Rail wheel out-of-roundness detection method based on correlation statistical characterization

Abstract

The embodiment of the invention provides a rail wheel out-of-roundness detection method based on correlation statistical characterization. Comprising the following steps: acquiring vibration acceleration discrete data and fluted disc rotation speed pulse number data of an axle box under a constant speed condition; calculating vibration displacement data of wheels of the train; dividing the filtered vibration displacement data, and extracting single wheel out-of-roundness characterization data; processing the single-time wheel out-of-roundness characterization data of the same wheel at different track positions to obtain multiple-time wheel out-of-roundness characterization data; determining the wave crest number of the multiple times of wheel out-of-roundness characterization data as a wheel out-of-roundness order; the difference value between the maximum value and the minimum value of the multiple times of wheel out-of-roundness characterization data is determined as a wheel out-of-roundness characterization wheel jump value; inputting the wheel out-of-roundness representation wheel jump value into a pre-established interpolation mapping model to obtain a final wheel jump value; and outputting the order and the final round jump value. The method can dynamically master the out-of-roundness condition of the wheel.

Description

Technical Field

[0001] The present application relates to the field of vehicle engineering technology, and in particular to a method for detecting out-of-roundness of a rail wheel based on relevant statistical characterization. Background Art

[0002] Wheel out-of-roundness refers to the phenomenon that the wheel radius shows a periodic jump along the circumferential direction. When the wheel out-of-roundness is serious, the wheel-rail interaction force is greater, which is easy to cause axle box cracks and other phenomena, posing a threat to driving safety. Therefore, timely detection of wheel out-of-roundness is crucial.

[0003] In the prior art, wheel out-of-roundness detection methods include static methods, using a wheel gauge or a wheel lathe to perform detection during planned repair or fault repair stages.

[0004] However, the use of underfloor wheel lathes for testing is expensive and cannot dynamically determine the out-of-roundness of the wheel. Summary of the invention

[0005] The embodiment of the present application provides a rail wheel out-of-roundness detection method based on relevant statistical characterization, so as to achieve the effect of dynamically grasping the wheel out-of-roundness situation.

[0006] In a first aspect, an embodiment of the present application provides a rail wheel out-of-roundness detection method based on relevant statistical characterization, which is applied to a computer device, including: obtaining discrete vibration acceleration data and sprocket speed pulse number data of an axle box of a running train under constant speed conditions; calculating the vibration displacement data of the train wheel according to the discrete vibration acceleration data; performing median filtering on the vibration displacement data to obtain filtered smooth vibration displacement data; dividing the smooth vibration displacement data according to the sprocket speed pulse number data and the total number of sprocket teeth, and extracting single wheel out-of-roundness characterization data; processing single wheel out-of-roundness characterization data of the same wheel at different track positions to obtain multiple wheel out-of-roundness characterization data; calculating the number of peaks of multiple wheel out-of-roundness characterization data to determine it as the wheel out-of-roundness order; calculating the difference between the maximum value and the minimum value of multiple wheel out-of-roundness characterization data to determine it as the wheel out-of-roundness characterization wheel jump value; inputting the wheel out-of-roundness characterization wheel jump value into a pre-established interpolation mapping model to obtain a final wheel out-of-roundness wheel jump value; and outputting the wheel out-of-roundness order and the final wheel out-of-roundness wheel jump value.

[0007] In a possible implementation, the calculation formula for the vibration displacement data is:

[0008]

[0009] In the formula, s(k) represents vibration displacement data, M represents the number of vibration acceleration discrete data, a(n) represents vibration acceleration discrete data, L represents the number of vibration acceleration discrete data, and v represents the vibration velocity frequency domain variable.

[0010] In a possible implementation, the smooth vibration displacement data is divided according to the sprocket speed pulse number data and the total number of sprocket teeth, and the single wheel out-of-roundness characterization data is extracted, including: calculating the number of data points collected by the vibration acceleration sensor when the wheel rotates a preset number of times according to the sprocket speed pulse number data and the total number of sprocket teeth, wherein the vibration acceleration sensor is installed on the axle box; on the smooth vibration displacement data, using the number of data points as a window, extracting a first smooth displacement subsequence and a second smooth displacement subsequence from the smooth vibration displacement data; determining the starting position of the wheel out-of-roundness characterization data according to the first smooth displacement subsequence and the second smooth displacement subsequence; dividing the smooth vibration displacement data according to the starting position to obtain each subsequence of the smooth vibration displacement data; and obtaining the single wheel out-of-roundness characterization data according to each subsequence.

[0011] In a possible implementation manner, the formula for determining the starting point position of the wheel out-of-roundness characterization data is:

[0012]

[0013] Where ξ represents the starting point of the wheel out-of-roundness characterization data, Max -1 {·} represents the τ value corresponding to the maximum value of the data, P represents the number of data points, x(η) represents the subsequence of the smoothed vibration displacement data with the first window being P, and y(η) represents the subsequence of the smoothed vibration displacement data with the second window being P.

