A train smoothness analysis method and system based on wheel-rail force

Abstract

The present application relates to the technical field of wheel-rail force, in particular to a train stability analysis method and system based on wheel-rail force, comprising the following steps: based on the real-time collected vertical force and lateral force data, the force value distribution of the train wheel and track contact point is segmented and counted, and the abnormal amplitude in the force fluctuation value is analyzed.The present application effectively identifies irregular vibration frequency and position by segmenting and counting the force value of the train wheel and track contact point, combined with abnormal amplitude analysis and screening of periodic abnormal parameters, improves the accuracy and processing speed of vibration identification, adjusts the trigger sensitivity and working threshold of the force sensor, optimizes the frequency distribution of the track irregularity area, enhances the response capability and adaptability of the monitoring system, and through the waveform feature parameter set extracted from the wheel dynamic force signal, the influence of the track gauge change on the waveform feature can be more comprehensively evaluated, and the dynamic stability analysis of the train is optimized, which provides data support for the train stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wheel-rail force, and particularly relates to a train stability analysis method and system based on wheel-rail force. BACKGROUND

[0002] Wheel-rail force technology focuses on the study and analysis of the interaction forces between train wheels and rails, including but not limited to wheel-rail normal force, lateral force and longitudinal force, which have a decisive influence on the track structure and train dynamic behavior during train operation. The measurement and calculation of wheel-rail force are crucial for understanding the stability, safety and efficiency of train operation. The technical field involves physical measurement, numerical simulation and the use of various sensors and calculation models to predict and optimize the distribution of these forces. Research helps to improve track design, train design and operation strategy, ensuring the stability of train operation and reducing the maintenance cost of railway system.

[0003] Among them, the train stability analysis method based on wheel-rail force involves the development and application of analysis technology based on wheel-rail interaction force to evaluate and ensure the running stability of the train. Generally, computational models are used to simulate the mechanical behavior between the wheel and the track, and to predict potential stability problems such as wheel derailment tendency or excessive vibration. These analysis results are used to design safer trains and track systems, as well as to develop more effective maintenance plans and operation protocols. The overall goal is to improve the safety and efficiency of railway transportation, reduce the risk of accidents, and optimize the design and operation conditions of trains.

[0004] The existing technology has obvious limitations in dynamic environment, especially in real-time data processing and vibration anomaly detection. Traditional technology relies on static models and intermittent data analysis, which is difficult to adapt to rapidly changing operating conditions, which to a large extent limits the timely response to sudden or non-periodic anomalies. The handling of track irregularities and abnormal vibrations is mostly a post-response, rather than a preventive strategy. This reactive approach not only increases the operation and maintenance cost, but also delays the problem solving time, affecting the overall safety and efficiency of the railway system. In addition, the existing technology lacks the ability to integrate and analyze multi-source data, resulting in an incomplete comprehensive evaluation of train stability, limiting the improvement of railway traffic safety and operation efficiency. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art and to propose a train stability analysis method and system based on wheel-rail force.

[0006] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: a train stability analysis method based on wheel-rail force, comprising the following steps,

[0007] S1: based on the real-time collected vertical force and lateral force data, the force value distribution of the train wheel and the track contact point is segmented and counted, the abnormal amplitude in the force fluctuation value is analyzed, the periodic abnormal parameters are screened through the frequency change trend, the irregular vibration frequency and position are determined, and the wheel-rail abnormal vibration characteristics are obtained;

[0008] S2: based on the wheel-rail abnormal vibration characteristics, the track gauge and the track direction parameter deviation in the detection range are corrected, the trigger sensitivity and the working threshold of the force sensor are adjusted, the frequency distribution of the track irregularity area is analyzed, and the monitoring frequency is reset, and the monitoring frequency optimization configuration is obtained;

[0009] S3: based on the monitoring frequency optimization configuration, the waveform characteristics are extracted from the wheel dynamic force signal, the energy distribution and the periodic change in the waveform are analyzed, the influence of the track gauge change on the coincidence of the waveform characteristics is evaluated, and the fluctuation interval is separated combined with the characteristic parameters, and the waveform characteristic parameter set is obtained;

[0010] S4: based on the waveform characteristic parameter set, the vibration frequency distribution characteristics of the track position are detected, the track distribution coefficient of the vibration influence is analyzed through space mapping, the dynamic distribution area affecting the train stability is screened, and the dynamic force stability parameter is calculated, and the train stability analysis result is obtained.

[0011] The application improves that the abnormal amplitude in the force fluctuation value is analyzed, and the abnormal amplitude in the force fluctuation value is analyzed.

[0012] S111: based on the real-time collected vertical force and lateral force data, the segmented statistics are carried out according to time or space interval, the total force value and the average force value of each segment are calculated, and the segmented statistical data are obtained;

[0013] S112: based on the segmented statistical data, the fluctuation analysis of the force value is carried out for each segment, and the formula is adopted:

[0014]

[0015] The standardization score Z of each segment is calculated i , the area with fluctuation higher than other segments is identified, and the abnormal amplitude is obtained, wherein x i represents the force value of the i th data point, represents the measured value of the vertical or lateral force collected in the target time or space segment, mu represents the average value of the force value in the data point, and sigma represents the standard deviation of the force value in the data point.

