Railway vehicle fault early warning device

Abstract

The invention relates to the technical field of rail vehicle fault early warning, in particular to a rail vehicle fault early warning device, which comprises a trackside frequency detection mechanism arranged at a rail position between adjacent stations when the vehicle speed is uniform and used for detecting the operation vibration frequency emitted by a rail; the carriage operation frequency detection mechanism is arranged outside the rail carriage and is used for detecting the carriage vibration frequency when the train runs at a constant speed in an initial detection period; and the vibration frequency receiving mechanism is arranged in the subway monitoring room and is used for receiving the operation vibration frequency and the carriage vibration frequency. Whether the compartment or the rail is abnormal or not is determined through the operation vibration frequency and the compartment vibration frequency, the fault level is determined through the compartment vibration peak value and the compartment vibration valley value of the compartment vibration frequency change curve, and the accuracy of rail vehicle fault early warning is improved.

Description

Technical Field

[0001] This invention relates to the field of rail vehicle fault early warning technology, and in particular to a rail vehicle fault early warning device. Background Technology

[0002] Routine inspections of rail vehicles are particularly important, especially during operation, as their high speed poses a significant safety hazard to passengers. Therefore, it is crucial to provide early warnings of rail vehicle malfunctions for troubleshooting.

[0003] Chinese Patent Publication No. CN120039292A discloses a method and storage medium for early warning of rail vehicle faults. The method includes: obtaining operational data of the rail vehicle; determining first operational data corresponding to a first train formation type and second operational data corresponding to a second train formation type; updating the first operational data using a first variable and updating the second operational data using a second variable; inputting the updated first and second operational data into an early warning model; the early warning model is pre-trained to recognize the first and second variables; and determining rail vehicle fault early warning information based on the output of the early warning model. This method configures variables for the corresponding operational data according to the train formation type, which not only improves the accuracy and applicability of the data but also enables the early warning model to cope with rail vehicles of different train formation types, exhibiting strong reusability and versatility.

[0004] Therefore, the existing technology has the following problems: In the process of early warning of rail vehicle faults, the lack of means to determine whether there is an abnormality in the car or track by the operating vibration frequency and the car vibration frequency, and to determine the fault level by the car vibration peak value and the car vibration valley value of the car vibration frequency change curve, leads to inaccurate early warning of rail vehicle faults. Summary of the Invention

[0005] Therefore, the present invention provides a rail vehicle fault early warning device to overcome the problem in the prior art that the fault early warning of rail vehicles is inaccurate due to the lack of means to determine whether there is an abnormality in the car or track by the running vibration frequency and the car vibration frequency, and to determine the fault level by the car vibration peak value and the car vibration valley value of the car vibration frequency change curve.

[0006] To achieve the above objectives, the present invention provides a rail vehicle fault early warning device, comprising: A trackside frequency detection mechanism is set at the track position when the vehicle speed is constant between adjacent stations to detect the operating vibration frequency emitted by the track. The carriage operating frequency detection mechanism is installed outside the railcar and is used to detect the carriage vibration frequency when the train is running at a constant speed within the initial detection cycle. A vibration frequency receiving mechanism, which is wirelessly connected to the trackside frequency detection mechanism and the carriage running frequency detection mechanism respectively, is installed in the track monitoring room and is used to receive the running vibration frequency and the carriage vibration frequency. The frequency analysis module, connected to the vibration frequency receiving mechanism, determines abnormal operating vibration frequencies and the number of abnormal operating vibration frequencies based on comparisons between several operating vibration frequencies and normal operating frequency ranges at each station. It then determines the cause of the abnormality based on the number of abnormal operating vibration frequencies compared to the operating vibration frequencies corresponding to a single travel interval, and determines the fault warning level based on the cause of the abnormality. Furthermore, it determines abnormal car vibration frequencies based on the car vibration frequencies, acquires abnormal car vibration frequency change curves based on these abnormal car vibration frequencies, determines the fault level based on the car vibration peak and valley values ​​of the car vibration frequency change curves, and adjusts the initial detection cycle based on the fault level and the interval between vibration extreme values ​​to obtain the target detection cycle. An alarm mechanism, connected to the frequency analysis module, is used to alarm for faults in operating rail vehicles.

[0007] Furthermore, the frequency analysis module includes: The first frequency analysis unit determines the number of abnormal operating vibration values ​​based on the comparison between the operating vibration frequency and the normal operating vibration frequency range, determines the travel interval between corresponding stations based on the abnormal operating vibration frequency values, determines several comparative operating vibration frequencies generated when multiple trains pass through the travel interval, and determines the cause of the anomaly based on the vibration frequency anomaly difference between the average value of the comparative operating vibration frequencies and the abnormal operating vibration frequency values. The second frequency analysis unit determines the abnormal car vibration frequency based on the comparison between the car vibration frequency and the normal operating vibration frequency range, and determines the fault level based on the car vibration difference between the car vibration peak value and the car vibration valley value. The fault warning unit determines the cause of the abnormality based on the abnormality of the carriage as a first-level fault warning level, and determines the cause of the abnormality based on the abnormality of the track as a second-level fault warning level. The causes of the anomalies include abnormalities in the carriages and abnormalities in the tracks.

[0008] Furthermore, the first frequency analysis unit determines the operating vibration frequency to be within the normal operating vibration frequency range if the operating vibration frequency is within the normal operating vibration frequency range; and determines the operating vibration frequency to be an abnormal value if the operating vibration frequency is outside the normal operating vibration frequency range and records the number of abnormal operating vibration values.

