CN113104063A - Comprehensive detection system and method for network rail tunnel - Google Patents
Comprehensive detection system and method for network rail tunnel Download PDFInfo
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Abstract
The invention relates to the technical field of comprehensive train detection, in particular to a comprehensive detection system and method for a network rail tunnel. The invention carries out information exchange, deep fusion and comprehensive analysis on the geometric parameter information and the inspection image information among the bow net detection system, the track detection system and the tunnel detection system through the vehicle-mounted processing system, so that the subsystems can share positioning information, clock information, computing resources, compensation information and the like, the information exchange and the information deep fusion are realized through the linkage of the vehicle-mounted processing system and other subsystems, the safety of the train is ensured in all directions, the overall maintenance level of the subway is improved, meanwhile, the running state of the train is comprehensively judged by determining whether the parameter value of the geometric parameter information exceeds the limit or whether abnormal information exists in the image information under the normal running of the train, therefore, the abnormal position, the abnormal reason and the abnormal condition are determined, and the found defects can be rechecked and subjected to multi-dimensional accurate matching.
Description
Technical Field
The invention relates to the technical field of comprehensive train detection, in particular to a comprehensive detection system and method for a network rail tunnel.
Background
In the different trip modes in current city, subway trip is more and more favored by citizens. Due to long-time operation of the subway, various unknown factors such as geological factors and the like, the safe operation of the train can be seriously damaged under the conditions that bolts of some equipment in the subway line are loosened, the equipment exceeds a set limit and the like. At present, a mode of manual detection is not suitable for urgent requirements of line automatic detection, a detection evaluation method and content cannot completely cover the whole system, although interconnection and intercommunication are partially realized among all service systems, a cooperative linkage mechanism is lacked, an 'information isolated island' still exists, the detection monitoring efficiency is low, and the operation requirements cannot be met.
Disclosure of Invention
The invention aims to provide a comprehensive detection system and method for a network rail tunnel, which can comprehensively ensure the safety of a train, recheck the found defects, realize multidimensional accurate matching, realize higher detection speed and more convenient and efficient detection operation.
In order to solve the technical problems, the technical scheme adopted by the invention for solving the technical problems is as follows:
a net rail tunnel comprehensive detection system comprises a bow net detection system, a track detection system, a tunnel detection system, a vehicle-mounted processing system and a power supply system for providing power for train net rail tunnel comprehensive detection; the pantograph-catenary detection system is used for acquiring pantograph-catenary geometric parameter information and a catenary patrol image of the train in real time; the track detection system is used for acquiring train attitude parameter information, track image information, steel rail section information and track geometric parameter information in real time; the tunnel detection system is used for acquiring 360-degree image information, tunnel section information and vehicle dynamic envelope line information of the whole tunnel in real time;
the vehicle-mounted processing system is used for comprehensively detecting and monitoring the pantograph-catenary running state, the track running state and the tunnel state, exchanging and comprehensively analyzing the parameter information and the image information among the pantograph-catenary running state, the track running state and the tunnel running state, and determining whether the parameter value of the parameter information exceeds the limit or whether abnormal information exists in the image information under the normal running of the train to comprehensively judge the running state of the train.
Further, the bow net detection system comprises: the system comprises a bow net parameter measuring module, a bow net video collecting module and an abnormality detecting module; the pantograph-catenary parameter measuring module is used for measuring the catenary geometric parameter information of the running train in real time and transmitting the catenary geometric parameter information to the vehicle-mounted processing system for storage; the pantograph-catenary video acquisition module is used for acquiring a real-time catenary patrol image of the running train; the abnormity detection module is used for detecting pantograph net abnormity in the pantograph net system of the running train.
Further, the track detection system comprises: the system comprises a track parameter measuring module, an inertia module, a track inspection module and a track section acquisition module; the track inspection module is used for acquiring image information of a track and a track bed and transmitting the image information to the vehicle-mounted processing system; the inertia module is used for measuring attitude parameter information when the train runs; the track parameter measuring module is used for measuring the track geometric parameter information of the running track in real time and transmitting the track geometric parameter information to the vehicle-mounted processing system for storage; the track section acquisition module is used for acquiring a section image of the running track.
Furthermore, the tunnel detection system comprises a limit detection module and a tunnel inspection module; the limit detection module is used for acquiring tunnel section information of a tunnel where a train is located and vehicle dynamic envelope information in real time; and the tunnel inspection module is used for acquiring 360-degree image information of the whole tunnel in real time and transmitting the acquired image information to the vehicle-mounted processing system.
The pantograph-catenary detection system further comprises a sensor module, wherein the sensor module comprises a current sensor, an acceleration sensor and a pressure sensor, the acceleration sensor is used for detecting hard points on a catenary, the acceleration sensor is arranged on the pantograph, and the acceleration sensor transmits the operation information of the pantograph to the vehicle-mounted processing system to confirm the hard point condition of the basic catenary; the pressure sensor is used for acquiring contact information of a contact net and a pantograph in real time, the pressure sensor transmits the contact information to the vehicle-mounted processing system to confirm the contact state of the pantograph and the pantograph, and the current sensor is used for acquiring current data of a train in real time.
Further, the track section acquisition module comprises a laser sensor and a section camera, wherein the laser sensor is used for emitting laser and irradiating the laser on the track section; the section camera is used for shooting the section of the track under laser irradiation to obtain a real-time track profile, and the vehicle-mounted processing module compares the track profile with a standard track profile to obtain a track abrasion position.
Furthermore, the clearance detection module comprises a clearance 3D assembly and a clearance radar assembly, the clearance 3D assembly is arranged at the head of the train, the clearance radar assembly is arranged at the tail of the train, the clearance 3D assembly is used for obtaining tunnel section parameters and vehicle dynamic envelope parameters in real time, and the clearance radar assembly is used for positioning and triggering the clearance 3D assembly at equal intervals to acquire tunnel section images.
Furthermore, the tunnel inspection module is arranged on a train head and comprises 3-6 inspection cameras, a light supplement device corresponding to the inspection cameras and an image analysis module, the 3-6 inspection cameras are uniformly distributed on the train head and used for acquiring inner wall images of different positions of the tunnel, and consecutive images of the train in the running process in the tunnel are shot through the inspection cameras; the image analysis module is used for enhancing and segmenting the acquired image and distinguishing tunnel defects and other interferents.
The invention also comprises a comprehensive detection method of the net rail tunnel, which comprises the following steps:
acquiring parameters and images of a bow net detection system, a track detection system and a tunnel detection system in a net rail tunnel integrated system;
determining whether the parameter value of the parameter information under the normal running of the train exceeds the limit or whether abnormal information exists in the image information,
if the parameter information is determined to be out of limit in the matched parameter value range, determining whether the linkage parameter matched with the parameter information is out of limit, judging whether the rail-tunnel integrated system is in an abnormal condition or not through the image corresponding to the parameter information and the linkage parameter, and obtaining the occurrence position of the abnormal condition;
and if the abnormal information appears in the image, confirming whether the linkage image matched with the image is abnormal or not, and confirming the reason of the abnormal information appearing in the rail-tunnel integrated system through the parameter information corresponding to the image and the linkage image.