[0016] The application improves that the wheel-rail abnormal vibration characteristics are obtained.

[0017] S121: based on the abnormal amplitude, the frequency components of the data are analyzed by using fast Fourier transform, the periodic abnormal parameters are screened, and the abnormal parameter set is obtained;

[0018] S122: Based on the abnormal parameter set, determine the irregular vibration frequency and position, using the formula:

[0019]

[0020] Get the wheel-rail abnormal vibration feature, where V(f q ,l) represents the vibration amplitude at position l and frequency f q , f represents the frequency, l represents the position on the track, A q is the amplitude of the qth frequency component, f q is the qth frequency component, φ q is the phase angle of the qth frequency component, N q is the number of frequency points, and c is the wave speed.

[0021] The application improves that the frequency distribution analysis step of the track irregularity area is specifically:

[0022] S211: Based on the wheel-rail abnormal vibration feature, compare the current track gauge and alignment data in the detection range with the standard value, and correct the track gauge and alignment data to obtain corrected track gauge and alignment data;

[0023] S212: Based on the corrected track gauge and alignment data, adjust the trigger sensitivity and working threshold of the force sensor to match the current track condition, and obtain the optimized sensor configuration;

[0024] S213: Based on the optimized sensor configuration, reacquire vibration data and perform frequency analysis, using the formula:

[0025]

[0026] Get the average frequency FH d of the track irregularity area, where FFT(fh u ) represents the frequency extracted from the force data of the uth measurement point by Fourier transform, N H is the total number of measurement points.

[0027] The application improves that the acquisition step of the monitoring frequency optimization configuration is specifically:

[0028] S221: Based on the average frequency of the track irregularity area, evaluate whether the current monitoring frequency setting captures key vibration data, identify the frequency range that needs to be adjusted, and obtain the current monitoring frequency configuration;

[0029] S222: Based on the current monitoring frequency configuration, analyze the data coverage efficiency and sensitivity of multiple frequency points, reconfigure the monitoring frequency, and match the track vibration condition to obtain the monitoring frequency optimization configuration.

[0030] The evaluation step of the influence of the gauge change on the coincidence of the waveform features is specifically:

[0031] S311: Extract the waveform features from the wheel power signal based on the monitoring frequency optimization configuration, analyze the energy distribution and periodic change in the waveform, and obtain waveform feature analysis results;

[0032] S312: Based on the waveform feature analysis results, use the formula:

[0033]

[0034] The influence of the gauge change on the coincidence of the waveform features is evaluated, wherein ΔSO represents the normalized deviation of the influence of the gauge change on the coincidence of the waveform features, E v,current represents the energy of the vth waveform feature under the current gauge, E v,standard represents the energy of the vth waveform feature under the standard gauge, N z is the total number of waveform features.

[0035] The acquisition step of the waveform feature parameter set is specifically:

[0036] S321: Based on the influence of the gauge change on the coincidence of the waveform features, separate the differentiated fluctuation intervals to obtain fluctuation interval data;

[0037] S322: Based on the fluctuation interval data, refine the feature parameters of each interval, including periodic change, energy distribution, and frequency response, and identify key fluctuation patterns and characteristics to obtain a waveform feature parameter set.

[0038] The acquisition step of the train stability analysis result is specifically:

[0039] S411: Based on the waveform feature parameter set, analyze the vibration frequency distribution characteristics of the track position, identify the vibration mode of multiple frequency components, and obtain vibration frequency distribution data;

[0040] S412: Based on the vibration frequency distribution data, analyze the influence coefficient of track interval vibration through spatial mapping to obtain a track distribution coefficient analysis result;

[0041] S413: Based on the track distribution coefficient analysis result, screen the dynamic distribution area that affects the stability of the train, and use the formula:

[0042]

[0043] Calculate the standard deviation Yσ of the dynamic force stability parameter of each area to obtain a train stability analysis result, wherein αp iStability weight coefficient representing the i-th region, xp i Vibration intensity of the i-th region, μp represents the average of vibration intensity of all regions, N Y Is the total number of regions.

