1084results about "Railway vehicle testing" patented technology

The invention provides a state monitoring and fault early warning method for a maglev vehicle system, relates to the technical field of urban rail transit data processing, and solves the problem of how to effectively combine an empirical threshold mode and a machine learning technology and give full play to a combination effect. The method comprises the steps of collecting operation data of a magnetic levitation vehicle system, performing corresponding data preprocessing operation, performing analysis and calculation in a mode of constructing a mathematical model, and evaluating the health state of the magnetic levitation vehicle system; constructing and training a fault prediction model, and inputting the evaluation data into the trained fault prediction model to obtain potential fault data of the magnetic levitation vehicle system; and generating early warning information based on the potential fault data, retrieving an expert experience library by using the early warning information, generating an intelligent decision result, and feeding back the intelligent decision result to the operation and maintenance personnel. According to the method, machine learning and expert experience are effectively combined, so that the real-time performance and the accuracy of fault early warning are improved.

The invention relates to the technical field of artificial intelligence and computer vision, discloses a subway key component fault detection method and system based on an AI visual large model, and aims to solve the problems of low detection precision, weak generalization ability, insufficient multi-mode understanding, poor real-time performance and lack of state evolution modeling in the prior art. The method comprises the following steps: acquiring images of key components through a multi-view industrial camera array, and performing distortion correction, illumination normalization and noise suppression; a pre-trained visual large model is utilized to extract deep space features, and modeling is carried out on a continuous frame feature sequence through bidirectional LSTM to capture a time sequence change trend. By introducing the large-scale visual large model and spatio-temporal joint modeling, the identification capability of tiny defects is improved, the discrimination stability is enhanced, high-precision and low-delay automatic detection is realized, the false alarm rate and the omission ratio are remarkably reduced, and the detection efficiency and the system maintainability are improved.

The invention provides a wheel tread derailment detection method and system for a railway vehicle. According to the method, jacking driving devices with jacking rotating wheels are symmetrically arranged on the two longitudinal sides of a track, a pressure sensing unit is arranged between the two jacking rotating wheels, optical detection units with orthogonal optical paths are installed on the outer sides, and displacement measurement units are integrated on the jacking rotating wheels, so that a three-dimensional detection network is formed. During detection, a top rotating wheel lifts a wheel step by step, a blocking signal, tread pressure and a vertical displacement parameter are synchronously collected, and a space mapping relation between a pressure decreasing path and a displacement abnormal point is established; converting the blocking signal into a blocking release rate, and calculating a displacement accumulated value in combination with a space mapping relation; and when the displacement accumulated value is continuously increased and the blocking release rate exceeds a threshold value, wheel-rail separation is judged, and jacking is controlled to be stopped. Through cooperative detection and analysis of multiple sensors, accurate judgment and self-adaptive jacking control of the wheel-rail separation state are achieved, and the problem of wheel diameter adaptability of a traditional fixed jacking mode is effectively solved.

The invention discloses a high-speed train body health monitoring system and method based on a distributed IMU, and belongs to the technical field of mechanical vibration testing, and the method comprises the steps: obtaining a preset structure topology model of a high-speed train body, recognizing a key structure region, and constructing a physical topology grouping mapping table; deploying a plurality of inertial measurement unit nodes to the key structure area and carrying out relevance division to obtain a physical topology group; the method comprises the following steps: receiving a real-time operation condition signal from a train control system, performing feature extraction on vibration data acquired by inertial measurement unit nodes in a physical topology group, generating a region collaborative feature vector, performing space-time anomaly analysis, positioning a damage generation region, and generating a residual life evaluation parameter according to the vibration feature variation of the damage generation region. According to the method, physical topology grouping mapping and working condition self-adaptive feature extraction technologies are adopted, space-time transmission consistency verification and an aging dynamic correction mechanism are combined, and accurate positioning of a damaged area and reliable evaluation of the residual life can be achieved.

