Vehicle fault monitoring system based on multi-dimensional data analysis

Abstract

The invention relates to the technical field of data processing, in particular to a vehicle fault monitoring system based on multi-dimensional data analysis, and the system comprises a tunnel monitoring unit which is used for collecting tunnel parameters; the single vehicle analysis unit is used for generating a fault experiment data packet; the test unit is used for simulating a fault scene, collecting operation fault information, generating an operation feedback data packet and generating a system feedback data packet; the checking unit is used for judging whether the data transmission is correct or not, determining maintenance information and calculating the average maintenance time of each same fault; the system evaluation unit is used for generating different fault interval time and the same fault interval time, adjusting the acquisition density of track parameters corresponding to the same fault and generating a fault reference list; according to the vehicle fault monitoring system based on the multi-dimensional data analysis, all the units are matched with one another, the fault finding efficiency is improved, and meanwhile the accuracy of the vehicle fault monitoring system based on the multi-dimensional data analysis is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a vehicle fault monitoring system based on multidimensional data analysis. Background Art

[0002] Big data technology has made significant progress in data collection, storage, processing and analysis. The large number of sensors and monitoring equipment deployed in the subway system can collect massive amounts of data in real time. At the same time, advanced data analysis algorithms and tools provide strong technical support for multi-dimensional data analysis. Through in-depth mining and analysis of these multi-dimensional data, the potential relationships and patterns between the data can be discovered, thereby more accurately diagnosing and predicting subway failures. With the acceleration of urbanization, the scale of urban rail transit construction continues to expand, and the number of subway lines continues to increase. This makes subway operation and management increasingly difficult, and the requirements for fault monitoring and management are also getting higher and higher. The fault monitoring system based on multi-dimensional data analysis can realize centralized monitoring and management of large-scale subways, improve the efficiency and accuracy of fault monitoring, and adapt to the needs of the rapid development of urban rail transit.

[0003] Chinese patent application publication number: CN117022146A discloses a dual-domain electronic and electrical architecture, working method and passenger vehicle for passenger vehicles. The invention provides a dual-domain electronic and electrical architecture, working method and passenger vehicle for passenger vehicles. The architecture includes: a vehicle intelligent cockpit control domain, a vehicle driving control domain, a central controller and a cloud platform; the vehicle intelligent cockpit control domain is used to receive information uploaded by multiple vehicle information collection devices, and the multiple collection devices have a gateway protocol conversion function; the vehicle driving control domain receives parameters of the vehicle driving execution structure, and the central controller fuses the multi-dimensional data of the vehicle intelligent cockpit control domain and the multi-dimensional data of vehicle driving and calculates them, and obtains the vehicle driving control implementation process and vehicle driving control or operation instructions based on the calculation results. The invention achieves close coordination of each domain controller through the domain controller architecture of the dual-domain structure, and can also achieve deep integration of business and full-scene linkage with the platform.

[0004] Chinese patent application publication number CN119691599A discloses an intelligent operation and maintenance method, device, and electronic equipment for subway faults. This invention provides an intelligent operation and maintenance method, device, and electronic equipment for subway faults, relating to the field of data processing. In this method, multi-source data for the target subway is acquired, including sensor data, maintenance records, and equipment logs. A fault knowledge graph is constructed based on the multi-source data. A deep learning model is used to fuse the multi-source data and the fault knowledge graph to obtain feature data and a fusion model. A fault tree is generated based on the feature data and the fusion model. Real-time monitoring data for the target subway is acquired. Fault prediction is performed on the real-time monitoring data using the fault tree to generate a multi-dimensional fault report. The technical solution provided by this application facilitates intelligent operation and maintenance of subway faults.

[0005] However, the above method has the following problems: it fails to verify the predicted faults through test trains, and fails to predict the faults that may occur in subway operation by monitoring the subway operating environment parameters when no faults occur in the subway. Summary of the Invention

[0006] To this end, the present invention provides a vehicle fault monitoring system based on multidimensional data analysis to overcome the problems in the prior art of failing to verify predicted faults through experimental trains and failing to predict possible faults in subway operation by monitoring subway operating environment parameters when no faults occur in the subway.

[0007] To achieve the above objectives, the present invention provides a vehicle fault monitoring system based on multidimensional data analysis, comprising:

[0008] a tunnel monitoring unit, which is used to collect tunnel parameters of each train during the train travel in the tunnel;

[0009] a single vehicle analysis unit connected to the tunnel monitoring unit, configured to predict predicted fault information of each single vehicle system based on the tunnel parameters to generate a fault experiment data packet and send it to the experimental train, wherein the fault experiment data packet includes a fault data packet and an experiment data packet;

[0010] A test unit connected to the single-vehicle analysis unit, configured to parse the fault data packet to simulate a fault scenario, collect operational fault information to generate an operational feedback data packet, and parse and run the experimental data packet on the system of the experimental train to generate a system feedback data packet;

[0011] a verification unit, connected to the single-vehicle analysis unit and the test unit, respectively, for determining whether data transmission is correct based on the system feedback data packet, and determining maintenance information and calculating the average maintenance time for each identical fault based on the operation feedback data packet and the fault data packet;

[0012] A system evaluation unit is connected to the tunnel monitoring unit, the single-vehicle analysis unit and the verification unit respectively, and is used to generate different fault intervals and the same fault interval according to the fault experiment data packet, and to adjust the collection density of track parameters corresponding to the same fault based on the same fault interval and the same fault average maintenance time, and to generate a fault reference list based on the different fault intervals and the maintenance information.