[0044] A train stability analysis system based on wheel-rail force, the system comprises:

[0045] The force value anomaly detection module is based on the vertical force and lateral force data, the force value distribution of train wheel and track contact point is segmented and counted, the abnormal amplitude in force fluctuation value is analyzed, the irregular vibration frequency and position are determined, and the wheel-rail abnormal vibration characteristics are obtained;

[0046] The gauge adjustment module is based on the wheel-rail abnormal vibration characteristics, corrects the gauge and alignment parameter deviation in the detection range, adjusts the trigger sensitivity and working threshold of the force sensor, and resets the monitoring frequency, and obtains the monitoring frequency optimization configuration;

[0047] The waveform recognition module is based on the monitoring frequency optimization configuration, extracts the waveform features from the wheel dynamic force signal, analyzes the energy distribution and periodic change in the waveform, evaluates the influence of gauge change on the coincidence of waveform features, and obtains the waveform feature parameter set;

[0048] The train stability evaluation module is based on the waveform feature parameter set, detects the vibration frequency distribution characteristics of the track position, analyzes the track distribution coefficient influenced by vibration, screens the dynamic distribution area influencing the train stability, and calculates the dynamic force stability parameter, and obtains the train stability analysis result.

[0049] Compared with the prior art, the advantages and positive effects of the present application are:

[0050] In the present application, by segmenting and counting the force value of train wheel and track contact point, combining abnormal amplitude analysis and periodic abnormal parameter screening, irregular vibration frequency and position are effectively identified, the accuracy and processing speed of vibration identification are improved, the trigger sensitivity and working threshold of the force sensor are adjusted, the response capability and adaptability of the monitoring system are enhanced by optimizing the frequency distribution of track irregularity area, the influence of gauge change on waveform features is more comprehensively evaluated through the waveform feature parameter set extracted from the wheel dynamic force signal, and the dynamic stability analysis of the train is optimized, which provides more comprehensive data support for train stability. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 A flowchart of a train stability analysis method based on wheel-rail force is proposed for the present application;

[0052] Figure 2 An analysis flowchart of abnormal amplitude in force fluctuation value is proposed in the present application;

[0053] Figure 3 Flow chart for obtaining wheel-rail abnormal vibration characteristics in the present application;

[0054] Figure 4 Flow chart for analyzing frequency distribution of track irregularity area in the present application;

[0055] Figure 5 Flow chart for obtaining monitoring frequency optimization configuration in the present application;

[0056] Figure 6 Flow chart for evaluating the influence of track gauge change on the coincidence of waveform characteristics in the present application;

[0057] Figure 7 Flow chart for obtaining waveform characteristic parameter set in the present application;

[0058] Figure 8 Flow chart for obtaining train stability analysis results in the present application. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical scheme and advantages of the present application clearer and more apparent, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0060] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0061] EMBODIMENT

[0062] Please refer to Figure 1 The present application provides a technical scheme: a train stability analysis method based on wheel-rail force, comprising the following steps:

[0063] S1: Based on the real-time collected vertical force and lateral force data, the force value distribution of the train wheel and track contact point is segmented and counted, the abnormal amplitude in the force fluctuation value is analyzed, the periodic abnormal parameters are screened through the frequency change trend, the irregular vibration frequency and position are determined, and the wheel-rail abnormal vibration characteristics are obtained;

[0064] S2: Based on the wheel-rail abnormal vibration characteristics, correct the track gauge and track orientation parameter deviations in the detection range, adjust the triggering sensitivity and working threshold of the force sensor, analyze the frequency distribution of the track irregularity area, and reset the monitoring frequency to obtain the optimized configuration of the monitoring frequency;

[0065] S3: Based on the optimized configuration of the monitoring frequency, extract the waveform features from the wheel force signal, analyze the energy distribution and periodic changes in the waveform, evaluate the influence of track gauge changes on the coincidence of waveform features, and separate the fluctuation interval combined with the feature parameters to obtain the waveform feature parameter set;

[0066] S4: Based on the waveform feature parameter set, detect the vibration frequency distribution characteristics of the track position, analyze the track distribution coefficient of vibration influence through spatial mapping, screen the dynamic distribution area that affects train stability, and calculate the dynamic force stability parameters to obtain the train stability analysis results.

[0067] The wheel-rail abnormal vibration characteristics include vibration frequency, vibration position, and abnormal amplitude. The optimized configuration of the monitoring frequency includes monitoring area adjustment information, sensor configuration, and frequency reset results. The waveform feature parameter set includes energy distribution degree, periodic variation index, and waveform alignment index. The train stability analysis results include vibration frequency distribution information and stability calculation parameters.

[0068] Please refer to Figure 2 The analysis steps of the abnormal amplitude in the force fluctuation value are as follows:

[0069] S111: Based on the real-time collected vertical force and lateral force data, segment statistics are performed according to time or spatial intervals, the total force value and average force value of each segment are calculated, and the segmented statistical data is obtained;

[0070] By dividing the data into multiple segments according to time intervals or spatial intervals, each segment contains a number of data points. When dividing according to time intervals, a fixed sampling time interval is selected, and all force data points collected within the time interval are accumulated to obtain the total force value of the time interval. Then, the average force value of the segment is calculated by averaging the data points. When dividing according to spatial intervals, a fixed interval segment is set according to the distance of the train running track, and all force data points belonging to the same interval segment are subjected to the same accumulation and averaging calculation. For the statistical results of each segment, the total force value and average force value are recorded in turn, and the statistical results of all segments are combined to generate complete segmented statistical data.