[0009] Furthermore, the first frequency analysis unit determines the cause of the anomaly as a carriage anomaly based on the fact that the difference between the average value of the operating vibration frequency and the abnormal value of the operating vibration frequency is less than a preset anomaly difference threshold; and determines the cause of the anomaly as a track anomaly based on the fact that the difference between the average value of the operating vibration frequency and the abnormal value of the operating vibration frequency is greater than or equal to a preset anomaly difference threshold.

[0010] Furthermore, the first frequency analysis unit determines that the vibration anomaly is an occasional vibration anomaly if the number of vibration anomalies is less than or equal to the vibration anomaly number threshold, and determines that the vibration anomaly is a continuous vibration anomaly if the number of vibration anomalies is greater than the vibration anomaly number threshold.

[0011] Furthermore, the second frequency analysis unit determines that the car vibration is normal if the car vibration frequency is within the normal operating vibration frequency range, and determines that the car vibration frequency is abnormal if the car vibration frequency is outside the normal operating vibration frequency range, indicating that the corresponding car has a fault.

[0012] Furthermore, the second frequency analysis unit segments the carriage vibration frequency change curve based on a preset time period to obtain a segmented carriage vibration frequency change curve. Based on the segmented carriage vibration frequency change curve, it determines the corresponding segmented carriage vibration peak value and segmented carriage vibration valley value. Based on the segmented carriage vibration difference between the segmented carriage vibration peak value and segmented carriage vibration valley value, it determines the fault level.

[0013] Furthermore, the second frequency analysis unit determines the fault level based on the comparison result between the vibration difference of the segmented carriages and the vibration difference threshold of the segmented carriages; Based on the fact that the vibration difference between the sections of the carriage is less than or equal to the vibration difference threshold between the sections of the carriage, the fault level is determined to be Level 1 fault. Based on the fact that the vibration difference between the sections of the carriage is greater than the threshold for the vibration difference between the sections of the carriage, the fault level is determined to be a level two fault. Among them, the first-level fault level is lower than the second-level fault level.

[0014] Furthermore, the frequency analysis module determines the initial value of the detection cycle based on the fault level, and determines the target detection cycle based on the comparison result between the initial value of the detection cycle and the interval of the vibration extreme value. The vibration extreme value interval is the interval within which the preset vibration extreme value appears within the abnormal carriage vibration frequency change curve, and the initial value of the detection cycle is the reference value of the initial detection cycle.

[0015] Furthermore, the frequency analysis module determines that the initial value of the detection cycle is set as the target detection cycle based on the vibration extreme value interval duration being greater than or equal to the vibration extreme value interval duration threshold. Based on the vibration extreme value interval duration being less than the vibration extreme value interval duration threshold, the initial value of the detection cycle is reduced according to the difference between the vibration extreme value interval duration threshold and the vibration extreme value interval duration to obtain the target detection cycle.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: In implementation, the rail vehicle fault early warning device adopts a dual-dimensional vibration detection architecture consisting of a trackside frequency detection mechanism and a carriage operating frequency detection mechanism. This architecture enables the vehicle to provide early warning of faults by combining the operating vibration frequency generated during vehicle operation with the carriage vibration frequency generated during its own operation. This effectively avoids the problems of misjudgment and missed judgment that are prone to occur with a single detection dimension, and improves the pertinence and reliability of fault identification. By using a frequency analysis module to determine the corresponding fault early warning level for different abnormal causes, it is possible to distinguish between mild events and events that require urgent handling. The combination of graded early warning and multi-channel alarm mechanisms can help operation and management personnel respond and handle the situation in a timely manner, significantly reduce the risk of fault escalation, and improve the safety level of subway operation.

[0017] Furthermore, during implementation, the first frequency analysis unit can accurately locate abnormal track intervals based on historical data comparison results and effectively distinguish between the two types of abnormal causes: track and carriage, avoiding confusion in fault investigation and providing clear guidance for maintenance personnel. The second frequency analysis unit achieves quantitative judgment of fault level through extreme value difference analysis of the abnormal carriage vibration frequency change curve, making the severity of the fault immediately clear. The fault early warning unit sets differentiated alarm modes based on different abnormal causes, intuitively conveying the risk level, and combined with the information push function, ensures that maintenance personnel receive fault information in a timely manner and respond quickly.

[0018] Furthermore, in implementation, the first frequency analysis unit first determines whether the operating vibration frequency is normal by checking if it falls within the normal operating vibration frequency range. This helps determine whether abnormal operating vibration is caused by track wear or cracks during subway operation, thus identifying potential causes affecting all subway cars and preventing track-related abnormal vibration frequencies from disrupting normal subway operations. The first frequency analysis unit also determines the cause of the anomaly by comparing the difference between the average operating vibration frequency and the abnormal operating vibration frequency. If the difference is less than a preset abnormality value... When the threshold is reached, the cause of the abnormality is determined to be a carriage abnormality. This means that during the operation of the subway carriage, the abnormality difference is within the controllable range of the preset abnormality difference threshold. The abnormal value of the operating vibration frequency is only caused by minor defects in some tracks. This can avoid the situation where there is an abnormality during subway operation, but the source of the abnormality cannot be accurately determined. The first frequency analysis unit determines that the vibration abnormality is sporadic when the number of vibration abnormal values ​​is less than or equal to the vibration abnormality number threshold. When the cause of the abnormality is determined, the comparison between the number of vibration abnormal values ​​and the vibration abnormality number threshold can determine whether the vibration abnormality is sporadic or continuous, and handle the corresponding vibration abnormality situation.