[0013] Furthermore, the bicycle analysis unit includes:

[0014] a fault analysis subunit connected to the tunnel monitoring unit, for drawing a tunnel parameter curve graph of each of the single-vehicle systems based on the tunnel parameters, and selecting tunnel parameters corresponding to outliers in the tunnel parameter curve graph to generate the predicted fault information;

[0015] The single vehicle system includes a door system, an air conditioning system, a traction system, a pantograph system, and a braking system.

[0016] Furthermore, the bicycle analysis unit further includes:

[0017] A fault prediction subunit is connected to the tunnel monitoring unit and is used to generate the fault experiment data packet in combination with the predicted fault information, fault time, fault location and simulation parameters, record the creation time of the fault experiment data packet and send the fault experiment data packet to the experimental train.

[0018] Furthermore, the testing unit includes:

[0019] An operation feedback subunit is connected to the single-vehicle analysis unit and is used to parse the fault data packet to simulate a fault scenario corresponding to the predicted fault information on the experimental train, and collect the operation fault information of the experimental train in the fault scenario to generate an operation feedback data packet.

[0020] Furthermore, the testing unit further includes:

[0021] The system feedback subunit is connected to the single-vehicle analysis unit and the operation feedback subunit respectively, and is used to parse the experimental data packet on the system of the experimental train and run in the fault scenario to generate the system feedback data packet.

[0022] Furthermore, the verification unit includes:

[0023] A transmission subunit, connected to the test unit, is used to determine whether the data transmission is correct based on the packet loss rate and integrity of the system feedback data packet, wherein:

[0024] If the packet loss rate is less than or equal to the preset packet loss rate and the integrity is greater than or equal to the preset integrity, it is determined that the data transmission is correct.

[0025] The preset packet loss rate is positively correlated with the amount of data in the fault experiment data packet; the preset integrity is negatively correlated with the amount of data in the fault experiment data packet.

[0026] Furthermore, the verification unit further includes:

[0027] The maintenance subunit is respectively connected to the single vehicle analysis unit, the test unit and the transmission subunit, and is used to determine the maintenance information by combining the operation feedback data packet and the fault data packet when determining that the data transmission is correct, and to perform maintenance on the train according to the maintenance information and calculate the average maintenance time of each identical fault.

[0028] Furthermore, the system evaluation unit includes:

[0029] An adjustment subunit is respectively connected to the tunnel monitoring unit, the single-vehicle analysis unit and the verification unit, and is used to generate different fault occurrence intervals and the same fault intervals according to the creation time of the fault experiment data packet, and to adjust the collection density of track parameters corresponding to the same fault based on the same fault intervals and the average repair time of the same fault. The collection density is the number of devices that collect the track parameters between adjacent stations.

[0030] Furthermore, the system evaluation unit further includes:

[0031] An alarm subunit is connected to the adjustment subunit and is used to determine frequent faults by analyzing the different fault intervals, compare the frequent faults with the maintenance information, and generate a fault reference list according to the comparison result.

[0032] Furthermore, the tunnel monitoring unit includes:

[0033] Vibration subunits are symmetrically arranged on both sides of the track to collect the track vibration frequency during the train's travel;

[0034] Noise subunits, symmetrically arranged on the train tunnel walls on both sides of the track, for collecting tunnel noise during train travel;

[0035] Wind pressure subunits, which are symmetrically arranged on the walls of the train tunnel on both sides of the track, are used to collect tunnel wind pressure during the train's travel;

[0036] a temperature subunit, which is arranged on the wall of the train tunnel and is used to collect the tunnel temperature during the train travel;

[0037] a humidity subunit, which is arranged on the wall of the train tunnel and is used to collect the tunnel humidity during the train travel;

[0038] The tunnel parameters include the track vibration frequency, the tunnel noise, the tunnel wind pressure, the tunnel temperature and the tunnel humidity.

