Train stability analysis method and system based on wheel-rail force

Abstract

The invention relates to the technical field of wheel-rail force, in particular to a train stability analysis method and system based on wheel-rail force, and the method comprises the following steps: based on vertical force and transverse force data collected in real time, carrying out segmented statistics on stress value distribution of contact points of train wheels and a track, and analyzing abnormal amplitude in force fluctuation values. According to the method, the stress values of the contact points of the train wheels and the rail are counted in a segmented mode, abnormal amplitude analysis and periodic abnormal parameter screening are combined, the irregular vibration frequency and position are effectively recognized, the vibration recognition accuracy and processing speed are improved, the triggering sensitivity and the working threshold value of the force sensor are adjusted, and the accuracy of vibration recognition is improved. In combination with optimization of frequency distribution of a track irregularity area, the response capability and adaptability of a monitoring system are enhanced, through a waveform characteristic parameter set extracted from a wheel power signal, the influence of track gauge change on waveform characteristics can be evaluated more comprehensively, dynamic stability analysis of a train is optimized, and data support is provided for train stability.

Description

Technical Field

[0001] The present invention relates to the technical field of wheel-rail forces, and particularly to a method and system for analyzing the ride comfort of a train based on wheel-rail forces. Background Art

[0002] Wheel-rail force technology focuses on the study and analysis of the interaction forces between train wheels and rails. These forces include, but are not limited to, wheel-rail normal forces, lateral forces, and longitudinal forces, which have a decisive impact on the track structure and the dynamic behavior of trains during train operation. The measurement and calculation of wheel-rail forces 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 computational models to predict and optimize the distribution of these forces. The research helps to improve track design, train design, and operation strategies, ensuring the smooth operation of trains and reducing the maintenance costs of the railway system.

[0003] Among them, the method for analyzing the ride comfort of a train based on wheel-rail forces involves the development and application of analysis techniques based on wheel-rail interaction forces to evaluate and ensure the smooth operation of trains. Usually, computational models are used to simulate the mechanical behavior between wheels and tracks and predict potential stability problems, such as the derailment tendency of wheels or excessive vibrations. These analysis results are used to design safer train and track systems, and are also used to formulate more effective maintenance plans and operation protocols. The overall purpose 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 technologies have obvious limitations in application in a dynamic environment, especially in the lack of efficiency in real-time data processing and vibration anomaly detection. Traditional technologies rely on static models and intermittent data analysis, and it is difficult to adapt to rapidly changing operating conditions, which greatly limits the timely response to sudden or non-periodic anomalies. The handling of track irregularities and abnormal vibrations is mostly a post-event response rather than a preventive strategy. This reactive handling method not only increases the operation and maintenance costs but also delays the problem-solving time, affecting the overall safety and efficiency of the railway system. In addition, the existing technologies have insufficient capabilities in integrating and analyzing multi-source data, resulting in an incomplete comprehensive assessment of train stability, limiting the improvement of railway transportation safety and operation efficiency. Summary of the Invention

[0005] The purpose of the present invention is to solve the drawbacks existing in the prior art, and to propose a method and system for analyzing the ride comfort of a train based on wheel-rail forces.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A method for analyzing the ride comfort of a train based on wheel-rail forces, including the following steps,

[0007] S1: Based on the vertically and horizontally directed force data collected in real time, perform segmented statistics on the distribution of the force values at the contact points between the train wheels and the track, analyze the abnormal amplitudes in the force fluctuation values, screen for periodic abnormal parameters through the frequency change trend, determine the irregular vibration frequencies and positions, and obtain the wheel-rail abnormal vibration characteristics;

[0008] S2: Based on the wheel-rail abnormal vibration characteristics, correct the deviation of the gauge and alignment parameters within the detection range, adjust the trigger sensitivity and working threshold of the force sensor, analyze the frequency distribution of the track unevenness area, and reset the monitoring frequency to obtain an optimized configuration of the monitoring frequency;

[0009] S3: Based on the optimized configuration of the monitoring frequency, extract the waveform characteristics from the wheel dynamic signal, analyze the energy distribution and periodic changes in the waveform, evaluate the overlapping effect of the gauge change on the waveform characteristics, and separate the fluctuation intervals in combination with the characteristic parameters to obtain a waveform characteristic parameter set;

[0010] S4: Based on the waveform characteristic parameter set, detect the vibration frequency distribution characteristics of the track position, analyze the track distribution coefficient affected by vibration through spatial mapping, screen for the dynamic distribution areas affecting the train stability, and calculate the dynamic force stability parameter to obtain the train stability analysis result.