Further, still include:
the method comprises the steps of acquiring a tunnel section image, a vehicle dynamic envelope curve and a track section image in real time, establishing a database of a standard tunnel, a vehicle dynamic envelope curve and a standard track profile in a normal state, wherein the standard tunnel profile in the database is matched with corresponding track profile parameters, the standard track profile is matched with corresponding track profile parameters, comparing the tunnel section profile with the standard tunnel profile at the same position in the database, comparing the vehicle dynamic envelope curve with the standard vehicle limit at the same position in the database to obtain the limit relation between the tunnel profile and the vehicle dynamic envelope curve, and comparing the section image with the standard track profile at the same position in the database to obtain the track abrasion position and the track state.
The invention has the beneficial effects that:
the invention carries out information exchange, depth fusion and comprehensive analysis on the geometric parameter information and the patrol image information among the bow net detection system, the track detection system and the tunnel detection system through the vehicle-mounted processing system, realizes the sharing of positioning information, clock information, computing resources, compensation information and the like among all subsystems, realizes the information exchange and the information depth fusion through the linkage of the vehicle-mounted processing system and other subsystems, comprehensively ensures the safety of a train, improves the overall maintenance level of the subway, and comprehensively judges the running state of the train by determining whether the parameter value of the geometric parameter information exceeds the limit or whether abnormal information exists in the image information under the normal running of the train, thereby determining the abnormal position, the abnormal reason and the abnormal condition, rechecking the found defects, accurately matching multiple dimensions and having higher detection speed, the precision is higher, and the detection operation is more convenient and efficient, cost greatly reduced.
Drawings
Fig. 1 is a schematic diagram of a comprehensive detection system and method for network rail and tunnel according to the present invention.
FIG. 2 is a schematic view of the detection process of the present invention.
Fig. 3 is a schematic view of a bow net detection system of the present invention.
FIG. 4 is a schematic view of the track detection system of the present invention.
Fig. 5 is a schematic diagram of a tunnel detection system of the present invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
Referring to fig. 1 to 5, a comprehensive detection system for a network rail and tunnel comprises a bow net detection system, a track detection system, a tunnel detection system, a vehicle-mounted processing system and a power supply system for providing power for comprehensive detection of the network rail and tunnel of a train; the pantograph-catenary detection system is used for acquiring pantograph-catenary geometric parameter information and a catenary patrol image of the train in real time; the track detection system is used for acquiring train attitude parameter information, track image information, steel rail section information and track geometric parameter information in real time; the tunnel detection system is used for acquiring 360-degree image information, tunnel section information and vehicle dynamic envelope line information of the whole tunnel in real time;
the vehicle-mounted processing system is used for comprehensively detecting and monitoring the pantograph network running state, the track running state and the tunnel state according to the acquired parameters and images of the pantograph network detecting system, the track detecting system and the tunnel detecting system, exchanging and comprehensively analyzing the parameter information and the image information among the pantograph network detecting system, the track detecting system and the tunnel detecting system, and determining whether the parameter value of the parameter information exceeds the limit or whether abnormal information exists in the image information under the normal running of the train to comprehensively judge the running state of the train.
The image information comprises a contact network inspection image letter, a track image letter, a steel rail section image letter, a 360-degree image letter of the whole tunnel and a tunnel section image letter of the train; the parameter information comprises bow net geometric parameter information, train attitude parameter information, track geometric parameter information and vehicle dynamic envelope information.
The invention carries out information exchange, depth fusion and comprehensive analysis on the geometric parameter information and the patrol image information among the bow net detection system, the track detection system and the tunnel detection system through the vehicle-mounted processing system, realizes the sharing of positioning information, clock information, computing resources, compensation information and the like among all subsystems, realizes the information exchange and the information depth fusion through the linkage of the vehicle-mounted processing system and other subsystems, comprehensively ensures the safety of a train, improves the overall maintenance level of the subway, and comprehensively judges the running state of the train by determining whether the parameter value of the geometric parameter information exceeds the limit or whether abnormal information exists in the image information under the normal running of the train, thereby determining the abnormal position, the abnormal reason and the abnormal condition, rechecking the found defects, accurately matching multiple dimensions and having higher detection speed, the precision is higher, and the detection operation is more convenient and efficient, cost greatly reduced.
The pantograph-catenary detection equipment detects pantograph-catenary kinetic parameters, catenary geometric parameters, pantograph-catenary relations, contact catenary states and the like by adopting a non-contact detection technology. The track detection equipment detects track dynamic geometric parameters, track states and the like by adopting a non-contact detection technology. The tunnel detection equipment detects tunnel limitation, tunnel state and the like by adopting a non-contact detection technology.
The data center receives various detection data and alarm information uploaded by the train device, so that multi-professional data fusion is realized, all-dimensional and multi-dimensional big data calculation and analysis are carried out on various off-line original data and real-time alarm defects reported by the vehicle-mounted detection monitoring device, comprehensive analysis is carried out on the quality state, the line defects and the like of the operation line, and powerful support and full-flow closed-loop tracking management are provided for maintenance and overhaul operation of the operation line.
The train is any one of an operation train and a detection train, the comprehensive detection system for the network rail and the tunnel is arranged on the train, and the data of the tunnel can be acquired more comprehensively by adopting a vehicle-mounted automatic detection and monitoring method.
The bow net detection system comprises: the system comprises a bow net parameter measuring module, a bow net video collecting module and an abnormality detecting module; the pantograph video acquisition module is used for acquiring real-time overhead contact system inspection images of the running train, transmitting the inspection images of the overhead contact system to a vehicle-mounted processing system for storage, the pantograph parameter measurement module is used for measuring the overhead contact system geometric parameter information of the running train in real time and transmitting the overhead contact system geometric parameter information to the vehicle-mounted processing system for storage, the abnormity detection module is used for detecting pantograph abnormity of the running train pantograph system and transmitting detection signals to the vehicle-mounted processing system, the vehicle-mounted processing module determines pantograph abnormity and positions according to the overhead contact system inspection images and the overhead contact system geometric parameter information before and after the detection signals, and determines pantograph abnormity conditions according to the detection signals and sends out corresponding alarm information.
The pantograph-catenary parameter measurement module measures height difference of guide height, pull-out value and positioning point contact line of a rigid contact net and a flexible contact net, and is required to adapt to the range of the maximum pull-out value +/-450 mm and the guide height 3900mm-6000mm of the rigid contact net and the flexible contact net, so that an area array camera is required to cover the measurement ranges of the height difference of the guide height, the pull-out value and the positioning point contact line in the rigid contact net and the flexible contact net and in a tunnel, and meanwhile, light source compensation is carried out through a laser so as to improve the detection precision.
The pantograph-catenary abnormal conditions comprise the conditions of catenary component falling, catenary loosening, electric spark phenomenon, hard spots, pressure, current overrun, pantograph abrasion and the like, linkage analysis is carried out between pantograph-catenary geometric parameters and pantograph-catenary inspection, the real defects of the pantograph-catenary can be found by comprehensive judgment, when one or more geometric parameters of a certain position in the pantograph-catenary geometric parameters are overrun, whether linkage parameters in a track detection system or a tunnel detection system matched with the geometric parameter information are overrun or not is confirmed, the linkage parameters are correlated with track image inspection and linkage parameter corresponding images of the same position, and the track inspection images and the linkage images are checked to determine a plurality of images before and after the position so as to complete comprehensive analysis.
