A vehicle fault monitoring system based on multi-dimensional data analysis

Abstract

The present application relates to the technical field of data processing, and especially relates to a vehicle fault monitoring system based on multi-dimensional data analysis, which comprises a tunnel monitoring unit for collecting tunnel parameters; a single vehicle analysis unit for generating fault experiment data packets; a test unit for simulating fault scenes, collecting running fault information, generating running feedback data packets, and generating system feedback data packets; a verification unit for determining whether data transmission is correct, determining repair information, and calculating average repair time of each same fault; a system evaluation unit for generating different fault interval times and same fault interval times, adjusting the collection density of track parameters corresponding to the same fault, and generating a fault reference list; the above-mentioned units cooperate with each other to improve the efficiency of fault discovery and further improve the accuracy of the vehicle fault monitoring system based on multi-dimensional data analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a vehicle fault monitoring system based on multi-dimensional data analysis. BACKGROUND

[0002] The big data technology has made significant progress in data collection, storage, processing and analysis, and a large number of sensors and monitoring devices deployed in the subway system can collect massive 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, potential relationships and rules between data can be found, so as to more accurately diagnose and predict subway faults. With the acceleration of urbanization process, the scale of urban rail transit construction is expanding, and the number of subway lines is continuously increasing, which makes the difficulty of subway operation management increasing, and the requirements for fault monitoring and management are also increasing. The fault monitoring system based on multi-dimensional data analysis can realize the centralized monitoring and management of large-scale subways, improve the efficiency and accuracy of fault monitoring, and adapt to the needs of rapid development of urban rail transit.

[0003] Chinese patent application publication No. CN117022146A discloses a dual-domain electronic and electrical architecture of a passenger vehicle, a working method and a passenger vehicle. The invention provides a dual-domain electronic and electrical architecture of a passenger vehicle, a working method and a passenger vehicle. 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 for receiving information uploaded by a plurality of vehicle information collection devices, and the plurality of collection devices have a protocol conversion function of a gateway; the vehicle driving control domain receives parameters of a vehicle driving execution structure; the central controller fuses and operates multi-dimensional data of the vehicle intelligent cockpit control domain and multi-dimensional data of the vehicle driving, and obtains a control implementation process of vehicle driving and a control or operation instruction of vehicle driving according to the operation result. The invention realizes close cooperation of each domain controller through the domain controller architecture of the dual-domain structure, and can realize deep fusion and full-scene linkage of business with the platform.

[0004] Chinese Patent Application Publication No. 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. The method involves acquiring multi-source data for the target subway, including sensor data, maintenance records, and equipment logs; constructing a fault knowledge graph based on the multi-source data; fusing the multi-source data and the fault knowledge graph using a deep learning model to obtain feature data and a fusion model; generating a fault tree based on the feature data and the fusion model; acquiring real-time monitoring data for the target subway; and predicting faults based 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 experimental trains, and it fails to predict the faults that may occur during subway operation by monitoring the subway operating environment parameters when no faults occur in the subway. Summary of the Invention

[0006] To address this, the present invention provides a vehicle fault monitoring system based on multidimensional data analysis, which overcomes the problems in the prior art that it fails to verify predicted faults through experimental trains and fails to predict faults that may occur during subway operation by monitoring subway operating environment parameters when no faults occur.

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

[0008] The tunnel monitoring unit is used to collect tunnel parameters for each train during its journey through the tunnel.

[0009] A single-vehicle analysis unit, which is 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 test data package and send it to the test train. The fault test data package includes a fault data package and a test data package.

[0010] The test unit, which is connected to the single-vehicle analysis unit, is used to parse the fault data packet to simulate fault scenarios, collect operational fault information to generate operational feedback data packets, and parse and run the experimental data packet on the system of the experimental train to generate system feedback data packets.

[0011] 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 based on the system feedback data packet, and to determine the maintenance information and calculate the average maintenance time for each identical fault by combining the operation feedback data packet and the fault data packet.

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

[0013] Further, the single-vehicle analysis unit comprises:

[0014] a fault analysis sub-unit connected with the tunnel monitoring unit, configured to plot a tunnel parameter curve of each single-vehicle system based on the tunnel parameters, select tunnel parameters corresponding to outliers in the tunnel parameter curve to generate the predicted fault information,

[0015] The single-vehicle system comprises a door system, an air conditioning system, a traction system, a pantograph system and a brake system.