[0011] The improvement of the present invention is that the analysis steps of the abnormal amplitude in the force fluctuation value are specifically as follows:

[0012] S111: Based on the vertically and horizontally directed force data collected in real time, perform segmented statistics at time or space intervals, calculate the sum of the force values and the average force value for each segment, and obtain the segmented statistical data;

[0013] S112: Based on the segmented statistical data, perform a fluctuation analysis of the force value for each segment, using the formula:

[0014]

[0015] Calculate the standardized score Z for each segment i , identify the area where the fluctuation is higher than other segments to obtain the abnormal amplitude, where x i represents the force value of the i-th data point, which is the measured value of the vertically or horizontally directed force collected within the target time or space segment, μ represents the average value of the force values within the data points, and σ represents the standard deviation of the force values within the data points.

[0016] The improvement of the present invention is that the steps for obtaining the wheel-rail abnormal vibration characteristics are specifically as follows:

[0017] S121: Based on the abnormal amplitude, use the fast Fourier transform to analyze the frequency components of the data, screen for periodic abnormal parameters, and obtain an abnormal parameter set;

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

[0019]

[0020] Obtain the wheel-rail abnormal vibration characteristics, 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 q-th frequency component, f q is the q-th frequency component, φ q is the phase angle of the q-th frequency component, N q is the number of frequency points, and c is the wave speed.

[0021] The improvement of the present invention is that the analysis steps of the frequency distribution in the track irregularity area are specifically as follows:

[0022] S211: Based on the wheel-rail abnormal vibration characteristics, compare the current gauge and alignment data within the detection range with the standard values, correct the gauge and alignment data, and obtain the corrected gauge and alignment data;

[0023] S212: Based on the corrected 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, re-collect vibration data and perform frequency analysis, using the formula:

[0025]

[0026] Obtain the average frequency FH d of the track irregularity area, where FFT(fh u ) represents the frequency extracted from the force data at the u-th measurement point through Fourier transform, and N H is the total number of measurement points.

[0027] The improvement of the present invention is that the acquisition steps of the optimized monitoring frequency configuration are specifically as follows:

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

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

[0030] The improvement of the present invention is that the evaluation step of the influence of gauge change on the coincidence of waveform features is specifically as follows:

[0031] S311: Based on the optimized configuration of the monitoring frequency, extract waveform features from the wheel dynamic signal, analyze the energy distribution and periodic changes in the waveform, and obtain the waveform feature analysis result;

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

[0033]

[0034] Evaluate the influence of gauge change on the coincidence of waveform features, where ΔSO represents the normalized deviation of the influence of gauge change on waveform feature coincidence, E v,current represents the energy of the v-th waveform feature under the current gauge, E v,standard represents the energy of the v-th waveform feature under the standard gauge, N z is the total number of waveform features.

[0035] The improvement of the present invention is that the acquisition step of the waveform feature parameter set is specifically as follows:

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

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

[0038] The improvement of the present invention is that the acquisition step of the train stability analysis result is specifically as follows:

[0039] S411: Based on the waveform feature parameter set, analyze the vibration frequency distribution characteristics of the track position, identify the vibration modes with multi-frequency components, and obtain the 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 the track distribution coefficient analysis result;

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

[0042]

[0043] Calculate the standard deviation Yσ of the dynamic force stability parameter of each area to obtain the train stability analysis result, where αp iThe stability weight coefficient representing the i-th region, xp i represents the vibration intensity of the i-th region, μp represents the average value of the vibration intensities of all regions, and N Y is the total number of regions.