The bow net video acquisition module is provided with a light supplement lamp and used for carrying out continuous video acquisition, shooting and video storage on flexible and rigid contact nets of subways, the bow net video acquisition module can send 20 frames per second of trigger signals, the vehicle-mounted processing system receives 20 frames of JPG image data per second shot by the continuous video camera and monitors the outline of the contact line in real time.
Bow net parameter measurement module, bow net video acquisition module, unusual detection module all set up at the train top, bow net video acquisition module, unusual detection module set up towards train advancing direction, bow net parameter measurement module sets up towards the train top, just bow net parameter measurement module sets up bow net video acquisition module, unusual detection module's top, on-vehicle processing system sets up inside the train carriage.
The system comprises a base network, a sensor module and a control module, wherein the sensor module comprises a current sensor, an acceleration sensor and a pressure sensor, the acceleration sensor is used for detecting hard spots on a contact network, the acceleration sensor is arranged on a pantograph, and the acceleration sensor transmits the operation information of the pantograph to a vehicle-mounted processing system to confirm the hard spot condition of the base network; the pressure sensor is used for acquiring contact information of a contact net and a pantograph in real time, the pressure sensor transmits the contact information to the vehicle-mounted processing system to confirm the contact state of the pantograph and the pantograph, the current sensor is used for acquiring current data of a train in real time, when arcing occurs, traction current appears to fall, and when the arcing is extinguished, the traction current appears to rise suddenly. Therefore, the current sensor comprehensively judges the current falling and rising processes as arcing, and the detection accuracy is improved.
The vibration compensation module is arranged at the bottom of the train and used for detecting the position deviation condition of the train and the top surface of the track in real time and transmitting the position deviation information to the vehicle-mounted processing system, and the vehicle-mounted processing system makes corresponding compensation for the geometric parameter information of the contact network according to the received position deviation information.
The measurement calculation of the static geometrical parameters is based on the rail plane, and when the vehicle body is not in rigid contact with the wheel set, the center line of the vehicle body is used as the reference in the situations, so that the deviation of the measurement calculation of the pull-out value is caused. According to the invention, the measurement and calculation of the inclination angle of the car body are realized through the position deviation of the 3D camera (vibration compensation module) and the top surface of the track, the real-time measurement of the inclination, the swing and the side rolling of the car body relative to the plane of the track is realized, and the measurement and calculation result of the geometric parameters is compensated.
Still include orientation module, orientation module sets up in the train bottom, orientation module is used for real-time detection train functioning speed and direction information to with functioning speed and direction information transmission to on-vehicle processing system, on-vehicle processing system patrols and examines image and functioning speed and direction information accurate positioning train position according to the contact net that receives.
The positioning module carries out non-contact measurement on the moving speed and the running direction of the vehicle by utilizing the Doppler principle, can adapt to different reflecting pavements, can overcome speed faults caused by a rotating speed sensor, can eliminate speed measurement errors caused by wheel idling, sliding and wheel diameter abrasion, improves the speed measurement and distance measurement precision of a train, and further realizes accurate positioning of the running position of the vehicle.
The contact net geometric parameter information comprises the height of the rigid and flexible contact nets, the height of the inner and outer contact nets of the tunnel, the pull-out value of the inner and outer contact nets of the tunnel, and the height difference of the contact lines of the positioning points.
The abnormal detection module comprises an arcing camera, an ultraviolet sensor and a temperature and humidity sensor, wherein the arcing camera is used for carrying out video acquisition on an interaction area of the pantograph-catenary and identifying the electric spark phenomenon on the pantograph and the contact line through the video acquired by the arcing camera; the ultraviolet sensor is used for detecting an ultraviolet signal generated by the bow net electric spark; the temperature and humidity sensor is used for acquiring temperature and humidity data when the train runs; and the vehicle-mounted processing system confirms the ultraviolet discharge intensity of the bow net electric spark according to the ultraviolet light signal and the temperature and humidity data.
Specifically, the arcing camera is used for carrying out video acquisition on an interaction area of the pantograph and the network, and arcing and sparks appearing on the pantograph and the contact line are identified through detection. The data output by the ultraviolet sensor are voltage signals, the ultraviolet signals and the temperature and humidity data are transmitted to the vehicle-mounted processing system, whether ultraviolet discharge exceeds the limit or not is judged through the vehicle-mounted processing system, and corresponding alarm signals are sent out.
The track detection system comprises: the system comprises a track parameter measuring module, an inertia module, a track inspection module and a track section acquisition module; the track inspection module is used for acquiring image information of a track and a track bed and transmitting the image information to the vehicle-mounted processing system; the inertia module is used for measuring attitude parameter information when the train runs; the track parameter measurement module is used for measuring track geometric parameter information of the running track in real time and transmitting the track geometric parameter information to the vehicle-mounted processing system for storage, the track section acquisition module is used for acquiring a section image of the running track, and the vehicle-mounted processing system determines the position of track abnormity and the reason of the track abnormity according to the received attitude parameter information, image information, section information and track geometric parameter information so as to obtain track maintenance information.
The track inspection module comprises a track camera and a track bed camera, the track camera is used for scanning the steel rail at equal intervals to obtain a steel rail surface image and a sleeper image and a fastener image which are connected with the steel rail, and the track bed camera is used for scanning the track bed at equal intervals to obtain a track bed image.
The rail surface images comprise rail inner side images, rail outer side images and rail top images, so that a rail camera can comprehensively obtain inspection images of the rail, whether the rail inspection part has defects or not is judged through the images, and judgment is carried out by adopting manual spot inspection or an image intelligent identification method of the center in the prior art.
The rail anomalies include rail clamp plate joint bolt loss, rail fastener loss (large glue under rail and plate, spring bar), iron tie plate bolt loss, turnout bolt loss, sleeper cracks, block falling, and rail corrugation (profile) wear.
The track geometric parameter information comprises track gauge, superelevation, height, track direction, triangular pits, vehicle body acceleration and other parameters of the track, and is used for completing track gauge irregularity detection, track gauge change rate, height irregularity detection, horizontal irregularity detection, track direction change rate, triangular pit detection, composite irregularity detection, curvature detection and curvature change rate.
The track section acquisition module comprises a laser sensor and a section camera, wherein the laser sensor is used for emitting laser and irradiating the laser on a track section; the section camera is used for shooting the section of the track under laser irradiation to obtain a real-time track profile, and the vehicle-mounted processing module compares the track profile with a standard track profile to obtain a track abrasion position.
By comparing the real-time track profile graph with the standard track profile, the abrasion of the top of the track, the abrasion of the side surface of the track, the abrasion of a set point and the like can be obtained, and the abrasion condition and the track state of the track are judged. And extracting the change value of the abrasion position to obtain the abrasion value. The specific implementation method of the abrasion calculation comprises the following steps: correcting the acquired outline angle of the rail-shaped data; extracting a vertical abrasion point and a side abrasion point of a standard steel rail; extracting vertical wearing points and side wearing points of worn steel rails; and matching the vertical abrasion points with the side abrasion points with the standard rails.