[0014] In a possible implementation manner, the calculation formula for the single wheel out-of-roundness characterization data is:

[0015]

[0016] In the formula, represents the single wheel out-of-roundness characterization data, H represents the number of subsequences, S h (k) represents each subsequence.

[0017] In a possible implementation, single wheel out-of-roundness characterization data at different track positions of the same wheel are processed to obtain multiple wheel out-of-roundness characterization data, including: randomly selecting a preset number of sequences from all single wheel out-of-roundness characterization data to determine as an initial cluster center sequence; for any single wheel out-of-roundness characterization data, calculating the cross-correlation value between any single wheel out-of-roundness characterization data and each sequence in the initial cluster center sequence to obtain a maximum correlation peak; dividing any single wheel out-of-roundness characterization data into a cluster center sequence corresponding to the maximum correlation peak; repeatedly executing the steps of "for any single wheel out-of-roundness characterization data, calculating the cross-correlation value between any single wheel out-of-roundness characterization data and each sequence in the initial cluster center sequence to obtain a maximum correlation peak; dividing any single wheel out-of-roundness characterization data into a cluster center sequence corresponding to the maximum correlation peak" until all cluster center sequences remain unchanged; and determining the cluster center sequence of the largest cluster among all cluster center sequences as the multiple wheel out-of-roundness characterization data.

[0018] In a possible implementation, the formula of the interpolation mapping model is:

[0019]

[0020] h j =J j -J j-1

[0021]

[0022] Where f(J) represents the final wheel out-of-roundness wheel runout value; J represents the input wheel out-of-roundness wheel runout value; J j Indicates the wheel runout value represented by the out-of-roundness of each wheel; U j Indicates the wheel runout value J of each wheel out of roundness j The corresponding actual detection round jump value data; d0 = 6 ((U1-U0) / h1-A)) / h1; d n =6((B-(U n -U n-1 ) / h n )) / h n ; The first-order derivative of f1(J) f1'(J) = A; f n The first derivative f of (J) n '(J)=B.

[0023] In a second aspect, an embodiment of the present application provides a rail wheel out-of-roundness detection method based on relevant statistical representation, including:

[0024] The signal acquisition module is used to obtain the discrete data of the vibration acceleration of the axle box and the number of pulses of the gear wheel speed of the running train under constant speed conditions.

[0025] The vibration displacement data generating module is used to calculate the vibration displacement data of the wheels of the train according to the vibration acceleration discrete data.

[0026] The median filter module is used to perform median filtering on the vibration displacement data to obtain filtered smooth vibration displacement data.

[0027] The single wheel out-of-roundness characterization data extraction module is used to divide the smooth vibration displacement data according to the sprocket speed pulse number data and the total number of sprocket teeth to extract the single wheel out-of-roundness characterization data.

[0028] The multiple wheel out-of-roundness characterization data acquisition module is used to process single wheel out-of-roundness characterization data of the same wheel at different track positions to obtain multiple wheel out-of-roundness characterization data.

[0029] The wheel out-of-roundness order determination module is used to calculate the number of peaks of multiple wheel out-of-roundness characterization data and determine it as the wheel out-of-roundness order.

[0030] The wheel out-of-roundness characterization wheel runout value determination module is used to calculate the difference between the maximum value and the minimum value of multiple wheel out-of-roundness characterization data, and determine it as the wheel out-of-roundness characterization wheel runout value.

[0031] The final wheel out-of-roundness wheel runout value determination module is used to input the wheel out-of-roundness characterization wheel runout value into a pre-established interpolation mapping model to obtain the final wheel out-of-roundness wheel runout value.

[0032] The detection result output module is used to output the wheel out-of-roundness order and the final wheel out-of-roundness wheel jump value.

[0033] In a third aspect, an embodiment of the present application provides a computer device, comprising: a memory, a processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory, so that the processor executes the first aspect above and / or various possible implementations of the first aspect.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0035] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0036] The rail wheel out-of-roundness detection method based on relevant statistical characterization provided in the embodiment of the present application can realize dynamic detection of wheel out-of-roundness by acquiring the acceleration discrete data and the gear wheel speed pulse number data collected by the sensor installed on the train axle box. In addition, by calculating the vibration displacement data of the train wheel according to the acceleration discrete data, eliminating high-frequency noise from the vibration displacement data through median filtering, extracting single wheel out-of-roundness characterization data according to the gear wheel speed pulse number data and the total number of gear teeth, and using the pre-established interpolation mapping model to process the wheel out-of-roundness characterization wheel jump value, it is possible to reduce the difference in collected data caused by factors such as noise, working conditions, and load, and more accurately reflect the out-of-roundness detection results of rail vehicle wheels. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0038] Figure 1 A schematic diagram of an application scenario of a rail wheel out-of-roundness detection method based on relevant statistical representation provided in an embodiment of the present application;

[0039] FIG2(1) is a schematic flow chart of a method for detecting out-of-roundness of a rail wheel based on relevant statistical characterization provided in an embodiment of the present application;

[0040] FIG. 2 (2) is an initial cluster center sequence O in an embodiment of the present application. c (k) Single wheel out-of-roundness table

[0042] Data collection Schematic diagram of the relationship between sequences;

[0043] Figure 3 A schematic diagram of the structure of a rail wheel out-of-roundness detection device based on relevant statistical characterization provided in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of the structure of the computer device provided for this application.

