CN115358012A - Method, equipment and medium for evaluating fatigue load coefficient of railway vehicle bogie - Google Patents
Method, equipment and medium for evaluating fatigue load coefficient of railway vehicle bogie Download PDFInfo
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Abstract
The invention discloses a railway vehicle bogie fatigue load coefficient evaluation method, computer equipment and a computer readable storage medium, which are used for solving the technical problem that the applicability of a standard sink coefficient beta and a standard side rolling coefficient alpha of a vertical fatigue load of a main body of a railway vehicle bogie frame is difficult to scientifically and effectively evaluate at present. The method comprises the following steps: acquiring actual measurement random sink coefficient time domain data and actual measurement random roll coefficient time domain data of a vertical fatigue load of a main body of a railway vehicle steering frame; low-pass filtering the actually measured random floating and sinking coefficient time domain data and the actually measured random rolling coefficient time domain data; performing statistical analysis by a rainflow counting method; and calculating by utilizing the actual sinking coefficient grading load spectrum and the actual rolling coefficient grading load spectrum and by using an equal damage principle to obtain an actual sinking equivalent load coefficient and an actual rolling equivalent load coefficient based on the shape of the standard load spectrum.
Description
Technical Field
The invention relates to the crossing field of the design and manufacture field of railway vehicle bogies and the technical field of computer information processing in equipment manufacture, in particular to a method for evaluating fatigue load coefficients of railway vehicle bogies, computer equipment and a computer readable storage medium.
Background
Existing standards "EN12663 railway installation-structural requirements of the railway vehicle body" (for short the EN12663 standard) and "EN13749-2011 railway applications, wheel set and bogie specifications: the requirement of the bogie frame (which may be abbreviated as the EN13749 standard) is a vehicle body and bogie frame strength specification established for european railway operating conditions. Due to the rapid development of China railways in recent years, the operating speed and the total mileage of China high-speed railways have led the world, and the vehicle fatigue load coefficient established based on the European standard is not enough to be applicable to the actual service environment of China.
The EN13749 standard specifies that the fatigue load of the bogie frame body is divided into a vertical fatigue load and a transverse fatigue load, wherein the vertical fatigue load needs to consider the superposition of the rolling and the floating of the vehicle body. The standard vertical fatigue load defining method comprises the following steps: f = (1 + rolling coefficient + sinking coefficient) × F z /2。
With respect to static part F z . In the existing standard load of the strength of the frame, the vertical load of the frame mainly refers to the load transferred to the frame of the bogie by the secondary suspension of the bogie frame. The EN13749 standard determines the vertical load of a bogie frame based on the load of a vehicle body above the bogie, and the operationThe load takes into account a safety factor of 1.2 for the load of the vehicle body. The vertical load calculation method of the bogie frame comprises the following steps: f z1 =F z2 =F z /2=((M v +1.2P 2 -2m + )g)/4，F z1 、F z2 Showing the vertical load, F, borne by the left and right side frames of the bogie z Represents the total vertical load of the bogie frame and has the unit of kilonewtons (kN), M v Indicating reconditioning vehicle mass, P 2 Represents the nominal workload, m + Representing the mass of the bogie, g is the gravity acceleration, and 9.8m/s 2 is taken.
Regarding the heave factor and the roll factor. The coefficient of floating and sinking is used for representing the vertical dynamic load generated by floating and sinking of the vehicle body to account for F z Percentage of (c). The standard heave factor β given in the EN13749 standard is 0.2, but no specific calculation method for the standard heave factor β is given. The roll coefficient is used for expressing the vertical dynamic load generated by the roll of the vehicle body to account for F z Is 0.1 given in the EN13749 standard, but again no method is given for calculating the standard roll coefficient a.
It can be seen that the floating coefficient and the rolling coefficient are the basis for the definition of the vertical fatigue load of the bogie frame main body, and whether the floating coefficient and the rolling coefficient can reflect the operation conditions of the Chinese railway vehicle is important for the design of the bogie frame strength. However, since no calculation method for the standard heave coefficient and the standard roll coefficient is given in the EN13749 standard and other standards, the evaluation of the applicability of the standard heave coefficient β and the standard roll coefficient α is difficult, and the study on the applicability of the fatigue load coefficient of the railway vehicle bogie specified by the EN13749 standard and other standards in the actual service environment in china is affected.