[0019] Furthermore, in implementation, the second frequency analysis unit determines whether the vibration of the subway car is abnormal. When the vibration frequency of the car is outside the normal operating frequency range, it is determined that the vibration frequency of the car is abnormal, and the corresponding car has a fault. This can accurately identify the problematic car and provide early warning. The second frequency analysis unit determines the fault level based on the vibration difference between the peak and trough values ​​of the segmented car vibrations. The peak and trough values ​​of the segmented car vibrations can express whether the subway is stable during uniform speed movement. When the vibration difference between the segmented car vibrations is less than or equal to the segmented car vibration difference threshold, the fault level is determined to be Level 1, indicating that the movement is unstable but does not pose a threat to the current subway car. When the vibration difference between the segmented car vibrations is greater than the segmented car vibration difference threshold, the fault level is determined to be Level 2, indicating that the current subway car is threatened and measures such as stopping for maintenance are required.

[0020] Furthermore, in implementation, the frequency analysis module determines the target detection period based on the comparison between the initial value of the detection period and the interval duration of vibration extreme values. This can be linked to the actual operating conditions of the subway car. When the interval duration of vibration extreme values ​​is greater than or equal to the threshold value, it indicates that the occurrence of vibration extreme values ​​is sporadic and does not meet the conditions for affecting the normal operation of the subway. In this case, the initial value of the detection period is set as the target detection period. When the interval duration of vibration extreme values ​​is less than the threshold value, it indicates that the occurrence of vibration extreme values ​​is continuous and meets the conditions for affecting the normal operation of the subway. The initial value of the detection period is reduced based on the difference between the threshold value and the interval duration of vibration extreme values ​​to obtain the target detection period. In this case, reducing the initial value of the detection period can shorten the initial detection period, which is more conducive to monitoring fault conditions that occur during subway operation. Attached Figure Description

[0021] Figure 1 This is a schematic diagram showing the arrangement of the rail vehicle fault early warning device in this embodiment; Figure 2 This is a schematic diagram of the frequency analysis module structure in this embodiment; Figure 3 This is a flowchart illustrating the process of determining the cause of the anomaly in this embodiment; Figure 4 This is a flowchart illustrating the process of determining the fault level in this embodiment.

[0022] In the diagram, 1-trackside frequency detection mechanism; 2-carriage operating frequency detection mechanism; 3-track; 4-carriage. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate 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 is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0026] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0027] Please see Figure 1 As shown, it is a schematic diagram of the structure of the rail vehicle fault early warning device in this embodiment; This embodiment provides a fault early warning device for rail vehicles, including: The trackside frequency detection mechanism 1 is set at the track position when the vehicle speed is constant between adjacent stations, and is used to detect the running vibration frequency emitted by the track 3. The carriage operating frequency detection mechanism 2 is installed outside the railcar 4 and is used to detect the carriage vibration frequency when the train is running at a constant speed within the initial detection cycle. A vibration frequency receiving mechanism, which is wirelessly connected to the trackside frequency detection mechanism and the carriage running frequency detection mechanism respectively, is installed in the track monitoring room and is used to receive the running vibration frequency and the carriage vibration frequency. The frequency analysis module, connected to the vibration frequency receiving mechanism, determines abnormal operating vibration frequencies and the number of abnormal operating vibration frequencies based on comparisons between several operating vibration frequencies and normal operating frequency ranges at each station. It then determines the cause of the abnormality based on the number of abnormal operating vibration frequencies compared to the operating vibration frequencies corresponding to a single travel interval, and determines the fault warning level based on the cause of the abnormality. Furthermore, it determines abnormal car vibration frequencies based on the car vibration frequencies, acquires abnormal car vibration frequency change curves based on these abnormal car vibration frequencies, determines the fault level based on the car vibration peak and valley values ​​of the car vibration frequency change curves, and adjusts the initial detection cycle based on the fault level and the interval between vibration extreme values ​​to obtain the target detection cycle. An alarm mechanism, connected to the frequency analysis module, is used to alarm for faults in operating rail vehicles.

[0028] In this embodiment of the invention, taking the detection of faults during subway operation as an example, the trackside frequency detection mechanism uses a high-precision piezoelectric vibration sensor as the core detection element. The sensor's detection frequency range is 10-100Hz, and the sampling accuracy is 0.1Hz, which meets the detection requirements of vibration frequency during subway uniform speed operation. The sensor is fixedly installed on the side of the track in the uniform speed section between adjacent subway stations by expansion bolts. The installation position is 50cm away from the track fastener and avoids vibration interference areas such as track joints and turnouts. The installation spacing is set according to the actual length of the subway line. The spacing between two adjacent trackside vibration sensors is 500m to ensure full coverage detection of the track in the constant speed section between adjacent stations. Each sensor integrates a wireless transmission module to upload the collected operating vibration frequency to the vibration frequency receiving mechanism in real time. In this embodiment, the carriage operating frequency detection mechanism adopts a miniature inertial vibration sensor. The frequency detection range is consistent with that of the trackside frequency detection mechanism. The miniature inertial vibration sensor is installed on the bottom outer side of each subway train carriage by strong magnetic adsorption. Two sensors are installed in each carriage, located at the front and rear of the carriage respectively, to ensure that the collected carriage vibration frequency is representative. The carriage operating frequency detection mechanism has a built-in timing module, and the initial detection cycle is set to 10s.