[0044] A train ride comfort analysis system based on wheel-rail forces, the system comprising:

[0045] The force value anomaly detection module performs segmented statistics on the force value distribution at the contact points between the train wheels and the track based on the vertical force and lateral force data, analyzes the abnormal amplitudes in the force fluctuation values, determines the irregular vibration frequencies and positions, and obtains the wheel-rail abnormal vibration characteristics;

[0046] The gauge adjustment module corrects the deviation of the gauge and alignment parameters within the detection range based on the wheel-rail abnormal vibration characteristics, adjusts the trigger sensitivity and working threshold of the force sensor, and re-sets the monitoring frequency to obtain an optimized configuration of the monitoring frequency;

[0047] The waveform recognition module extracts waveform characteristics from the wheel dynamic signal based on the optimized configuration of the monitoring frequency, analyzes the energy distribution and periodic changes in the waveform, and evaluates the overlapping influence of the gauge change on the waveform characteristics to obtain a waveform characteristic parameter set;

[0048] The train stability evaluation module detects the vibration frequency distribution characteristics of the track position based on the waveform characteristic parameter set, analyzes the track distribution coefficient affected by the vibration, screens the dynamic distribution regions affecting the train stability, and calculates the dynamic force stability parameter to obtain the train stability analysis result.

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

[0050] In the present invention, by performing segmented statistics on the force values at the contact points between the train wheels and the track, combined with the analysis of abnormal amplitudes and the screening of periodic abnormal parameters, the irregular vibration frequencies and positions are effectively identified, improving the accuracy and processing speed of vibration identification. By adjusting the trigger sensitivity and working threshold of the force sensor and combining with the optimization of the frequency distribution of the track unevenness region, the response ability and adaptability of the monitoring system are enhanced. Through the waveform characteristic parameter set extracted from the wheel dynamic signal, the influence of the gauge change on the waveform characteristics can be more comprehensively evaluated, and the dynamic stability analysis of the train can be optimized, providing more comprehensive data support for the train stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flowchart of a train ride comfort analysis method based on wheel-rail forces proposed by the present invention;

[0052] Figure 2 is a flowchart of the analysis of the abnormal amplitude in the force fluctuation value in the present invention;

[0053] Figure 3 This is the flowchart for obtaining the characteristics of abnormal wheel-rail vibration in the present invention;

[0054] Figure 4 This is the analysis flowchart for the frequency distribution of the track irregularity area in the present invention;

[0055] Figure 5 This is the flowchart for obtaining the optimal configuration of the monitoring frequency in the present invention;

[0056] Figure 6 This is the evaluation flowchart for the overlapping influence of gauge change on waveform characteristics in the present invention;

[0057] Figure 7 This is the flowchart for obtaining the waveform characteristic parameter set in the present invention;

[0058] Figure 8 This is the flowchart for obtaining the analysis result of train stability in the present invention. Detailed implementation manners

[0059] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0061] Embodiment

[0062] Please refer to Figure 1 , the present invention provides a technical solution: a train ride quality analysis method based on wheel-rail force, including the following steps:

[0063] S1: Based on the vertically and horizontally acting force data collected in real time, perform segmented statistics on the distribution of the acting force values at the contact points between the train wheels and the track, analyze the abnormal amplitudes in the force fluctuation values, screen the periodic abnormal parameters through the frequency change trend, determine the irregular vibration frequencies and positions, and obtain the characteristics of abnormal wheel-rail vibration;

[0064] S2: Based on the abnormal wheel-rail vibration characteristics, correct the deviations of gauge and alignment parameters within the detection range, adjust the trigger sensitivity and working threshold of the force sensor, analyze the frequency distribution of the track irregularity area, and reset the monitoring frequency to obtain an optimized configuration of the monitoring frequency.

[0065] S3: Based on the optimized configuration of the monitoring frequency, extract waveform characteristics from the wheel dynamic signal, analyze the energy distribution and periodic changes in the waveform, evaluate the overlapping impact of gauge changes on the waveform characteristics, and separate the fluctuation intervals in combination with characteristic parameters to obtain a waveform characteristic parameter set.

[0066] S4: Based on the waveform characteristic parameter set, detect the vibration frequency distribution characteristics of the track position, analyze the track distribution coefficient affected by vibration through spatial mapping, screen the dynamic distribution areas affecting train stability, and calculate the dynamic force stability parameters to obtain the train stability analysis results.

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

[0068] Please refer to Figure 2 , and the analysis steps for the abnormal amplitude in the force fluctuation value are specifically as follows:

[0069] S111: Based on the vertically and horizontally collected force data in real time, conduct segmented statistics at time or space intervals, calculate the sum and average force value of each segment to obtain segmented statistical data.