The invention discloses a high-speed rail vehicle-mounted equipment fault prediction and health management method, and relates to the technical field of high-speed rail vehicle-mounted equipment fault prediction, and the method comprises the steps: monitoring high-speed rail vehicle-mounted equipment through employing a high-precision sensor network, and obtaining an original monitoring data set; cleaning the original monitoring data set by adopting a multi-source data integration and preprocessing method to obtain a high-quality data set, and extracting key features based on the high-quality data set to obtain a feature data set; constructing a fault prediction model based on a deep learning method and the feature data set, inputting the high-quality data set to the fault prediction model, and outputting a fault early warning result; based on the fault early warning result, a decision support method is adopted to evaluate the equipment state, and a maintenance strategy suggestion is obtained; obtaining a comprehensive analysis report based on the maintenance strategy suggestion and the equipment evaluation state; environmental data and historical maintenance record data from a ground monitoring system are integrated, and the data are fused by using a weighted average method.

The invention discloses a motor bearing fault diagnosis method, device and equipment and a medium, and relates to the technical field of motor bearing fault diagnosis, and the method comprises the steps: obtaining a vibration signal and a current signal of a motor bearing, and carrying out the feature extraction of the vibration signal and the current signal, obtaining a first time domain feature, a first frequency domain feature and a first energy feature of the vibration signal and a second time domain feature, a second frequency domain feature and a second energy feature of the current signal; carrying out feature fusion and feature dimension reduction on the first time domain feature, the first frequency domain feature, the first energy feature, the second time domain feature, the second frequency domain feature and the second energy feature to obtain a feature after fusion dimension reduction, and taking the feature after fusion dimension reduction as input; and determining whether the motor bearing has a fault and the fault type when the fault exists by using the fault diagnosis model. According to the invention, the fault diagnosis precision can be improved.

The invention relates to the field of gear fault diagnosis, in particular to a rack rail train driving gear fault diagnosis method, system and device, and solves the problem that fault features cannot be effectively extracted from rack rail section vibration signals in the prior art. The method comprises the following steps: identifying a transition section vibration signal of a transition device; identifying a toothed rail section vibration signal; the rack rail section vibration signals are processed; and fault diagnosis is realized according to the meshing frequency and the sideband information. According to the time domain characteristics of the vibration signals, the vibration signals generated when the driving gear passes through the rack rail section can be accurately recognized from the vibration signals, and self-adaptive recognition of the vibration signals of the rack rail section is achieved. According to the method, noise interference in time-frequency distribution of toothed rail section vibration signals is removed, time-frequency aggregation of the time-frequency distribution is improved, fault features of the driving gear are extracted from the time-frequency distribution, fault diagnosis of the driving gear is achieved, health state monitoring of the toothed rail train driving gear is achieved, and train operation safety is guaranteed.

The invention discloses a bogie detection system and a control method thereof, and the system comprises a guide rail which is provided with a detection station; the height detection device is arranged above the detection station, and the height detection device is used for detecting the height of the bogie; the first image detection device is arranged below the detection station, and the first image detection device is used for carrying out image detection on the bottom of the bogie while carrying out height detection; and the second image detection device is arranged on the side surface of the detection station and is used for carrying out image detection on the side surface of the bogie while carrying out height detection. Bogie height parameter and multi-view image acquisition and multi-modal data fusion analysis are synchronously completed at a single station, the defect recognition accuracy is improved, the labor intensity of detection personnel is remarkably reduced, the repeated configuration of equipment is reduced through station integrated design, the detection period is effectively shortened, and the workshop space is saved. And an efficient intelligent detection solution is provided for manufacturing of rail transit equipment.

The invention discloses an electric inertia simulation braking power test bench and an electric inertia control method thereof, and relates to the technical field of electric transmission simulation of flywheels, the electric inertia simulation braking power test bench comprises a motor, an incremental encoder, a main shaft, a flywheel, a force arm frame, a mechanical friction braking torque sensor, an electric transmission control system and a brake disc; an incremental encoder is installed at the rear end of the motor, the transmission end of the motor is connected with the main shaft through a coupler, the flywheel is installed on the main shaft, a brake disc is installed on the force arm frame, and meanwhile the force arm frame is connected with a mechanical friction braking torque sensor. According to the invention, the angular speed, the angular displacement and the mechanical friction braking torque are synchronously collected, and the motor torque is calculated and given, so that the test bench operates in a state of approaching an equivalent flywheel and equivalently having no system resistance; the algorithm can control the total error within a very small range; accurate matching of rotational inertia is achieved, and the control precision can meet the high-precision test requirement. And investment and operation cost are saved.