[0029] In implementation, the rail vehicle fault early warning device adopts a dual-dimensional vibration detection architecture consisting of a trackside frequency detection mechanism and a carriage operating frequency detection mechanism. This architecture enables the vehicle to provide early warnings of faults by combining the operating vibration frequency generated during vehicle operation with the carriage vibration frequency generated during its own operation. This effectively avoids the misjudgment and missed judgment problems that are prone to occur with a single detection dimension, and improves the pertinence and reliability of fault identification. Through the frequency analysis module, the device determines the corresponding fault early warning level for different abnormal causes, which can distinguish between mild events and events that require emergency handling. The combination of graded early warning and multi-channel alarm mechanisms can help operation and management personnel respond and handle the situation in a timely manner, significantly reduce the risk of fault escalation, and improve the safety level of subway operation.

[0030] Please see Figure 2 As shown, it is a schematic diagram of the frequency analysis module structure in this embodiment; Specifically, the frequency analysis module includes: The first frequency analysis unit determines the number of abnormal operating vibration values ​​based on the comparison between the operating vibration frequency and the normal operating vibration frequency range, determines the travel interval between corresponding stations based on the abnormal operating vibration frequency values, determines several comparative operating vibration frequencies generated when multiple trains pass through the travel interval, and determines the cause of the anomaly based on the vibration frequency anomaly difference between the average value of the comparative operating vibration frequencies and the abnormal operating vibration frequency values. The second frequency analysis unit determines the abnormal car vibration frequency based on the comparison between the car vibration frequency and the normal operating vibration frequency range, and determines the fault level based on the car vibration difference between the car vibration peak value and the car vibration valley value. The fault warning unit determines the cause of the abnormality based on the abnormality of the carriage as a first-level fault warning level, and determines the cause of the abnormality based on the abnormality of the track as a second-level fault warning level. The causes of the anomalies include abnormalities in the carriages and abnormalities in the tracks.

[0031] In this embodiment of the invention, the first frequency analysis unit can pre-store basic information and historical operating vibration frequency data of each driving section, and can retrieve comparative operating vibration frequency data of the driving section. It first compares the collected operating vibration frequency with the preset normal operating vibration frequency range, counts the number of abnormal operating vibration frequency values, and then locates the abnormal driving section according to the location of the trackside frequency detection mechanism corresponding to the abnormal operating vibration frequency value. Subsequently, it retrieves 100 sets of comparative operating vibration frequencies of the same time period and the same vehicle type in the abnormal driving section within the past 30 days, calculates the average value, and finally determines the cause of the abnormality as a carriage abnormality or a track abnormality by comparing the difference between the abnormal operating vibration frequency value and the average value with the preset abnormality difference threshold. In this embodiment of the invention, the second frequency analysis unit has a built-in curve generation and segmentation algorithm, which can convert abnormal carriage vibration frequency data into a visual abnormal carriage vibration frequency change curve, and automatically segment it according to a preset time period of 1 minute, and calculate the vibration peak and valley values ​​of each segment of the carriage vibration frequency change curve to obtain the vibration difference of the segmented carriages. In this embodiment of the invention, the fault warning unit includes an audible and visual alarm unit and an information push unit. The audible and visual alarm unit is installed in the monitoring room and uses a red warning light and a buzzer. Different fault warning levels correspond to different alarm modes. Specifically, the first-level fault warning level uses a constantly lit red light and an intermittent buzzer alarm; the second-level fault warning level uses a flashing red light and a continuous buzzer alarm.

[0032] In implementation, the first frequency analysis unit can accurately locate the abnormal track interval based on the comparison results of historical data, and effectively distinguish between the two types of abnormal causes: track and carriage, avoiding confusion in the direction of fault investigation and providing clear guidance for maintenance personnel; the second frequency analysis unit realizes the quantitative judgment of fault level through extreme value difference analysis of the vibration frequency change curve of abnormal carriage, making the severity of the fault clear at a glance; the fault early warning unit sets differentiated alarm modes based on different abnormal causes, intuitively conveys the risk level, and at the same time, combined with the information push function, ensures that maintenance personnel receive fault information in a timely manner and respond quickly.

[0033] Specifically, the first frequency analysis unit determines the operating vibration frequency to be within the normal operating vibration frequency range if the operating vibration frequency is within the normal operating vibration frequency range; and determines the operating vibration frequency to be an abnormal value if the operating vibration frequency is outside the normal operating vibration frequency range and records the number of abnormal operating vibration values.

[0034] Please see Figure 3 The diagram shown is a flowchart illustrating the process of determining the cause of an anomaly in this embodiment. Specifically, the first frequency analysis unit determines the cause of the abnormality as a carriage abnormality based on the fact that the difference between the average value of the operating vibration frequency and the abnormal value of the operating vibration frequency is less than a preset abnormality difference threshold; and determines the cause of the abnormality as a track abnormality based on the fact that the difference between the average value of the operating vibration frequency and the abnormal value of the operating vibration frequency is greater than or equal to a preset abnormality difference threshold.

[0035] Specifically, the first frequency analysis unit determines an occasional vibration anomaly based on the number of vibration anomalies being less than or equal to a threshold number of vibration anomalies, and determines a continuous vibration anomaly based on the number of vibration anomalies being greater than the threshold number of vibration anomalies.