[0071] S112: Based on the segmented statistical data, the fluctuation analysis of the force value is performed for each segment using the formula:

[0072]

[0073] The standardized score Z of each segment is calculated i, identify the area with higher fluctuation than other segments, and obtain abnormal amplitude, where x i represents the force value of the i th data point, represents the measured value of vertical or lateral force collected within the target time or spatial segment, μ represents the average value of force values within the data point, and σ represents the standard deviation of force values within the data point, used to measure the dispersion of force value data points within the segment relative to the average value;

[0074] with the following data, x i = 100 N (force value of a certain data point, obtained through monitoring), μ = 85 N (average value of force values within the segment, obtained by summing the data points within the segment and dividing by the number of points), and σ = 10 N (standard deviation of force values), the numerical values are substituted into the formula to calculate:

[0075]

[0076] The result shows that the standardized fluctuation index of this data point is 1.5, indicating that its deviation from the average value within the segment is moderate. By comparing the fluctuation index with other segments, the area with significant fluctuation can be identified, thereby obtaining abnormal amplitude data.

[0077] Please refer to Figure 3 The steps for obtaining the wheel-rail abnormal vibration characteristics are as follows:

[0078] S121: Based on the abnormal amplitude, use fast Fourier transform to analyze the frequency components of the data, filter periodic abnormal parameters, and obtain an abnormal parameter set;

[0079] Through fast Fourier transform for frequency domain analysis of force data, the time domain force signal collected is subjected to discrete Fourier transform, the amplitude and phase of the signal at each frequency component are calculated, the main frequency components are extracted according to the amplitude size and the corresponding frequency points are recorded, and the corresponding amplitude is associated to generate a frequency distribution table. The points with amplitude significantly higher than other frequencies are screened and marked, and the points are defined as periodic abnormal parameters. The abnormal parameters are classified and combined to form an abnormal parameter set containing frequency points and corresponding amplitudes. Finally, the data is further classified according to the abnormal parameter set.

[0080] S122: Based on the abnormal parameter set, determine the irregular vibration frequency and position, using the formula:

[0081]

[0082] Obtain the wheel-rail abnormal vibration characteristics, where V(f q , l) represents the vibration amplitude at position l and frequency f q , A represents the quantified expression of vibration characteristics, f represents the frequency, used to specify the specific frequency considered in vibration analysis, l represents the position on the track, used to locate the spatial coordinates of vibration measurement and analysis, and Aq is the amplitude of the qth frequency component, indicating the intensity of the frequency vibration, f q is the qth frequency component, is the specific frequency point identified in the Fourier transform analysis, φ q is the phase angle of the qth frequency component, describing the phase difference of the frequency component relative to the reference point, N q is the number of frequency points, c is the wave speed;

[0083] The monitoring frequency range is 0-200Hz, the sampling frequency is 1000Hz, the signal acquisition time is 10 seconds, the significant frequency points are obtained by calculating the Fourier transform of the signal, if the amplitude detection value A1=0.5, A2=0.3, the frequency point f1=50Hz, f2=120Hz, the phase angle φ1=π / 6, φ2=π / 4, the track position l=10m, the wave speed c is 500m / s, the above parameters are substituted into the formula:

[0084]

[0085] The first frequency component calculation (f1=50Hz):

[0086] Calculate the angle:

[0087]

[0088] Add the phase angle:

[0089]

[0090] Take the sine value:

[0091] sin(6.805)≈0.536;

[0092] Amplitude calculation:

[0093] 0.5×0.536=0.268;

[0094] The second frequency component calculation (f2=120Hz):

[0095] Calculate the angle:

[0096]

[0097] Add the phase angle:

[0098]

[0099] Take the sine value:

[0100] sin(15.865)≈0.311;

[0101] Amplitude calculation:

[0102] 0.3 x 0.311 = 0.0933;

[0103] Total amplitude calculation:

[0104] V(50, 10) = 0.268 + 0.0933 = 0.3613;

[0105] The result shows that the quantified value of the vibration feature is 0.3613, reflecting the abnormal vibration intensity at this position and frequency.

[0106] Please refer to Figure 4 , the frequency distribution analysis step of the track irregularity area is specifically:

[0107] S211: Based on the wheel-rail abnormal vibration feature, the current track gauge and alignment data in the detection range are compared with the standard value, and the track gauge and alignment data are corrected to obtain the corrected track gauge and alignment data;

[0108] First, the collected results of the current track gauge and alignment data are preprocessed, and relevant measurement parameters are extracted, including track gauge deviation and alignment deviation angle. Then, by analyzing the numerical variation of all measurement points in the detection range, the difference between each measurement point is calculated and compared, and the differential calculation method is used to determine the change trend of the track gauge and alignment deviation. According to this trend, an estimated curve of nonlinear change is generated, and the parameters of the estimated results are adjusted for fitting, so as to reflect the track gauge and alignment change in sections. The track gauge and alignment data of each measurement point are gradually approached to the standard value by using the decreasing adjustment method, and repeated calibration is performed until the deviation amount and deviation angle of all data are within the set range, thereby obtaining the corrected data results.