The invention provides the unsupervised learning-based fault diagnosis method and system for detecting wheel out of round, which belongs to the technical field of machine learning fault diagnosis. Characteristic signals of train wheels will be acquired. The pre-trained detection model is adopted to process the characteristic signals of the train wheels to be detected, so as to obtain the wheel roundness state results. As for the collected characteristic signals, the invention constructs a subway wheel out of round detection method based on unsupervised learning for collected characteristic signals, which are deployed to computer equipment capable of executing computer programs, inputting characteristic signal data collected by a subway wheel out of round monitoring device based on rail wayside response into the computer equipment to obtain the wheel roundness state.

The invention relates to the technical field of rail transit monitoring, and discloses a multi-sensor fusion rail transit electric passenger car bogie dynamic performance monitoring method. The method comprises the steps that multi-source heterogeneous sensing data such as bogie vibration acceleration time sequence signals, wheel-rail contact force distribution data, bearing temperature gradient data and structural strain field data are collected; analyzing the vibration signal to extract a frequency band energy feature vector, performing spatial gridding mapping on the contact force data to generate a distribution matrix, and performing time-space alignment fusion on the two to form a primary fusion feature set; positioning a bearing temperature anomaly region through a deep convolutional network, and generating a secondary fusion feature set in combination with a structural strain concentration coefficient; and inputting the two-stage feature set into a dynamic performance evaluation model, outputting a performance degradation index set of scores of axle box bearing wear degree, framework fatigue cumulant, wheel set tread damage and the like, generating a maintenance priority sequence and triggering a preventive maintenance instruction according to the performance degradation index set, and realizing accurate monitoring and efficient operation and maintenance management and control of the dynamic performance of the bogie.

The invention relates to the technical field of subway tunnel fire experiment, and discloses a variable-pressure reduced-size subway tunnel / train compartment double-long-narrow-space fire experiment platform which comprises a tunnel body, a ventilation unit, a monitoring unit, a rail unit and a train unit, and a fire source structure is arranged in the train unit. According to the variable-pressure reduced-size subway tunnel / train compartment double-long-narrow-space fire experiment platform, a fire source structure is arranged, when a fire experiment needs to be carried out, a top fan, two side fans and a main fan are started through a wireless receiver, the wind speed during high-speed running of a train is simulated, and then a remote control igniter is remotely and wirelessly connected; the top fan, the two side fans and the main fan are remotely turned off after a certain time, a real fire disaster is simulated, the fire source structure can simulate the effect that the fire source continuously burns the multiple carriages after a train runs at a high speed, and the authenticity is high.

The invention relates to the technical field of wheel-rail force, in particular to a train stability analysis method and system based on wheel-rail force, and the method comprises the following steps: based on vertical force and transverse force data collected in real time, carrying out segmented statistics on stress value distribution of contact points of train wheels and a track, and analyzing abnormal amplitude in force fluctuation values. According to the method, the stress values of the contact points of the train wheels and the rail are counted in a segmented mode, abnormal amplitude analysis and periodic abnormal parameter screening are combined, the irregular vibration frequency and position are effectively recognized, the vibration recognition accuracy and processing speed are improved, the triggering sensitivity and the working threshold value of the force sensor are adjusted, and the accuracy of vibration recognition is improved. In combination with optimization of frequency distribution of a track irregularity area, the response capability and adaptability of a monitoring system are enhanced, through a waveform characteristic parameter set extracted from a wheel power signal, the influence of track gauge change on waveform characteristics can be evaluated more comprehensively, dynamic stability analysis of a train is optimized, and data support is provided for train stability.