The measurement of the three postures of rolling, pitching and direction of the vehicle body and the vertical vibration acceleration, the horizontal vibration acceleration and the longitudinal vibration acceleration of the vehicle body is completed by an inertia module, the inertia module comprises three accelerometers and three inclinometers, the accelerometers and strain gages of the inclinometers are caused to change under external excitation, and the sensors convert the body changes into digital signals and output the digital signals to a vehicle-mounted processing system, so that the measurement of the posture and the acceleration of each direction of the vehicle body is realized.
The track parameter measuring module, the inertia module, the track inspection module and the track section acquisition module are all arranged at the bottom of the train, the track inspection module is arranged above the track, the track section acquisition module is arranged above the ballast bed, and the vehicle-mounted processing system and the power module are arranged in a train carriage.
The train positioning system is characterized by further comprising a radar speed measuring module, wherein the radar speed measuring module is arranged at the bottom of the train and used for detecting the running speed and the mileage data of the train in real time and transmitting the speed and the mileage data to the vehicle-mounted processing system, and the vehicle-mounted processing system is mapped into train positioning information according to the received speed and mileage data.
The radar speed measurement module can carry out non-contact measurement to vehicle velocity of motion and direction of travel, can adapt to different reflection road surfaces, can overcome the velocity fault that tachometric transducer arouses, can eliminate the error that tests the speed that arouses because of wheel idle running, slides and wheel footpath wearing and tearing, improves the train and tests the speed, range finding precision, and then realizes the accurate positioning of vehicle running position.
The tunnel detection system comprises a limit detection module and a tunnel inspection module; the limit detection module is used for acquiring tunnel section information of a tunnel where a train is located and vehicle dynamic envelope information in real time; the tunnel inspection module is used for acquiring 360-degree image information of the whole tunnel in real time and transmitting the acquired image information to the vehicle-mounted processing system; and the vehicle-mounted processing system is used for receiving the tunnel image information transmitted by the tunnel inspection module and the tunnel section information and the vehicle dynamic envelope information transmitted by the limit detection module, and determining whether the tunnel has potential safety hazards or not according to the tunnel image information and the tunnel section information and the vehicle dynamic envelope information at the positions corresponding to the tunnel image information.
And the vehicle-mounted processing system obtains limit information according to the comparison between the tunnel section information and the standard tunnel section and the comparison between the vehicle dynamic envelope information and the standard vehicle limit, if the limit information exceeds the limit, whether linkage parameters matched with the limit information exceed the limit or not is confirmed, and the limit early warning prompt is carried out on the limit exceeding by combining the positioning information, and then tunnel image information and linkage images of the time before and after the position corresponding to the limit information are confirmed to determine the tunnel defects.
And the vehicle-mounted processing system obtains the tunnel information according to the tunnel image information, if the tunnel information is abnormal, whether the linkage image matched with the image is abnormal is determined, and then the tunnel information, the limit information corresponding to the tunnel information and the linkage image and the parameter information corresponding to the linkage image are determined to determine the reason of the abnormality of the tunnel or the vehicle.
The vehicle-mounted processing system transmits the received information to a ground data center, the data center stores more parameter information and image information into a database, the terminal display connected with the database displays the profile information of the tunnel section in real time, and the image acquisition information is stored on a host server in real time for subsequent analysis and query.
The limit detection module comprises a limit 3D assembly and a limit radar assembly, the limit 3D assembly is arranged at the head of a train, the limit radar assembly is arranged at the tail of the train, the limit 3D assembly is used for obtaining tunnel section parameters and vehicle dynamic envelope parameters in real time, the limit radar assembly is used for positioning and triggering the limit 3D assembly at equal intervals to acquire tunnel section images, and one frame of data is acquired at a distance of 5 mm.
The boundary 3D assembly comprises 5 boundary cameras, an included angle between every two adjacent boundary cameras is 72 degrees, the boundary cameras are used for obtaining tunnel parameters and tunnel section images of a train in the driving process, and the vehicle-mounted processing system obtains coherent tunnel parameters and tunnel section information according to the measured tunnel parameters and the tunnel section images collected by the 5 boundary cameras.
The invention adopts 5 cameras to cover all the fields of view required by detection, and the cameras are arranged at positions with an included angle of 72 degrees, so that the fields of view of the cameras can cover the nearest position and the farthest position of the section in the tunnel. And measuring the tunnel section parameters by using a triangulation distance measuring principle. In order to measure parameters, the internal parameters and the external parameters of the five cameras need to be calibrated independently, the actual physical coordinates of the point clouds acquired by the five cameras are calculated independently, and finally the physical coordinates of the five cameras are spliced and fused into information with complete sections in the prior art. Wherein the two cameras facing the track assume a parameter compensation function.
The boundary limit 3D assembly further comprises a laser assembly, the position relation between the train and the tunnel is confirmed through the laser assembly and the boundary limit camera, the running stability is guaranteed, meanwhile, the abnormity between the train and the rail can be obtained according to the relation between the train and the tunnel, and tunnel detection errors caused by foreign matters on the rail and other reasons are avoided.
And acquiring point cloud data at the overlapped part of the visual fields of two adjacent cameras according to the distribution condition of the laser components of each limiting camera according to corresponding rules. Calculating to obtain physical coordinates by combining point cloud data acquired by a camera with respective camera calibration data, splicing to obtain complete tunnel section information, tunnel contours and real-time changes of lines of a vehicle dynamic envelope line, and judging the overrun condition of equipment and a vehicle; and displaying the tunnel profile and the vehicle dynamic envelope curve through a 3D effect diagram to show a 3D model of the tunnel, and judging the type of the tunnel in real time.
The third part is to display vehicle operation information in real time, including time and location (station, speed and kilometer).
The tunnel inspection module is arranged on a train head and comprises 3-6 inspection cameras, a light supplement device corresponding to the inspection cameras and an image analysis module, the 3-6 inspection cameras are uniformly distributed on the train head and used for acquiring inner wall images of different positions of the tunnel, and consecutive images of the train in the running process in the tunnel are shot by the inspection cameras; the image analysis module is used for enhancing and segmenting the acquired image and distinguishing tunnel defects and other interferents.
The vehicle-mounted processing system can realize the work of data acquisition, compression, storage, analysis, alarm and the like. In addition, the image analysis module can automatically detect and identify defects such as water leakage, block falling, wet stain and duct piece dislocation on the side wall or the top of the tunnel based on the existing intelligent image identification algorithm, and timely returns a detection result to the vehicle-mounted processing system, the vehicle-mounted processing system automatically uploads tunnel section information before and after the defect, vehicle dynamic envelope lines and tunnel inner wall images to a data center on the ground for subsequent maintenance and analysis, and the data center is responsible for presentation, display, statistics, hard disk data analysis and the like of various alarm defects of the tunnel.
The limit detection module further comprises a mileage positioning component, the mileage positioning component is used for detecting train running speed and mileage data in real time and transmitting the speed and mileage data to the vehicle-mounted processing system, and the vehicle-mounted processing system correspondingly confirms the platform and the tunnel according to the speed and mileage data and the tunnel section information so as to trigger the limit 3D component to acquire the tunnel section information.
