algorithm framework and approximated the optimal solution through numerous iterative operations. However， such strategies typically faced the limitations of slow convergence speed and the tendency to fall into local extrema. Therefore， neural networks were used to directly learn and analyze the complex mapping relationship between unbalance vibration response and unbalance， thus realizing high-precision unbalance identification. A sufficient unbalance vibration dataset with labels was constructed by simulating the rotor dynamics model. A feature fusion mechanism was designed to address the multi-dimensional complex-valued characteristics of unbalanced data. At the core algorithm level， a CNN-GRU hybrid model was constructed. In this model， CNN was responsible for extracting local spatial features from vibration data， while GRU captured temporal dependencies within the vibration data. By integrating information from both spatial and temporal domains， the model’s generalization ability and recognition accuracy were significantly enhanced. The unbalance recognition results of test set data and experimental bench demonstrate that this method may accurately predict the unbalance of the rotors， providing a rapid and accurate guide for dynamic balancing in the field without trial weights.
Aircraft fuel lines contained various structures such as bends， branches and welded seams， which exhibiedted complex fluid-solid coupling mechanics behaviors under external loads and internal fluid coupling. The microstructural parameters of the welded areas were obtained through metallographic testing and microtensile testing， and a high-precision finite element model of the welded fuel lines with different local structures was constructed. The modal test results show that the errors between tests and simulations are less than 10%， which verify the accuracy of modelling and simulation. The effects of different branch pipe bending radii and pipe diameter ratios on the mechanics properties of the pipelines were investigated. It is found that increasing the bending radii and pipe diameter ratios may help to make the distribution of the flow velocity and pressure fields in the pipelines more uniform， and the intrinsic frequency decreases with the increase of the bending radius； and when the bending radius decreases but the pipe diameter ratio increases， the peak of the random vibratory stresses decreases. The applications of a fuel line model that accurately models welded seams to analyze the effect of local structures on the mechanics properties of the line may provide a basis for line design and optimization.
Aiming at the problems of LIMs metro vehicle lateral swayings， correlation analyses between the characteristics of track irregularities and the stability indicators of the vehicles were studied. The dynamics software SIMPACK was used to establish a dynamics model of the LIM vehicles. The influences of track irregularity on the vehicle dynamics performances were simulated and the sensitive wavelength ranges of track geometric irregularity for the LIMs vehicle swayings were analyzed. Results show that the low-frequency lateral swaying of carbody occurs at about 2 Hz when the vehicles are travelling at high speed in a straight line， which is related to the lateral track periodic irregularity with wavelengths of 10~13 m.The suspension mode frequency of the vehicles determines the sensitive wavelength range of different track irregularities. Alignment irregularity has the most significant influences on the wheel-rail lateral forces and the lateral accelerations of the carbodies. For the carbody lateral swayings， the sensitive wavelengths of alignment irregularity are as 10~13 m and 17~20 m. The sensitive wavelengths of vertical irregularity are as 5~8 m and 12~17 m. The sensitive wavelengths of both twist and crosslevel irregularity are as 5~8 m and 15~20 m. The excitation frequency of track irregularity with a sensitive wavelength band of 10~13 m is close to the frequency of the upper sway mode of the vehicles. The excitation frequency of track irregularity with a sensitive wavelength band of 15~20 m is close to the frequencies of pitch mode and roll mode of the vehicles.
During the mount tests of a certain type of hook， Cd-Ti coating of latch was delaminated， and caused the latch slide downward， then the suspension might deflected， posing a risk of suspension detachment. A research on this risk was conducted. Based on cohesive zone model， a finite element model of the hook under load was established， incorporating predefined vertical cracks and interfacial cracks. The crack damage factor was employed to determine the crack failure modes， and a local contact model of the stop arm/latch was developed to investigate the variations in friction coefficient during coating interface debonding. Results reveal that during the hook's load-bearing processes， in-plane shear stress differences at the interfaces between the latch coating and the substrate induce interfacial debonding， without the formation of vertical cracks. Meanwhile， the friction coefficient at the contact interfaces temporarily drops below 0.05 during debonding， which is lower than the critical sliding friction coefficient required to prevent latch slippages.