The invention relates to the technical field of rail traffic monitoring, and discloses an urban rail vehicle driving analysis system based on digital twinning, which is characterized in that vehicle vibration and traction motor current signals are collected in real time through a vehicle-mounted sensing unit, and an edge feature extraction unit calculates energy ratio features of a predefined fault frequency band; the data transmission unit transmits the characteristic data and the working condition information to a digital twin platform, a digital fault tree model is arranged in the platform, and a cross-physical domain fault conduction path is analyzed and recognized through multi-source data coupling. Through combination of frequency band energy ratio characteristics and fault tree logical reasoning, electromechanical coupling fault risks which cannot be found by traditional isolated monitoring are effectively captured, meanwhile, a diagnosis threshold value is dynamically adjusted according to the wheel-rail adhesion state, and the diagnosis reliability under the complex working condition is improved.

The invention discloses an intelligent train bogie defect detection and fault identification system and method. The intelligent train bogie defect detection and fault identification system comprises a control module, a data acquisition module, a single-chip microcomputer module and a data sending module. The control module controls other modules to work and transmit data; the data acquisition module, the single chip microcomputer module and the data sending module are respectively connected with the control module; the data acquisition module comprises a high-definition imaging unit, a radar ranging unit and an infrared temperature measurement unit; the single-chip microcomputer module comprises an image processing unit, a defect detection unit and a fault identification unit. The data sending module comprises a coding unit and a wireless communication unit; and the fault judgment unit clusters the processed fault feature map and the fault information trained by the database to obtain the fault information, so that intelligent train bogie defect detection and fault identification are realized, the labor amount of workers is reduced, the bogie maintenance efficiency is improved, and the train operation safety is guaranteed.

The invention provides a water-rich stratum tunnel train simulation excitation signal generation method and device, and relates to the technical field of engineering test, and the method comprises the steps: obtaining train operation parameters, wheel track vibration waves and track characteristic parameters; performing frequency band division on the wheel track vibration wave, calculating time delay, performing phase compensation on each frequency band according to the time delay, and generating a composite waveform to obtain a first signal; performing frequency domain correction on the first signal according to the train operation parameters, and adjusting the amplitude of each frequency band according to the corrected normalized energy of each frequency band to obtain a second signal; frequency bands of the second signal are divided according to the track characteristic parameters, the center frequency of each frequency band is determined, phase compensation is carried out according to impedance corresponding to the center frequency, and a third signal is obtained; and generating a simulation excitation signal based on the third signal and the train operation parameters. The excitation signal generated by the invention has the composite characteristics of high-frequency transient impact and low-frequency rigid motion, and can provide high-fidelity excitation input for related tests of train vibration load.

The invention relates to the technical field of train fault diagnosis, in particular to a fault diagnosis method for a train transmission system, and overcomes the defects that weak features are difficult to extract and fault features are difficult to effectively distinguish in the prior art. In combination with the strong feature extraction capability of a deep network model constructed by an InceptionResNetV2 network, local and global features are captured by using a parallel multi-branch structure and a residual adaptive fusion mechanism, and through multi-scale perception and residual cross-layer connection, the capture capability of the super-huge bearing weak fault time sequence-frequency domain features can be significantly enhanced, and the fault detection accuracy is improved. And finally, pre-training a model by using source domain data, extracting parameterized knowledge of the model, and migrating to a target domain to realize parameter fine tuning and feature adaptation, so that the generalization performance and convergence speed of fault diagnosis can be improved, different fault categories can be effectively separated, and feature aggregation of the same category can be realized.

The invention relates to the technical field of train data analysis, in particular to a data analysis method for train operation faults, which comprises the following steps: carrying out data fusion processing on train operation brake signals acquired by a vibration sensor, an acoustic emission sensor and a state detection device to generate multi-dimensional brake time sequence data; brake fusion features are generated from the multi-dimensional brake time sequence data through a cross-modal fusion model, and the cross-modal fusion model is constructed based on a gating feature interaction layer and a convolution feature extraction layer; and the brake fusion features are used to generate brake fault severity through a space-time joint model, the space-time joint model is constructed based on an improved LSTM architecture, and optimization training is carried out through a physics constraint loss function. According to the method, the accuracy and reliability of predictive fault diagnosis of the subway braking system are remarkably improved through the LSTM architecture of multi-modal data fusion, cross-modal feature modeling and physical constraint embedding on the train operation braking data detected beside the track and the detection data of the braking system.