[0045] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0046] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0047] First, the terms involved in this application are explained:

[0048] Wheel out-of-roundness refers to the phenomenon that the wheel radius shows periodic jitter along the circumferential direction.

[0049] In order to clearly understand the technical solution of the present application, the solution of the prior art is first introduced in detail. In order to solve the problem of urban travel difficulties, urban rail transit with the advantages of safety, comfort, speed, large passenger capacity, energy saving and environmental protection has developed rapidly. As an important component of the running gear of urban rail transit vehicles, the failure of the wheel is directly related to driving safety. The out-of-roundness of the wheel makes the contact impact between the wheel and the rail more severe, the wheel-rail force is enhanced, and the vibration of vehicle track components such as wheelsets, axle boxes and rails is intensified. Long-term periodic impact will lead to a decrease in the service life of vehicle track system components. Based on the hazards caused by wheel out-of-roundness, it is necessary to conduct timely and accurate detection and take corresponding measures to prevent adverse effects on train operation. In the prior art, the method for detecting wheel out-of-roundness includes using a wheel diameter gauge or a wheel-free lathe to perform detection during the planned repair or fault repair stage. However, the cost of using a wheel-free lathe for detection is high.

[0050] In response to the above technical problems, the inventors have come up with the idea of ​​calculating the vibration displacement data of the wheels of the train based on the discrete data of the vibration acceleration of the axle box of the running train under constant speed conditions, dividing the vibration displacement data into single cycles based on the sprocket speed pulse number data, extracting single wheel out-of-roundness characterization data, processing the single wheel out-of-roundness characterization data generated by collecting data from the same wheel at different track positions, obtaining multiple wheel out-of-roundness characterization data, determining the wheel out-of-roundness order according to the number of peaks of the multiple wheel out-of-roundness characterization data, and determining the difference between the maximum and minimum values ​​of the wheel out-of-roundness characterization data as the wheel out-of-roundness characterization wheel jump value, and obtaining the final wheel out-of-roundness wheel jump value based on the pre-established interpolation mapping model and the wheel out-of-roundness characterization wheel jump value. Dynamic detection of wheel out-of-roundness is achieved by using computer equipment to collect discrete data of vibration acceleration and data of pulse number of gear disc speed. In addition, wheel out-of-roundness characterization data is extracted and the wheel jump value characterizing wheel out-of-roundness is processed using a pre-established interpolation mapping model. This can reduce the difference in collected data caused by factors such as noise, working conditions, and load, and more accurately reflect the out-of-roundness detection results of rail vehicle wheels.

[0051] Based on the above creative findings, the inventor proposed the technical solution of the present application.

[0052] Figure 1 Schematic diagram of application scenario of the rail wheel out-of-roundness detection method based on relevant statistical representation provided in the embodiment of the present application. Figure 1 As shown, the application scenario of this embodiment includes: a receiving device 101, a processor 102 and a display device 103. It is understandable that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the rail wheel out-of-roundness detection method based on relevant statistical characterization. In other feasible implementations of the present application, the above architecture may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown may be implemented in hardware, software, or a combination of software and hardware.

[0053] In a specific implementation process, the receiving device 101 may be an input / output interface or a communication interface, which is used to obtain the discrete data of vibration acceleration of the axle box and the data of the number of gear speed pulses of the running train under constant speed conditions.

[0054] Processor 102 can calculate the vibration displacement data of the wheels of the train according to the discrete data of vibration acceleration; perform median filtering on the vibration displacement data to obtain smooth vibration displacement data after filtering; divide the smooth vibration displacement data according to the number of pulses of the sprocket speed and the total number of teeth of the sprocket to extract single wheel out-of-roundness characterization data; process the single wheel out-of-roundness characterization data of the same wheel at different track positions to obtain multiple wheel out-of-roundness characterization data; calculate the number of peaks of the multiple wheel out-of-roundness characterization data to determine it as the wheel out-of-roundness order; calculate the difference between the maximum value and the minimum value of the multiple wheel out-of-roundness characterization data to determine it as the wheel out-of-roundness characterization wheel jump value; input the wheel out-of-roundness characterization wheel jump value into a pre-established interpolation mapping model to obtain a final wheel out-of-roundness wheel jump value; output the wheel out-of-roundness order and the final wheel out-of-roundness wheel jump value.