[0109] S212: Based on the corrected track gauge and alignment data, the trigger sensitivity and working threshold of the force sensor are adjusted to match the current track condition, and the optimized sensor configuration is obtained;

[0110] First, the average and maximum values of the sensor force corresponding to the corrected data under different track conditions are calculated, and the adjustment factor of the sensor sensitivity is determined in combination with the correction parameters of the track gauge and alignment. According to the change of the adjustment factor, the force range of the sensor is updated in sections, so that the sensitivity can adapt to the track change condition. At the same time, the fluctuation interval of the force range covering the track gauge and alignment data is analyzed, and the parameter configuration is gradually adjusted until the error of the sensitivity is within the allowable range and the working threshold of the sensor can cover the change range of the track condition, forming an optimized sensor configuration scheme matched with the current track condition.

[0111] S213: Based on the optimized sensor configuration, the vibration data is re-collected, and frequency analysis is performed, using the formula:

[0112]

[0113] Obtaining the average frequency FH of the track irregularity region d wherein FFT (fh u ) represents the frequency extracted from the force data of the u-th measuring point by Fourier transform, N H is the total number of measuring points;

[0114] The following data is collected, fh1 = 12 Hz, fh2 = 15 Hz and fh3 = 10 Hz (frequency data of three measuring points respectively), N H = 3 (total number of measuring points), the values are substituted into the formula:

[0115]

[0116] The result shows that the average frequency of the track irregularity region is 12.33 Hz, which provides a key indicator for analyzing the characteristics of track irregularity and can be used for further analysis of vibration modes or optimization of track design scheme.

[0117] Please refer to Figure 5 , the obtaining step of the monitoring frequency optimization configuration is specifically:

[0118] S221: Based on the average frequency of the track irregularity region, evaluate whether the current monitoring frequency setting captures key vibration data, identify the frequency range that needs to be adjusted, and obtain the current monitoring frequency configuration;

[0119] The collected frequency data is sorted and classified, the distribution characteristics of the key frequency points are extracted, the frequency range is divided into multiple intervals, the frequency occurrence times and the corresponding vibration intensity values in each interval are counted, according to the distribution characteristics of the vibration intensity, the range of high frequency vibration region and low frequency vibration region is identified, and the frequency weight of each interval is determined, according to the key frequency point coverage range and the current monitoring setting, it is judged whether there is a monitoring frequency omission or repeated coverage, the missing key frequency points are supplemented into the monitoring frequency range, the repeated coverage frequency points are adjusted and de-duplicated, and the current monitoring frequency configuration is obtained after comparison and analysis.

[0120] S222: Based on the current monitoring frequency configuration, analyze the data coverage efficiency and sensitivity of multiple frequency points, reset the monitoring frequency, and match the track vibration condition, to obtain the monitoring frequency optimization configuration;

[0121] The coverage range and vibration data collection rate of each frequency point are calculated, the proportion of effective vibration signals in the coverage range is counted, the frequency points with high coverage efficiency are screened, and the integrity of the frequency configuration is re-evaluated, the interval of each frequency point is optimized in a segmented adjustment manner to ensure the uniform distribution between different frequency points, and the matching between the monitoring frequency and the track vibration is monitored through testing adjustment, the monitoring frequency points are increased in the area with high vibration intensity, and the frequency distribution density is appropriately reduced in the area with low vibration, finally the adjusted monitoring frequency range matches the track vibration condition, and the optimized monitoring frequency configuration is obtained.

[0122] Please refer to Figure 6 The evaluation step of the influence of the change of the track gauge on the coincidence of the waveform characteristics is specifically:

[0123] S311: Based on the optimized monitoring frequency configuration, the waveform characteristics are extracted from the wheel dynamic signal, the energy distribution and periodic change in the waveform are analyzed, and the waveform characteristic analysis result is obtained;

[0124] The collected wheel dynamic signal is preprocessed, including segmented interception and denoising processing, the segmented interception is based on the time window division of the signal according to the frequency range set in the optimized configuration, the denoising processing adopts the comparison of the collected reference signal to eliminate the abnormal part with large deviation from the reference signal, then the distribution characteristics of the segmented signal in the time domain and the frequency domain are calculated, the main frequency component of the signal and the energy proportion at different frequencies are analyzed, the waveform periodic change of the time domain signal is extracted, the periodic change characteristics are represented by the peak value, the valley value and the average value of the signal in each period, and the waveform of each segment is normalized for comparative analysis, and finally the analysis results in the time domain and the frequency domain are comprehensively analyzed.