[0039] Compared with the existing technology, the beneficial effect of the present invention is that the present invention collects parameters in the subway tunnel, analyzes and predicts problems that may occur in the operation of subway trains, collects parameters such as temperature, humidity, wind pressure in the tunnel, and combines them with train operation status data to discover hidden dangers of equipment failure in advance. When the tunnel temperature rises abnormally, it indicates that there is an overheating problem in the power supply equipment or the train traction system. Timely warning and processing can avoid safety accidents caused by equipment failure. Monitoring parameters such as track vibration frequency can timely discover problems such as track wear and deformation. Through long-term monitoring and analysis of parameters, the replacement cycle of equipment can be predicted, and the reserve of spare parts can be reasonably planned to avoid shortages of spare parts. The cost waste caused by insufficient or backlog of equipment can be eliminated by analyzing the collected parameter data, which can reveal the shortcomings of existing technologies and equipment and provide direction for technological improvement and innovation. The environmental parameters in the tunnel can be collected to monitor the impact of subway operation on the surrounding environment. Monitoring the parameters of the subway tunnel can effectively monitor the status of the tunnel itself and promptly discover problems in the tunnel. The spatial environment of the subway tunnel is relatively small. When small faults that are difficult to find occur during the operation of the subway train, they can be reflected in the environmental parameters of the subway tunnel. Monitoring the environmental parameters of the subway tunnel can effectively improve the efficiency of discovering problems while effectively improving the accuracy of the vehicle fault monitoring system based on multi-dimensional data analysis.

[0040] Furthermore, the present invention sends predicted fault data to an experimental train, simulates the parameter operating environment of the running train, and reviews the predicted faults on the experimental train. This allows early detection of train operation faults. Based on theoretical analysis, it is predicted that the train traction system may have an overheating fault. However, during the simulated operation of the experimental train, combined with actual load, ambient temperature and other parameters for testing, it is possible to determine whether the prediction is accurate, avoid misjudgment or missed judgment, and improve the reliability of fault diagnosis. The simulated operating environment of the experimental train provides a platform for in-depth research on fault mechanisms. Under controllable conditions, the process of fault occurrence and changes in related parameters are observed, the root cause of the fault is analyzed, and faults in train operation are discovered in advance. Corresponding preventive measures can be taken on the actual train. Based on the review results of the predicted faults by the experimental train, a more accurate maintenance plan can be formulated. Clarify the time, location and extent of the fault, arrange maintenance resources reasonably, avoid unnecessary maintenance work and waste of resources, and avoid further damage to the equipment by discovering and solving the fault problem in advance, thereby extending the service life of the equipment. Solving the fault problem in advance can effectively reduce the occurrence of train delays and suspensions, ensure the punctual operation of trains, ensure that the trains are in good operating condition, reduce problems caused by faults, and further improve the accuracy of the vehicle fault monitoring system based on multi-dimensional data analysis.

[0041] Furthermore, the present invention combines the fault data packet and the experimental data packet and sends them to the experimental train. In the simulated fault scenario, the integrity of the feedback data packet of the experimental data packet is detected to determine whether the train's communication is normal in different fault scenarios. In the simulated fault scenario, this method can comprehensively detect the response capability of the communication system under different fault conditions, and accurately determine whether the communication system can remain stable in the event of a fault by analyzing the integrity of the feedback data packet. Based on the results of a large number of simulation experiments, the weak links of the communication system in different fault scenarios can be discovered, so that the communication system can be optimized and improved in a targeted manner. If it is found that data packet loss often occurs in a certain fault scenario, resulting in communication interruption, corresponding correction measures can be added to the system design. Fault mechanisms or redundant measures can improve the overall performance and safety of the communication system. Ensuring the normal operation of the train communication system under various fault scenarios is the key to ensuring the safe operation of the train. By simulating faults and detecting communication conditions in advance, potential communication risks can be discovered in time and corresponding measures can be taken to solve them, avoiding train accidents caused by communication failures. Such simulation tests can be carried out before the actual operation of the train or during regular maintenance. Problems can be discovered and solved before they cause actual faults or accidents. At the same time, the integrity of the fault data packet transmission can be reflected through the detection of the integrity of the experimental data packet transmission. While ensuring the integrity in the fault simulation, the accuracy of the vehicle fault monitoring system based on multi-dimensional data analysis is further improved.

[0042] Furthermore, the present invention adds equipment for collecting tunnel parameters corresponding to frequently occurring identical accidents by combining the time when the same fault occurs and the average time used for repairing the same fault so as to be able to discover faults earlier. At the same time, the frequent faults are determined based on the intervals between different faults and the information on repairs is combined to generate a reference list of frequent faults. Adding equipment for collecting tunnel parameters corresponding to frequently occurring identical accidents can improve the monitoring density and frequency of these key areas and fault types. By collecting tunnel parameters more frequently and more meticulously, subtle changes and abnormal trends in parameters can be captured earlier. Combining the time when the same fault occurs and the average time used for repairs can help to gain a deeper understanding of the occurrence patterns and characteristics of faults. At the same time, the average repair time can reflect the complexity and handling difficulty of the fault, providing a more comprehensive reference for fault diagnosis, thereby improving the accuracy of fault diagnosis. Accuracy and reliability, identify frequent faults and generate a reference list of frequent faults in combination with maintenance information, so that maintenance personnel can clearly understand which faults in the tunnel system are the most common and important. According to the reference list of frequent faults and the analysis of fault occurrence time and maintenance time, a more scientific and reasonable maintenance plan can be formulated. For frequent faults, preventive maintenance measures can be arranged in advance, such as regular inspections, replacement of wearing parts, etc., to reduce the probability of failure. At the same time, the maintenance time can be reasonably arranged to avoid large-scale maintenance work during peak traffic hours or important operating periods, reducing the impact on the normal use of the tunnel. The reference list of frequent faults can serve as an important basis for emergency response. When a fault occurs, maintenance personnel can quickly determine the type of fault and the possible scope of impact based on the list, further improving the accuracy of the vehicle fault monitoring system based on multidimensional data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a structural block diagram of a vehicle fault monitoring system based on multidimensional data analysis according to the present invention;