To thoroughly analyze the characteristics of supersonic flow fields and enhance the efficiency of numerical computations， an efficient acceleration algorithm was designed. The algorithm herein fully leveraged the CPU-GPU heterogeneous parallel architecture and achieved data transmission and processing through asynchronous streaming， significantly accelerating the computational processes of supersonic flow field numerical simulations. The results demonstrate that the computational speed of GPU parallel processing is markedly faster than that of CPU serial processing， and the speedup ratio exhibits a pronounced increasing trend as the scale of the flow field grid expands. GPU parallel computing may effectively improve the computational speed of supersonic flow field simulations， providing a robust parallel computing method for the design， optimization， performance evaluation， and development of supersonic aircrafts.
The forward kinematics equation of the six-degree-of-freedom parallel mechanisms was nonlinear and strongly coupled， and generally did not have a symbolic positive solution， which was not conducive to the real-time feedback control of the robots. Thus， a “7-4” Stewart-type parallel mechanism was designed with weak coupling in structures but decoupled in motions. The forward kinematics equation and link length coordination equation were solved analytically， and the singularity research was carried out. Firstly， based on the “2-1” kinematic links， a six-degree-of-freedom “7-4” Stewart-type parallel mechanism was synthesized， and the structural coupling characteristics of the mechanisms were analyzed based on the azimuth feature set theory. Secondly， based on 13 compatible equations and the theory of tetrahedral geometry， an analytical algorithm for solving the forward kinematics equation was proposed. At the same time， it was proved that the number of real solutions under general configuration was 8（they were symmetrical about the same plane ）. Then， according to the geometric constraint relationship between the moving ball hinges， the link length coordination equation was constructed. It is found that the equation also has a symbolic solution. The Jacobian matrix of the mechanisms was derived， and various singular types were analyzed. Finally， the internal relationship between the forward kinematics and singularity of the parallel mechanisms was analyzed.
In WJGL， the instability in water jet from low-frequency pressure pulsations （0~500 Hz） in fluid supply systems needed urgent resolution. A water pressure pulsation attenuator was designed using flexible liners for coupling vibrations and Herschel-Quincke tubes （HQ tubes） for phase cancellation， achieving dual filtering. A transfer matrix model was established based on one-dimensional analytical approach and electro-hydraulic analogy principle， and the feasibility was experimentally verified through prototype testing. The influence mechanism of key parameters， such as HQ tube structures， insert tube lengths， and liner elastic modulus on low-frequency pulsation attenuation were quantitative analyzed in simulations. The results show that the damping performance of polyurethane liners improves as their elastic modulus decreases， while the loss angle negatively affects transmission loss. Under certain conditions， changing the insert tube lengths has minimal impact on transmission loss（TL）， but increasing the HQ tube lengths and reducing the diameter enhances attenuation.
To investigate the effects of tribodynamics behavior in MRF on the wear mechanism of sealing pairs， the dynamic sealing wear model was established considering coupling effects of surface roughness and iron particles， with ANSYS employed for microscopic contact mechanics simulation. Additionally， the friction and wear performance of piston rods with different surface roughnesses in two media was evaluated by self-developed reciprocating dynamic seal wear test device. The results demonstrate that the instantaneous contact between rough texture and iron particles in sealing interfaces alleviates fatigue cracks on O-ring surfaces under alternating shearing actions of asperities， and help to avoid the micro-cutting effects of iron particles on the surface piston rods. As a result， the sealing pairs exhibit good tribological performance in MRF environment. However， iron particles situated between polished sample and O-ring are continuously subjected to the transverse shear forces and longitudinal extrusion pressures by micro-texture. Consequently， numerous iron particles are forced to embed in the O-rings， and obvious furrow-like wear marks are produced on the surfaces of reciprocating piston rods， causing premature sealing failure due to the two-body abrasive wear.
To address the challenges in achieving high-stiffness locking during the deployment of segmented space telescopes using sleeve mechanisms， a staged locking scheme was proposed for the secondary mirror sleeve-supported deployment mechanisms of long-focal-length， large-aperture space telescopes. Contact models between expansion petals and inner sleeve walls were established， and the normal contact behavior and load distribution patterns during the locking processes were investigated. Bernoulli-Euler beam theory was employed to analyze the normal contact forces between the expansion petals and the sleeve. The relationship among inter-stage locking force， design clearance and locking point positions was derived and validated through finite element methods. The Palmgreen formula was applied to analyze the ascending displacement of the driving ring's inclined surfaces. The design parameters of the inclined surfaces were determined， and finite element methods were employed to analyze the inter-stage locking force when rounded values were assigned to the ascending displacement. The magnitude and distribution patterns of the locking forces were obtained. An experimental prototype of the locking mechanisms was constructed， the experimental results indicate that rolling cylinders smoothly roll into place， the locking forces of the expansion flaps meet expectations， and the feasibility of the sleeve locking mechanism design is confirmed.