[0070] By dividing the data into multiple segments at time intervals or space intervals, each segment contains several data points. When dividing by time intervals, a fixed sampling time interval is selected, and all force data points collected within this time interval are added up to obtain the total force value for this time period, and then the average value of the data points is calculated to obtain the average force value for this segment. When dividing by space intervals, fixed interval segments are 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 addition and average calculation. For the statistical results of each segment, the total force value and average force value are recorded in sequence, and the statistical results of all segments are summarized to generate complete segmented statistical data.

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

[0072]

[0073] Calculate the standardized score Z for each segment. i, identify the region with fluctuations higher than other segments to obtain the abnormal amplitude, where x i represents the force value of the i-th data point, which is the measured value of the vertical or lateral force collected within the target time or space segment. μ represents the average value of the force values within the data points, and σ represents the standard deviation of the force values within the data points, which is used to measure the degree of dispersion of the force value data points within this segment relative to the average value;

[0074] There is the following data, x i = 100N (the force value of a certain data point, obtained through monitoring), μ = 85N (the average value of the force values within the segment, obtained by summing the data points within the segment and dividing by the number of points), σ = 10N (the standard deviation of the force values), substitute the values into the formula for calculation:

[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 with the fluctuation indices of other segments, the regions with significant fluctuations can be identified, thereby obtaining the abnormal amplitude data.

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

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

[0079] Through the fast Fourier transform for frequency-domain analysis of the force data, perform discrete Fourier transform on the collected time-domain force signal, calculate the amplitude and phase of the signal at each frequency component, extract the main frequency components according to the amplitude size and record their corresponding frequency points, and at the same time associate the corresponding amplitudes to generate a frequency distribution table, screen and mark the points with amplitudes significantly higher than other frequencies, define the points as periodic abnormal parameters, then classify and merge the abnormal parameters to form an abnormal parameter set containing frequency points and corresponding amplitudes, and finally further classify the data according to the abnormal parameter set.

[0080] S122: Based on the abnormal parameter set, determine the irregular vibration frequencies and positions, and use the formula:

[0081]

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

[0083] The monitoring frequency range is 0 - 200 Hz. By sampling at a frequency of 1000 Hz and collecting signals for 10 seconds, significant frequency points are obtained by calculating the Fourier transform of the signal. If the amplitude detection value A 1 = 0.5, A 2 = 0.3, the frequency point f 1 = 50 Hz, f 2 = 120 Hz, the phase angle φ 1 = π / 6, φ 2 = π / 4, the orbital position l = 10 m, and the wave speed c is 500 m / s. Substitute the above parameters into the formula:

[0084]

[0085] Calculation of the first frequency component (f 1 = 50 Hz):

[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] Calculation of the second frequency component (f 2 = 120 Hz):

[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 × 0.311 = 0.0933;

[0103] Total amplitude calculation:

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

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

[0106] Please refer to Figure 4 , and the analysis steps of the frequency distribution in the track irregularity area are specifically as follows:

[0107] S211: Based on the abnormal vibration characteristics of the wheel-rail, compare the current gauge and alignment data within the detection range with the standard values, correct the gauge and alignment data, and obtain the corrected gauge and alignment data;

[0108] First, preprocess the acquisition results of the current gauge and alignment data, extract relevant measurement parameters, including gauge deviation and alignment deviation angle. Then, by analyzing the numerical changes of all measurement points within the detection range, calculate the difference degree between each measurement point, use the differential calculation method to determine the change trend of the gauge and alignment deviation, generate an estimated curve of non-linear change according to this trend, fit by adjusting the parameters of the estimated result to reflect the gauge and alignment changes in segments, apply the decreasing adjustment method to the gauge and alignment data of each measurement point to gradually approach the standard value, and repeatedly calibrate until the deviation and deviation angle of all data are within the set range, so as to obtain the corrected data result.

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

[0110] First, calculate the average and maximum values of the sensor force corresponding to the corrected data under different track conditions, determine the adjustment factor of the sensor sensitivity in combination with the correction parameters of the gauge and alignment, update the force range of the sensor in segments according to the change of the adjustment factor, so that the sensitivity can adapt to the track change condition. At the same time, analyze the fluctuation range of the force range covering the gauge and alignment data, and gradually adjust the parameter configuration 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 matching the current track condition.