[0044] Figure 2 This is a structural block diagram of a test unit according to an embodiment of the present invention;

[0045] Figure 3 A logic diagram for determining data transmission according to an embodiment of the present invention;

[0046] Figure 4 A logic diagram for adjusting the acquisition density according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0048] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0049] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0050] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0051] See also Figure 1 As shown in FIG, which is a structural block diagram of a vehicle fault monitoring system based on multidimensional data analysis of the present invention, an embodiment of the present invention provides a vehicle fault monitoring system based on multidimensional data analysis, including:

[0052] a tunnel monitoring unit, which is used to collect tunnel parameters of each train during the train travel in the tunnel;

[0053] A single vehicle analysis unit, connected to the tunnel monitoring unit, is used to predict the predicted fault information of each single vehicle system based on the tunnel parameters to generate a fault experiment data packet and send it to the test train. The fault experiment data packet includes a fault data packet and an experiment data packet;

[0054] The test unit is connected to the single-vehicle analysis unit and is used to parse fault data packets to simulate fault scenarios, collect operational fault information to generate operational feedback data packets, and parse and run experimental data packets on the experimental train system to generate system feedback data packets;

[0055] The verification unit is connected to the single vehicle analysis unit and the test unit respectively, and is used to determine whether the data transmission is correct according to the system feedback data packet, and to determine the maintenance information and calculate the average maintenance time of each identical fault by combining the operation feedback data packet and the fault data packet;

[0056] The system evaluation unit is connected to the tunnel monitoring unit, the single vehicle analysis unit and the verification unit respectively, and is used to generate different fault intervals and the same fault intervals according to the fault experiment data packet, and to adjust the collection density of track parameters corresponding to the same fault based on the same fault interval and the same fault average repair time, and to generate a fault reference list based on different fault intervals and repair information.

[0057] Specifically, the bicycle analysis unit includes:

[0058] The fault analysis subunit is connected to the tunnel monitoring unit and is used to draw tunnel parameter curves for each vehicle system based on tunnel parameters, and select tunnel parameters corresponding to outliers in the tunnel parameter curves to generate predicted fault information.

[0059] The bicycle system includes door system, air conditioning system, traction system, pantograph system and braking system.

[0060] It is understandable that when a train door malfunctions, the train's airtightness decreases. During high-speed travel, air in the tunnel will enter the train through gaps, or air from the train will flow out. This will disrupt the originally stable wind pressure distribution in the tunnel. Monitoring changes in tunnel wind pressure can determine whether the train door system has malfunctioned.

[0061] If the dehumidification components of the air conditioning system malfunction, causing the dehumidification function to fail or weaken, the moisture in the carriage cannot be effectively discharged. This may cause the moisture in the carriage to penetrate into the tunnel through various gaps, thereby causing the local humidity in the tunnel to rise. If the air conditioning system cannot cool, the heat in the carriage cannot be exchanged with the air in the tunnel through the air conditioning system, and the tunnel temperature will drop. Monitoring the tunnel humidity and tunnel temperature can determine whether the subway air conditioning system has malfunctioned.

[0062] Failure of the motor or gearbox in the traction system will transmit vibration to the track and generate special noise. A comprehensive analysis of the track vibration frequency and tunnel noise can determine whether the traction system has failed.

[0063] When the pantograph slide plate is severely worn, the surface is uneven, or there are defects in the contact wire, the contact state between the pantograph and the catenary deteriorates, the friction intensifies, and the friction noise increases significantly. When the train runs at high speed in the tunnel, the tunnel wind pressure will exert force on the pantograph. Under normal circumstances, the pantograph is designed with reasonable aerodynamic performance and can maintain a stable working state within a certain wind pressure range. When the structural components of the pantograph are loose, deformed or damaged, the dynamic stability of the pantograph will be affected by the wind pressure in the tunnel. Monitoring the tunnel noise and tunnel wind pressure can determine whether the pantograph system has a fault.

[0064] When the brake shoe appears uneven wear, surface cracks or improper installation, the contact between the brake shoe and the wheel tread during braking will be uneven, and this uneven contact will produce pulse friction, causing the braking force on the wheel to be uneven, thereby causing changes in the frequency of rail vibration, and some parts of the braking system, such as the brake lever, connecting pin, etc., if loose, will produce impact noise during train operation and braking. These noises are usually low-frequency sounds and can become part of the tunnel noise. A comprehensive analysis of rail vibration frequency, tunnel noise and tunnel temperature can determine whether the braking system has failed.