The contact characteristics and wear properties of Si3N4 high temperature resistant all ceramic radial spherical plain bearings were studied under high temperature conditions. Based on thermal-mechanics coupling finite element simulation method， the temperature fields of the inner and outer rings of the spherical plain bearings were analyzed. The wear tests were carried out by using self-developed high-temperature spherical plain bearing test machine. The wear surface morphology and composition of different contact areas of inner and outer rings were analyzed by scanning electron microscope and energy dispersive spectrometer， and the wear mechanism of all ceramic spherical plain bearings was proved. Results show that the wear processs of all ceramic spherical plain bearings at 400 °C are mainly divided into a running-in period with a significant increase in wear and a stable wear period with a slow increase in wear. The wear areas are mainly divided into core bearing areas dominated by adhesive wear and oxidation wear， and non-core bearing areas dominated by abrasive wear. The wear loss of the spherical plain bearings is as 108 μm under the condition of 200 N radial load， 2 Hz swing frequency and ±25° swing angle， 25 000 swing times. The wear resistance is good and the running state is stable.
Hexapod robots often sinked and got stuck when walking in clay environment， which had a negative impact on the walking stability and energy consumption. The adhesion and shear resistance properties of clay were considered. A mechanics model of leg lifting block for a hexapod robot during foot sinking was established. The correlation between foot subsidence and leg lifting resistance was revealed， and the foot-ground mechanics experimental platform was designed and constructed. Based on this platform， foot-ground mechanics experiments were conducted on hexapod robots under three gaits. Data on sinking and blocking forces were obtained. The accuracy of the mechanics model was verified by comparing with the calculated results of theoretical model. Finally， EDEM software was used to simulate the clay environment and perform foot-ground contact simulations. The variation law of the internal mechanics behavior of clay was revealed. The comparisons of the results from simulation， mechanics model prediction and experiments show that the data change trends are basically the same.
In order to predict the fatigue life of rolling bearings， a modified critical plane was established according to the bearing crack extension mode， which transformed the cracks from two-dimensional extension to three-dimensional extension. Combining the Smith-Watson-Topper-fatigue indicator parameter（SWT-FIP） method with S-N curve， based on the modified critical plane method and the modified Paris model， the equivalent elliptical crack area difference was used as fatigue crack extension characterization quantity， and the equivalent crack length was calculated to establish a full-cycle fatigue life prediction model of the rolling bearings covering crack initiation life， crack extension life and fatigue spalling life. The full-cycle fatigue life prediction model was validated with 2 types of accelerated life test datasets of bearings， and compared with L-P model and original Paris model. Results show that the full-cycle fatigue life prediction model of rolling bearings may predict the fatigue life of bearings more accurate than that of original Paris model and L-P model.
To realize the accurate online measurement of axis straightness errors for deep-hole parts， a multi-sensor integrated measurement system was constructed by eddy current displacement sensors， electromagnetic ultrasonic transducer， rotary encoder and laser interferometer. By analyzing the arrangement and distribution of spatial sample points， a rough error filtering method was proposed based on sine and cosine distribution characteristics， and the influences of random errors were reduced by Kalman filtering method， and data information closer to the real contour of parts was obtained. Based on the principle of approaching the minimum area， the evaluation of axis straightness errors was transformed into a parameter optimization problem， and the IZOA was adopted to solve this problem. In the comparative measurement experiments of commercial laser trackers， the measurement error of the developed measurement system is only 0.053 mm in the length range of 1500 mm deep-hole parts（with an inner diameter of 150 mm）， and the straightness measurement errors are less than 0.065 mm/m， which meets the requirements of enterprises（0.15 mm/m） and may effectively guide the machining processes of deep-hole parts.
To extract weak features and diagnose rolling bearing faults， a new resonance demodulation method was proposed based on subbands-reconstructed and-rearranged dual-tree complex wavelet packet transform（SRR-DTCWPT） and peak frequency extraction. The SRR-DTCWPT-based frequency band division method was fine， maintained the advantages of DTCWPT in approximate translation invariance and less spectral energy leakage， and solved the problem of band misalignment. The resonance demodulation method based on SRR-DTCWPT and peak frequency extraction did not require the participation of any indicator， might extract frequency bands at any position， avoided the influences of strong impact interference， and automated the calculation processes. The proposed method was compared with Fast Kurtogram and Autogram in simulation and case studies， and the results demonstrate the effectiveness and efficiency of the proposed method.