[0016] Further, the single-vehicle analysis unit further comprises:

[0017] a fault prediction sub-unit connected with the tunnel monitoring unit, configured 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 experiment train.

[0018] Further, the test unit comprises:

[0019] a running feedback sub-unit connected with the single-vehicle analysis unit, configured to analyze the fault data packet to simulate a fault scene corresponding to the predicted fault information on the experiment train, collect running fault information of the experiment train in the fault scene to generate a running feedback data packet.

[0020] Further, the test unit further comprises:

[0021] a system feedback sub-unit connected with the single-vehicle analysis unit and the running feedback sub-unit respectively, configured to analyze the experiment data packet on the system of the experiment train and generate the system feedback data packet in the fault scene.

[0022] Further, the verification unit comprises:

[0023] a transmission sub-unit connected with the test unit, configured to determine whether the data transmission is correct according to the packet loss rate and completeness of the system feedback data packet, wherein,

[0024] if the packet loss rate is less than or equal to a preset packet loss rate and the completeness is greater than or equal to a preset completeness, determining that the data transmission is correct,

[0025] The preset packet loss rate is positively correlated with the data amount in the fault experimental data packet, and the preset completeness is negatively correlated with the data amount in the fault experimental data packet.

[0026] Further, the checking unit further comprises:

[0027] The maintenance subunit is connected with the single train analysis unit, the test unit and the transmission subunit respectively, and is used to determine the maintenance information in combination with the operation feedback data packet and the fault data packet in the state that the data transmission is determined to be correct, and to perform maintenance on the train according to the maintenance information and calculate the average maintenance time of the same fault.

[0028] Further, the system evaluation unit comprises:

[0029] The adjustment subunit is connected with the tunnel monitoring unit, the single train analysis unit and the checking unit respectively, and is used to generate different fault interval times and the same fault interval time according to the creation time of the fault experimental data packet, and to adjust the collection density of the track parameter corresponding to the same fault based on the same fault interval time and the average maintenance time of the same fault, the collection density being the number of devices for collecting the track parameter between adjacent stations.

[0030] Further, the system evaluation unit further comprises:

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

[0032] Further, the tunnel monitoring unit comprises:

[0033] The vibration subunit is symmetrically arranged on both sides of the track, and is used to collect the track vibration frequency during the train running;

[0034] The noise subunit is symmetrically arranged on the train tunnel wall on both sides of the track, and is used to collect the tunnel noise during the train running;

[0035] The wind pressure subunit is symmetrically arranged on the train tunnel wall on both sides of the track, and is used to collect the tunnel wind pressure during the train running;

[0036] The temperature subunit is arranged on the train tunnel wall, and is used to collect the tunnel temperature during the train running;

[0037] A humidity subunit is arranged on the train tunnel wall to collect tunnel humidity during train running.

[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 prior art, the beneficial effects of the present application are that the present application collects parameters in the subway tunnel, analyzes and predicts problems occurring in the subway train operation, collects parameters such as temperature, humidity and wind pressure in the tunnel, combines train operation state data, can find equipment fault hidden dangers in advance, when the tunnel temperature abnormally rises, indicating that the power supply equipment or the train traction system has an overheating problem, timely warning and processing can avoid safety accidents caused by equipment failure, monitoring track vibration frequency and other parameters can find out problems such as track wear and deformation in time, through long-term monitoring and analysis of parameters, the replacement cycle of equipment can be predicted, the storage of spare parts can be reasonably planned, and the cost waste caused by insufficient or excessive spare parts can be avoided, analyzing the collected parameter data can find out the shortcomings of the existing technology and equipment, provide direction for technical improvement and innovation, collect environmental parameters in the tunnel, can monitor the influence of subway operation on the surrounding environment, monitor the parameters of the subway tunnel, can effectively monitor the state of the tunnel itself, find out problems in the tunnel in time, the space environment of the subway tunnel is small, when small faults that are not easy to find out occur during the operation of the subway train, they can be reflected in the environmental parameters of the subway tunnel, monitor the environmental parameters of the subway tunnel, effectively improve the efficiency of finding out problems, and effectively improve the accuracy of the vehicle fault monitoring system based on multi-dimensional data analysis.