[0055] The display device 103 may be used to display the out-of-roundness detection result of the railway vehicle wheel.

[0056] The display device may also be a touch display screen, which is used to receive user instructions while displaying the above content to achieve operational interaction with the user.

[0057] It should be understood that the above-mentioned processor can be implemented by the processor reading instructions in the memory and executing the instructions, or it can be implemented by a chip circuit.

[0058] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0059] FIG2(1) is a flow chart of a method for detecting out-of-roundness of a rail wheel based on relevant statistical characterization provided in an embodiment of the present application. As shown in FIG2(1), the method includes:

[0060] S201: Obtaining discrete data of vibration acceleration of the axle box and data of the number of pulses of gear speed of the running train under constant speed conditions.

[0061] Specifically, the vibration acceleration discrete signal collected by the vibration acceleration sensor in the vertical bearing area of ​​the axle box seat of the running train is obtained, and the vibration acceleration discrete data a(k) is obtained through analog-to-digital conversion by the data acquisition board (k=0, 1, 2, ..., M-1). The gear speed pulse number signal collected by the speed sensor at the end of the axle of the running train is obtained, and the gear speed pulse number data r is obtained through analog-to-digital conversion by the data acquisition board i ,(i=0,1,2,…,N-1).

[0062] S202: Calculate the vibration displacement data of the wheels of the train according to the vibration acceleration discrete data.

[0063] Specifically, the calculation formula for vibration displacement data is:

[0064]

[0065] Where s(k) represents vibration displacement data, M represents the number of vibration acceleration discrete data, a(n) represents vibration acceleration discrete data, L represents the number of vibration acceleration discrete data, v represents vibration velocity frequency domain variable, and exponential function e is used to convert discrete signals into frequency domain.

[0066] S203: Perform median filtering on the vibration displacement data to obtain filtered smoothed vibration displacement data.

[0067] Specifically, the vibration displacement data s(k) is subjected to median filtering with a window length of 2q+1, and the formula is as follows:

[0068] S(k)=Med(s(kq),…,s(k),…,s(k+q),(q=0,1,2,…,M-1)

[0069] Where Med{·} represents the median of the data s. S(k) represents the smoothed vibration displacement data after median filtering.

[0070] S204: dividing the smooth vibration displacement data according to the sprocket speed pulse number data and the total number of sprocket teeth, and extracting single wheel out-of-roundness characterization data.

[0071] Specifically, the process of extracting single wheel out-of-roundness characterization data includes Sa1 to Sa5:

[0072] Sa1: Calculate the number of data points collected by the vibration acceleration sensor when the wheel rotates a preset number of times based on the sprocket speed pulse number data and the total number of sprocket teeth, where the vibration acceleration sensor is installed on the axle box.

[0073] Specifically, firstly, according to the gear wheel speed pulse number data r i Calculate the average value of the gear speed pulse number data

[0074]

[0075] Wherein, N represents the number of gear speed pulse data.

[0076] Specifically, according to the total number of teeth R of the sprocket, the time WT for the wheel to rotate 3 / 2 of a circle is calculated.

[0077]

[0078] Where ΔT represents the sampling interval.

[0079] Specifically, the number of data points collected by the vibration acceleration sensor at time WT is P = WT·f a , where f a Indicates the data sampling rate.

[0080] Sa2: On the smoothed vibration displacement data, taking the number of data points as a window, extracting the first smoothed displacement subsequence and the second smoothed displacement subsequence from the smoothed vibration displacement data.

[0081] Specifically, in the smooth vibration displacement data S(k), the number of data points P collected by the vibration acceleration sensor when the wheel rotates 3 / 2 circles is used as a window for calculation to find the starting position ξ of the wheel out-of-roundness characterization data. Set the first smooth displacement subsequence to x(η)=S(k), (k=1,2,...,P), and the second smooth displacement subsequence to y(η)=S(k), (k=P+1,...,2P).

[0082] Sa3: Determine the starting position of the wheel out-of-roundness characterization data according to the first smoothed displacement subsequence and the second smoothed displacement subsequence.

[0083] Specifically, the formula for determining the starting point position ξ of the wheel out-of-roundness characterization data is:

[0084]

[0085] Where Max -1 {·} represents the value of τ corresponding to the maximum value of the data.

[0086] Sa4: Divide the smooth vibration displacement data according to the starting point position to obtain subsequences of the smooth vibration displacement data.

[0087] Specifically, starting from position ξ, the smooth vibration displacement data S(k) is divided according to the length P-ξ to obtain each subsequence S of the smooth vibration displacement data S(k): h (k),(h=1,2,...,H).

[0088] Sa5: According to each subsequence, obtain the single wheel out-of-roundness characterization data.

[0089] Specifically, the calculation formula for the single wheel out-of-roundness characterization data is:

[0090]

[0091] In the formula, represents the single wheel out-of-roundness characterization data, H represents the number of subsequences, S h (k) represents each subsequence.

