A health management method and system for train running gear

Abstract

The present invention provides a health management method for a train running gear, including: data collection and processing, obtaining status monitoring data of key components of the train running gear, the key components of the train running gear include box bearings, wheelset treads, gear sets, gear bearings, motor bearings, and the status monitoring data include characteristic frequency fault magnitude, axle temperature, ambient temperature, and vibration effective value; health evaluation, quantitative scoring of the health status of key components of the train running gear, including: key parameter deduction value calculation and health value calculation; maintenance strategy formulation, determining the health level of the corresponding key components of the train running gear according to the health value and formulating the maintenance strategy; remaining life prediction, predicting the remaining life of the corresponding key components of the train running gear according to the health level. The present invention combines multi-parameter data to quantitatively evaluate the health status of key components of the running gear, which can effectively monitor wear faults and reveal safety hazards.

Description

Technical Field

[0001] The present invention belongs to the technical field of train operation safety monitoring, and in particular relates to a health management method and system for a train running gear. Background Art

[0002] Bearings and gears are the most important parts of the running gear of urban rail trains. They work at high speeds and high pressures and are prone to failure. As the load-bearing end that is in long-term contact with the track, the wheelset is greatly affected by the road and ground conditions. Therefore, it is necessary to establish a quantitative method to continuously monitor the health status of the running gear, evaluate the health status and change trends of the train running gear, identify hidden dangers early, and guide maintenance.

[0003] In the existing running gear component fault diagnosis algorithm, the vibration signal is usually resonantly demodulated, and then a fast Fourier transform is performed to obtain the corresponding spectrum. It is determined whether the spectrum has a preset characteristic frequency of the rotating component to determine whether the monitored component has a fault. It is then determined whether the impact intensity reaches a preset impact intensity threshold to obtain the alarm level. However, determining the alarm level only by the impact intensity cannot fully reflect the development trend of the monitored component failure. There are other monitoring parameters that can reflect the development trend of the failure, especially wear failure. In addition, the differences in vibration signals caused by differences in hardware and train operating environment cannot be avoided, which may lead to false alarms, increase the maintenance workload, and pose a safety hazard that the running gear failure cannot be repaired in a timely manner. Summary of the invention

[0004] Based on the above problems, a first aspect of the present invention proposes a health management method for a train running gear, comprising:

[0005] S1: Data collection and processing, obtaining the status monitoring data of key components of the train running gear, including box bearings, wheelset treads, gear sets, gear bearings, motor bearings, and status monitoring data including characteristic frequency fault value, shaft temperature, ambient temperature, and vibration effective value;

[0006] S2: Health evaluation, quantitative scoring of the health status of key components of the train running gear, including:

[0007] S21: Calculation of deduction points for key parameters, including characteristic frequency fault quantity, temperature, and original vibration

[0008] Effective value;

[0009] S22: Calculate the health value, perform weighted statistics on the deduction value of each key parameter to obtain the health value;

[0010] S3: Maintenance strategy formulation: determine the health level of the corresponding key components of the train running gear according to the health value and formulate a maintenance strategy;

[0011] S4: Remaining life prediction, the remaining life of key components of the train running gear corresponding to the health level value.

[0012] Preferably, S1 applies a polling collection mechanism, the collection interval is 8 to 12 seconds, and the number of collection points for each data packet is 1024 points.

[0013] Furthermore, the calculation method of the deduction value of S21 includes referring to the threshold classification of the warning and alarm levels, or referring to the fitting benchmark value of the key parameter, and normalizing it in combination with the defined full score deduction value to obtain the key parameter deduction value.

[0014] Specifically, the calculation formula of the characteristic frequency fault amount deduction value Y1 is:

[0015]

[0016] Among them, X1 is the characteristic frequency fault value; Z1 is the full score deduction value of the characteristic frequency fault value, Z1∈[60,80]; X A is the warning threshold, X B is the level I alarm threshold, X C is the II level alarm threshold, corresponding to the health deduction value constant Z A , Z B , Z C , the corner marks A, B, and C are the thresholds that will be reached at the next level.

[0017] Specifically, the calculation formula for the temperature deduction value Y2 is:

[0018]

[0019] in,

[0020]

[0021]

[0022] Among them, X21 is the shaft temperature, and the temperature rise X22 = X21-X23, where X23 is the ambient temperature; Z2 is the temperature deduction score, with a maximum deduction score of 50-70 points; X D1 is the shaft temperature warning threshold, X E1 is the shaft temperature alarm threshold, corresponding to the health deduction value constant Z D1 , Z E1 , X D2 is the temperature rise threshold, X E2 is the temperature rise alarm threshold, corresponding to the health deduction value constant Z D2 , Z E2 , the subscripts D and E represent the thresholds that will be reached at the next level.