[0040] Further, the application can find the fault problem of train operation in advance by sending the predicted fault data to the experimental train, checking the predicted fault on the experimental train by simulating the parameter operation environment of the train, and determining whether the prediction is accurate by testing the actual load, environmental temperature and other parameters during the simulation operation of the experimental train, avoiding misjudgment or omission, improving the reliability of fault diagnosis, and providing a platform for in-depth study of fault mechanism by the simulation operation environment of the experimental train. Under controllable conditions, the process of fault occurrence and the change of related parameters are observed, the root cause of the fault is analyzed, the fault problem of train operation is found in advance, corresponding preventive measures can be taken on the actual train, and more accurate maintenance plans can be made based on the review results of the predicted fault of the experimental train. The time, position and degree of fault occurrence are determined, the maintenance resources are reasonably arranged, unnecessary maintenance work and resource waste are avoided, the fault problem is found and solved in advance, further damage to the equipment caused by the fault is avoided, thereby prolonging the service life of the equipment, solving the fault problem in advance, effectively reducing the occurrence of train delay and shutdown, ensuring the on-time operation of the train, ensuring the train in good running state, reducing the problems caused by faults, and further improving the accuracy of the vehicle fault monitoring system based on multi-dimensional data analysis.

[0041] Further, the application can find the fault problem of train operation in advance by sending the predicted fault data to the experimental train, checking the predicted fault on the experimental train by simulating the parameter operation environment of the train, and determining whether the prediction is accurate by testing the actual load, environmental temperature and other parameters during the simulation operation of the experimental train, avoiding misjudgment or omission, improving the reliability of fault diagnosis, and providing a platform for in-depth study of fault mechanism by the simulation operation environment of the experimental train. Under controllable conditions, the process of fault occurrence and the change of related parameters are observed, the root cause of the fault is analyzed, the fault problem of train operation is found in advance, corresponding preventive measures can be taken on the actual train, and more accurate maintenance plans can be made based on the review results of the predicted fault of the experimental train. The time, position and degree of fault occurrence are determined, the maintenance resources are reasonably arranged, unnecessary maintenance work and resource waste are avoided, the fault problem is found and solved in advance, further damage to the equipment caused by the fault is avoided, thereby prolonging the service life of the equipment, solving the fault problem in advance, effectively reducing the occurrence of train delay and shutdown, ensuring the on-time operation of the train, ensuring the train in good running state, reducing the problems caused by faults, and further improving the accuracy of the vehicle fault monitoring system based on multi-dimensional data analysis.

[0042] Further, the application increases the collection device of the tunnel parameters corresponding to the same frequent accident by combining the time of the same frequent accident and the average time of the same frequent accident repair, so as to discover the fault earlier, and determines the frequent fault reference list by combining the information of the repair according to the interval time of the different frequent accidents, increases the collection device of the tunnel parameters corresponding to the same frequent accident, can improve the monitoring density and frequency of the key areas and the fault types, and can capture the subtle changes and abnormal trends of the parameters earlier by collecting the tunnel parameters more frequently and more carefully, and combining the time of the same frequent accident and the average time of the repair, can help to understand the occurrence law and characteristics of the fault more deeply, and at the same time, the average repair time can reflect the complexity and processing difficulty of the fault, and provide more comprehensive reference for the fault diagnosis, so as to improve the accuracy and reliability of the fault diagnosis, determines the frequent fault and generates 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 and the fault occurrence time and the repair time, can make more scientific and reasonable maintenance plan, for the frequent fault, can arrange preventive maintenance measures in advance, such as periodic inspection, replacement of vulnerable parts, etc., to reduce the probability of fault occurrence, at the same time, reasonably arrange the repair time, avoid large-scale repair work in the peak traffic period or important operation period, reduce the influence on the normal use of the tunnel, 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, further improve the accuracy of the vehicle fault monitoring system based on multi-dimensional data analysis. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The structure block diagram of the vehicle fault monitoring system based on multi-dimensional data analysis of the application is shown in the figure;

[0044] Figure 2 The structure block diagram of the test unit of the embodiment of the application is shown in the figure;

[0045] Figure 3 The logic diagram of the data transmission of the embodiment of the application is shown in the figure;

[0046] Figure 4 The logic diagram of the adjustment of the collection density of the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0047] In order to make the purpose and advantages of the application more clear and obvious, the application is further described below in combination with the embodiments; it should be understood that the specific embodiments described herein are only used to explain the application, and do not limit the application.

[0048] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art will appreciate that the embodiments are only used to explain the technical principles of the present application, and are not intended to limit the protection scope of the present application.