[0092] S205: Processing single wheel out-of-roundness characterization data at different track positions of the same wheel to obtain multiple wheel out-of-roundness characterization data.

[0093] Specifically, for the same wheel, multiple word wheel out-of-roundness characterization data generated by collecting data at different track positions are processed, and the specific process of obtaining multiple wheel out-of-roundness characterization data includes Sb1 to Sb5:

[0094] Sb1: Randomly select a preset number of sequences from all single wheel out-of-roundness characterization data and determine them as the initial cluster center sequence.

[0095] Specifically, from Z single wheel out-of-roundness characterization data Randomly select C sequences from the sequence as the initial cluster center sequence O c (k), where c is 1, 2, 3, ..., C. Figure 2 (2) is the initial cluster center sequence O in the embodiment of the present application. c (k) and single wheel out-of-roundness characterization data Schematic diagram of the sequence relationship.

[0096] Sb2: For any single wheel out-of-roundness characterization data, calculate the cross-correlation value between any single wheel out-of-roundness characterization data and each sequence in the initial cluster center sequence to obtain the maximum correlation peak value.

[0097] Specifically, calculate the zth Sequence and cth O c (k) The cross-correlation value of the sequence, the maximum correlation peak CP zc The calculation formula is:

[0098]

[0099] Sb3: Divide any single wheel out-of-roundness characterization data into the cluster center sequence corresponding to the maximum correlation peak.

[0100] Specifically, for each Find the maximum correlation peak CP zc The corresponding c, the Divide into cluster c.

[0101] Sb4: Repeat the steps of "for any single wheel out-of-roundness characterization data, calculate the cross-correlation value between any single wheel out-of-roundness characterization data and each sequence in the initial cluster center sequence to obtain the maximum correlation peak; divide any single wheel out-of-roundness characterization data into the cluster center sequence corresponding to the maximum correlation peak" until all cluster center sequences remain unchanged.

[0102] Specifically, the vibration displacement sequence in each cluster is G c Represents the size of the cth cluster, and the cluster center sequence O is continuously updated using the following formula c (k) Until the cluster center sequence no longer changes.

[0103]

[0104] Sb5: Among all the cluster center sequences, the cluster center sequence of the largest cluster is determined as multiple wheel out-of-roundness characterization data.

[0105] Specifically, find the largest cluster O c (k) As multiple wheel out-of-roundness characterization data.

[0106] S206: Calculate the number of peaks of the wheel out-of-roundness characterization data for multiple times and determine it as the wheel out-of-roundness order.

[0107] Specifically, the number of peaks in multiple wheel out-of-roundness characterization data is defined as the wheel out-of-roundness order.

[0108] S207: Calculate the difference between the maximum value and the minimum value of the wheel out-of-roundness characterization data for multiple times, and determine it as the wheel runout value characterizing the wheel out-of-roundness.

[0109] Specifically, the difference between the maximum value and the minimum value in the secondary wheel out-of-roundness characterization data is defined as the wheel out-of-roundness characterization wheel jump value.

[0110] S208: Inputting the wheel runout value representing the wheel out-of-roundness into a pre-established interpolation mapping model to obtain a final wheel out-of-roundness wheel runout value.

[0111] Specifically, the wheel out-of-roundness characterization wheel runout value is input into an interpolation mapping model constructed according to the wheel out-of-roundness characterization wheel runout value and actual detected wheel runout value data, and the final wheel out-of-roundness wheel runout value of the running train is output.

[0112] S209: Outputting the wheel out-of-roundness order and the final wheel out-of-roundness wheel jump value.

[0113] Specifically, the wheel out-of-roundness order and the final wheel out-of-roundness wheel jump value are output to a display screen, so that the staff can timely inspect and repair the train wheels based on the wheel out-of-roundness order and the final wheel out-of-roundness wheel jump value.

[0114] In summary, by acquiring the acceleration discrete data and the gear speed pulse number data collected by the sensor installed on the train axle box, the dynamic detection of wheel out-of-roundness can be realized. In addition, the vibration displacement data is obtained by continuous processing of the acceleration discrete data, and the high-frequency noise of the vibration displacement data is eliminated by median filtering. The single wheel out-of-roundness characterization data is extracted according to the gear speed pulse number data and the total number of gear teeth, and the wheel jump value representing the wheel out-of-roundness is processed using the pre-established interpolation mapping model, which can reduce the differences in the collected data caused by noise, working conditions, load and other factors, and more accurately reflect the out-of-roundness detection results of rail vehicle wheels.

[0115] In another embodiment of the present application, due to the complexity of the actual operating environment of the train, there is an error between the calculated wheel out-of-roundness characterization wheel jump value and the actual detection wheel jump value data obtained by standard equipment detection. To solve this problem, it is necessary to use an interpolation mapping model to process the wheel out-of-roundness characterization wheel jump value. The establishment process of the interpolation mapping model includes:

[0116] S301: Characterizing wheel runout value J according to multiple wheel out-of-roundness j (j=0,1,2,…,n) Determine the round-trip value interval [a,b], so that a=J0 <J1<…<J n-1 <J n = b, where each wheel out-of-roundness represents the wheel runout value J j Corresponding to an actual detection round jump value data U j ,(j=0,1,2,…,n).