[0125] S312: Based on the waveform characteristic analysis result, the formula:

[0126]

[0127] The influence of the change of the track gauge on the coincidence of the waveform characteristics is evaluated, wherein ΔSO represents the normalized deviation of the influence of the change of the track gauge on the coincidence of the waveform characteristics, E v,current represents the energy of the vth waveform characteristic under the current track gauge, which is the energy value obtained in actual measurement, E v,standard represents the energy of the vth waveform characteristic under the standard track gauge, which is the energy value for comparison, N z is the total number of waveform characteristics;

[0128] If the total number of waveform characteristics N z = 3, the waveform characteristic energies of the current track gauge are E 1,current = 12, E 2,current = 18 and E 3,current= 15, the waveform feature energy of the standard track gauge is E 1,standard = 10, E 2,standard = 15 and E 3,standard = 12, the data is substituted into the formula:

[0129]

[0130] The score value and square value of each item are calculated:

[0131]

[0132] The results show that the influence of track gauge change on the coincidence of waveform features is quantified as 0.377, indicating that track gauge change significantly affects the stability and consistency of the waveform.

[0133] Please refer to Figure 7 The acquisition steps of the waveform feature parameter set are as follows:

[0134] S321: Based on the influence of track gauge change on the coincidence of waveform features, the differentiated fluctuation intervals are separated to obtain fluctuation interval data;

[0135] By analyzing the data collected by the optimized monitoring configuration, the similarity and overlap degree between different waveforms are determined, the similarity index in each wave band is calculated, the fluctuation area significantly affected by track gauge change is identified, the area usually shows high waveform coincidence or significant waveform deviation, then the clustering algorithm is applied to group the waveform data, the data with similar waveform features are aggregated together, thereby realizing effective separation of different fluctuation intervals, each fluctuation interval is independently identified according to its statistical characteristics such as peak value, mean value and standard deviation, finally detailed information is extracted according to the distinguished fluctuation interval data, including the waveform length, coincidence degree of the interval and the unique characteristics that distinguish it from other intervals, thereby obtaining detailed fluctuation data of each interval.

[0136] S322: Based on the fluctuation interval data, the feature parameters of each interval are refined, including periodic change, energy distribution and frequency response, and the key fluctuation mode and characteristics are identified, thereby obtaining the waveform feature parameter set;

[0137] Further refine the characteristic parameters of each fluctuation interval, calculate the periodic variation, energy distribution and frequency response of each interval through time-frequency analysis, the periodic variation is evaluated through the consistency of the repeating pattern and time interval of the waveform, the energy distribution is quantified through the degree of aggregation and dispersion of energy within each cycle, and the frequency response is drawn through the response curve to show the contribution of each frequency component to the total vibration, the parameters of each interval are recorded and compared in detail to identify the significant or representative fluctuation mode, and the common and different characteristics of each mode are analyzed, and finally a comprehensive set of waveform characteristic parameters is obtained based on the refined analysis.

[0138] Please refer to Figure 8 The acquisition step of the train stability analysis result is specifically:

[0139] S411: Based on the waveform characteristic parameter set, analyze the vibration frequency distribution characteristics of the track position, identify the vibration mode of multiple frequency components, and obtain vibration frequency distribution data;

[0140] Divide the track vibration signal into multiple paragraphs according to the track interval, extract the frequency components of each signal, classify all frequency components, and summarize the vibration energy values in each frequency interval according to the frequency range. At the same time, calculate the proportion of the main frequency component in each frequency segment. By comparing the frequency distribution characteristics of each segment, the superposition characteristics of multiple frequency components are identified, and single frequency and multi-frequency mixed areas are separated. According to the energy distribution and proportion of the multi-frequency components, classify and record, further analyze the spatial distribution rule of the track vibration frequency characteristics, and form the vibration frequency distribution data.

[0141] S412: Based on the vibration frequency distribution data, analyze the influence coefficient of the track interval vibration through spatial mapping, and obtain the track distribution coefficient analysis result;

[0142] Divide the track into fixed-length interval segments, calculate the average energy value and the contribution rate of the main frequency according to the vibration frequency distribution of each interval segment, convert the data into feature points in spatial coordinates, form a spatial mapping diagram of track vibration, and evaluate the vibration influence degree of each track segment according to the vibration characteristics of each interval in the mapping diagram, combined with the track gauge and track orientation data. Through comprehensive analysis of the correlation between vibration frequency and spatial position of each segment, the range of high-impact and low-impact intervals is marked, and the track distribution coefficient analysis result is formed.