[0036] The operating vibration frequencies collected from each station are compared with the preset normal operating frequency range of 20-50Hz. If a certain operating vibration frequency exceeds this range, it is determined to be an abnormal value of the operating vibration frequency, and the number of abnormal values ​​of operating vibration is recorded. At the same time, based on the sensor position corresponding to the abnormal value of operating vibration, the driving interval where the abnormal value of operating vibration is located is determined, that is, the interval between stations is determined. In this embodiment of the invention, historical comparative operating vibration frequency data of the operating section corresponding to the abnormal operating vibration value are retrieved. The operating vibration frequency of the same type of train passing through the operating section corresponding to the abnormal operating vibration value within the same time period within 30 days is retrieved, totaling 100 sets. The retrieval period of 30 days belongs to the short-to-medium-term cycle of subway operation. It can cover different operating conditions such as differences in passenger flow on weekdays and weekends, vibration characteristics during morning and evening peak hours, and the impact of short-term weather changes. It can also exclude abnormal vibrations caused by occasional interference on a single day, such as temporary track cleaning or temporary failure of individual trains. Furthermore, it can avoid the failure of the benchmark due to slow changes such as track wear and aging of vehicle components in long-term data such as more than 3 months. This ensures that the benchmark data matches the actual operating status of the current track and vehicle. In this embodiment of the invention, the average value of the comparative operating vibration frequency is calculated by an arithmetic average algorithm; In this embodiment of the invention, a preset abnormal difference threshold is set to 5Hz. When the subway is running at a constant speed, even if the track and the carriage are normal, the vibration frequency will fluctuate slightly due to slight changes in passenger flow and differences in instantaneous wheel-rail contact. However, actual measurement data shows that the maximum difference of this normal fluctuation is usually ≤3-4Hz. It can be understood that setting the preset abnormal difference threshold to 5Hz can cover the normal fluctuation range, avoid misjudging normal small fluctuations as faults, and ensure the fault tolerance of the judgment. If the difference between the average operating vibration frequency and the abnormal operating vibration frequency is less than 5Hz, the cause of the abnormality is determined to be an abnormality in the carriage. If the difference between the average operating vibration frequency and the abnormal value of the operating vibration frequency is greater than or equal to 5Hz, the cause of the abnormality is determined to be a track abnormality. In this embodiment, the threshold for the number of vibration anomalies is set to 3, the initial detection cycle is set to 10s, the uniform operating speed of the subway is about 30km / h, the length of the uniform speed section between adjacent stations in the core urban area is usually 500-1000m, which corresponds to 2-4 trackside frequency detection institutions, the time for a single train to pass through a running section is about 60-120s, and 6-12 sets of running vibration frequencies can be collected. The threshold for the number of 3 vibration anomalies is about 1 / 4 to 1 / 2 of the sample size of a single train passing through. This 1 / 4 to 1 / 2 ratio can effectively eliminate random interference during a single train passing through, such as the instantaneous intrusion of small debris into the wheel and rail or vibration fluctuations caused by instantaneous passenger flow fluctuations. Such interferences usually only result in 1-2 anomalies and will not appear in 3 or more consecutively, thus avoiding misjudging random fluctuations as persistent faults. For regularly operating subway lines, the station spacing needs to be determined based on passenger density and urban planning layout in the area covered by the line. The industry standard is as follows: In the core urban area, where the population is dense and passenger demand is high, the station spacing is usually 0.8-1.2km; in the suburban area or near the city center, where the population is relatively sparse, the station spacing can be increased to 1.5-2.5km; and in the far suburbs, where the coverage is wide and passenger density is low, the station spacing can be further increased to 2.5-5km. From starting to reaching a constant speed of 30-40 km / h, a subway train needs to accelerate for 200-300 meters. Similarly, from a constant speed to stopping at the station, it needs to decelerate for 200-300 meters. This data is a commonly used measured value in the rail transit industry. Taking the station spacing of different sections of a regular subway line as an example; Core urban area section: Based on the minimum station spacing of 0.8km, the length of the uniform speed section is 800m-200m-200m, i.e., the deceleration and acceleration sections = 400m; based on the maximum station spacing of 1.2km, the length of the uniform speed section is 1200m-300m-300m, i.e., the deceleration and acceleration sections = 600m. Considering the error of small-spacing stations and the detection requirements, the length of the uniform speed section in the core urban area is taken as 500-1000m, which matches the operation scenario where subway stations are dense in the core urban area and the uniform speed section should not be too long. Suburban section: Based on a station spacing of 1.5km, the length of the constant speed section is 1500m - 200m - 200m, which includes the deceleration and acceleration sections = 1100m; based on a station spacing of 2.5km, the length of the constant speed section is 2500m - 300m - 300m, which includes the deceleration and acceleration sections = 1900m. Suburban section: Based on a station spacing of 5km, the length of the constant speed section is 5000m - 300m - 300m, which is the deceleration and acceleration section = 4400m; If the number of vibration anomalies is less than or equal to 3, it is determined to be an occasional vibration anomaly. If the number of vibration anomalies is greater than 3, then it is determined to be a continuous vibration anomaly.

[0037] In implementation, the first frequency analysis unit first determines whether the operating vibration frequency is normal by checking if it falls within the normal operating vibration frequency range. This helps determine if abnormal operating vibration is caused by track wear or cracks during subway operation, thus identifying potential causes affecting all subway cars and preventing track-related abnormal vibration frequencies from disrupting normal subway operations. The first frequency analysis unit then determines the cause of the abnormality by comparing the difference between the average operating vibration frequency and the abnormal value. If the difference is less than a preset threshold, the abnormality is considered normal. When the value is set, the abnormality is determined to be a carriage abnormality. This means that during the operation of the subway carriage, the abnormality difference is within the controllable range of the preset abnormality difference threshold. The abnormal value of the operating vibration frequency is only caused by minor defects in some tracks. This can avoid the situation where there is an abnormality during subway operation, but the source of the abnormality cannot be accurately determined. The first frequency analysis unit determines that the vibration abnormality is sporadic when the number of vibration abnormal values ​​is less than or equal to the vibration abnormality number threshold. It can determine whether the vibration abnormality is sporadic or continuous by comparing the number of vibration abnormal values ​​with the vibration abnormality number threshold when the cause of the abnormality is determined, and then handle the corresponding vibration abnormality.