[0065] Specifically, the present application collects parameters in the subway tunnel, analyzes and predicts problems that occur during subway train operation, collects parameters such as temperature, humidity, and wind pressure in the tunnel, and combines train operation state data to detect equipment failure hazards in advance. When the tunnel temperature abnormally rises, it indicates that the power supply equipment or train traction system has overheating problems, and timely warning and processing can avoid safety accidents caused by equipment failure. Monitoring parameters such as rail vibration frequency can detect problems such as rail wear and deformation in a timely manner. Through long-term monitoring and analysis of parameters, the replacement cycle of equipment can be predicted, and the storage of spare parts can be reasonably planned to avoid cost waste caused by insufficient or excessive spare parts. Analyzing the collected parameter data can find the shortcomings of existing technology and equipment, providing direction for technical improvement and innovation. Collecting environmental parameters in the tunnel can monitor the impact of subway operation on the surrounding environment, and monitoring parameters of the subway tunnel can effectively monitor the state of the tunnel itself and detect problems in the tunnel in a timely manner. The space environment of the subway tunnel is small, and small faults that are not easy to detect during the operation of the subway train can be reflected in the environmental parameters of the subway tunnel. Monitoring the environmental parameters of the subway tunnel can effectively improve the efficiency of problem detection and the accuracy of the vehicle fault monitoring system based on multi-dimensional data analysis.

[0066] Specifically, the single vehicle analysis unit further comprises:

[0067] A fault prediction sub-unit connected to the tunnel monitoring unit, configured to generate a fault experiment data packet in combination with the predicted fault information, fault time, fault location and simulation parameters, record the creation time of the fault experiment data packet and send the fault experiment data packet to the experimental train.

[0068] It can be understood that the fault is determined to be instantaneous, intermittent or continuous, and the corresponding duration is set. For intermittent faults, multiple fault occurrence and recovery time periods need to be planned.

[0069] It can be understood that the fault location is located, and it is clear that the fault occurs at which position of the tunnel and which part of the train.

[0070] It is understandable that the train operation parameters are simulated and the train operation status parameters such as speed, load, voltage, current, frequency, etc. are recorded when the fault is detected.

[0071] During implementation, the collected data such as predicted fault information, fault time, fault location and simulation parameters are filled into the corresponding fields according to the designed data packet structure.

[0072] See also Figure 2 As shown in FIG, which is a structural block diagram of a test unit according to an embodiment of the present invention, the test unit includes:

[0073] The operation feedback subunit is connected to the single-vehicle analysis unit and is used to parse the fault data packet to simulate the fault scenario corresponding to the predicted fault information on the experimental train, and collect the operation fault information of the experimental train in the fault scenario to generate an operation feedback data packet.

[0074] It is understood that the operating environment and system parameters of the test train are set according to the simulation parameters, and corresponding fault simulation equipment or tools are prepared for the predicted fault type. If it is an electrical fault, a device is needed to simulate a short circuit or open circuit; for mechanical faults, relevant components need to be adjusted or damaged to simulate wear or damage. According to the fault time specified in the fault data packet, the fault simulation is started at the corresponding time, and various sensors installed on the test train are used to collect real-time operating data of the train under the fault scenario, focusing on monitoring characteristic information related to the predicted fault.

[0075] Specifically, the test unit also includes:

[0076] The system feedback subunit is connected to the single-vehicle analysis unit and the operation feedback subunit respectively, and is used to parse the experimental data packet on the system of the experimental train and run the generated system feedback data packet in the fault scenario.

[0077] It can be understood that the experimental data packet is parsed, the data in the experimental data packet is transmitted between various systems on the train, a transmission result is generated, and the transmission result is summarized to generate a system feedback data packet.

[0078] Specifically, the present invention sends predicted fault data to an experimental train, simulates the parameter operating environment of the running train, and reviews the predicted faults on the experimental train. This allows for early detection of train operation faults. Based on theoretical analysis, it predicts that the train traction system may experience an overheating fault. However, during the simulated operation of the experimental train, testing is conducted in combination with actual load, ambient temperature and other parameters to determine whether the prediction is accurate, avoid misjudgments or missed judgments, and improve the reliability of fault diagnosis. The simulated operating environment of the experimental train provides a platform for in-depth research on fault mechanisms. Under controllable conditions, the process of fault occurrence and changes in related parameters are observed, the root causes of the faults are analyzed, and train operation faults are discovered in advance. Appropriate preventive measures can be taken on the actual train. Based on the review results of the predicted faults by the experimental train, a more accurate maintenance plan can be formulated. Clarify the time, location and extent of the fault, arrange maintenance resources reasonably, avoid unnecessary maintenance work and waste of resources, and avoid further damage to the equipment by discovering and solving the fault problem in advance, thereby extending the service life of the equipment. Solving the fault problem in advance can effectively reduce the occurrence of train delays and suspensions, ensure the punctual operation of trains, ensure that the trains are in good operating condition, reduce problems caused by faults, and further improve the accuracy of the vehicle fault monitoring system based on multi-dimensional data analysis.