Disclosure of Invention
The invention provides a railway vehicle bogie fatigue load coefficient evaluation method, computer equipment and a computer readable storage medium, which are used for solving the technical problem that the applicability of a standard sink-float coefficient beta and a standard side-rolling coefficient alpha of a railway vehicle bogie frame body vertical fatigue load is difficult to scientifically and effectively evaluate at present.
According to a first aspect of the present invention, there is provided a railway vehicle bogie fatigue load coefficient evaluation method for evaluating a standard heave coefficient β and a standard side roll coefficient α of a vertical fatigue load of a railway vehicle bogie frame body, comprising: acquiring actual measurement random heave coefficient time domain data and actual measurement random roll coefficient time domain data of a vertical fatigue load of a main body of a railway vehicle steering frame, wherein the actual measurement random heave coefficient time domain data reflect the time variation condition of an actual measurement random heave coefficient beta ', the actual measurement random roll coefficient time domain data reflect the time variation condition of an actual measurement random roll coefficient alpha', the actual measurement random heave coefficient beta '= (A1 (z) + A2 (z))/2, and the actual measurement random roll coefficient alpha' = (A1 (z) -A2 (z))/2, wherein A1 (z) and A2 (z) are vertical accelerations acquired by a vehicle body sleeper beam air spring left and right acceleration sensors respectively, and the unit of the vertical acceleration is g, and g =9.8m/s ^2; low-pass filtering is carried out on the actually measured random sinking coefficient time domain data and the actually measured random rolling coefficient time domain data, wherein the low-pass filtering is used for filtering an actually measured random sinking coefficient beta 'corresponding to vertical acceleration caused by local elastic vibration of a vehicle body in the actually measured random sinking coefficient time domain data and an actually measured random rolling coefficient alpha' corresponding to vertical acceleration caused by local elastic vibration of the vehicle body in the actually measured random rolling coefficient time domain data, and the purposes of reserving the actually measured random sinking coefficient beta 'corresponding to rigidity acceleration of the vehicle body in the actually measured random sinking coefficient time domain data and the actually measured random rolling coefficient alpha' corresponding to rigidity acceleration of the vehicle body in the actually measured random rolling coefficient time domain data are achieved; carrying out statistical analysis on actual measurement random float-sink coefficient time domain data and actual measurement random side rolling coefficient time domain data after low-pass filtering by a rain flow counting method, and then respectively obtaining an actual float-sink coefficient graded load spectrum and an actual side rolling coefficient graded load spectrum, wherein the actual float-sink coefficient graded load spectrum is used for reflecting the corresponding relation between the actual float-sink coefficient amplitude and the circulation times of each stage, and the actual side rolling coefficient graded load spectrum is used for reflecting the corresponding relation between the actual side rolling coefficient amplitude and the circulation times of each stage; calculating by utilizing the actual sinking coefficient graded load spectrum and the actual rolling coefficient graded load spectrum and through an equal damage principle to obtain an actual sinking equivalent load coefficient and an actual rolling equivalent load coefficient based on a standard load spectrum shape; and comparing the actual float-sink equivalent load coefficient with the standard float-sink equivalent load coefficient beta, evaluating the standard float-sink coefficient beta according to the ratio of the standard float-sink coefficient beta to the actual float-sink equivalent load coefficient, comparing the actual roll equivalent load coefficient with the standard roll coefficient alpha, and evaluating the standard roll coefficient alpha according to the ratio of the standard roll coefficient alpha to the actual roll equivalent load coefficient.