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

[0112]

[0113] Obtain the average frequency FH of the track irregularity area d , where FFT(fh u ) represents the frequency extracted from the force data at the u - th measurement point through Fourier transform, and N H is the total number of measurement points;

[0114] The following data is collected, fh 1 = 12Hz, fh 2 = 15Hz, and fh 3 = 10Hz (frequency data of three measurement points respectively), N H = 3 (total number of measurement points), substitute the values into the formula:

[0115]

[0116] The result shows that the average frequency of the track irregularity area is 12.33Hz, which provides a key index for analyzing the track irregularity characteristics and can be used for further analyzing vibration modes or optimizing track design schemes.

[0117] Please refer to Figure 5 , and the specific steps for obtaining the monitored frequency optimization configuration are as follows:

[0118] S221: Based on the average frequency of the track irregularity area, evaluate whether the currently monitored frequency settings capture key vibration data, identify the frequency range that needs to be adjusted, and obtain the currently monitored frequency configuration;

[0119] Sort and classify the collected frequency data, extract the distribution characteristics of key frequency points, divide the frequency range into multiple intervals, count the frequency occurrence times and corresponding vibration intensity values in each interval, identify the ranges of high - frequency vibration areas and low - frequency vibration areas according to the distribution characteristics of vibration intensity, and at the same time determine the frequency weights of each interval. According to the coverage range of key frequency points and the current monitoring settings, judge whether there are omissions or duplicate coverage of monitored frequencies. Supplement the omitted key frequency points into the monitored frequency range, and adjust and remove duplicates for the repeatedly covered frequency points. After comparison and analysis, obtain the current monitored frequency configuration.

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

[0121] Calculate the coverage range and vibration data acquisition rate for each frequency point, count the proportion of valid vibration signals within the coverage range, screen for frequency points with high coverage efficiency and re-evaluate the integrity of the frequency configuration, optimize the intervals between each frequency point in a segmented adjustment manner to ensure uniform distribution between different frequency points. At the same time, adjust the matching of the monitoring frequency and the track vibration through testing. Increase the monitoring frequency points for areas with higher vibration intensity and appropriately reduce the frequency distribution density for areas with lower 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 steps for the influence of gauge change on the coincidence of waveform features are specifically as follows:

[0123] S311: Based on the optimized monitoring frequency configuration, extract waveform features from the wheel dynamic signal, analyze the energy distribution and periodic changes in the waveform, and obtain the waveform feature analysis results;

[0124] Preprocess the collected wheel dynamic signal, including segmented interception and denoising. The basis for segmented interception is to divide the signal into time windows according to the frequency range set in the optimized configuration. The denoising process uses the collected reference signal for comparison and eliminates abnormal parts with large deviations from the reference signal. Subsequently, calculate the distribution characteristics of the time domain and frequency domain of the segmented signal, analyze the main frequency components of the signal and the energy proportion at different frequencies, extract the waveform periodic changes of the time domain signal, and characterize the periodic change characteristics by statistically calculating the peak, valley, and average values of the signal within each period. Combine the energy distribution characteristics within the frequency range, perform normalization processing on each waveform segment for comparative analysis, and finally synthesize the analysis results of the time domain and frequency domain.

[0125] S312: Based on the waveform feature analysis results, use the formula:

[0126]

[0127] Evaluate the influence of gauge change on the coincidence of waveform features, where ΔSO represents the normalized deviation of the influence of gauge change on the coincidence of waveform features, E v,current represents the energy of the v-th waveform feature under the current gauge, which is the energy value obtained in actual measurement, and E v,standard represents the energy of the v-th waveform feature under the standard gauge, which is the energy value used as a comparison benchmark, and N z is the total number of waveform features;

[0128] If the total number of waveform features N z = 3, and the waveform feature energies of the current gauge are E 1,current = 12, E 2,current = 18, and E 3,current= 15, the waveform feature energies of the standard gauge are E 1,standard = 10, E 2,standard = 15 and E 3,standard = 12. Substitute the data into the formula:

[0129]

[0130] Calculate the fractional value and square value of each term:

[0131]

[0132] The result shows that the impact of gauge change on the coincidence of waveform features is quantified as 0.377, indicating that the gauge change significantly affects the stability and consistency of the waveform.