[0049] It should be noted that, in the description of the present application, the terms indicating the direction or positional relationship of "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description, and does not indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0050] In addition, it should be further noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connection" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the internal communication of two elements. Those skilled in the art can understand the specific meaning of the above terms in the present application according to the specific circumstances.

[0051] Please refer to Figure 1 As shown in the structure block diagram of the vehicle fault monitoring system based on multi-dimensional data analysis of the present application, the present application provides a vehicle fault monitoring system based on multi-dimensional data analysis, comprising:

[0052] The tunnel monitoring unit is used to collect tunnel parameters of each train in the tunnel during the train driving process;

[0053] The single vehicle analysis unit is connected with the tunnel monitoring unit, and 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 experimental train. The fault experiment data packet includes a fault data packet and an experiment data packet;

[0054] The test unit is connected with the single vehicle analysis unit, and is used to analyze the fault data packet to simulate a fault scene, collect running fault information to generate a running feedback data packet, and analyze and run the experiment data packet on the system of the experimental train to generate a system feedback data packet;

[0055] The checking unit is connected with 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 determine the repair information and calculate the average repair time of each same fault in combination with the running feedback data packet and the fault data packet;

[0056] The system evaluation unit is connected with the tunnel monitoring unit, the single vehicle analysis unit and the verification unit respectively, and is used for generating different fault interval times and same fault interval times according to the fault experimental data packet, adjusting the collection density of the track parameters corresponding to the same fault based on the same fault interval times and the same fault average maintenance time, and generating a fault reference list based on the different fault interval times and the maintenance information.

[0057] Specifically, the single vehicle analysis unit comprises:

[0058] The fault analysis sub-unit is connected with the tunnel monitoring unit, and is used for drawing a tunnel parameter curve of each single vehicle system based on the tunnel parameters, selecting tunnel parameters corresponding to outliers in the tunnel parameter curve to generate prediction fault information,

[0059] The single vehicle system comprises a door system, an air conditioning system, a traction system, a pantograph system and a brake system.

[0060] It can be understood that when the train door fails, the air tightness of the train decreases, and in the process of high-speed running of the train, the air in the tunnel will enter the train through the gap or the air in the train will flow out, which will interfere with the original stable air pressure distribution in the tunnel, and the change of the tunnel air pressure can determine whether the train door system fails;

[0061] If the dehumidification part of the air conditioning system fails, the dehumidification function is invalid or weakened, and the humidity in the car cannot be effectively discharged, which may cause the humidity in the car to penetrate into the tunnel through various gaps, thereby causing the local humidity of the tunnel to rise, and if the air conditioning system cannot cool, the heat in the car cannot be exchanged with the air in the tunnel through the air conditioning system, and the temperature of the tunnel will decrease, and the monitoring of the tunnel humidity and the tunnel temperature can determine whether the subway air conditioning system fails;

[0062] The motor or gear box of the traction system will transmit vibration to the track, and at the same time, special noise will be generated, and the comprehensive analysis of the track vibration frequency and the tunnel noise can determine whether the traction system fails;

[0063] When the pantograph slide plate is seriously worn, the surface is uneven, or the overhead line has defects, the contact state between the pantograph and the overhead line will deteriorate, and the friction will intensify, which will cause the friction noise to increase significantly, and when the train runs at high speed in the tunnel, the tunnel air pressure will generate a force on the pantograph. Under normal circumstances, the pantograph is designed to have reasonable aerodynamic performance, and can maintain a stable working state within a certain range of air pressure. When the structural parts of the pantograph are loose, deformed or damaged, the dynamic stability of the pantograph will be affected under the action of the tunnel air pressure. The monitoring of the tunnel noise and the tunnel air pressure can determine whether the pantograph system fails;

[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] Understandably, this involves simulating train operating parameters and recording the train's operating status parameters at the moment a fault is detected, such as speed, load, voltage, current, and frequency.

[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] Please see Figure 2 As shown, this is a structural block diagram of the test unit according to an embodiment of the present invention. The test unit includes:

[0073] The operation feedback subunit, which is connected to the single-vehicle analysis unit, is used to parse the fault data packet, 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 operation feedback data packet.