[0117] S302: Establish an interpolation mapping model f(J).

[0118]

[0119] Specifically, in order to make U j =f(J j ),(k=0,1,2,…,n),f j (J) The following formula needs to be satisfied:

[0120]

[0121] In the formula, h j The difference between the wheel runout values ​​of two adjacent wheels is calculated as follows:

[0122] h j =J j -J j-1

[0123] Specifically, variables M0, M1, ..., M n By solving n+1 linear equations, the linear equations satisfy the following formula:

[0124]

[0125] Among them, the parameter μ j and λ j The calculation formula is:

[0126]

[0127] For boundary conditions, the first-order derivative of f1(J) is f1'(J) = A, f n The first derivative f of (J) n '(J) = B, then d0 = 6((U1-U0) / h1-A)) / h1; d n =6((B-(U n -U n-1 ) / h n )) / h n .

[0128] In summary, establishing an interpolation mapping model for wheel out-of-roundness characterization wheel runout values ​​can reduce the error between the calculated wheel out-of-roundness characterization wheel runout values ​​and the wheel runout value data detected by standard equipment, thereby improving the accuracy of detection.

[0129] Figure 3 A schematic diagram of the structure of a rail wheel out-of-roundness detection device based on relevant statistical characterization provided in an embodiment of the present application is shown in FIG. Figure 3As shown, the device includes a signal acquisition module 301, a vibration displacement data generation module 302, a median filtering module 303, a single wheel out-of-roundness characterization data extraction module 304, a multiple wheel out-of-roundness characterization data acquisition module 305, a wheel out-of-roundness order determination module 306, a wheel out-of-roundness characterization wheel jump value determination module 307, a final wheel out-of-roundness wheel jump value determination module 308 and a detection result output module 309.

[0130] The signal acquisition module 301 is used to acquire the discrete data of the vibration acceleration of the axle box and the pulse number data of the gear speed of the running train under the condition of constant speed.

[0131] The vibration displacement data generating module 302 is used to calculate the vibration displacement data of the wheels of the train according to the vibration acceleration discrete data.

[0132] The median filter module 303 is used to perform median filtering on the vibration displacement data to obtain filtered smoothed vibration displacement data.

[0133] The single wheel out-of-roundness characterization data extraction module 304 is used to divide the smooth vibration displacement data according to the sprocket speed pulse number data and the total number of sprocket teeth to extract the single wheel out-of-roundness characterization data.

[0134] The multiple wheel out-of-roundness characterization data acquisition module 305 is used to process single wheel out-of-roundness characterization data of the same wheel at different track positions to obtain multiple wheel out-of-roundness characterization data.

[0135] The wheel out-of-roundness order determination module 306 is used to calculate the number of peaks of multiple wheel out-of-roundness characterization data and determine it as the wheel out-of-roundness order.

[0136] The wheel out-of-roundness characterization wheel runout value determination module 307 is used to calculate the difference between the maximum value and the minimum value of multiple wheel out-of-roundness characterization data, and determine it as the wheel out-of-roundness characterization wheel runout value.

[0137] The final wheel out-of-roundness and wheel runout value determining module 308 is used to input the wheel out-of-roundness characterizing wheel runout value into a pre-established interpolation mapping model to obtain the final wheel out-of-roundness and wheel runout value.

[0138] The detection result output module 309 is used to output the wheel out-of-roundness order and the final wheel out-of-roundness wheel jump value.

[0139] In a possible implementation, the vibration displacement data generation formula in the vibration displacement data generation module 302 is:

[0140]

[0141] In the formula, s(k) represents vibration displacement data, M represents the number of vibration acceleration discrete data, a(n) represents vibration acceleration discrete data, L represents the number of vibration acceleration discrete data, and v represents the vibration velocity frequency domain variable.

[0142] In a possible implementation, the single wheel out-of-roundness characterization data extraction module 304 is specifically used to calculate the number of data points collected by the vibration acceleration sensor when the wheel rotates a preset number of times based on the sprocket speed pulse number data and the total number of sprocket teeth, wherein the vibration acceleration sensor is installed on the axle box; on the smooth vibration displacement data, using the number of data points as a window, extracting a first smooth displacement subsequence and a second smooth displacement subsequence from the smooth vibration displacement data; determining the starting position of the wheel out-of-roundness characterization data based on the first smooth displacement subsequence and the second smooth displacement subsequence; dividing the smooth vibration displacement data based on the starting position to obtain each subsequence of the smooth vibration displacement data; and obtaining the single wheel out-of-roundness characterization data based on each subsequence.