The invention provides a railway vehicle defect detection method, which comprises the following steps: constructing an acoustic-visual fusion inspection system, and combining with an inspection robot to detect the defects of multiple parts of a train; the system comprises a system triggering module, a defect positioning module, an acoustic signal acquisition module, a visual data acquisition module, a data transmission module, an acoustic signal processing module, a visual data processing module and a feature fusion module. On the acoustic part, defect positioning and defect diagnosis are realized by collecting acoustic signals, filtering irrelevant information and extracting characteristic frequency; for the visual part, collecting image data, filtering the data, extracting feature points, and registering to carry out defect detection on point cloud data; for a specific part, acoustic and visual feature fusion needs to be carried out, and then defects are diagnosed; the method is suitable for defect detection of the rail train, defect detection of a plurality of key parts of the train can be covered through sound and vision fusion, the detection precision is improved, and the labor burden is relieved.

The invention belongs to the technical field of intelligent operation and maintenance of train transmission systems, and discloses a train transmission system fault detection and positioning method under variable working conditions. The sample diversity is enhanced by constructing a pseudo fault sample library; eliminating noise interference in the data by using a residual shrinkage feature extraction module, and extracting representative features; a self-adaptive alignment mechanism is designed to map different working condition health samples to a unified feature space, and the influence of working condition fluctuation on data distribution is relieved; health and pseudo fault feature pairs are constructed, working condition attributes and essential attributes of health states are revealed, and relation scores of the feature pairs are calculated by using a Colmogorov-Arnodel network relation. According to the method, the train transmission system fault is accurately detected and positioned under variable working conditions, the dependence of a data driving method on fault data and the dependence on data distribution consistency are overcome, and important engineering application value is provided for high-performance, long-life and safe and stable operation of the train transmission system in China.

The invention relates to the technical field of damage identification, in particular to a structural defect identification method for brake shoe displacement and damage characteristics of a railway wagon, which comprises the following steps of: in the whole life cycle of the railway wagon, dividing a braking process into a plurality of stages, and performing hierarchical filing according to working condition types and service stages, constructing a brake shoe multi-working-condition structure response file; through longitudinal comparison along a time axis in the same working condition layer and transverse comparison among different working condition layers, screening out a brake shoe ectopic evolution mark and an initial brake shoe damage evolution mark which are sensitive to a structure state; encoding all evolution marks according to a spatial position, a working condition layer and a service stage to form a brake shoe ectopic and damage structure fingerprint sequence; and outputting an identification result including the structure defect type and the severity level, and generating a maintenance priority. According to the method, a space-time coupling mechanism of acoustic emission energy and temperature gradient is introduced to recognize early microcrack propagation, and the recognition sensitivity of recessive fatigue and complex damage modes is improved.

The invention discloses a wheel rail vibration boundary condition testing method and device and a medium. Relates to the field of high-speed trains, and solves the problem of testing wheel-rail vibration boundary conditions on a scaling wheel-rail vibration test bench. According to the invention, from the collection of the real-time axle load value, the real-time torque and the six-component data, the interaction and vibration characteristics between the wheel rails can be reflected more accurately. Through comprehensive processing and analysis of data, feedback control data is finally generated and used for adjusting operation parameters of the test bench. Based on the real-time adjustment of feedback control data, the intelligent and automatic level of the test bench is improved, and the dynamic control of the test process is realized. Compared with a full-scale test bench, the scaling rolling test bench has lower operation and maintenance cost, and can meet the research requirements on the vibration characteristics of the high-speed train.

This application relates to the field of health monitoring in rail transit, and discloses a method, device, and system for monitoring the health of a bogie. The method includes: acquiring the stress on the surface of a predetermined location on the main body of a composite bogie when deformation occurs; the predetermined location includes key parts of the main body and areas where the main body and components are connected by fasteners; determining the degree of damage to the main body; and predicting the remaining life of the composite bogie based on the stress and the degree of damage. Therefore, the health monitoring method in this application, by acquiring the stress on the surface of a predetermined location on the main body of the composite bogie and determining the degree of damage to the bogie main body, and then predicting the remaining life of the composite bogie based on the degree of damage and the acquired stress, ensures the structural safety of the composite bogie and reduces maintenance costs.