[0023] Specifically, the calculation formula of the original vibration effective value deduction value Y3 is:

[0024]

[0025] Among them, X rms is the original vibration effective value, Xq is the vibration reference value, Xq=q×n 2 +P, a, q, P are constants, q = 0.0002 ~ 0.0005, P = 0.6 ~ 1.5, n is the rotational speed; Z3 is the full score deduction value of the original vibration effective value, Z3∈[40,60].

[0026] Furthermore, the formula for calculating the health value in S22 is:

[0027] Y=100-(b×Y1+c×Y2+d×Y3)

[0028] Among them, Y1 is the deduction value of the characteristic frequency fault quantity, Y2 is the deduction value of the temperature, Y3 is the deduction value of the original vibration effective value, and b, c, and d are constants.

[0029] Furthermore, S22 also includes taking the deduction value for a continuous period of time as a whole and counting the health value in the form of a moving sliding window.

[0030] A second aspect of the present invention provides a health management system for a train running gear, comprising:

[0031] The data acquisition and processing module obtains the status monitoring data of the key components of the train running gear. The key components of the train running gear include box bearings, wheel set treads, gear sets, gear bearings, and motor bearings. The status monitoring data includes characteristic frequency fault value, shaft temperature, ambient temperature, and vibration effective value;

[0032] The health assessment module quantitatively scores the health status of key components of the train running gear, including:

[0033] a) Key parameter deduction value unit, key parameters include characteristic frequency fault quantity, temperature, original vibration effective value;

[0034] b) Health value calculation unit, which performs weighted statistics on each key parameter to obtain the health value;

[0035] The maintenance strategy formulation module determines the health level of the corresponding key components of the train running gear according to the health value and formulates the maintenance strategy;

[0036] The remaining life prediction module predicts the remaining life of the corresponding key components of the train running gear according to the health level.

[0037] Preferably, the data acquisition and processing module includes a temperature-vibration integrated composite sensor, a preprocessor, and a data processing board.

[0038] The present invention constructs a health management and evaluation system for key components of the train running gear through the fusion of multi-parameter and multi-source data, analysis of the running gear operation mechanism, and the application of big data processing technology, based on hierarchical and graded maintenance strategies. In addition to the fault alarm requirements that can be met by existing technical algorithms, the present invention also combines the value changes of different parameters to quantitatively evaluate the health status of key components of the running gear, monitor the development trend of key components of the running gear before a fault alarm occurs, effectively monitor wear faults, and provide urban rail operating users with intuitive maintenance strategies to eliminate the safety hazards of running gear faults that cannot be repaired in a timely manner. In addition, by using statistics in the form of moving sliding windows, false alarms caused by differences in hardware and train operating environments can be avoided, reducing maintenance workload. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings help to further understand the present application. For the convenience of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0040] Figure 1 is a flow chart of a health management method for a train running gear in one embodiment;

[0041] Figure 2 This is a schematic diagram of a health assessment process in an embodiment;

[0042] Figure 3 A schematic diagram of a flow chart of calculating a characteristic frequency fault amount deduction value in an embodiment;

[0043] Figure 4 A schematic diagram of a flow chart of temperature deduction value calculation in one embodiment;

[0044] Figure 5 A schematic diagram of a flow chart of calculating the deduction value of the original vibration effective value in one embodiment;

[0045] Figure 6 A schematic diagram of health value calculation in an embodiment;

[0046] Figure 7 This is a schematic diagram of the structure of a health management system for a train running gear in one embodiment;

[0047] Figure 8 Schematic diagram of the workflow of the data acquisition and processing module in one embodiment. DETAILED DESCRIPTION

[0048] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention.

[0049] Figure 1Flow chart of a health management method for a train running gear in one embodiment of the present invention. The method in this embodiment includes:

[0050] S1: Data collection and processing, obtaining the status monitoring data of key components of the train running gear, including box bearings, wheelset treads, gear sets, gear bearings, motor bearings, and status monitoring data including characteristic frequency fault value, shaft temperature, ambient temperature, and vibration effective value;

[0051] S2: Health evaluation, quantitative scoring of the health status of key components of the train running gear;

[0052] S3: Maintenance strategy formulation: determine the health level of the corresponding key components of the train running gear according to the health value and formulate a maintenance strategy;

[0053] S4: Remaining life prediction: predict the remaining life of the corresponding key components of the train running gear according to the health level.