[0143] In a possible implementation, the formula for determining the starting point position of the wheel out-of-roundness characterization data in the single wheel out-of-roundness characterization data extraction module 304 is:

[0144]

[0145] Where ξ represents the starting point of the wheel out-of-roundness characterization data, Max -1 {·} represents the τ value corresponding to the maximum value of the data, P represents the number of data points, x(η) represents the subsequence of the smoothed vibration displacement data with the first window being P, and y(η) represents the subsequence of the smoothed vibration displacement data with the second window being P.

[0146] In a possible implementation, the calculation formula of the single wheel out-of-roundness characterization data in the single wheel out-of-roundness characterization data extraction module 304 is:

[0147]

[0148] In the formula, represents the single wheel out-of-roundness characterization data, H represents the number of subsequences, S h (k) represents each subsequence.

[0149] In one possible implementation, the multiple wheel out-of-roundness characterization data acquisition module 305 is specifically used to randomly select a preset number of sequences from all single wheel out-of-roundness characterization data to determine them as the initial cluster center sequence; for any single wheel out-of-roundness characterization data, calculate the cross-correlation value between any single wheel out-of-roundness characterization data and each sequence in the initial cluster center sequence to obtain the maximum correlation peak; divide any single wheel out-of-roundness characterization data into the cluster center sequence corresponding to the maximum correlation peak; repeat the steps of "for any single wheel out-of-roundness characterization data, calculate the cross-correlation value between any single wheel out-of-roundness characterization data and each sequence in the initial cluster center sequence to obtain the maximum correlation peak; divide any single wheel out-of-roundness characterization data into the cluster center sequence corresponding to the maximum correlation peak" until all cluster center sequences remain unchanged; among all cluster center sequences, the cluster center sequence of the largest cluster is determined as the multiple wheel out-of-roundness characterization data.

[0150] In a possible implementation, the formula of the interpolation mapping model is:

[0151]

[0152] h j =J j -J j-1

[0153]

[0154] Where f(J) represents the final wheel out-of-roundness wheel runout value; J represents the input wheel out-of-roundness wheel runout value; J j Indicates the wheel runout value represented by the out-of-roundness of each wheel; U j Indicates the wheel runout value J of each wheel out of roundness j The corresponding actual detection round jump value data; d0 = 6 ((U1-U0) / h1-A)) / h1; d n =6((B-(U n -U n-1 ) / h n )) / h n ; The first-order derivative of f1(J) f1'(J) = A; f n The first derivative f of (J) n '(J)=B.

[0155] The rail wheel out-of-roundness detection device based on relevant statistical characterization provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effects are similar, and this embodiment will not be described in detail here.

[0156] Figure 4 This is a schematic diagram of the structure of the computer device provided in this application. Figure 4As shown, the computer device provided in this embodiment includes: at least one processor 401 and a memory 402. Optionally, the computer device also includes a communication component 403. The processor 401, the memory 402 and the communication component 403 are connected via a bus 404.

[0157] In a specific implementation process, at least one processor 401 executes the computer-executable instructions stored in the memory 402, so that at least one processor 401 executes the above method.

[0158] The specific implementation process of the processor 401 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0159] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0160] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0161] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0162] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0163] An embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0164] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0165] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0166] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0167] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0169] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0170] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0171] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for detecting out-of-roundness of a rail wheel based on relevant statistical characterization, characterized in that: Applicable to computer equipment, including: Obtain the discrete data of vibration acceleration of the axle box and the number of pulses of gear speed of the running train under constant speed conditions; Calculating vibration displacement data of the wheels of the train according to the vibration acceleration discrete data; Performing median filtering on the vibration displacement data to obtain filtered smoothed vibration displacement data; According to the sprocket speed pulse number data and the total number of sprocket teeth, the smooth vibration displacement data is divided to extract single wheel out-of-roundness characterization data; Processing single wheel out-of-roundness characterization data at different track positions of the same wheel to obtain multiple wheel out-of-roundness characterization data; Calculate the number of peaks of the multiple wheel out-of-roundness characterization data to determine the number of peaks as the wheel out-of-roundness order; Calculating the difference between the maximum value and the minimum value of the multiple wheel out-of-roundness characterization data to determine the difference as the wheel out-of-roundness characterization wheel runout value; Inputting the wheel out-of-roundness characterization wheel runout value into a pre-established interpolation mapping model to obtain a final wheel out-of-roundness wheel runout value; Output the wheel out-of-roundness order and the final wheel out-of-roundness wheel runout value.

2. The method according to claim 1, characterized in that The calculation formula of the vibration displacement data is: In the formula, s(k) represents the vibration displacement data, M represents the number of the vibration acceleration discrete data, a(n) represents the vibration acceleration discrete data, L represents the number of the vibration acceleration discrete data, and v represents the vibration velocity frequency domain variable.