[0133] Please refer to Figure 7 , and the steps for obtaining the waveform feature parameter set are specifically as follows:

[0134] S321: Based on the impact of gauge change on the coincidence of waveform features, separate the differentiated fluctuation intervals to obtain the fluctuation interval data;

[0135] By analyzing the data collected from the optimized monitoring configuration to determine the similarity and overlap degree between different waveforms, by calculating the similarity index within each waveband, identify the fluctuation regions significantly affected by the gauge change. The regions usually show a high degree of waveform coincidence or significant waveform deviation. Then apply the clustering algorithm to group the waveform data, aggregate the data with similar waveform features together, so as to effectively separate different fluctuation intervals. Each fluctuation interval is independently identified according to its statistical characteristics such as peak value, mean value, and standard deviation. Finally, extract detailed information based on the distinguished fluctuation interval data, including the waveform length, coincidence degree of the interval, and unique features different from other intervals, so as to obtain the detailed fluctuation data of each interval.

[0136] S322: Based on the fluctuation interval data, refine the characteristic parameters of each interval, including periodic change, energy distribution, and frequency response, and identify the key fluctuation patterns and characteristics to obtain the waveform feature parameter set;

[0137] Further refine the characteristic parameters of each fluctuation interval. Calculate the periodic changes, energy distribution, and frequency response of each interval through time-frequency analysis. The periodic changes are evaluated by the repetition pattern of the waveform and the consistency of the time interval. The energy distribution is quantified by the degree of energy aggregation and dispersion within each period. The frequency response is obtained by plotting the response curve to show the contribution of each frequency component to the total vibration. The parameters of each interval are recorded in detail and compared to identify the significant or representative fluctuation patterns. At the same time, analyze the commonalities and differences of each pattern. Finally, obtain a comprehensive set of waveform characteristic parameters based on the refined analysis.

[0138] Please refer to Figure 8 , and the steps for obtaining the train stability analysis results are specifically as follows:

[0139] S411: Based on the set of waveform characteristic parameters, analyze the vibration frequency distribution characteristics of the track position, identify the vibration patterns of multi-frequency components, and obtain the vibration frequency distribution data;

[0140] Divide the track vibration signal into multiple segments according to the track intervals, extract the frequency components of each segment of the signal, statistically classify all the frequency components, summarize the vibration energy values within each frequency interval by segment according to the frequency range, and at the same time calculate the proportion of the main frequency components within each frequency segment. By comparing the frequency distribution characteristics of each section, identify the superimposed characteristics of multi-frequency components, separate the single-frequency and multi-frequency mixed regions, classify and record according to the energy distribution and proportion of multi-frequency components, and further analyze the spatial distribution law of the track vibration frequency characteristics to 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 to obtain the track distribution coefficient analysis result;

[0142] Divide the track into interval segments of a fixed length. According to the vibration frequency distribution of each interval segment, calculate its average energy value and the contribution rate of the main frequency. Convert the data into characteristic points in the spatial coordinates to form a spatial mapping diagram of the track vibration. Based on the vibration characteristics of each interval in the mapping diagram, combined with the gauge and alignment data, evaluate the vibration influence degree of each section of the track. By comprehensively analyzing the correlation between the vibration frequency and spatial position of each section, mark the ranges of the high-influence intervals and low-influence intervals to form the track distribution coefficient analysis result.

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

[0144]

[0145] Calculate the standard deviation Yσ of the dynamic force stability parameter for each area, which is used to evaluate the stability fluctuation on the entire track, and obtain the train stability analysis result. Among them, αp i represents the stability weight coefficient of the i-th area, and xp i represents the vibration intensity of the i-th area, which is the vibration measurement value of this area directly obtained from the data. μp represents the average value of the vibration intensities of all areas, and N Y is the total number of areas;

[0146] If the track distribution includes 4 areas, and the area weight coefficients are αp 1 = 0.3, αp 2 = 0.4, αp 3 = 0.2, and αp 4 = 0.1, and the vibration intensities are xp 1 = 15, xp 2 = 20, xp 3 = 10, and xp 4 = 5, calculate the weighted average value of the vibration intensity, and through weight normalization:

[0147]

[0148] Substitute into the formula to calculate the standard deviation:

[0149]

[0150] Calculate item by item:

[0151]

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

[0153] A train ride comfort analysis system based on wheel-rail forces, the system includes:

[0154] The force value abnormal detection module, based on the vertical force and lateral force data, conducts segmented statistics on the force value distribution at the contact points between the train wheels and the track, analyzes the abnormal amplitudes in the force fluctuation values, determines the irregular vibration frequencies and positions, and obtains the wheel-rail abnormal vibration characteristics;

[0155] The gauge adjustment module, based on the wheel-rail abnormal vibration characteristics, corrects the deviations of the gauge and alignment parameters within the detection range, adjusts the trigger sensitivity and working threshold of the force sensor, and resets the monitoring frequency to obtain the optimized configuration of the monitoring frequency;

[0156] Based on the optimized configuration of the monitoring frequency, the waveform recognition module extracts waveform features from the wheel dynamic signal, analyzes the energy distribution and periodic changes in the waveform, evaluates the overlapping influence of gauge changes on the waveform features, and obtains a set of waveform feature parameters;

[0157] Based on the set of waveform feature parameters, the train stability evaluation module detects the vibration frequency distribution characteristics of the track position, analyzes the track distribution coefficient affected by vibration, screens the dynamic distribution areas affecting train stability, and calculates the dynamic force stability parameters to obtain the train stability analysis result.

[0158] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A train stability analysis method based on wheel-rail force, characterized in that: The following steps are involved: S1: Based on the vertical force and lateral force data collected in real time, the force distribution of the contact point between the train wheel and the track is statistically analyzed in sections, 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 abnormal wheel-rail vibration characteristics are obtained; S2: Based on the abnormal wheel-rail vibration characteristics, the deviation of the track gauge and track direction parameters within the detection range is corrected, the trigger sensitivity and working threshold of the force sensor are adjusted, the frequency distribution of the track unevenness area is analyzed, and the monitoring frequency is reset to obtain the optimal configuration of the monitoring frequency; S3: Based on the optimized configuration of the monitoring frequency, waveform features are extracted from the wheel force signal, energy distribution and periodic changes in the waveform are analyzed, the overlap effect of the track gauge change on the waveform features is evaluated, and the fluctuation interval is separated in combination with the characteristic parameters to obtain a waveform characteristic parameter set; S4: Based on the waveform characteristic parameter set, the vibration frequency distribution characteristics of the track position are detected, the track distribution coefficient affected by the vibration is analyzed through spatial mapping, the dynamic distribution area affecting the train stability is screened, and the dynamic force stability parameter is calculated to obtain the train stability analysis result.

2. The train stability analysis method based on wheel-rail force according to claim 1, characterized in that: The specific steps for analyzing the abnormal amplitude in the force fluctuation value are: S111: Based on the vertical force and lateral force data collected in real time, segmented statistics are performed according to time or space intervals, and the sum of the force values ​​and the average force value of each segment are calculated to obtain segmented statistical data; S112: Based on the segmented statistical data, a force value fluctuation analysis is performed on each segment using the formula: Calculate the normalized score Z for each segment i , identify the area where the fluctuation is higher than other segments and obtain the abnormal amplitude, where x i represents the force value of the ith data point, represents the measured value of the vertical or lateral force collected within the target time or space period, μ represents the mean value of the internal force value of the data point, and σ represents the standard deviation of the internal force value of the data point.

3. The train stability analysis method based on wheel-rail force according to claim 1, characterized in that: The steps for obtaining the abnormal wheel-rail vibration characteristics are specifically as follows: S121: Based on the abnormal amplitude, using fast Fourier transform to analyze the frequency components of the data, screening periodic abnormal parameters, and obtaining an abnormal parameter set; S122: Based on the abnormal parameter set, determine the irregular vibration frequency and position, using the formula: The wheel-rail abnormal vibration characteristics are obtained, where V(f q ,l) means at position l and frequency f q The vibration amplitude under the condition, 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.