[0054] S2 specifically includes:

[0055] S21: Calculation of deduction points for key parameters, including characteristic frequency fault quantity, temperature, and original vibration effective value;

[0056] S22: Calculate the health value, perform weighted statistics on the deduction value of each key parameter, and obtain the health value.

[0057] For reference Figure 2 , a flow chart of S2 health assessment in one embodiment of the present invention, wherein,

[0058] S21: Calculation of key parameter deduction values, respectively calculating the healthy deduction values ​​of temperature value, characteristic frequency fault value and original vibration effective value;

[0059] S22: Health value calculation: the health score values ​​of key components are integrated and calculated to obtain the health score values ​​of key components.

[0060] In a preferred embodiment, the calculation method of the deduction value of S21 includes referring to the threshold classification of the warning and alarm levels, or referring to the fitting benchmark value of the key parameter, and normalizing it in combination with the defined full score deduction value to obtain the key parameter deduction value.

[0061] In the preferred embodiment, the characteristic frequency fault value deduction value is calculated by combining the thresholds of the early warning, level I alarm, and level II alarm of the characteristic frequency fault value of the key components as a reference for the graded calculation, and a linear normalization method is used to calculate the characteristic frequency health deduction value Y1 according to the following formula:

[0062]

[0063] Among them, X1 is the characteristic frequency fault value; Z1 is the full score deduction value of the characteristic frequency fault value, with a maximum deduction value of 60-80 points; X A is the warning threshold, X B is the level I alarm threshold, X C is the II level alarm threshold, corresponding to the health deduction value constant Z A , Z B , Z C , the corner marks A, B, and C are the thresholds that will be reached at the next level.

[0064] Figure 3 The figure is a flow chart of the characteristic frequency fault value deduction value calculation in a specific embodiment. The key components in this embodiment are bearings, treads, and gear sets. The characteristic frequency fault value X1 of the parameter output in the data acquisition and processing steps is used as the source data, and the thresholds of the early warning, level I alarm, and level II alarm of the characteristic frequency fault value of the key components are combined for calculation to obtain the characteristic frequency health deduction value.

[0065] In the preferred embodiment, the temperature deduction value is calculated by combining the temperature value warning and alarm thresholds, the difference, linear normalization and other calculation methods, and the temperature value health deduction value Y2 is calculated as follows:

[0066]

[0067] in,

[0068]

[0069]

[0070] Among them, X21 is the shaft temperature, and the temperature rise X22 = X21-X23, where X23 is the ambient temperature; Z2 is the temperature deduction score, with a maximum deduction score of 50-70 points; X D1 is the shaft temperature warning threshold, X E1 is the shaft temperature alarm threshold, corresponding to the health deduction value constant Z D1 , Z E1 , X D2 is the temperature rise threshold, X E2 is the temperature rise alarm threshold, corresponding to the health deduction value constant Z D2 , Z E2 , the subscripts D and E represent the thresholds that will be reached at the next level.

[0071] Figure 4The figure is a flow chart of temperature deduction value calculation in a specific embodiment. In this embodiment, the output parameters of shaft temperature X21 and ambient temperature X23 in the data acquisition and processing steps are used as source data, and the shaft temperature health deduction value Y21 is calculated based on the shaft temperature X21. The ambient temperature health deduction value Y22 is calculated based on the difference between the shaft temperature X21 and the ambient temperature X23, that is, the temperature rise X22. The larger value of Y21 and Y22 is taken to obtain the temperature health deduction value Y2.

[0072] In a preferred embodiment, the original vibration effective value deduction value is combined with the fitted vibration reference value to calculate the original vibration effective value health deduction value Y3 according to a certain ratio, as shown in the following formula:

[0073]

[0074] Among them, X rms is the original vibration effective value, Xq is the vibration reference value, Xq=q×n 2 +P, a, q, P are constants, depending on the project, q = 0.0002 ~ 0.0005, P = 0.6 ~ 1.5, n is the speed, which can be the wheel speed or motor speed, determined according to the location of the monitoring component where the measuring point is located; Z3 is the full score deduction value of the original vibration effective value, limited to 40-60 points.