3. The method according to claim 1, characterized in that The step of dividing the smooth vibration displacement data according to the sprocket speed pulse number data and the total number of sprocket teeth to extract single wheel out-of-roundness characterization data includes: Calculate the number of data points collected by the vibration acceleration sensor when the wheel rotates a preset number of times according to the sprocket speed pulse number data and the total number of teeth on the sprocket, wherein the vibration acceleration sensor is installed on the axle box; On the smooth vibration displacement data, taking the number of data points as a window, extracting a first smooth displacement subsequence and a second smooth displacement subsequence from the smooth vibration displacement data; Determining a starting point position of the wheel out-of-roundness characterization data according to the first smooth displacement subsequence and the second smooth displacement subsequence; Dividing the smooth vibration displacement data according to the starting point position to obtain subsequences of the smooth vibration displacement data; According to each of the subsequences, the single wheel out-of-roundness characterization data is obtained.

4. The method according to claim 3, characterized in that The formula for determining the starting point position of the wheel out-of-roundness characterization data is: Where, ξ represents the starting point of the wheel out-of-roundness characterization data, Max -1 {·} represents the τ value corresponding to the maximum value of the data, P represents the number of data points, x(η) represents the subsequence of the smoothed vibration displacement data whose first window is P, and y(η) represents the subsequence of the smoothed vibration displacement data whose second window is P.

5. The method according to claim 3, characterized in that: The calculation formula for the single wheel out-of-roundness characterization data is: In the formula, represents the single wheel out-of-roundness characterization data, H represents the number of subsequences, S h (k) represents each of the subsequences.

6. The method according to claim 1, characterized in that The processing of single wheel out-of-roundness characterization data at different track positions of the same wheel to obtain multiple wheel out-of-roundness characterization data includes: Randomly select a preset number of sequences from all single wheel out-of-roundness characterization data to determine them as initial cluster center sequences; For any single wheel out-of-roundness characterization data, calculate the cross-correlation value between the any single wheel out-of-roundness characterization data and each sequence in the initial cluster center sequence to obtain the maximum correlation peak value; Dividing any single wheel out-of-roundness characterization data into a cluster center sequence corresponding to the maximum correlation peak value; Repeat the steps of "for any single wheel out-of-roundness characterization data, calculating the cross-correlation value between the any single wheel out-of-roundness characterization data and each sequence in the initial cluster center sequence to obtain the maximum correlation peak value; and dividing the any single wheel out-of-roundness characterization data into the cluster center sequence corresponding to the maximum correlation peak value" until all cluster center sequences remain unchanged; The cluster center sequence of the largest cluster among all the cluster center sequences is determined as the multiple wheel out-of-roundness characterization data.

7. The method according to claim 1, characterized in that The formula of the interpolation mapping model is: h j =J j -J j-1 Where f(J) represents the final wheel out-of-roundness wheel runout value; J represents the input wheel out-of-roundness wheel runout value; J j Indicates the wheel runout value represented by the out-of-roundness of each wheel; U j The wheel runout value J representing the out-of-roundness of each wheel j The corresponding actual detection round jump value data; d0=6((U1-U0) / h1-A)) / h1; d n =6((B-(U n -U n-1 ) / h n )) / h n ; The first-order derivative of f1(J) f1'(J) = A; f n The first derivative f of (J) n '(J)=B.

8. A rail wheel out-of-roundness detection device based on relevant statistical characterization, characterized in that: Applicable to computer equipment, including: A signal acquisition module is used to obtain the discrete data of vibration acceleration of the axle box and the number of pulses of gear speed of the running train under constant speed conditions; A vibration displacement data generating module, used for calculating the vibration displacement data of the wheels of the train according to the vibration acceleration discrete data; A median filter module, used for performing median filtering on the vibration displacement data to obtain filtered smooth vibration displacement data; A single wheel out-of-roundness characterization data extraction module is used to divide the smooth vibration displacement data according to the sprocket speed pulse number data and the total number of sprocket teeth to extract the single wheel out-of-roundness characterization data; A multiple wheel out-of-roundness characterization data acquisition module is used to process single wheel out-of-roundness characterization data of the same wheel at different track positions to obtain multiple wheel out-of-roundness characterization data; A wheel out-of-roundness order determination module is used to calculate the number of peaks of the multiple wheel out-of-roundness characterization data and determine it as the wheel out-of-roundness order; A wheel out-of-roundness characterization wheel runout value determination module, used for calculating the difference between the maximum value and the minimum value of the plurality of wheel out-of-roundness characterization data, and determining the difference as the wheel out-of-roundness characterization wheel runout value; A final wheel out-of-roundness wheel runout value determination module is used to input the wheel out-of-roundness characterization wheel runout value into a pre-established interpolation mapping model to obtain a final wheel out-of-roundness wheel runout value; The detection result output module is used to output the wheel out-of-roundness order and the final wheel out-of-roundness wheel jump value.

9. A computer device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

11. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 7 when being executed by a processor.

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