Since one-dimensional feature vectors might not retain temporal feature information， but neural networks had good effects on image recognition， an image data set constructed by fault sound signals of centrifugal pumps was used to conduct centrifugal pump fault diagnosis. A Bayesian optimized multiscale DenseNet fault diagnosis method was proposed for centrifugal pump sound signals. One-dimensional time series acoustic signals were transformed into two-dimensional image through Gram angle field， and the time information and fault characteristics were preserved. Then multiscale dense blocks were used to extract image features to enhance image feature reuse. The dropout layer and L2 regularization method were used to prevent overfitting， and Bayesian optimization algorithm was adopted to determine neural network hyperparameters. Finally， experimental verification was performed using centrifugal pump acoustic signals， and comparisons were made with other diagnostic methods. The results show that the Bayesian optimization multiscale DenseNet diagnosis model has a fault recognition rate of 99.5% for the test set.
A rapid single-exposure HDR defect recognition algorithm was proposed for highly reflective metal surfaces. This algorithm was based on detail enhancement techniques and CycleGAN. The input low dynamic range （LDR） images were first converted to HSV color space and processed with guided filtering to obtain luminance and detail layers. The CycleGAN network was then used to enhance the dynamic range of these layers separately. The enhanced luminance and detail layers were weighted and fused， followed by filtering and denoising to produce an HDR image suitable for defect recognition. Defects were identified in the HDR image using threshold segmentation， feature selection， and morphological processing. This single-exposure algorithm was experimentally compared with three classic single-exposure algorithms and one multi-exposure algorithm. The evaluation was based on five metrics： peak signal-to-noise ratio（PSNR）， image entropy， processing time， gray histograms， and recognition results. The experimental results indicate that the algorithm herein outperforms three other single-exposure algorithms in effectively addressing overexposure issues， achieving results comparable to multi-exposure algorithms. Additionally， it has a shorter processing time， making it suitable for online detection. Furthermore， this algorithm demonstrates superior capability in extracting image detail information compared to other algorithms， resulting in higher accuracy in recognition.
To optimize the three objectives of efficiency， energy consumption and impacts at the same time， a multi-objective trajectory planning model was proposed based on an improved SSA. Firstly， the artificial potential field method （APF） was used for path planning to obtain the shortest and collision-free path of the manipulator grasping the materials， and the key motion sequence was extracted to establish a multi-objective function. Then， aiming at the problems of multi-objective salp swarm algorithm （MSSA）， such as poor diversity of initial population， easy to fall into local optimum and slow convergence in solution set space， an improved algorithm namely logistic-sine multi-objective salp swarm algorithm（LMSSA）was proposed. The algorithm combined logistic-sine chaotic mapping， pinhole imaging learning strategy and golden sine development strategy to optimize the control nodes of the seventh-order B-spline curve and complete the multi-objective motion trajectory planning of the robotic arms. Finally， the trajectory planning model was applied to the actual grasping tasks of the manipulator UR16e by building MATLAB-CoppeliaSim-UR16e experimental platform. Experimental results show that based on LMSSA， the manipulator motion planning method realizes the accurate， efficient and energy-saving motion trajectory planning of the manipulator， and is successfully applied to the actual operation scenes.
To achieve a high-precision and highly generalizable thermal error model of machine tools， a thermal image input-based ResNet method was proposed for thermal error modeling of CNC machine tool spindles. A thermal image dataset labelled was constructed with thermal error rounding， and a ResNet-based classification model was trained for thermal error prediction using thermal images as inputs. Considering the regression characteristics of the machine tool thermal error time series， a regression output layer was constructed by integrating the probabilities of different classification labels from the classification output layer in a weighted manner， enabling thermal error regression prediction without retraining. The deep features of thermal images and the classification performance of the ResNet model were visualized， confirming the effectiveness of ResNet in feature extraction and strong classification ability. Finally， the ResNet model was compared with GoogLeNet and VGGNet models under different operating conditions， demonstrating the high accuracy and generalization of the ResNet-based thermal error classification and regression models.