The method comprises the steps of utilizing vehicle vibration information collected by inertial navigation equipment to transform a vehicle model contour to obtain a vibrated contour, judging by combining standard vehicle limit information, outputting an alarm when a limit point exists, and outputting current positioning information.
And calculating by combining the inspection image obtained by inspection with camera calibration data to obtain a physical coordinate, and obtaining the profile of the section of the tunnel through data fusion. And judging whether the tunnel contour and the tunnel limit standard template exceed the limit, alarming and outputting the exceeding limit, and outputting the current positioning information.
And determining the position of the train according to the running speed and the mileage data, and judging whether the train is in a tunnel area or a platform area at present. If the tunnel region exists, the limited 3D component works; if the platform area is the platform area, the limiting 3D assembly is closed, laser in the limiting 3D assembly is prevented from radiating human eyes, and personal safety is guaranteed.
The invention also comprises a comprehensive detection method of the net rail tunnel, which comprises the following steps:
acquiring parameters and images of a bow net detection system, a track detection system and a tunnel detection system in a net rail tunnel integrated system; the images comprise a contact network inspection image, a track image, a steel rail section image, a 360-degree image of the whole tunnel and a tunnel section image of the train; the parameters comprise bow net geometric parameters, train attitude parameters, track geometric parameters and vehicle dynamic envelope lines;
determining whether the parameter value of the parameter information under the normal running of the train exceeds the limit or whether abnormal information exists in the image information,
if the parameter information is determined to be out of limit in the matched parameter value range, determining whether the linkage parameter matched with the parameter information is out of limit, judging whether the rail-tunnel integrated system is in an abnormal condition or not through the image corresponding to the parameter information and the linkage parameter, and obtaining the occurrence position of the abnormal condition;
and if the abnormal information appears in the image, confirming whether the linkage image matched with the image is abnormal or not, and confirming the reason of the abnormal information appearing in the rail-tunnel integrated system through the parameter information corresponding to the image and the linkage image.
Performing linkage analysis between the pantograph parameters, the pantograph images, the track geometry, the track inspection and the tunnel section parameters, comprehensively judging to find the real defects of train detection, when the train outline exceeds the limit at a certain position in the tunnel section, determining whether the track geometry parameters matched with the train outline parameter information exceed the limit, the pantograph parameters fall off onto the track and whether the tunnel section falls off, correlating the track inspection, the pantograph images and the tunnel inspection at the same position, checking the tunnel inspection images to determine whether the position is a tunnel entrance or exit, considering that the train outline at the position does not exceed the limit if the position is the tunnel entrance or exit, and judging whether the inner wall of the tunnel falls off according to the tunnel inspection, the track inspection, the pantograph images, the pantograph parameters and the tunnel inspection and the comprehensive judgment to find the real defects of train detection The conditions such as water leakage and the like, whether the track is in charge or worn, whether a contact net falls off on the track, confirming that one or more reasons cause parameter abnormity according to parameter information and images, and performing targeted maintenance to complete comprehensive analysis
Further comprising:
the method comprises the steps of acquiring a tunnel section image, a vehicle dynamic envelope curve and a track section image in real time, establishing a database of a standard tunnel, a vehicle dynamic envelope curve and a standard track profile in a normal state, wherein the standard tunnel profile in the database is matched with corresponding track profile parameters, the standard track profile is matched with corresponding track profile parameters, comparing the tunnel section profile with the standard tunnel profile at the same position in the database, comparing the vehicle dynamic envelope curve with the standard vehicle limit at the same position in the database to obtain the limit relation between the tunnel profile and the vehicle dynamic envelope curve, and comparing the section image with the standard track profile at the same position in the database to obtain the track abrasion position and the track state.
And determining the abnormal situation of the pantograph according to the pantograph system image, determining the position of the abnormal situation of the pantograph and the reason of the abnormal situation according to the pantograph images before and after the abnormal situation and the geometric parameters, and sending corresponding alarm information.
The pantograph-catenary geometric parameters comprise the height of a rigid flexible contact net, the height of an inner contact net and an outer contact net of the tunnel, the pull-out value of the inner contact net and the outer contact net of the tunnel, and the height difference of contact lines of positioning points.
When the pantograph-catenary abnormal condition is an electric spark phenomenon, identifying the electric spark phenomenon on a pantograph and a contact wire through a pantograph-catenary system image, acquiring ultraviolet light information and real-time temperature and humidity data generated by pantograph-catenary electric spark, and calculating ultraviolet radiation energy generated by pantograph-catenary electric spark according to the ultraviolet light information and the temperature and humidity data to represent the electric spark intensity.
According to the invention, the size parameter of the pantograph system problem is preliminarily judged through the contact net geometric parameter information, the image information of the problem can be rapidly confirmed through the real-time video acquired by the pantograph video acquisition module, otherwise, the real-time video acquired by the pantograph video acquisition module can be used for carrying out precision correction on the contact net geometric parameter information acquired by the pantograph parameter measurement module, and the detection precision is ensured.
This embodiment still includes:
monitoring front and rear 3-6 frames of contact net inspection images when the pull-out value of the contact net is +/-300 mm- +/-450 mm, the height of the contact line is 3900mm-4000mm, the height difference of the contact line is 0-180mm and the pantograph net electric spark area is less than 25 pixels;
when the pulling value of the contact net is +/-350 mm, the height of the contact line is larger than 5500mm or smaller than 4000mm, the height difference of the contact line is 0-180mm, and the pantograph net electric spark area is smaller than 25 pixels, monitoring front and back 6-10 frames of contact net polling images;
if the catenary patrol image and the catenary geometric parameter information appear in the threshold edge wandering and do not exceed the threshold, comprehensively analyzing and determining geometric parameter abnormal information according to the patrol image information of the pantograph-catenary, preventing the occurrence of electric sparks and ensuring the driving safety.
And when the pulling value of the contact net is +/-350 mm, the height of the contact line is 4000mm-5500 mm, the height difference of the contact line is 0-180mm and the pantograph net electric spark area is more than 25 pixels, monitoring 11-15 frames of contact net polling images before and after.
And the contact network inspection image and the contact network geometric parameter information do not exceed the threshold value, the ultraviolet radiation energy generated by the pantograph-catenary electric spark is calculated according to the ultraviolet light information and the temperature and humidity data to represent the intensity of the electric spark, so that the related positions can be quickly checked, timely maintenance is carried out, and the driving safety is ensured.
The bow net parameter measuring module of the car roof is composed of an area-array camera and a laser, mainly completes the measurement of the geometric parameters of a rigid contact net and a flexible contact net, and the bow net video acquisition module completes the real-time monitoring of the outline of a contact line; the arcing camera and the ultraviolet sensor detect sparks generated by the pantograph and the contact line in an off-line manner in real time; the contact type strain pressure sensor calculates the irregularity of a contact line, the pressure of a contact net and the current of a pantograph; and meanwhile, a module can be reserved on the roof of the vehicle for a high-definition imaging module for upgrading subsequent equipment. The vehicle bottom vibration compensation module is arranged on a vehicle body, when the vehicle travels in a curve or bend section, the vehicle body inclines towards an inner rail surface due to the fact that the inner rail and the outer rail are ultrahigh, so that the deviation of measurement and calculation of a pull-out value is inevitably caused, and the 3D camera takes a rail plane as a reference datum for compensation. The speed measuring module (positioning module) is arranged at the bottom of the vehicle (right above or laterally above the fixing bolt on the inner side of the steel rail) and is used for acquiring speed information in a relatively open position.