4. The train stability analysis method based on wheel-rail force according to claim 1, characterized in that: The analysis steps of the frequency distribution of the track irregularity area are specifically as follows: S211: Based on the abnormal wheel-rail vibration characteristics, compare the current track gauge and track direction data within the detection range with the standard value, correct the track gauge and track direction data, and obtain corrected track gauge and track direction data; S212: Based on the corrected track gauge and track direction data, adjusting the trigger sensitivity and working threshold of the force sensor to match the current track condition, and obtaining an optimized sensor configuration; S213: Based on the optimized sensor configuration, re-collect vibration data and perform frequency analysis using the formula: Get the average frequency FH ​​of the track irregularity area d , 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.

5. The train stability analysis method based on wheel-rail force according to claim 1, characterized in that: The steps for obtaining the monitoring frequency optimization configuration are specifically as follows: S221: Based on the average frequency of the track irregularity area, evaluating whether the current monitoring frequency setting captures key vibration data, identifying the frequency range that needs to be adjusted, and obtaining the current monitoring frequency configuration; S222: Based on the current monitoring frequency configuration, the data coverage efficiency and sensitivity of multiple frequency points are analyzed, the monitoring frequency is reset, and the track vibration condition is matched to obtain an optimized monitoring frequency configuration.

6. The train stability analysis method based on wheel-rail force according to claim 1, characterized in that: The steps for evaluating the influence of the track gauge change on the overlap of waveform characteristics are specifically as follows: S311: extracting waveform features from the wheel force signal based on the monitoring frequency optimization configuration, analyzing energy distribution and periodic changes in the waveform, and obtaining waveform feature analysis results; S312: Based on the waveform feature analysis result, the formula is used: Evaluate the influence of track gauge change on the overlap of waveform features, where ΔSO represents the normalized deviation of the influence of track gauge change on the overlap of waveform features, and E v,current represents the energy of the vth waveform feature under the current track gauge, E v,standard represents the energy of the vth waveform feature under standard track gauge, N z is the total number of waveform features.

7. The train stability analysis method based on wheel-rail force according to claim 1, characterized in that: The steps for obtaining the waveform characteristic parameter set are specifically as follows: S321: Separating differentiated fluctuation intervals based on the overlapping influence of the track gauge change on the waveform characteristics to obtain fluctuation interval data; S322: Based on the fluctuation interval data, the characteristic parameters of each interval are refined, including periodic changes, energy distribution and frequency response, and key fluctuation patterns and characteristics are identified to obtain a waveform characteristic parameter set.

8. The train stability analysis method based on wheel-rail force according to claim 1, characterized in that: The steps for obtaining the train stability analysis result are specifically as follows: S411: Analyze the vibration frequency distribution characteristics of the track position based on the waveform characteristic parameter set, identify the vibration mode of multiple frequency components, and obtain vibration frequency distribution data; S412: Based on the vibration frequency distribution data, analyzing the influence coefficient of the orbital interval vibration through spatial mapping to obtain an orbital distribution coefficient analysis result; S413: Based on the track distribution coefficient analysis result, the dynamic distribution area affecting the train stability is screened, using the formula: Calculate the standard deviation Yσ of the dynamic force stability parameter of each area to obtain the train stability analysis result, where αp i represents the stability weight coefficient of the ith region, xp i represents the vibration intensity of the ith region, μp represents the average vibration intensity of all regions, and N Y is the total number of regions.

9. A train stability analysis system based on wheel-rail force, characterized in that: According to the train stability analysis method based on wheel-rail force according to any one of claims 1 to 8, the system comprises: 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 abnormal wheel-rail vibration characteristics; The gauge adjustment module corrects the gauge and rail direction parameter deviations within the detection range based on the abnormal wheel-rail vibration characteristics, adjusts the trigger sensitivity and working threshold of the force sensor, and resets the monitoring frequency to obtain an optimized configuration of the monitoring frequency; The waveform recognition module extracts waveform features from the wheel force signal based on the optimized configuration of the monitoring frequency, analyzes the energy distribution and periodic changes in the waveform, evaluates the overlap effect of the track gauge change on the waveform features, and obtains a waveform feature parameter set; The train stability assessment module detects the vibration frequency distribution characteristics of the track position based on the waveform characteristic parameter set, analyzes the track distribution coefficient affected by the vibration, screens the dynamic distribution area affecting the train stability, and calculates the dynamic force stability parameter to obtain the train stability analysis result.

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