[0079] See also Figure 3 As shown, it is a logic diagram for determining data transmission according to an embodiment of the present invention, and the verification unit includes:

[0080] The transmission subunit is connected to the test unit and is used to determine whether the data transmission is correct based on the packet loss rate and integrity of the system feedback data packet.

[0081] If the packet loss rate is less than or equal to the preset packet loss rate and the integrity is greater than or equal to the preset integrity, the data transmission is determined to be correct.

[0082] If the packet loss rate is greater than the preset packet loss rate or the integrity is less than the preset integrity, it is determined that the data transmission is erroneous.

[0083] In a specific embodiment, the preset packet loss rate is set to 5% and the preset integrity is set to 90%. If the packet loss rate is 2% which is less than the preset packet loss rate and the integrity is 98% which is greater than the preset integrity, then it is determined that the data transmission is correct.

[0084] If the packet loss rate is 9% greater than the preset packet loss rate and the integrity is 97% greater than the preset integrity, it is determined that the data transmission is erroneous.

[0085] If the packet loss rate is 3% which is greater than the preset packet loss rate and the integrity is 84% ​​which is less than the preset integrity, it is determined that the data transmission is erroneous.

[0086] The preset packet loss rate is positively correlated with the amount of data in the fault experiment data packet; the preset integrity is negatively correlated with the amount of data in the fault experiment data packet.

[0087] It can be understood that the larger the amount of data in the fault experiment data packet, the greater the system calculation amount, and the greater the probability of packet loss. Therefore, the preset packet loss rate is positively correlated with the amount of data in the fault experiment data packet; the larger the amount of data in the fault experiment data packet, the greater the probability of data loss after transmission. Therefore, the preset integrity is negatively correlated with the amount of data in the fault experiment data packet.

[0088] Specifically, the present invention combines the fault data packet and the experimental data packet and sends them to the experimental train. In the simulated fault scenario, the integrity of the feedback data packet of the experimental data packet is detected to determine whether the train's communication is normal in different fault scenarios. In the simulated fault scenario, this method can comprehensively detect the response capability of the communication system under different fault conditions, and accurately determine whether the communication system can remain stable in the event of a fault by analyzing the integrity of the feedback data packet. Based on the results of a large number of simulation experiments, the weak links of the communication system in different fault scenarios can be discovered, so that the communication system can be optimized and improved in a targeted manner. If it is found that data packet loss often occurs in a certain fault scenario, resulting in communication interruption, corresponding correction measures can be added to the system design. Fault mechanisms or redundant measures can improve the overall performance and safety of the communication system. Ensuring the normal operation of the train communication system under various fault scenarios is the key to ensuring the safe operation of the train. By simulating faults and detecting communication conditions in advance, potential communication risks can be discovered in time and corresponding measures can be taken to solve them, avoiding train accidents caused by communication failures. Such simulation tests can be carried out before the actual operation of the train or during regular maintenance. Problems can be discovered and solved before they cause actual faults or accidents. At the same time, the integrity of the fault data packet transmission can be reflected through the detection of the integrity of the experimental data packet transmission. While ensuring the integrity in the fault simulation, the accuracy of the vehicle fault monitoring system based on multi-dimensional data analysis is further improved.

[0089] Specifically, the verification unit also includes:

[0090] The maintenance subunit is connected to the single vehicle analysis unit, the test unit and the transmission subunit respectively. It is used to determine the maintenance information by combining the operation feedback data packet and the fault data packet when determining that the data transmission is correct, and to perform maintenance on the train according to the maintenance information and calculate the average maintenance time for each identical fault.

[0091] During implementation, the system compares the actual fault characteristics in the operational feedback data package with the predicted fault type in the fault data package. Based on the data in the operational feedback data package, the system assesses the severity of the actual fault. The specific location of the fault in the operational feedback data package is compared with the fault location information in the fault data package. Combining the information from both packages, the system identifies the triggering factors for the fault. Correlation analysis is performed on various data in the operational feedback data package to identify relevant changes and potential patterns before and after the fault occurs. The system then identifies the components to be repaired, defines the repair content, plans the repair time and method, records repair recommendations, and generates repair information.

[0092] See also Figure 4 As shown in FIG. 1 , which is a logic diagram for adjusting the acquisition density according to an embodiment of the present invention, the system evaluation unit includes:

[0093] The adjustment subunit is connected to the tunnel monitoring unit, the single vehicle analysis unit and the verification unit respectively, and is used to generate different fault occurrence intervals and the same fault intervals according to the creation time of the fault experiment data packet, and to adjust the collection density of track parameters corresponding to the same fault based on the same fault interval and the same fault mean repair time.

[0094] If the interval time between the same faults is less than or equal to the preset interval time and the average repair time for the same faults is greater than or equal to the preset average time, it is determined that the collection density is increased.