Acquiring images of a track and a track bed and a section image of an operating track in real time, detecting abnormal fault points in a track system, if the abnormal fault points exist, confirming track abnormal parameters according to track geometric parameters and attitude parameters of corresponding positions, and judging the reason of track abnormity according to the images and the section image of the track and the track bed so as to obtain track maintenance information; otherwise, the track geometric parameters and the attitude parameters are corrected in real time according to the images and the section images of the track and the track bed so as to obtain track maintenance information.
Confirming track abnormal parameters according to the track geometric parameters and the attitude parameters of the corresponding positions, and judging the reasons of track abnormality according to the track, the images of the track bed and the section images; and correcting the geometric parameters and the attitude parameters of the track in real time according to the images and the section images of the track and the track bed so as to obtain track maintenance information, and comprehensively analyzing the routing inspection information and the geometric parameters of the track to obtain the abnormal position and the abnormal reason of the track, so that the detection precision is further improved.
Confirming track abnormal parameters according to the track geometric parameters and the attitude parameters of the corresponding positions, and judging the reasons of track abnormality according to the track, the images of the track bed and the section images;
the rail gauge obtained in real time exceeds a preset threshold value, at the moment, the train can shake or the train wheels and the steel rail are not in complete contact and idle running, the condition of the steel rail at the moment can be judged through the images and the section images of the track and the track bed, namely, the condition is caused by the reason that the sleeper of the track bed is broken, the steel rail fastener is loosened, the steel rail has a large broken edge and the like, the abnormal reason can be judged quickly, the track maintenance information is obtained, and the follow-up maintenance is carried out in pertinence.
Otherwise, correcting the geometric parameters and the attitude parameters of the track in real time according to the images and the section images of the track and the track bed so as to obtain track maintenance information;
specifically, according to the images of the track and the track bed and the section images obtained in real time, when abnormal conditions, namely, a sleeper of the track bed is broken, a steel rail fastener is loosened, a steel rail has large broken edges and the like, occur in the images, the track gauge, the height, the track direction, the triangular pit and the vehicle body acceleration of the track exceed a set threshold value, if the abnormal conditions can not cause driving safety, the geometric parameters and the attitude parameters are corrected in real time, and false alarm is avoided; if the device can be used temporarily, but is not beneficial to driving safety, a maintenance alarm should be sent out to maintain the corresponding position in time.
If the track geometric parameter information appears in the threshold edge loitering and does not exceed the threshold, shortening the sampling interval of the cross-section image, the track and the image of the track bed, calling 8-11 frames of images before and after the parameter abnormal point, determining the geometric parameter abnormal information through multi-frame image comprehensive analysis, preventing the track abnormality and ensuring the driving safety.
When the track gauge is 1450mm, the height range of the left track and the right track is +/-100 mm, the range of the transverse acceleration of the train is +/-8 m/s, and the composite irregularity range is +/-120 mm, the sampling interval of the sectional images is 0.25m, and the image sampling interval of the tracks and the track bed is 1.6 mm.
When the track parameters exceed the threshold values, the track information and the positions are calculated by the geometric parameters, the related positions can be rapidly checked according to the inspection images and the section images, timely maintenance is carried out, and the driving safety is guaranteed.
The preferable scheme is that the track outline acquisition interval is 0.25m and the image acquisition adopts a linear array camera sampling interval (advancing direction) of 1 mm; calculating the geometric parameters of the acquired orbit contour based on the wavelength through an inertial reference method and 2D point cloud data; the linear array camera collects 1000 data groups to assemble a JPG image to form a track inspection image, and the track inspection image comprises left and right track inspection and track bed inspection;
the method comprises the steps of acquiring tunnel section information, a vehicle dynamic envelope curve and a 360-degree image of the whole tunnel where a train is located in real time, detecting an abnormal position in a tunnel system, and determining whether potential safety hazards exist in the tunnel according to the whole tunnel image, the tunnel section information of the position corresponding to the whole tunnel image and the vehicle dynamic envelope curve.
The abnormal position information comprises the information of the overrun of the tunnel equipment and the overrun of the vehicle and the position of the information, is used for analyzing the total to obtain the abnormal reason, combines the actual parameters with the image, reduces the image calculation amount, has small calculation amount for detecting the tunnel contour and the vehicle contour parameters, has no special requirement on the system performance, and has low cost.
Further comprising:
comparing the tunnel section information with a standard tunnel section, and comparing the vehicle dynamic envelope line with a standard vehicle limit to obtain limit information, and when the limit information exceeds a threshold value, confirming tunnel defects by tunnel image information of time before and after a position corresponding to the limit information;
and when the tunnel information is abnormal, confirming the abnormal reason of the tunnel or the train by the tunnel information and the corresponding limit information.
The method comprises the steps of acquiring tunnel section images in real time, establishing a database of a standard tunnel and a vehicle dynamic envelope line under a normal state by the vehicle dynamic envelope line, comparing the tunnel section outline with the standard tunnel outline at the same position in the database according to the fact that the standard tunnel outline in the database is matched with corresponding track outline parameters in real time, and comparing the vehicle dynamic envelope line with the standard vehicle limit at the same position in the database so as to obtain the limit relation between the tunnel outline and the vehicle dynamic envelope line.
When the dynamic envelope line of the vehicle does not have an over-limit point, the vehicle speed is 0-80km/h, and the abnormal area of the inspection image is less than 15mm x 15mm, monitoring the information of front and rear 3-6 frames of tunnel images; and if no abnormity occurs in the routing inspection, the tunnel images before and after monitoring are not needed.
When the dynamic envelope line of the vehicle does not have an exceeding limit point, if the abnormal area of the patrol image appears in the threshold edge loitering and does not exceed the threshold, comprehensively analyzing 3-6 frames of tunnel image information before and after monitoring to determine the geometric parameter abnormal information.
When more than 1 over-limit point appears on the vehicle dynamic envelope curve, the vehicle speed is 0-100km/h, and the abnormal area of the inspection image is more than or equal to 15mm, monitoring the information of 11-15 frames of tunnel images before and after.
When the dynamic envelope of the vehicle has more than 1 overrun point and the abnormal area of the inspection image is more than or equal to 15mm, the relevant positions can be quickly inspected by monitoring 11-15 frames of tunnel image information before and after the monitoring, the maintenance is carried out in time, and the driving safety is ensured.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (10)
1. A net rail tunnel comprehensive detection system is characterized by comprising a bow net detection system, a track detection system, a tunnel detection system, a vehicle-mounted processing system and a power supply system for providing power supply for train net rail tunnel comprehensive detection; the pantograph-catenary detection system is used for acquiring pantograph-catenary geometric parameter information and a catenary patrol image of the train in real time; the track detection system is used for acquiring train attitude parameter information, track image information, steel rail section information and track geometric parameter information in real time; the tunnel detection system is used for acquiring 360-degree image information, tunnel section information and vehicle dynamic envelope line information of the whole tunnel in real time;
the vehicle-mounted processing system is used for comprehensively detecting and monitoring the pantograph-catenary running state, the track running state and the tunnel state, exchanging and comprehensively analyzing the parameter information and the image information among the pantograph-catenary running state, the track running state and the tunnel running state, and determining whether the parameter value of the parameter information exceeds the limit or whether abnormal information exists in the image information under the normal running of the train to comprehensively judge the running state of the train.