[0038] Specifically, the second frequency analysis unit determines that the car vibration is normal if the car vibration frequency is within the normal operating vibration frequency range, and determines that the car vibration frequency is abnormal if the car vibration frequency is outside the normal operating vibration frequency range, indicating that the corresponding car has a fault.

[0039] In this embodiment of the invention, the normal operating frequency range is set to 20-50Hz. The vibrations generated by wheel-rail contact and the vibrations of the carriage structure itself are the main vibration sources when the subway train is running at a constant speed of 30-40km / h. Industry measured data show that... Normal wheel-rail contact vibrations typically occur at frequencies between 25-45 Hz, such as the elastic contact vibration between the rail and wheelset, and the buffer vibration of the rail fasteners. Normal vibrations in a car body structure typically occur at frequencies between 20-35Hz, such as vibration damping in the chassis suspension system and slight resonance in the car body. The 20-50Hz range can cover the frequency range of the two core normal vibrations mentioned above, ensuring that vibrations under normal operating conditions will not be misjudged as abnormal. The second frequency analysis unit determines that the vibration of the carriage is normal based on the fact that the collected carriage vibration frequency is within the preset normal operating frequency range, and only records the data without any subsequent special processing. If the vibration frequency of the carriage is not within the normal operating frequency range, it is determined that the vibration frequency of the carriage is abnormal, and the corresponding carriage is found to have a fault.

[0040] Specifically, the second frequency analysis unit segments the carriage vibration frequency change curve based on a preset time period to obtain a segmented carriage vibration frequency change curve. Based on the segmented carriage vibration frequency change curve, it determines the corresponding segmented carriage vibration peak value and segmented carriage vibration valley value. Based on the segmented carriage vibration difference between the segmented carriage vibration peak value and segmented carriage vibration valley value, it determines the fault level.

[0041] Please see Figure 4 The diagram shown is a flowchart illustrating the process of determining the fault level in this embodiment. Specifically, the second frequency analysis unit determines the fault level based on the comparison between the vibration difference of the segmented carriages and the vibration difference threshold of the segmented carriages; Based on the fact that the vibration difference between the sections of the carriage is less than or equal to the vibration difference threshold between the sections of the carriage, the fault level is determined to be Level 1 fault. Based on the fact that the vibration difference between the sections of the carriage is greater than the threshold for the vibration difference between the sections of the carriage, the fault level is determined to be a level two fault. Among them, the first-level fault level is lower than the second-level fault level.

[0042] In this embodiment of the invention, the abnormal carriage vibration frequency change curve is evenly segmented based on a preset time period of 1 minute to obtain a carriage vibration frequency change segmented curve. If the abnormal vibration of a carriage lasts for 5 minutes, then 5 carriage vibration frequency change segmented curves are generated. For each carriage vibration frequency change segmented curve, the segmented carriage vibration peak value and segmented carriage vibration valley value corresponding to each carriage vibration frequency change segmented curve are determined. In this embodiment of the invention, a preset threshold for the vibration difference between different sections of the carriage is set to 10Hz. Level 1 faults, or minor faults such as loose small parts at the bottom of the carriage or slight aging of the suspension system, only cause small fluctuations in vibration. The difference between the peak and trough vibration values ​​of a section of the carriage is typically ≤8Hz. For example, in a certain subway line, the vibration difference was 5-7Hz when the bolts at the bottom of the carriage were loose. Level 2 faults, or severe faults such as suspension spring failure, deformation of the carriage chassis structure, or severe wear of the wheelsets, disrupt the vibration stability of the carriage, causing large and violent fluctuations in vibration. The difference between the peak and trough vibration values ​​of a section of the carriage is generally >12Hz. For example, in a measured case of broken suspension springs, the vibration difference can reach 15-20Hz. Therefore, 10Hz is precisely the dividing point between the vibration differences of the two types of faults, accurately distinguishing the severity of the fault and avoiding confusion between minor and major faults. It is understood that the data listed in this embodiment is based on the actual operating environment of the subway for ease of understanding. In practical applications, the data can be changed according to the actual usage scenario. If the vibration difference between the sections of the carriage is less than or equal to the vibration difference threshold between the sections of the carriage, the fault level is determined to be Level 1 fault. If the vibration difference between the sections of the carriage exceeds the threshold value for vibration difference between the sections of the carriage, the fault level is determined to be a level two fault.

[0043] In implementation, the second frequency analysis unit determines whether the vibration of the subway car is abnormal. When the vibration frequency of the car is outside the normal operating frequency range, it is determined that the vibration frequency of the car is abnormal, and the corresponding car has a fault. This can accurately identify the problematic car and provide early warning. The second frequency analysis unit determines the fault level based on the vibration difference between the peak and valley values ​​of the segmented car vibrations. The peak and valley values ​​of the segmented car vibrations can express whether the subway is stable during uniform speed movement. When the vibration difference between the segmented car vibrations is less than or equal to the segmented car vibration difference threshold, the fault level is determined to be Level 1, indicating that the movement is unstable but does not pose a threat to the current subway car. When the vibration difference between the segmented car vibrations is greater than the segmented car vibration difference threshold, the fault level is determined to be Level 2, indicating that the current subway car is threatened and measures such as stopping for maintenance are required.