According to an embodiment of the method for evaluating the fatigue load factor of a railway vehicle bogie according to the invention, said standard heave factor β and said standard roll factor α are respectively the standard "EN13749-2011 railway application, wheel set and bogie specifications: standard heave coefficient beta and standard roll coefficient alpha specified in the requirements of bogie frame; the standard load spectrum is for standard "EN13749-2011 railway applications, wheel set and bogie specifications: bogie frame fatigue load spectrum as specified in "requirements for bogie frame"; the step of calculating the actual sinking and floating equivalent load coefficient and the actual rolling equivalent load coefficient based on the standard load spectrum shape by utilizing the actual sinking and floating coefficient graded load spectrum and the actual rolling coefficient graded load spectrum and an equal damage principle comprises the following steps: respectively substituting the relevant data of the actual sinking-floating coefficient graded load spectrum and the relevant data of the actual rolling coefficient graded load spectrum into the formula eq =｛[L t /(f d L 1 (N 1 +N 2 (1.2)^m+N 3 (1.4)^m))]∑(n i ∆F i The float-sink equivalent load coefficient and the side-roll equivalent load coefficient are obtained by the calculation of ^ m) } (1 \8260m), wherein L t For safe operation kilometers, and specifically 1500 kilometers L 1 The number of kilometers of the actual sinking coefficient graded load spectrum or the actual rolling coefficient graded load spectrum is N is 1 multiplied by 10 of the number of equivalent load cycles 7 ，∆F i Lam is the actual heave coefficient amplitude of each level in the actual heave coefficient graded load spectrum or the actual roll coefficient amplitude of each level in the actual roll coefficient graded load spectrum, n i To said actual floatThe circulation times of each level in the sinking coefficient grading load spectrum or the circulation times of each level in the actual rolling coefficient grading load spectrum, f d Respectively taking 1, 0.5 and 0.3 as damage coefficients and corresponding welding structure base materials and welding lines, respectively taking m as S-N curve parameters, taking 3 as the welding line and taking 5 as the base materials, and calculating N when the sinking-floating equivalent load coefficient is calculated 1 、N 2 And N 3 Respectively taking 600 ten thousand times, 200 ten thousand times and 200 ten thousand times, and calculating N when the equivalent load coefficient of the side rolling is calculated 1 、N 2 And N 3 30 ten thousand times, 10 ten thousand times and 10 ten thousand times respectively.
According to an embodiment of the method for evaluating a fatigue load coefficient of a railway vehicle bogie, the low-pass filtering the time domain data of the actually measured random heave coefficient and the time domain data of the actually measured random roll coefficient comprises: and filtering data with the frequency more than 3Hz in the actually measured random floating and sinking coefficient time domain data and the actually measured random rolling coefficient time domain data.
According to an embodiment of the method for evaluating a fatigue load factor of a railway vehicle bogie, the method further comprises predicting the reliability of the standard heave factor β and the standard roll factor α, wherein the predicting the reliability of the standard heave factor β and the reliability of the standard roll factor α comprises: carrying out statistical analysis on actual measurement random floating coefficient time domain data and actual measurement random roll coefficient time domain data after low-pass filtering by a rain flow counting method, then respectively obtaining an actual measurement random floating coefficient distribution probability density function and an actual measurement random roll coefficient distribution probability density function, then respectively obtaining a floating coefficient empirical distribution cumulative probability function and a roll coefficient empirical distribution cumulative probability function by the actual measurement random floating coefficient distribution probability density function and the actual measurement random roll coefficient distribution probability density function, and finally respectively predicting the credibility of the standard floating coefficient beta and the standard roll coefficient alpha by the floating coefficient empirical distribution cumulative probability function and the roll coefficient empirical distribution cumulative probability function.
According to the embodiment of the method for evaluating the fatigue load coefficient of the railway vehicle bogie, if the ratio of the standard sinking coefficient beta to the actual sinking equivalent load coefficient is lower than a set threshold value and/or the ratio of the standard rolling coefficient alpha to the actual rolling equivalent load coefficient is lower than a set threshold value, a modification suggestion for improving the standard sinking coefficient beta and/or the standard rolling coefficient alpha is given.
According to an embodiment of the method for evaluating the fatigue load factor of a railway vehicle bogie according to the invention, the modification proposal is to specify the following standard "EN13749-2011 railway application, wheel set and bogie: the standard roll coefficient α specified in the truck frame requirements "was adjusted from 0.1 to 0.15.
According to the embodiment of the method for evaluating the fatigue load coefficient of the railway vehicle bogie, a plurality of different railway lines are selected, actual measurement random floating coefficient time domain data and actual measurement random side rolling coefficient time domain data of the vertical fatigue load of the main body of the railway vehicle bogie running on each railway line in the different railway lines are respectively obtained, actual floating equivalent load coefficients and actual side rolling equivalent load coefficients corresponding to the railway lines in the different railway lines one by one are obtained through calculation, the ratio of the standard floating coefficient beta to the maximum value in the actual floating equivalent load coefficients corresponding to the railway lines in the different railway lines one by one is defined as a floating coefficient safety margin (which can be represented by a symbol S), the ratio of the standard side rolling coefficient alpha to the maximum value in the actual side rolling equivalent load coefficients corresponding to the railway lines in the different railway lines one by one is defined as a side rolling coefficient safety margin, the standard floating coefficient beta is evaluated through the floating coefficient safety margin, and the standard side rolling coefficient alpha is evaluated through the side rolling coefficient safety margin.