[0143] S413: Based on the track distribution coefficient analysis result, screen the dynamic distribution area that affects the train stability, and use the formula:

[0144]

[0145] The standard deviation Yσ of the dynamic force stability parameter of each region is calculated, which is used to evaluate the stability fluctuation on the whole track, and the train stability analysis result is obtained, wherein, αp i represents the stability weight coefficient of the i th region, xp i represents the vibration intensity of the i th region, the vibration measurement value of the region directly obtained from the data, and μp represents the average value of the vibration intensity of all regions, N Y is the total number of regions;

[0146] If the track distribution contains 4 regions, the region weight coefficients are αp1 = 0.3, αp2 = 0.4, αp3 = 0.2 and αp4 = 0.1 respectively, and the vibration intensities are xp1 = 15, xp2 = 20, xp3 = 10 and xp4 = 5 respectively, the weighted average value of the vibration intensity is calculated, and the weight is normalized:

[0147]

[0148] The standard deviation is calculated by substituting the formula:

[0149]

[0150] The items are calculated one by one:

[0151]

[0152] The result shows that the standard deviation of the dynamic force stability parameter is 23.21, which reflects the fluctuation degree of the vibration intensity of different regions on the track, and indicates the overall uniformity and fluctuation level of the track dynamic stability.

[0153] A train stability analysis system based on wheel-rail force, the system comprises:

[0154] The force value anomaly detection module performs segmented statistics on the force value distribution of the contact point between the train wheel and the track based on the vertical force and lateral force data, analyzes the abnormal amplitude in the force fluctuation value, determines the irregular vibration frequency and position, and obtains the wheel-rail abnormal vibration characteristics;

[0155] The gauge adjustment module corrects the deviation of the gauge and the alignment parameter in the detection range based on the wheel-rail abnormal vibration characteristics, adjusts the triggering sensitivity and working threshold of the force sensor, and resets the monitoring frequency, and obtains the monitoring frequency optimization configuration;

[0156] The waveform recognition module extracts the waveform characteristics from the wheel dynamic force signal based on the monitoring frequency optimization configuration, analyzes the energy distribution and periodic change in the waveform, evaluates the influence of the gauge change on the coincidence of the waveform characteristics, and obtains the waveform characteristic parameter set;

[0157] The train stability evaluation module detects vibration frequency distribution characteristics of the track position based on the waveform feature parameter set, analyzes track distribution coefficients of vibration influence, screens dynamic distribution areas influencing train stability, and calculates dynamic force stability parameters to obtain train stability analysis results.

[0158] The above is only the preferred embodiment of the present application, and does not limit the present application in other forms. Any person skilled in the art can use the disclosed technical content to make changes or modifications into equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution content of the present application, according to the technical essence of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A train ride smoothness analysis method based on wheel-rail forces, characterized by, The method comprises the following steps: S1: based on the real-time collected vertical force and lateral force data, the force value distribution of the train wheel and track contact point is segmented and counted, the abnormal amplitude in the force fluctuation value is analyzed, the periodic abnormal parameters are screened through the frequency change trend, the irregular vibration frequency and position are determined, and the wheel-rail abnormal vibration characteristics are obtained; S2: based on the wheel-rail abnormal vibration characteristics, the track gauge and track orientation parameter deviation in the detection range is corrected, the trigger sensitivity and working threshold of the force sensor is adjusted, the frequency distribution of the track irregularity area is analyzed, and the monitoring frequency is reset, and the monitoring frequency optimization configuration is obtained; S3: based on the monitoring frequency optimization configuration, the waveform characteristics are extracted from the wheel dynamic force signal, the energy distribution and periodic change in the waveform are analyzed, the influence of the track gauge change on the coincidence of the waveform characteristics is evaluated, and the fluctuation interval is separated combined with the characteristic parameters, and the waveform characteristic parameter set is obtained; S4: based on the waveform characteristic parameter set, the vibration frequency distribution characteristics of the track position are detected, the track distribution coefficient of the vibration influence is analyzed through spatial mapping, the dynamic distribution area affecting the train stability is screened, and the dynamic force stability parameter is calculated, and the train stability analysis result is obtained.

2. The wheel-rail force-based train ride quality analysis method of claim 1, wherein, The analysis step of the abnormal amplitude in the force fluctuation value is specifically: S111: based on the real-time collected vertical force and lateral force data, the segmented statistics is carried out according to time or space interval, the total force value and average force value of each segment are calculated, and the segmented statistical data is obtained; S112: based on the segmented statistical data, the fluctuation analysis of the force value is carried out for each segment, and the formula is used: Calculate the normalized score Z for each segment i Identify the region with fluctuation higher than other segments to get the abnormal amplitude, where x i represents the force value of the i th data point, represents the measured value of the vertical or horizontal force collected within the target time or space segment, μ represents the average value of the force value within the data point, and σ represents the standard deviation of the force value within the data point.

3. The wheel-rail force-based train ride quality analysis method of claim 1, wherein, The acquisition step of the wheel-rail abnormal vibration characteristics is specifically: S121: based on the abnormal amplitude, the frequency component of the data is analyzed by using fast Fourier transform, the periodic abnormal parameters are screened, and the abnormal parameter set is obtained; S122: based on the abnormal parameter set, the irregular vibration frequency and position are determined, and the formula is used: obtaining a wheel rail abnormal vibration feature, wherein V(f q , l) represents a vibration amplitude at position l and frequency f q , f represents a frequency, l represents a position on a track, A q is an amplitude of a qth frequency component, f q is a qth frequency component, φ q is a phase angle of a qth frequency component, N q is a frequency point number, and c is a wave speed.