[0044] Specifically, the frequency analysis module determines the initial value of the detection cycle based on the fault level, and determines the target detection cycle based on the comparison result between the initial value of the detection cycle and the interval of the vibration extreme value. The vibration extreme value interval is the interval within which the preset vibration extreme value appears within the abnormal carriage vibration frequency change curve, and the initial value of the detection cycle is the reference value of the initial detection cycle.

[0045] Specifically, the frequency analysis module determines that the initial value of the detection cycle is set as the target detection cycle based on the vibration extreme value interval duration being greater than or equal to the vibration extreme value interval duration threshold. Based on the vibration extreme value interval duration being less than the vibration extreme value interval duration threshold, the initial value of the detection cycle is reduced according to the difference between the vibration extreme value interval duration threshold and the vibration extreme value interval duration to obtain the target detection cycle.

[0046] In this embodiment, the initial detection cycle is 10s, and the initial value of the detection cycle is 10s. The initial detection cycle is fine-tuned according to the fault level. The initial value of the detection cycle for the first-level fault level is still 10s, and the initial value of the detection cycle for the second-level fault level is reduced to 8s. The vibration extreme value interval is the time interval between peaks or valleys that exceed the normal frequency range. The time interval is calculated by extracting the timestamp difference between two adjacent extreme points in the abnormal vibration frequency change curve. The target detection cycle is determined based on the comparison between the initial value of the detection cycle and the interval between extreme vibration values. In this embodiment of the invention, the threshold for the interval between vibration extreme values ​​is set to 30s. If the interval between vibration extreme values ​​is ≥30s, it means that only one abnormal extreme value appears in three consecutive detection cycles, indicating that the occurrence of abnormal extreme values ​​is sparse and discontinuous. The existing 10s detection cycle is sufficient to completely capture the abnormal features, and no adjustment is needed. If the interval is <30s, such as 20s or 10s, multiple abnormal extreme values ​​may appear in one detection cycle, or the interval between adjacent vibration extreme values ​​may be shorter than the detection cycle. In this case, the 10s detection cycle will miss some extreme value information, and the detection cycle needs to be shortened to ensure data integrity. The setting of 30s can form a 3-fold correlation with the initial detection cycle, which avoids frequent adjustments to the detection cycle due to the interval being too short, and ensures that high-frequency abnormalities are not missed due to the interval being too long. The vibration extreme value interval threshold is a quantitative boundary for judging the frequency of abnormal car vibration. Its core purpose is to dynamically adjust the detection cycle. When the interval between two adjacent vibration extreme values ​​in the abnormal car vibration frequency change curve exceeds the peak and trough values ​​of the normal range of 20-50Hz by ≥30s, it indicates that the abnormal vibration is sparse, and the original detection cycle should be maintained. When the interval is <30s, it indicates that the abnormal vibration is frequent, and the detection cycle needs to be shortened to improve the monitoring accuracy. If the interval between extreme vibration values ​​is greater than or equal to 30 seconds, it indicates that the frequency of abnormal vibration is low. Therefore, the initial value of the current detection cycle is directly set as the target detection cycle to match the interval pattern of normal and abnormal carriage vibration. If the interval between extreme vibration values ​​is less than 30 seconds, it indicates that abnormal vibrations in the carriage occur frequently, and the detection cycle needs to be shortened to improve monitoring accuracy. The initial value of the detection cycle is reduced proportionally based on the difference between the threshold value and the extreme vibration interval, thus obtaining the target detection cycle. For example, if the interval between extreme vibration values ​​is 20 seconds, the difference between it and the 30-second threshold is 10 seconds, so the initial value of the detection cycle for the second-level fault is reduced by 2 seconds from 8 seconds, and the final target detection cycle is set to 6 seconds. If the interval between extreme vibration values ​​is 10 seconds, the difference is 20 seconds, so the initial value of 8 seconds is reduced by 4 seconds, and the target detection cycle is set to 4 seconds.

[0047] In implementation, the frequency analysis module determines the target detection period based on the comparison between the initial value of the detection period and the interval duration of vibration extreme values. This can be linked to the actual operation of the subway car. When the interval duration of vibration extreme values ​​is greater than or equal to the threshold value, it indicates that the occurrence of vibration extreme values ​​is sporadic and does not meet the conditions for affecting the normal operation of the subway. In this case, the initial value of the detection period is set as the target detection period. When the interval duration of vibration extreme values ​​is less than the threshold value, it indicates that the occurrence of vibration extreme values ​​is continuous and meets the conditions for affecting the normal operation of the subway. The initial value of the detection period is reduced based on the difference between the threshold value and the interval duration of vibration extreme values ​​to obtain the target detection period. In this case, reducing the initial value of the detection period can shorten the initial detection period, which is more conducive to monitoring faults that occur during subway operation.

[0048] 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.