[0075] Figure 5 The figure is a flow chart of calculating the deduction value of the original vibration effective value in a specific embodiment. rms As the source data, combined with the wheel speed or motor speed provided by the train system, the fitting parameter X is calculated q As the vibration reference value (the vibration reference value is generally between 1 and 5 g), the original vibration effective value Xrms and the fitting parameter Xq are calculated according to the above formula to obtain the vibration effective value health deduction value Y3.

[0076] In another preferred embodiment, the characteristic frequency health deduction value Y1, the temperature value health deduction value Y2, and the original vibration effective value health deduction value Y3 are used as source data, and the full score is limited to 100 points. The health value Y is calculated according to the following formula:

[0077] Y=100-(b×Y1+c×Y2+d×Y3)

[0078] Among them, b, c, and d are constants set according to specific projects, b=0.4~1, c=0.7~1, and d=0.2~1.

[0079] Figure 6This is a calculation flow of the health value in a specific embodiment. The key components in this embodiment are bearings, treads, and gear sets. The characteristic frequency health deduction value Y1, temperature value health deduction value Y2, and original vibration effective value health deduction value Y3 are integrated and calculated to obtain the health score value Y of each key component.

[0080] In a more preferred embodiment, in order to avoid false alarms caused by hardware and train differences, S1 applies a polling collection mechanism with a collection interval of 8 to 12 seconds and 1024 points for each data packet; S22 uses the health deduction value of M consecutive data packets as an overall package, and uses a moving sliding window with an interval of 1 packet for statistical analysis, and calculates the health value Y of the key components at each moment according to the following formula:

[0081]

[0082] According to another embodiment, in the S3 maintenance strategy formulation step, six health levels are formulated according to the health management score of each component, including three status levels and three fault levels, namely healthy, sub-healthy, early fault, warning, level I warning, and level II warning. In combination with different health levels, corresponding maintenance strategies for key components of the running gear of urban rail trains are formulated. Taking the axle box bearing as an example, the corresponding maintenance strategy is shown in the following table:

[0083] Table 1 Health level and maintenance strategy of urban rail train axle box bearings

[0084]

[0085]

[0086] According to another embodiment, in the remaining life prediction step S4, the corresponding life of key components is predicted based on different health value levels. Taking the axle box bearing as an example, its corresponding remaining life is shown in the following table:

[0087] Table 2 Health level and expected health life of axle box bearings of urban rail trains

[0088]

[0089] Figure 7 It is a structural schematic diagram of a health management system 700 for a train running gear in an embodiment of the present invention, comprising a data acquisition and processing module 701, which is used to obtain status monitoring data of key components of the train running gear; a health evaluation module 702, which quantitatively scores the health status of key components of the train running gear based on the data provided by the data acquisition and processing module; a maintenance strategy formulation module 703, which is used to provide a basis and strategy for the maintenance of key components of the urban rail train running gear; and a life prediction module 704, which is used to estimate the life of key components of the urban rail train running gear.

[0090] In a preferred embodiment, the data acquisition and processing module includes a temperature and vibration integrated composite sensor for front-end sensing, a preprocessor for data tag processing, and a data processing board, and finally outputs relevant monitoring parameter values.

[0091] Figure 8 This is a workflow diagram of the data acquisition and processing module in a specific embodiment. The key components in this embodiment are bearings, treads, and gear sets. The composite sensor at the front end senses vibration and temperature signals. The vibration signal is output as the effective value of vibration Xrms through the processor, and the impact value is obtained through resonance demodulation. Then, through analysis, Fourier transform (FFT), etc., the output is the characteristic frequency fault value X1. The temperature signal is output as the shaft temperature X21 and the ambient temperature X22 through the processor.

[0092] Although the contents of the present application are specifically demonstrated and described in conjunction with the preferred embodiments, those skilled in the art should understand that various changes made to the present application in form and detail without departing from the spirit and scope of the present application as defined by the appended claims and without making any creative work are within the scope of protection of the present application.