4. The wheel-rail force-based train ride quality analysis method of claim 1, wherein, The analysis step of the frequency distribution of the track irregularity area is specifically: S211: based on the wheel-rail abnormal vibration characteristics, the current track gauge and track orientation data in the detection range and the standard value are compared, the track gauge and track orientation data are corrected, and the corrected track gauge and track orientation data are obtained; S212: based on the corrected track gauge and track orientation data, the trigger sensitivity and working threshold of the force sensor is adjusted, the current track condition is matched, and the optimized sensor configuration is obtained; S213: based on the optimized sensor configuration, the vibration data is re-collected, and the frequency analysis is carried out, and the formula is used: obtaining an average frequency FH of the track irregularity region d where FFT(fh u ) represents a frequency extracted from force data of the u-th measuring point by Fourier transform, N H is the total number of measuring points.

5. The wheel-rail force-based train ride quality analysis method of claim 1, wherein, The acquisition step of the monitoring frequency optimization configuration is specifically: S221: based on the average frequency of the track irregularity area, whether the key vibration data is captured by the current monitoring frequency setting is evaluated, the frequency range needing to be adjusted is identified, and the current monitoring frequency configuration is obtained; S222: based on the current monitoring frequency configuration, the data coverage efficiency and sensitivity of the multiple frequency points are analyzed, the monitoring frequency is reset, and the track vibration condition is matched, and the monitoring frequency optimization configuration is obtained.

6. The wheel-rail force-based train ride quality analysis method of claim 1, wherein, The evaluation step of the influence of the track gauge change on the coincidence of the waveform characteristics is specifically: S311: Extract waveform features from the wheel power signal based on the monitoring frequency optimization configuration, analyze the energy distribution and periodic changes in the waveform, and obtain waveform feature analysis results; S312: Based on the waveform feature analysis results, use the formula: The influence of the gauge variation on the coincidence of the waveform features is evaluated, where ΔSO represents the normalized deviation of the influence of the gauge variation on the coincidence of the waveform features, E v,current represents the energy of the vth waveform feature under the current gauge, E v,standard represents the energy of the vth waveform feature under the standard gauge, N z is the total number of the waveform features.

7. The wheel-rail force-based train ride quality analysis method of claim 1, wherein, The waveform feature parameter set acquisition step is specifically: S321: Based on the influence of track gauge changes on waveform features, separate the differentiated fluctuation intervals to obtain fluctuation interval data; S322: Based on the fluctuation interval data, refine the feature parameters of each interval, including periodic changes, energy distribution, and frequency response, and identify key fluctuation patterns and characteristics to obtain a waveform feature parameter set.

8. The wheel-rail force-based train ride quality analysis method of claim 1, wherein, The train stability analysis result acquisition step is specifically: S411: Based on the waveform feature parameter set, analyze the vibration frequency distribution characteristics of the track position, identify the vibration mode of multiple frequency components, and obtain vibration frequency distribution data; S412: Based on the vibration frequency distribution data, analyze the influence coefficient of track interval vibration through spatial mapping to obtain track distribution coefficient analysis results; S413: Based on the track distribution coefficient analysis results, screen dynamic distribution areas that affect train stability, and use the formula: The standard deviation Yσ of the dynamic force stability parameters of each region is calculated, and the train stability analysis result is obtained, where αp i is the stability weight coefficient of the ith region, xp i is the vibration intensity of the ith region, μp represents the average value of the vibration intensity of all regions, N Y is the total number of regions.

9. A train ride smoothness analysis system based on wheel-rail forces, characterized by, The train stability analysis method based on wheel-rail force according to any one of claims 1-8 is executed, and the system comprises: The force value anomaly detection module performs segmented statistics on the force value distribution of the train wheel and track contact point based on vertical force and lateral force data, analyzes the abnormal amplitude in force fluctuation value, determines irregular vibration frequency and position, and obtains wheel-rail abnormal vibration characteristics; The track gauge adjustment module corrects the track gauge and track direction parameter deviations within the detection range based on the wheel-rail abnormal vibration characteristics, adjusts the triggering sensitivity and working threshold of the force sensor, and resets the monitoring frequency to obtain monitoring frequency optimization configuration; The waveform recognition module extracts waveform features from the wheel power signal based on the monitoring frequency optimization configuration, analyzes the energy distribution and periodic changes in the waveform, evaluates the influence of track gauge changes on waveform features, and obtains a waveform feature parameter set; The train stability evaluation module detects the vibration frequency distribution characteristics of the track position based on the waveform feature parameter set, analyzes the track distribution coefficient of vibration influence, screens dynamic distribution areas that affect train stability, and calculates dynamic force stability parameters to obtain train stability analysis results.

Images