A comprehensive review of the innovative applications and development of laser additive manufacturing technology in high-end equipment manufacturing was provided. Firstly， the basic principles and advantages were introduced， including the ability to achieve integrated manufacturing of complex structures， optimized design of materials and structures， and improvement of component performance. Further， the innovative opportunities brought by laser additive manufacturing technology to high-end equipment manufacturing in aspects were discussed such as new material development， new process innovations， new structures design， and new functions integration. The challenges faced in the applications of laser additive manufacturing technology in high-end equipment manufacturing were analyzed， such as technical difficulties in material system development and new material applications， manufacturing equipment development， online monitoring and quality control technology during the manufacturing processes， and improvement of post-processing technologies. Finally， the future development trends of laser additive manufacturing technology for high-end equipment were outlooked.
Experimental analyses were conducted on the color mixing effect of the developed full⁃color FDM 3D printers， verifying the color mixing uniformity of the designed mixing nozzles. Thus， an RGB⁃CMYKW color matching calibration algorithm was proposed based on FDM 3D printing of polylactic acid（PLA） filament and BP neural network. CMYW four⁃color silk materials were mixed in different proportions to obtain 529 mixed color samples. Using SOFV⁃1xi image acquisition equipment to extract color （RGB） information from mixed color samples under standard D50 light source， and combining the color information extracted from the standard color card under the same acquisition conditions for color correction， 529 RGB-CMYW color conversion sub samples were obtained. The sub samples were used for training to determine the FDM 3D printing full⁃color matching calibration algorithm. The developed full⁃color FDM 3D printer was used to print 24 color chips， and the corresponding color differences among the printed color chips and the standard 24 color card were analyzed and calculated. Results indicate that the color error difference is generally small and the color features may be well replicated. At the same time， actual 3D color model design and printing are carried out， further verifying the reliability and practicality of the proposed color matching algorithm.
A multi-scale ternary numerical model of “process-microstructure-properties” was proposed to investigate the relationships of the processing parameters of metal SLM， the microstructure of the formed parts and the fatigue properties. In detail， to analyze the evolution processes of temperature field， velocity field， and pore defects under different processing parameters， the meso dynamics process of the molten pool was studied with consideration of multiple physical field coupling phenomena such as recoil force. Using the temperature field data， the microstructure distribution of the representative volume element（RVE） was obtained based on cellular automaton model， and the effects of processing parameters on grain sizes and defect characteristics were investigated. Finally， the hazard levels of defects were evaluated under different processing parameters by stress intensity factors， and the macro fatigue strength of corresponding RVE was predicted. The results show that the proposed multi-scale model may effectively predict the fatigue properties of SLM metal parts under different processing parameters. This work provides a reference for optimizing SLM processing parameters.
To address issues such as incomplete selection of evaluation indicators and unreasonable processing of evaluation information in the traditional cloud manufacturing paradigm during the evaluation of collaborative service entities， a novel evaluation method for cloud manufacturing collaborative service entities was proposed based on R-GRA. Firstly， by comprehensively considering the production capacity， economic strength， organizational capability， risk response ability， and sustainable development capability of cloud manufacturing service entities， a systematic evaluation index system with multiple sub-indices was established， and the entropy weight method was used to determine the indicator weights. Secondly， taking into account the fuzziness and uncertainty of evaluation information in the collaborative service entity evaluation processes， an evaluation model for cloud manufacturing collaborative service entities was constructed based on R-GRA. By integrating the weights of the aforementioned evaluation indicators， the rough number interval difference coefficient for each collaborative service entity was calculated to yield the optimal evaluation results. Finally， the proposed model was validated for the scientific validity， reasonableness， and effectiveness through a case study of cloud manufacturing in electronic medical devices.
In order to improve the intelligent， efficient， and convenient development of wind turbine blade health monitoring technology， a wind turbine blade surface defect detection method was proposed based on improved YOLOv5s algorithm according to target recognition technology. Firstly， the original backbone network of YOLOv5s was replaced with an AFPN to enhance the network's learning ability. Secondly， the CBAM was embedded into the backbone extraction network， which enhanced the model's ability to extract surface defect features of leaves. Then， the minimum point distance intersection over union（MPDIoU） loss function was used to replace the CIoU loss function， improving the precision of bounding box localization. Finally， an improved detection method was used to detect defects in the blades of a certain wind turbine unit. The detection results show that the improved algorithm improves precision， recall and mean average precision（mAP） by 4.1%， 2.9% and 4.8%， respectively， reaching as 91.9%， 89.3% and 93.5%， which has significant precision advantages and better model stability.