The invention provides a rail vehicle collision test system, which relates to the technical field of rail vehicle collision tests and comprises a collision simulation device, a wall body, a test vehicle body and a data acquisition device. The test vehicle body is configured to move towards the wall body at a preset speed, and dummy passengers are carried in the test vehicle body. The collision simulation device comprises an energy absorption assembly and an adjusting assembly, the energy absorption assembly is installed on the wall and suitable for colliding with the test vehicle body, and the adjusting assembly is configured to respond to the crumple amount of the energy absorption assembly in the collision process and adjust the counter-acting force applied by the energy absorption assembly to the test vehicle body so as to simulate the collision process of the real vehicle with the energy absorption structure. The data acquisition device is arranged on the dummy passenger and is suitable for acquiring data information of secondary collision of the dummy passenger in the test vehicle body.

The invention provides a multi-modal intelligent diagnosis method and system for temperature vibration of a high-speed rail bearing based on a working condition spectrogram, and relates to the technical field of intelligent diagnosis, and the method comprises the steps: synchronously collecting vibration, temperature distribution and phase angle signals of a bearing, and extracting a self-adaptive reference baseline in combination with an operation working condition; and obtaining a temperature vibration joint sequence through phase alignment, calculating a thermal vibration coupling intensity spectrum, and comparing the thermal vibration coupling intensity spectrum with a baseline to obtain circumferential deviation distribution. And after dynamic threshold correction, mapping the over-limit phase interval into a fault space coordinate on the bearing raceway, and determining a fault type according to frequency spectrum characteristics. According to the invention, accurate space positioning and type identification of high-speed rail bearing faults are realized, and the adaptability and accuracy of diagnosis are improved.

The invention discloses an online detection device for a rack rail wheel, relates to the technical field of rail transit vehicles, and aims to solve the problem that the meshing relation between the rack rail wheel and a rack cannot be actually detected. The gear tooth structures of the detection rack and the line rack are consistent in specification; the propelling device is arranged on one side of the detection rack and used for driving the detection rack to move in the direction perpendicular to the extending direction of the detection rack; the pressing device is arranged on the other side of the detection rack, and the pressing device is used for providing pre-tightening force enabling the detection rack to reset in the direction perpendicular to the extending direction of the detection rack; through the arrangement, the actual meshing center distance between the rack rail wheel and the rack can be directly detected.

The present application relates to the technical field of sealing test, in particular to a railway clamp testing device, which comprises a testing table, a fixing mechanism arranged on the testing table, and an air pipe in communication with an internal air supply device of the testing table, one end of the air pipe being in communication with a connector; the connector comprises a pipe body, a distance sensor, and an adjusting piece, one end of the pipe body being rotatably provided with a connecting pipe, the pipe body being internally provided with a contraction part and a piston piece. In the railway clamp testing device, the piston plate and the contraction part are arranged to cooperate with each other in the pipe body, so that the piston plate can isolate the inside of the pipe body by moving in or out, and then the piston plate can automatically displace according to the change of air pressure, on the one hand, the driving of the connecting pipe is realized, and the number of times of twisting the connecting pipe is reduced, on the other hand, the piston plate can move according to the air leakage position, so that the air leakage position can be obtained according to the moving state, and whether the clamp leaks air can be quickly understood.

The invention relates to a test stand for testing bogies of rail vehicles, comprising a load impact unit (100) for generating a linear force acting on a bogie to be tested. The load impact unit (100) comprises a hydraulic cylinder (110) having a piston rod (114). The hydraulic cylinder (110) is a double-acting cylinder, the cylinder chambers of which are subjected to hydraulic fluid by a bidirectional axial piston pump (140). The test stand further comprises a transmission device (120) for transmitting the force acting on the bogie, wherein the transmission device (120) is operatively connected to an end of the piston rod (114).

Laser lighting is used to capture high resolution images of a moving train as the train passes through a portal. On the respective sides, top and bottom of the portal a plurality of cameras are mounted to capture the images of the train. In certain applications a de-speckler is added to refine the light to its most functional use.