2. The net rail tunnel integrated detection system of claim 1, wherein the bow net detection system comprises: the system comprises a bow net parameter measuring module, a bow net video collecting module and an abnormality detecting module; the pantograph video acquisition module is used for acquiring real-time contact network polling images of the running train and transmitting the polling images of the contact network to the vehicle-mounted processing system for storage, the pantograph parameter measurement module is used for measuring the geometric parameter information of the contact network of the running train in real time and transmitting the geometric parameter information of the contact network to the vehicle-mounted processing system for storage, and the abnormity detection module is used for detecting pantograph abnormity of the pantograph system of the running train and transmitting detection signals to the vehicle-mounted processing system.
3. The net-rail tunnel integrated detection system of claim 1, wherein the rail detection system comprises: the system comprises a track parameter measuring module, an inertia module, a track inspection module and a track section acquisition module; the track inspection module is used for acquiring image information of a track and a track bed and transmitting the image information to the vehicle-mounted processing system; the inertia module is used for measuring attitude parameter information when the train runs; the track parameter measuring module is used for measuring the track geometric parameter information of the running track in real time and transmitting the track geometric parameter information to the vehicle-mounted processing system for storage; the track section acquisition module is used for acquiring a section image of the running track.
4. The comprehensive detection system of network rails and tunnels of claim 1, wherein the tunnel detection system comprises a boundary detection module and a tunnel inspection module; the limit detection module is used for acquiring tunnel section information of a tunnel where a train is located and vehicle dynamic envelope information in real time; and the tunnel inspection module is used for acquiring 360-degree image information of the whole tunnel in real time and transmitting the acquired image information to the vehicle-mounted processing system.
5. The comprehensive detection system for network rails and tunnels as claimed in claim 2, wherein the pantograph detection system further comprises a sensor module, the sensor module comprises a current sensor, an acceleration sensor and a pressure sensor, the acceleration sensor is used for detecting a hard spot on a catenary, the acceleration sensor is arranged on the pantograph, and the acceleration sensor transmits the operation information of the pantograph to the vehicle-mounted processing system to confirm the hard spot condition of the basic network; the pressure sensor is used for acquiring contact information of a contact net and a pantograph in real time, the pressure sensor transmits the contact information to the vehicle-mounted processing system to confirm the contact state of the pantograph and the pantograph, and the current sensor is used for acquiring current data of a train in real time.
6. The comprehensive detection system for network rails and tunnels as claimed in claim 3, wherein the rail section acquisition module comprises a laser sensor and a section camera, the laser sensor is used for emitting laser and irradiating the laser on the rail section; the section camera is used for shooting the section of the track under laser irradiation to obtain a real-time track profile, and the vehicle-mounted processing module compares the track profile with a standard track profile to obtain a track abrasion position.
7. The comprehensive detection system for network rails and tunnels as claimed in claim 4, wherein the boundary detection module comprises a boundary 3D component and a boundary radar component, the boundary 3D component is arranged at the head of the train, the boundary radar component is arranged at the tail of the train, the boundary 3D component is used for obtaining tunnel section parameters and vehicle dynamic envelope parameters in real time, and the boundary radar component is used for positioning and equidistantly triggering the boundary 3D component to acquire tunnel section images.
8. The network rail tunnel comprehensive detection system of claim 4, wherein the tunnel inspection module is arranged on a train head, the tunnel inspection module comprises 3-6 inspection cameras, a light supplement device corresponding to the inspection cameras and an image analysis module, the 3-6 inspection cameras are uniformly distributed on the train head, the inspection cameras are used for acquiring inner wall images of different positions of the tunnel, and consecutive images of the train in the running process in the tunnel are shot through the inspection cameras; the image analysis module is used for enhancing and segmenting the acquired image and distinguishing tunnel defects and other interferents.
9. A comprehensive detection method for a network rail tunnel is characterized by comprising the following steps:
acquiring parameter information and image information of a bow net detection system, a track detection system and a tunnel detection system in a net rail tunnel integrated system;
determining whether the parameter value of the parameter information under the normal running of the train exceeds the limit or whether abnormal information exists in the image information,
if the parameter information is determined to be out of limit in the matched parameter value range, determining whether the linkage parameter matched with the parameter information is out of limit, judging whether the rail-tunnel integrated system is in an abnormal condition or not through the image corresponding to the parameter information and the linkage parameter, and obtaining the occurrence position of the abnormal condition;
and if the abnormal information appears in the image, confirming whether the linkage image matched with the image is abnormal or not, and confirming the reason of the abnormal information appearing in the rail-tunnel integrated system through the parameter information corresponding to the image and the linkage image.