To improve the reliability of self-locked performances of the function transformation mechanisms in multi-functional clamp-shear-grab integrated attachment， the contact performance of the mechanisms under self-locked states was studied， and original prototype and test prototype of the mechanisms were designed based on test conditions， and then tests were conducted. Initially， the prototypes of function transformation mechanisms were developed， and a self-locked mechanism was proposed based on slider-groove structure. By thoroughly considering the elastic deformations， adhesions， pileup deformations， and sliding shear contact states under self-locked states， an innovative contact model was proposed. Based on these， the self-locked states and contact characteristic distributions of slider-groove in contact areas were studied through finite element analyses. Finally， experiments were designed to verify the feasibility of the model and design. Experimental results show that the prototype may achieve self-locked function and exhibits good mechanics performance， and the proposed contact model outperforms traditional model by 59.3% in terms of maximum shear stress relative error.
Given the substantial vibrational excitation caused by inspection equipment on transmission lines and unclear vibration mechanism， the dynamic characteristics of power transmission lines under the influences of moving loads was investigated to explore their dynamic responses and vibration patterns. A three-dimensional dynamic nonlinear cable model was developed， and dynamics modeling of the inspection robots was conducted. Using the combined Lagrange-Ritz method， dynamics equations of the coupled systems of moving loads and power transmission lines were discretized. Numerical simulations were performed with MATLAB to analyze the impacts of nonlinear effects on the power transmission lines. Simulations were conducted for typical scenarios involving varying speeds of moving loads and different installation height differences of the power transmission lines. The results show that under the influences of moving loads， the power transmission lines exhibit significant nonlinear large-displacement characteristics and terminal effectiveness， with a maximum nonlinearity factor for axial force increments reaching 1.677. Increase the moving load speeds（0.5~2 m/s） results in increases of 2.4%， 3.9%， and 4.4% in lateral displacement， longitudinal displacement and axial force increments， respectively， with terminal effect amplitudes increasing by 140%， 138%， and 225%. Increasing the installation height difference （0~10 m） reduces the vertical sag distance of the transmission lines， with longitudinal displacement and axial force increments decreasing by 7.3% and 6.2% respectively， and a maximum reduction in terminal effect vibration frequency of 50%. These findings provide theoretical reference for the engineering designs of similar cable structures under moving loads.
In response to the urgent demands for daily maintenance and inspection of oil and gas pipelines， a novel modular pipeline inspection robot named RoboChain-Ⅰ， featuring adaptive deformation capabilities， was proposed herein. Unlike most wheel-based pipeline robots， the robot adopted a cell-inspired modular biomimetic design with more flexible joint redundant rotational degrees of freedom（DOF）， allowing the robot to actively deform in response to pipelines with varying shapes and diameters. Each module was equipped with dual-wheel independent drive， and a pair of pitch and yaw actuation mechanisms were installed at the front and rear. The modules were connected by passive elastic damping support structures or controllable electromagnetic adhesion-separation rigid structures， which improved the robot's ability to navigate complex pipelines and adapt to various environments. The forces acting on the robots during their motions inside the pipeline were modeled， and kinematics simulations were conducted using Adams. The selection of design parameters for the model was validated accordingly. A comprehensive series of experiments were conducted to evaluate RoboChain-Ⅰ's performance， including terrestrial locomotion， straight pipe traversal， elbow pipe navigation， diameter-varying pipeline adaptation， and active mother-child separation. Experimental results validate the robot's effectiveness and reliability in performing inspection tasks within complex three-dimensional pipeline networks with diameters ranging from 175~440 mm， demonstrating maximum velocities of 0.87 m/s on flat surfaces and 0.4 m/s within pipelines.
Aiming at the problems of automatic extraction of magnetic memory signal features and quantitative identification of defect levels in oil and gas pipelines， a multimodal fusion model was proposed combining residual neural network and graph neural network（ResGNNet）. The original magnetic memory signals of defects of different depths on L245N pipeline steels were collected by metal magnetic memory detector. In order to realize automatic feature extraction， the complete information of the original magnetic memory signals was retained， and the relationship among samples was taken into account. The original signals were converted into a node graph by K nearest neighbor-dynamic time warping， and the original signals were converted into a 2D image by Gram angle field. The designed graph neural network and residual neural network may automatically extract the embedded feature vectors of 1D signals and 2D images respectively. The multimodal embedded feature vectors were fused， weighted and screened by multi-head self-attention mechanism， and then input into the Softmax classification module to complete the defect level identification. The model verification results show that the accuracy of quantitative identification of pipeline defect levels reaches 93%.