Claims

1. A health management method for a train running gear, characterized in that: include: S1: Data collection and processing, obtaining the status monitoring data of key components of the train running gear, the train running gear Key components include box bearings, wheelset treads, gear sets, gear bearings, motor bearings, and the condition monitoring data Including characteristic frequency fault value, shaft temperature, ambient temperature, and vibration effective value; S2: Health evaluation, quantitative scoring of the health status of key components of the train running gear, including: S21: Calculation of deduction points for key parameters, where the key parameters include characteristic frequency fault quantity, temperature, and original vibration effective value; The calculation method of the deduction value includes referring to the threshold classification of the warning and alarm levels, or referring to the fitting benchmark value of the key parameter, and normalizing it with the defined full score deduction value to obtain the key parameter deduction value; the characteristic frequency fault The calculation formula of the amount deduction value Y1 is: Among them, X1 is the characteristic frequency Fault value; Z1 is the full score deduction value of the characteristic frequency fault value, Z1∈[60,80]; X A is the warning threshold, X B For I Level alarm threshold, X C is the II level alarm threshold, corresponding to the health deduction value constant Z A , Z B , Z C , corner mark A, B and C take the threshold that will be reached at the next level; S22: Calculate the health value, perform weighted statistics on the deduction value of each key parameter to obtain the health value; S3: Maintenance strategy formulation, determine the health level of the corresponding key components of the train running gear according to the health value and formulate maintenance strategies Repair strategy; S4: Remaining life prediction: predict the remaining life of the corresponding key components of the train running gear based on the health value.

2. The health management method for train running gear according to claim 1, characterized in that: S1 uses polling The collection mechanism has a collection interval of 8 to 12 seconds and 1024 points are collected for each data packet.

3. The health management method for train running gear according to claim 1, characterized in that: Temperature deduction value The calculation formula for Y2 is: in, X21 is the shaft temperature, temperature rise X22 = X21-X23, where X23 is the ambient temperature; Z2 is the temperature deduction Full score, with 50-70 points as the deduction limit; X D1 is the shaft temperature warning threshold, X E1 Axis temperature alarm threshold Values, corresponding to the health deduction constant Z D1 , Z E1 , X D2 is the temperature rise threshold, X E2 is the temperature rise alarm threshold, Corresponding to the health deduction constant Z D2 , Z E2 , the subscripts D and E represent the thresholds that will be reached at the next level.

4. The health management method for train running gear according to claim 1, characterized in that: The calculation formula of the original vibration effective value deduction value Y3 is: Among them, X rms is the original vibration effective value, Xq is the vibration reference value, Xq=q×n 2 +P, q, P are constant number, q = 0.0002 ~ 0.0005, P = 0.6 ~ 1.5, n is the speed; Z3 is the original vibration effective value deduction full score value, Z3∈[40,60]。 5. The train running gear health management method according to claim 1, characterized in that: The formula for calculating the health value in S22 is Y=100-(b×Y1+c×Y2+d×Y3) Among them, Y1 is the deduction value of characteristic frequency fault quantity, Y2 is the deduction value of temperature, and Y3 is the deduction value of original vibration effective value. score, b, c, d are constants.

6. The health management method for train running gear according to claim 5, characterized in that: S22 also includes The deduction value for a continuous period of time is taken as a whole, and the health value is calculated in the form of a moving sliding window.

7. A health management system for a train running gear, characterized in that: include: The data acquisition and processing module uses a polling acquisition mechanism to obtain the status monitoring data of key components of the train running gear, wherein the key components of the train running gear include box bearings, wheel set treads, gear sets, gear bearings, and motor bearings. The status monitoring data includes characteristic frequency fault value, shaft temperature, ambient temperature, and vibration effective value; The health evaluation module quantitatively scores the health status of the key components of the train running gear, including: a) Key parameter deduction value unit, the key parameters include characteristic frequency fault quantity, temperature, original vibration The calculation method of the deduction value includes referring to the threshold classification of the warning and alarm levels, or referring to the fitting benchmark value of the key parameters, and normalizing the deduction value in combination with the defined full score value to obtain the deduction value of the key parameter; the calculation formula of the characteristic frequency fault amount deduction value Y1 is: Among them, X1 is the characteristic frequency fault value; Z1 is the characteristic frequency The maximum score for fault quantity deduction, Z1∈[60,80]; X A is the warning threshold, X B is the level I alarm threshold, X C is the II level alarm threshold, corresponding to the health deduction value constant Z A , Z B , Z C , corner marks A, B, and C take the threshold value that will be reached at the next level; b) Health value calculation unit, which performs weighted statistics on each key parameter to obtain the health value; The maintenance strategy formulation module determines the health level of the corresponding key components of the train running gear according to the health value and formulates the maintenance strategy; The remaining life prediction module predicts the remaining life of the corresponding key components of the train running gear based on the health value.

8. The health management system for train running gear according to claim 7, characterized in that: Said The data acquisition and processing module includes a temperature and vibration integrated composite sensor, a preprocessor, and a data processing board.

Images