[0095] If the interval time between the same faults is greater than the preset interval time or the average repair time for the same faults is less than the preset average time, it is determined that the acquisition density is reduced.

[0096] In a specific embodiment, the preset interval time is set to 30 hours and the preset average time is set to 2 hours. If the interval time between the same fault is 15 hours, which is less than the preset interval time, and the average repair time for the same fault is 4.5 hours, which is greater than the preset average time, it is determined that the collection density is increased.

[0097] If the interval time between the same fault is 90 hours, which is greater than the preset interval time, and the average repair time for the same fault is 6 hours, which is greater than the preset average time, it is determined that the collection density is reduced.

[0098] If the interval time between the same faults is 25 hours, which is less than the preset interval time, and the average repair time for the same faults is 0.5, which is less than the preset average time, it is determined to reduce the collection density.

[0099] The preset interval time is positively correlated with the train running interval time, while the preset average time is negatively correlated with the train running interval time;

[0100] It is understandable that the longer the train running interval, the fewer trains run, and the smaller the probability of failure, so the preset interval time is positively correlated with the train running interval time; the shorter the train running interval, the more trains run, and the number of failures is relatively more, resulting in increased maintenance time, so the preset average time is negatively correlated with the train running interval time.

[0101] The collection density is the number of devices collecting track parameters between adjacent sites.

[0102] Specifically, the system evaluation unit also includes:

[0103] The alarm subunit is connected to the adjustment subunit and is used to determine frequent faults by analyzing different fault intervals, compare the frequent faults with maintenance information, and generate a fault reference list based on the comparison results.

[0104] Specifically, the tunnel monitoring unit includes:

[0105] Vibration subunits are symmetrically arranged on both sides of the track to collect the track vibration frequency during the train's travel;

[0106] Noise subunits are symmetrically arranged on the train tunnel walls on both sides of the track to collect tunnel noise during train travel;

[0107] Wind pressure subunits are symmetrically arranged on the train tunnel walls on both sides of the track to collect tunnel wind pressure during train travel;

[0108] A temperature subunit is installed on the wall of the train tunnel to collect the tunnel temperature during the train's travel;

[0109] A humidity subunit is installed on the wall of the train tunnel to collect the tunnel humidity during the train's travel;

[0110] Tunnel parameters include track vibration frequency, tunnel noise, tunnel wind pressure, tunnel temperature and tunnel humidity.

[0111] Specifically, the application increases the collection device of the tunnel parameters corresponding to the same frequent accident by combining the time of the same fault occurrence and the average time of the same fault repair, so as to discover the fault earlier, and determines the frequent fault reference list by combining the repair information of the frequent fault occurrence interval time, increases the collection device of the tunnel parameters corresponding to the same frequent accident, can improve the monitoring density and frequency of these key areas and fault types, and can capture the subtle changes and abnormal trends of the parameters earlier by more frequent and more detailed collection of the tunnel parameters, and combining the time of the same fault occurrence and the average time of the repair, can help to understand the occurrence regularity and characteristics of the fault more deeply, at the same time, the average repair time can reflect the complexity and processing difficulty of the fault, and provide more comprehensive reference for fault diagnosis, thereby improving the accuracy and reliability of fault diagnosis, determining the frequent fault and generating the frequent fault reference list by combining the repair information, so that the maintenance personnel can clearly understand which faults in the tunnel system are the most common and important, according to the analysis of the frequent fault reference list, fault occurrence time and repair time, a more scientific and reasonable maintenance plan can be made, for the frequent fault, preventive maintenance measures such as periodic inspection and replacement of vulnerable parts can be arranged in advance, so as to reduce the probability of fault occurrence, at the same time, the repair time is reasonably arranged to avoid large-scale repair work during the peak traffic period or important operation period, thereby reducing the influence on the normal use of the tunnel, and the frequent fault reference list can be used as an important basis for emergency response, when the fault occurs, the maintenance personnel can quickly judge the type and possible influence range of the fault according to the list, thereby further improving the accuracy of the vehicle fault monitoring system based on multi-dimensional data analysis.

[0112] So far, the technical solutions of the application have been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the application, and the technical solutions after the changes or replacements will fall within the protection scope of the application.

[0113] The above description is only the preferred embodiments of the application and is not used to limit the application; for those skilled in the art, the application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A vehicle fault monitoring system based on multidimensional data analysis, characterized in that: include: a tunnel monitoring unit, which is used to collect tunnel parameters of each train during the train travel in the tunnel; a single vehicle analysis unit connected to the tunnel monitoring unit, configured to predict predicted fault information of each single vehicle system based on the tunnel parameters to generate a fault experiment data packet and send it to the experimental train, wherein the fault experiment data packet includes a fault data packet and an experiment data packet; A test unit connected to the single-vehicle analysis unit, configured to parse the fault data packet to simulate a fault scenario, collect operational fault information to generate an operational feedback data packet, and parse and run the experimental data packet on the system of the experimental train to generate a system feedback data packet; a verification unit, connected to the single-vehicle analysis unit and the test unit, respectively, for determining whether data transmission is correct based on the system feedback data packet, and determining maintenance information and calculating the average maintenance time for each identical fault based on the operation feedback data packet and the fault data packet; A system evaluation unit is connected to the tunnel monitoring unit, the single-vehicle analysis unit and the verification unit respectively, and is used to generate different fault intervals and the same fault interval according to the fault experiment data packet, and to adjust the collection density of track parameters corresponding to the same fault based on the same fault interval and the same fault average maintenance time, and to generate a fault reference list based on the different fault intervals and the maintenance information.