10. The comprehensive detection method of network rail and tunnel of claim 9, further comprising:
the method comprises the steps of acquiring a tunnel section image, a vehicle dynamic envelope curve and a track section image in real time, establishing a database of a standard tunnel, a vehicle dynamic envelope curve and a standard track profile in a normal state, wherein the standard tunnel profile in the database is matched with corresponding track profile parameters, the standard track profile is matched with corresponding track profile parameters, comparing the tunnel section profile with the standard tunnel profile at the same position in the database, comparing the vehicle dynamic envelope curve with the standard vehicle limit at the same position in the database to obtain the limit relation between the tunnel profile and the vehicle dynamic envelope curve, and comparing the section image with the standard track profile at the same position in the database to obtain the track abrasion position and the track state.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114670898A (en) * | 2022-04-19 | 2022-06-28 | 北京运达华开科技有限公司 | Multifunctional track detection system |
| CN114701543A (en) * | 2022-04-27 | 2022-07-05 | 中铁第四勘察设计院集团有限公司 | High-precision equipment limit detection early warning system and method based on big data |
| CN114771603A (en) * | 2022-04-25 | 2022-07-22 | 北京铁科英迈技术有限公司 | Centralized monitoring system and method for vehicle-mounted detection system of comprehensive inspection vehicle |
| CN115158412A (en) * | 2022-06-14 | 2022-10-11 | 深圳市远望谷信息技术股份有限公司 | Railway wagon running quality online detection method and device, electronic equipment and medium |
| CN115610479A (en) * | 2022-09-23 | 2023-01-17 | 北京京天威科技发展有限公司 | Railway line state inspection system and method |
| CN117309875A (en) * | 2023-09-20 | 2023-12-29 | 北京运达华开科技有限公司 | Non-contact type bow net contact hard point detection device and method |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2249122A1 (en) * | 2009-04-27 | 2010-11-10 | DB Netz AG | Method for detecting components in overhead wire assemblies of rail vehicles, in particular railroad vehicles |
| CN102710756A (en) * | 2012-05-18 | 2012-10-03 | 中国铁道科学研究院基础设施检测研究所 | Detection data real-time synchronization method and processing method of overhead line system |
| CN103552579A (en) * | 2013-11-18 | 2014-02-05 | 南车洛阳机车有限公司 | Comprehensive detection train for freight heavy haul railway |
| EP2838752A2 (en) * | 2012-04-19 | 2015-02-25 | Siemens AG Österreich | Method and device for monitoring pantograph failure |
| CN106379343A (en) * | 2016-10-21 | 2017-02-08 | 宝鸡中车时代工程机械有限公司 | Metro network rail comprehensive detection vehicle |
| US20180156737A1 (en) * | 2015-05-12 | 2018-06-07 | Camlin Italy S.R.L. | System and method for the video inspection of a pantograph along an overhead contact line |
| CN110588709A (en) * | 2019-09-10 | 2019-12-20 | 中国铁道科学研究院集团有限公司 | Railway infrastructure detection system |
| CN110606106A (en) * | 2019-09-26 | 2019-12-24 | 北京唐智科技发展有限公司 | Comprehensive monitoring system and method for safe operation of train and fault diagnosis instrument |
| CN110806193A (en) * | 2019-11-27 | 2020-02-18 | 上海应用技术大学 | Subway tunnel deformation detection system |
| CN111201176A (en) * | 2017-09-19 | 2020-05-26 | 西门子交通有限责任公司 | Bogie track monitoring |
| CN111398306A (en) * | 2019-12-28 | 2020-07-10 | 昆山高新轨道交通智能装备有限公司 | Urban rail transit operation comprehensive detection system |
| CN111579560A (en) * | 2020-05-28 | 2020-08-25 | 成都国铁电气设备有限公司 | Defect positioning device and method used in tunnel |
| CN111666947A (en) * | 2020-05-26 | 2020-09-15 | 成都唐源电气股份有限公司 | Pantograph head offset measurement method and system based on 3D imaging |
| CN211689698U (en) * | 2019-10-30 | 2020-10-16 | 昆山高新轨道交通智能装备有限公司 | Dual-purpose net rail detection car of highway-railway |
| CN112406947A (en) * | 2020-11-26 | 2021-02-26 | 中国铁道科学研究院集团有限公司 | Network rail fusion detection system, detection method and detection device |
-
2021
- 2021-06-09 CN CN202110640300.6A patent/CN113104063A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2249122A1 (en) * | 2009-04-27 | 2010-11-10 | DB Netz AG | Method for detecting components in overhead wire assemblies of rail vehicles, in particular railroad vehicles |
| EP2838752A2 (en) * | 2012-04-19 | 2015-02-25 | Siemens AG Österreich | Method and device for monitoring pantograph failure |
| CN102710756A (en) * | 2012-05-18 | 2012-10-03 | 中国铁道科学研究院基础设施检测研究所 | Detection data real-time synchronization method and processing method of overhead line system |
| CN103552579A (en) * | 2013-11-18 | 2014-02-05 | 南车洛阳机车有限公司 | Comprehensive detection train for freight heavy haul railway |
| US20180156737A1 (en) * | 2015-05-12 | 2018-06-07 | Camlin Italy S.R.L. | System and method for the video inspection of a pantograph along an overhead contact line |
| CN106379343A (en) * | 2016-10-21 | 2017-02-08 | 宝鸡中车时代工程机械有限公司 | Metro network rail comprehensive detection vehicle |
| CN111201176A (en) * | 2017-09-19 | 2020-05-26 | 西门子交通有限责任公司 | Bogie track monitoring |
| CN110588709A (en) * | 2019-09-10 | 2019-12-20 | 中国铁道科学研究院集团有限公司 | Railway infrastructure detection system |
| CN110606106A (en) * | 2019-09-26 | 2019-12-24 | 北京唐智科技发展有限公司 | Comprehensive monitoring system and method for safe operation of train and fault diagnosis instrument |
| CN211689698U (en) * | 2019-10-30 | 2020-10-16 | 昆山高新轨道交通智能装备有限公司 | Dual-purpose net rail detection car of highway-railway |
| CN110806193A (en) * | 2019-11-27 | 2020-02-18 | 上海应用技术大学 | Subway tunnel deformation detection system |
| CN111398306A (en) * | 2019-12-28 | 2020-07-10 | 昆山高新轨道交通智能装备有限公司 | Urban rail transit operation comprehensive detection system |
| CN111666947A (en) * | 2020-05-26 | 2020-09-15 | 成都唐源电气股份有限公司 | Pantograph head offset measurement method and system based on 3D imaging |
| CN111579560A (en) * | 2020-05-28 | 2020-08-25 | 成都国铁电气设备有限公司 | Defect positioning device and method used in tunnel |
| CN112406947A (en) * | 2020-11-26 | 2021-02-26 | 中国铁道科学研究院集团有限公司 | Network rail fusion detection system, detection method and detection device |
Non-Patent Citations (4)
| Title |
|---|
| 姚东: "高速铁路基础设施综合检测监测技术探讨", 《铁道标准设计》 * |
| 王卫东: "《高速铁路基础设施动态检测技术》", 31 May 2017 * |
| 王卫东: "高速铁路基础设施综合检测技术", 《铁路技术创新》 * |
| 许晓东: "城市轨道交通网轨融合智能运维方案研究", 《现代城市轨道交通》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114670898A (en) * | 2022-04-19 | 2022-06-28 | 北京运达华开科技有限公司 | Multifunctional track detection system |
| CN114771603A (en) * | 2022-04-25 | 2022-07-22 | 北京铁科英迈技术有限公司 | Centralized monitoring system and method for vehicle-mounted detection system of comprehensive inspection vehicle |
| CN114771603B (en) * | 2022-04-25 | 2024-01-05 | 北京铁科英迈技术有限公司 | Centralized monitoring system and method for vehicle-mounted detection system of comprehensive inspection vehicle |
| CN114701543A (en) * | 2022-04-27 | 2022-07-05 | 中铁第四勘察设计院集团有限公司 | High-precision equipment limit detection early warning system and method based on big data |
| CN114701543B (en) * | 2022-04-27 | 2023-10-17 | 中铁第四勘察设计院集团有限公司 | High-precision equipment limit detection early warning system and method based on big data |
| CN115158412A (en) * | 2022-06-14 | 2022-10-11 | 深圳市远望谷信息技术股份有限公司 | Railway wagon running quality online detection method and device, electronic equipment and medium |
| CN115610479A (en) * | 2022-09-23 | 2023-01-17 | 北京京天威科技发展有限公司 | Railway line state inspection system and method |
| CN115610479B (en) * | 2022-09-23 | 2023-09-15 | 北京京天威科技发展有限公司 | Railway line state inspection system and method |
| CN117309875A (en) * | 2023-09-20 | 2023-12-29 | 北京运达华开科技有限公司 | Non-contact type bow net contact hard point detection device and method |
| CN117309875B (en) * | 2023-09-20 | 2024-04-09 | 北京运达华开科技有限公司 | Non-contact type bow net contact hard point detection device and method |
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