[0074] Understandably, the operating environment and system parameters of the experimental train are set according to the simulation parameters, and corresponding fault simulation equipment or tools are prepared for the predicted fault types. If it is an electrical fault, a device to simulate short circuits or open circuits needs to be prepared; 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 package, the fault simulation is started at the corresponding time, and various sensors installed on the experimental train are used to collect the train's operating data in real time 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, which is connected to the single-vehicle analysis unit and the operation feedback subunit respectively, is used to parse experimental data packets on the experimental train system and generate system feedback data packets in fault scenarios.

[0077] Understandably, the experimental data packets are parsed, the data in the experimental data packets are transmitted between various systems of the train, the transmission results are generated, and the transmission results are summarized to generate system feedback data packets.

[0078] Specifically, this invention sends predicted fault data to an experimental train. By simulating the train's operating environment and parameters, the predicted faults are verified on the experimental train. This allows for the early detection of potential train malfunctions. For example, theoretical analysis might predict overheating in the train's traction system. However, by testing the experimental train under simulated conditions with actual loads and ambient temperatures, the accuracy of the prediction can be determined, avoiding misjudgments or omissions and improving the reliability of fault diagnosis. The simulated operating environment of the experimental train provides a platform for in-depth research into fault mechanisms. Under controllable conditions, observing the fault occurrence process and related parameter changes allows for analysis of the root causes of the faults, enabling early detection of train malfunctions. This allows for the implementation of corresponding preventative measures on actual trains. Based on the verification results of predicted faults on the experimental train, more precise maintenance plans can be developed. By clearly identifying the time, location, and extent of a fault, maintenance resources can be allocated rationally to avoid unnecessary maintenance work and resource waste. By identifying and resolving faults in advance, further damage to equipment can be prevented, thereby extending the equipment's service life. Proactive fault resolution can effectively reduce train delays and stoppages, ensure on-time train operation, maintain trains in good operating condition, reduce problems caused by faults, and further improve the accuracy of vehicle fault monitoring systems based on multi-dimensional data analysis.

[0079] Please see Figure 3 As shown, this is a logic diagram for determining data transmission according to an embodiment of the present invention. The verification unit includes:

[0080] The 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 data packets returned by the system.

[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, then the data transmission is considered correct.

[0082] If the packet loss rate is greater than the preset packet loss rate or the packet integrity is less than the preset packet integrity, then the data transmission is determined to be incorrect.

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

[0084] If the packet loss rate is 9%, which is greater than the preset packet loss rate, and the integrity rate is 97%, which is greater than the preset integrity rate, then the data transmission is considered to be faulty.

[0085] If the packet loss rate is 3%, which is greater than the preset packet loss rate, and the integrity rate is 84%, which is less than the preset integrity rate, then the data transmission is judged to be incorrect.

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

[0087] It is understandable that the larger the amount of data in the fault test data packet, the greater the system's computational load 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 test data packet. Conversely, the larger the amount of data in the fault test 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 test data packet.

[0088] Specifically, this invention merges fault data packets and experimental data packets and sends them to an experimental train. Under simulated fault scenarios, the integrity of the feedback data packets from the experimental data packets is checked to determine whether the train's communication is normal under different fault conditions. In simulated fault scenarios, this method can comprehensively test the communication system's ability to cope with different fault conditions. By analyzing the integrity of the feedback data packets, it accurately determines whether the communication system can remain stable during faults. Based on the results of numerous simulation experiments, weaknesses in the communication system under different fault scenarios can be identified, allowing for targeted optimization and improvement of the communication system. If it is found that data packet loss frequently leads to communication interruptions under a certain fault scenario, corresponding correction mechanisms can be added to the system design. Error correction mechanisms or redundancy measures enhance the overall performance and security of the communication system. Ensuring the normal operation of the train communication system under various fault scenarios is crucial for the safe operation of trains. By simulating faults and testing communication conditions in advance, potential communication hazards can be identified in a timely manner, and corresponding measures can be taken to resolve them, avoiding train operation accidents caused by communication failures. Such simulation tests, conducted before actual train operation or during regular maintenance, can detect and resolve problems before they lead to actual faults or accidents. At the same time, the integrity of fault data packet transmission can be reflected by testing 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 testing unit, and the transmission subunit. It is used to determine maintenance information by combining the operation feedback data packet and the fault data packet when the data transmission is determined to be correct, and to carry out maintenance on the train based on the maintenance information and calculate the average maintenance time for each identical fault.