2. The vehicle fault monitoring system based on multidimensional data analysis according to claim 1, characterized in that: The bicycle analysis unit includes: a fault analysis subunit connected to the tunnel monitoring unit, for drawing a tunnel parameter curve graph of each of the single-vehicle systems based on the tunnel parameters, and selecting tunnel parameters corresponding to outliers in the tunnel parameter curve graph to generate the predicted fault information; The single vehicle system includes a door system, an air conditioning system, a traction system, a pantograph system, and a braking system.

3. The vehicle fault monitoring system based on multidimensional data analysis according to claim 2, characterized in that: The bicycle analysis unit further includes: A fault prediction subunit is connected to the tunnel monitoring unit and is used to generate the fault experiment data packet in combination with the predicted fault information, fault time, fault location and simulation parameters, record the creation time of the fault experiment data packet and send the fault experiment data packet to the experimental train.

4. The vehicle fault monitoring system based on multidimensional data analysis according to claim 3, characterized in that: The testing unit comprises: An operation feedback subunit is connected to the single-vehicle analysis unit and is used to parse the fault data packet to simulate a fault scenario corresponding to the predicted fault information on the experimental train, and collect the operation fault information of the experimental train in the fault scenario to generate an operation feedback data packet.

5. The vehicle fault monitoring system based on multidimensional data analysis according to claim 4, characterized in that: The testing unit further includes: The system feedback subunit is connected to the single-vehicle analysis unit and the operation feedback subunit respectively, and is used to parse the experimental data packet on the system of the experimental train and run in the fault scenario to generate the system feedback data packet.

6. The vehicle fault monitoring system based on multidimensional data analysis according to claim 5, characterized in that: The verification unit includes: A transmission subunit, connected to the test unit, is used to determine whether the data transmission is correct based on the packet loss rate and integrity of the system feedback data packet, wherein: If the packet loss rate is less than or equal to the preset packet loss rate and the integrity is greater than or equal to the preset integrity, it is determined that the data transmission is correct. The preset packet loss rate is positively correlated with the amount of data in the fault experiment data packet; the preset integrity is negatively correlated with the amount of data in the fault experiment data packet.

7. The vehicle fault monitoring system based on multidimensional data analysis according to claim 6, characterized in that: The verification unit further includes: The maintenance subunit is respectively connected to the single vehicle analysis unit, the test unit and the transmission subunit, and is used to determine the maintenance information by combining the operation feedback data packet and the fault data packet when determining that the data transmission is correct, and to perform maintenance on the train according to the maintenance information and calculate the average maintenance time of each identical fault.

8. The vehicle fault monitoring system based on multidimensional data analysis according to claim 7, characterized in that: The system evaluation unit includes: An adjustment subunit is respectively connected to the tunnel monitoring unit, the single-vehicle analysis unit and the verification unit, and is used to generate different fault occurrence intervals and the same fault intervals according to the creation time of the fault experiment data packet, and to adjust the collection density of track parameters corresponding to the same fault based on the same fault intervals and the average repair time of the same fault. The collection density is the number of devices that collect the track parameters between adjacent stations.

9. The vehicle fault monitoring system based on multidimensional data analysis according to claim 8, characterized in that: The system evaluation unit also includes: An alarm subunit is connected to the adjustment subunit and is used to determine frequent faults by analyzing the different fault intervals, compare the frequent faults with the maintenance information, and generate a fault reference list according to the comparison result.

10. The vehicle fault monitoring system based on multidimensional data analysis according to claim 9, characterized in that: The tunnel monitoring unit includes: Vibration subunits are symmetrically arranged on both sides of the track to collect the track vibration frequency during the train's travel; Noise subunits, symmetrically arranged on the train tunnel walls on both sides of the track, for collecting tunnel noise during train travel; Wind pressure subunits, which are symmetrically arranged on the walls of the train tunnel on both sides of the track, are used to collect tunnel wind pressure during the train's travel; a temperature subunit, which is arranged on the wall of the train tunnel and is used to collect the tunnel temperature during the train travel; a humidity subunit, which is arranged on the wall of the train tunnel and is used to collect the tunnel humidity during the train travel; The tunnel parameters include the track vibration frequency, the tunnel noise, the tunnel wind pressure, the tunnel temperature and the tunnel humidity.

Images