[0049] 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 fault early warning device for rail vehicles, characterized in that, include: A trackside frequency detection mechanism is set at the track position when the vehicle speed is constant between adjacent stations to detect the operating vibration frequency emitted by the track. The carriage operating frequency detection mechanism is installed outside the railcar and is used to detect the carriage vibration frequency when the train is running at a constant speed within the initial detection cycle. A vibration frequency receiving mechanism, which is wirelessly connected to the trackside frequency detection mechanism and the carriage running frequency detection mechanism respectively, is installed in the track monitoring room and is used to receive the running vibration frequency and the carriage vibration frequency. The frequency analysis module is connected to the vibration frequency receiving mechanism. Based on the comparison results of several operating vibration frequencies between each station and the normal operating frequency range, it determines the abnormal value of the operating vibration frequency and the number of abnormal operating vibration values. Based on the number of abnormal operating vibration values ​​and the operating vibration frequency corresponding to a single driving section, it determines the cause of the abnormality. Based on the cause of the abnormality, it determines the fault warning level. Based on the vibration frequency of the carriage, the abnormal carriage vibration frequency is determined, and the abnormal carriage vibration frequency change curve is obtained based on the abnormal carriage vibration frequency. Based on the vibration peak value and vibration valley value of the carriage vibration frequency change curve, the fault level is determined, and the initial detection cycle is adjusted based on the fault level and the interval between vibration extreme values ​​to obtain the target detection cycle. An alarm mechanism, connected to the frequency analysis module, is used to alarm for faults in operating rail vehicles.

2. The rail vehicle fault early warning device according to claim 1, characterized in that, The frequency analysis module includes: The first frequency analysis unit determines the number of abnormal operating vibration values ​​based on the comparison between the operating vibration frequency and the normal operating vibration frequency range, determines the travel interval between corresponding stations based on the abnormal operating vibration frequency values, determines several comparative operating vibration frequencies generated when multiple trains pass through the travel interval, and determines the cause of the anomaly based on the vibration frequency anomaly difference between the average value of the comparative operating vibration frequencies and the abnormal operating vibration frequency values. The second frequency analysis unit determines the abnormal car vibration frequency based on the comparison between the car vibration frequency and the normal operating vibration frequency range, and determines the fault level based on the car vibration difference between the car vibration peak value and the car vibration valley value. The fault warning unit determines the cause of the abnormality based on the abnormality of the carriage as a first-level fault warning level, and determines the cause of the abnormality based on the abnormality of the track as a second-level fault warning level. The causes of the anomalies include abnormalities in the carriages and abnormalities in the tracks.

3. The rail vehicle fault early warning device according to claim 2, characterized in that, The first frequency analysis unit determines the operating vibration frequency to be within the normal operating vibration frequency range if the operating vibration frequency is within the normal operating vibration frequency range; if the operating vibration frequency is outside the normal operating vibration frequency range, it determines it to be an abnormal value of the operating vibration frequency and records the number of abnormal values ​​of the operating vibration.

4. The rail vehicle fault early warning device according to claim 3, characterized in that, The first frequency analysis unit determines the cause of the abnormality as a carriage abnormality based on the fact that the difference between the average value of the operating vibration frequency and the abnormal value of the operating vibration frequency is less than a preset abnormality difference threshold; and determines the cause of the abnormality as a track abnormality based on the fact that the difference between the average value of the operating vibration frequency and the abnormal value of the operating vibration frequency is greater than or equal to a preset abnormality difference threshold.

5. The rail vehicle fault early warning device according to claim 4, characterized in that, The first frequency analysis unit determines an occasional vibration anomaly based on the number of vibration anomalies being less than or equal to a threshold number of vibration anomalies, and determines a continuous vibration anomaly based on the number of vibration anomalies being greater than the threshold number of vibration anomalies.

6. The rail vehicle fault early warning device according to claim 5, characterized in that, The second frequency analysis unit determines that the car vibration is normal if the car vibration frequency is within the normal operating vibration frequency range, and determines that the car vibration frequency is abnormal if the car vibration frequency is outside the normal operating vibration frequency range, indicating that the corresponding car has a fault.

7. The rail vehicle fault early warning device according to claim 6, characterized in that, The second frequency analysis unit segments the carriage vibration frequency change curve based on a preset time period to obtain a segmented carriage vibration frequency change curve. Based on the segmented carriage vibration frequency change curve, it determines the corresponding segmented carriage vibration peak value and segmented carriage vibration valley value. Based on the segmented carriage vibration difference between the segmented carriage vibration peak value and segmented carriage vibration valley value, it determines the fault level.

8. The rail vehicle fault early warning device according to claim 7, characterized in that, The second frequency analysis unit determines the fault level based on the comparison between the vibration difference of the segmented carriages and the vibration difference threshold of the segmented carriages; Based on the fact that the vibration difference between the sections of the carriage is less than or equal to the vibration difference threshold between the sections of the carriage, the fault level is determined to be Level 1 fault. Based on the fact that the vibration difference between the sections of the carriage is greater than the threshold for the vibration difference between the sections of the carriage, the fault level is determined to be a level two fault. Among them, the first-level fault level is lower than the second-level fault level.

9. The rail vehicle fault early warning device according to claim 8, characterized in that, The frequency analysis module determines the initial value of the detection cycle based on the fault level, and determines the target detection cycle based on the comparison result between the initial value of the detection cycle and the interval of the vibration extreme value. The vibration extreme value interval is the interval within which the preset vibration extreme value appears within the abnormal carriage vibration frequency change curve, and the initial value of the detection cycle is the reference value of the initial detection cycle.

10. The rail vehicle fault early warning device according to claim 9, characterized in that, The frequency analysis module determines that the initial value of the detection cycle is set as the target detection cycle based on the vibration extreme value interval duration being greater than or equal to the vibration extreme value interval duration threshold. Based on the vibration extreme value interval duration being less than the vibration extreme value interval duration threshold, the initial value of the detection cycle is reduced according to the difference between the vibration extreme value interval duration threshold and the vibration extreme value interval duration to obtain the target detection cycle.

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