[0091] During implementation, the actual fault characteristics in the operation feedback data packet are compared with the predicted fault types in the fault data packet. Based on the data in the operation feedback data packet, the severity of the actual fault is assessed. The specific location of the fault in the operation feedback data packet is verified with the fault location information in the fault data packet. Combining the information from both data packets, the triggering factors of the fault are identified. Correlation analysis is performed on various data in the operation feedback data packet to find relevant changes and potential patterns before and after the fault. The components to be repaired are identified, the repair content is formulated, the repair time and method are planned, repair suggestions are recorded, and repair information is generated.

[0092] Please see Figure 4 As shown, this is a logic diagram for adjusting the acquisition density in an embodiment of the present invention. The system evaluation unit includes:

[0093] The adjustment subunit, connected to the tunnel monitoring unit, single-vehicle analysis unit, and verification unit respectively, is used to generate different fault occurrence intervals and identical fault intervals based on the creation time of the fault test data package, and to adjust the acquisition density of track parameters corresponding to the same fault based on the identical fault interval and the average maintenance time of the same fault.

[0094] If the interval between identical faults is less than or equal to the preset interval and the average repair time for identical faults is greater than or equal to the preset average time, then the data collection density is increased.

[0095] If the interval between identical faults is greater than the preset interval or the average repair time for identical faults is less than the preset average time, then the data collection density will be reduced.

[0096] In one specific embodiment, a preset interval time is set to 30 hours, and a preset average time is set to 2 hours. If the interval time for 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, then it is determined that the data collection density should be increased.

[0097] If the interval between identical faults is 90 hours, which is greater than the preset interval, and the average repair time for the same fault is 6 hours, which is greater than the preset average time, then the data collection density will be reduced.

[0098] If the interval between the same faults is 25 hours, which is less than the preset interval, and the average repair time for the same faults is 0.5 hours, which is less than the preset average time, then the data collection density will be reduced.

[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 interval, the fewer trains run, and the lower the probability of malfunctions. Therefore, the preset interval time is positively correlated with the train interval time. Conversely, the shorter the train interval, the more trains run, and the more malfunctions occur, leading to increased maintenance time. Therefore, the preset average time is negatively correlated with the train interval time.

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

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

[0103] The alarm subunit, which is connected to the adjustment subunit, is used to identify frequently occurring faults by analyzing different fault interval times, compare the frequently occurring faults with maintenance information, and generate a fault reference list based on the comparison results.

[0104] Specifically, the tunnel monitoring unit includes:

[0105] The vibration sub-unit is symmetrically arranged on both sides of the track to collect the track vibration frequency during train operation;

[0106] The noise sub-units are symmetrically arranged on the tunnel walls on both sides of the track to collect tunnel noise during train operation.

[0107] The wind pressure sub-unit is symmetrically arranged on the tunnel walls on both sides of the track to collect tunnel wind pressure during train operation;

[0108] Temperature subunit, which is installed on the wall of the train tunnel, is used to collect the tunnel temperature during train operation;

[0109] The humidity subunit is installed on the wall of the train tunnel to collect the tunnel humidity during train operation;

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

[0111] Specifically, this invention increases the number of data acquisition devices for tunnel parameters corresponding to frequently occurring, similar incidents by combining the time of occurrence of the same fault with the average time taken to repair it, thus enabling earlier fault detection. Simultaneously, it identifies frequently occurring faults based on different fault occurrence intervals and generates a reference list of frequently occurring faults by combining repair information. Increasing the number of data acquisition devices for tunnel parameters corresponding to frequently occurring, similar incidents improves the monitoring density and frequency for these key areas and fault types. More frequent and detailed collection of tunnel parameters allows for earlier detection of subtle changes and abnormal trends. Combining the time of occurrence of the same fault with the average time taken to repair it helps to gain a deeper understanding of the fault's occurrence patterns and characteristics. Furthermore, the average repair time reflects the complexity and difficulty of handling the fault, providing a more comprehensive reference for fault diagnosis, thereby improving fault diagnosis capabilities. The system ensures accuracy and reliability by identifying frequent faults and generating a reference list of frequent faults based on maintenance information. This allows maintenance personnel to clearly understand which faults in the tunnel system are the most common and critical. Analysis of the reference list, fault occurrence times, and maintenance schedules enables the development of more scientific and reasonable maintenance plans. For frequent faults, preventative maintenance measures, such as regular inspections and replacement of vulnerable parts, can be arranged in advance to reduce the probability of fault occurrence. Simultaneously, maintenance times can be rationally scheduled to avoid large-scale maintenance work during peak traffic periods or important operating times, minimizing the impact on normal tunnel operation. The reference list of frequent faults serves as a crucial basis for emergency response. When a fault occurs, maintenance personnel can quickly determine the type of fault and its potential impact range based on the list, further improving the accuracy of the vehicle fault monitoring system based on multi-dimensional data analysis.

[0112] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A vehicle fault monitoring system based on multidimensional data analysis, characterized in that, include: The tunnel monitoring unit is used to collect tunnel parameters for each train during its journey through the tunnel. A single-vehicle analysis unit, which is 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 test data package and send it to the test train. The fault test data package includes a fault data package and a test data package. The test unit, which is connected to the single-vehicle analysis unit, is used to parse the fault data packet to simulate fault scenarios, collect operational fault information to generate operational feedback data packets, and parse and run the experimental data packet on the system of the experimental train to generate system feedback data packets. 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 based on the system feedback data packet, and to determine the maintenance information and calculate the average maintenance time for each identical fault by combining the operation feedback data packet and the fault data packet. The system evaluation unit, which is connected to the tunnel monitoring unit, the single-vehicle analysis unit, and the verification unit, is used to generate different fault interval times and the same fault interval times based on the fault test data packets, adjust the acquisition density of track parameters corresponding to the same fault based on the same fault interval times and the average maintenance time of the same fault, and generate a fault reference list based on the different fault interval times and the maintenance information.

2. The vehicle fault monitoring system based on multidimensional data analysis according to claim 1, characterized in that, The single-vehicle analysis unit includes: The fault analysis subunit, connected to the tunnel monitoring unit, is used to plot tunnel parameter curves for each vehicle system based on the tunnel parameters, and to generate the predicted fault information by selecting the tunnel parameters corresponding to outliers in the tunnel parameter curves. 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 single-vehicle analysis unit also includes: The fault prediction subunit, which is connected to the tunnel monitoring unit, is used to generate the fault experiment data packet by combining 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 test unit includes: The operation feedback subunit, which is connected to the single-vehicle analysis unit, is used to parse the fault data packet to simulate the fault scenario corresponding to the predicted fault information on the experimental train, and to collect the operation fault information of the experimental train in the fault scenario to generate the operation feedback data packet.

5. The vehicle fault monitoring system based on multidimensional data analysis according to claim 4, characterized in that, The test unit also 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 generate the system feedback data packet in the fault scenario.

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 data packets returned by the system. If the packet loss rate is less than or equal to a preset packet loss rate and the integrity is greater than or equal to a preset integrity, then the data transmission is determined to be correct. The preset packet loss rate is positively correlated with the amount of data in the fault test data packet; the preset integrity is negatively correlated with the amount of data in the fault test 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 connected to the single-vehicle analysis unit, the testing 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 the data transmission is determined to be correct, and to perform maintenance on the train based on the maintenance information and calculate the average maintenance time for 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: The adjustment subunit, which is connected to the tunnel monitoring unit, the single-vehicle analysis unit and the verification unit respectively, is used to generate different fault interval times and the same fault interval times according to the creation time of the fault test data packet, and to adjust the acquisition density of track parameters corresponding to the same fault based on the same fault interval time and the average maintenance time of the same fault, wherein the acquisition density is the number of devices that acquire 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, connected to the adjustment subunit, is used to determine frequently occurring faults by analyzing the different fault interval times, compare the frequently occurring faults with the maintenance information, and generate a fault reference list based on the comparison results.

10. The vehicle fault monitoring system based on multidimensional data analysis according to claim 9, characterized in that, The tunnel monitoring unit includes: The vibration sub-unit is symmetrically arranged on both sides of the track to collect the track vibration frequency during train operation; The noise sub-units are symmetrically arranged on the tunnel walls on both sides of the track to collect tunnel noise during train operation. The wind pressure sub-unit is symmetrically arranged on the tunnel walls on both sides of the track to collect tunnel wind pressure during train operation; A temperature subunit is installed on the wall of the train tunnel to collect the tunnel temperature during train operation; A humidity subunit is installed on the wall of the train tunnel to collect tunnel humidity during train operation; The tunnel parameters include the track vibration frequency, the tunnel noise, the tunnel wind pressure, the tunnel temperature, and the tunnel humidity.

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