According to the embodiment of the method for evaluating the fatigue load coefficient of the railway vehicle bogie, the time domain data of the actually measured random sink coefficient and the time domain data of the actually measured random roll coefficient of the vertical fatigue load of the main body of the railway vehicle bogie frame in the new wheel state and the abrasion wheel state, which run on each railway line in a plurality of different railway lines, are respectively obtained, the actual sink equivalent load coefficient and the actual roll equivalent load coefficient which are in one-to-one correspondence with the new wheel state and the abrasion wheel state of each railway line in the plurality of different railway lines are obtained through calculation, the ratio of the standard sink coefficient beta to the maximum value in the actual sink equivalent load coefficients which are in one-to-one correspondence with the new wheel state and the abrasion wheel state of each railway line in the plurality of different railway lines is defined as the sink coefficient safety margin, the ratio of the standard sink coefficient alpha to the maximum value in the actual sink equivalent load coefficients which are in one-to-one correspondence with the new wheel state and the abrasion wheel state of each railway line in the plurality of different railway lines is defined as the sink coefficient safety margin, the standard sink coefficient safety margin is evaluated through the standard sink coefficient alpha.
According to a second aspect of the present invention, there is provided a computer device comprising a processor coupled to a memory for storing a computer program or instructions, the processor being configured to execute the computer program or instructions in the memory, such that the computer device performs the method for evaluating a fatigue load coefficient of a railway vehicle bogie according to the first aspect.
According to a third aspect of the present invention, there is provided a computer-readable storage medium storing a computer program or instructions which, when executed, causes the computer to execute the railway vehicle bogie fatigue load coefficient evaluation method of the first aspect described above.
Tests show that the evaluation results of the standard sinking coefficient beta and the standard side rolling coefficient alpha of the railway vehicle bogie frame main body vertical fatigue load by the railway vehicle bogie fatigue load coefficient evaluation method provided by the invention reflect that the standard sinking coefficient beta and the standard side rolling coefficient alpha have larger overall safety margin, which is highly consistent with the fact that the existing high-speed train main body structure has less failure. Furthermore, it was also found by experiments that the standard side-rolling coefficient α of the vertical fatigue load of the bogie frame body of a railway vehicle of an individual railway track (a track under severe conditions) is small, and it is sufficient to explain that the method for evaluating a fatigue load coefficient of a railway vehicle bogie according to the present invention can effectively evaluate the applicability of the bogie side-rolling fatigue load coefficient based on the measured data.
The computer device and the computer-readable storage medium of the present invention realize computer software for the method of evaluating a fatigue load coefficient of a railway vehicle bogie of the present invention. In the application process, the evaluation results of the standard sink coefficient beta and the standard side rolling coefficient alpha of the vertical fatigue load of the bogie frame main body of the railway vehicle can be obtained by collecting the big data of the railway vehicle operated on the Chinese railway line and inputting the collected data into the computer equipment for data processing and analysis, so that the standard sink coefficient beta and the standard side rolling coefficient alpha can be more accurately and reliably evaluated and corrected in time.
The method for evaluating the fatigue load coefficient of the railway vehicle bogie provides a scientific and effective research tool for the research on the applicability of the fatigue load coefficient of the railway vehicle bogie specified by standards such as EN13749 standard and the like in the actual service environment of China, and is favorable for further strengthening and improving the domestic autonomous research and development capability and level of key core equipment such as a high-speed train.
The invention is further described with reference to the following figures and detailed description. Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to assist in understanding the relevant embodiments, and are incorporated in and constitute a part of this specification, with the understanding that the present disclosure is to be considered an exemplification of the invention and is not intended to limit the invention to that as illustrated and described herein.
Fig. 1 is a schematic diagram showing arrangement positions of left and right acceleration sensors of air springs of body bolster in a method for evaluating fatigue load coefficient of railway vehicle bogie according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of calculating an actually measured random heave coefficient β' by acquiring vertical acceleration through left and right acceleration sensors of air springs of a body bolster in the method for evaluating a fatigue load coefficient of a railway vehicle bogie according to the embodiment of the present invention.
Fig. 3 is a schematic diagram of calculating an actually measured random roll coefficient α' by acquiring vertical acceleration through left and right acceleration sensors of air springs of a body bolster in the method for evaluating a fatigue load coefficient of a railway vehicle bogie according to the embodiment of the present invention.
Fig. 4 is a standard "EN13749-2011 railway application, wheel set and truck specification: the requirement of bogie frame "stipulates standard load spectrum shape schematic diagram.
Fig. 5 shows the actual sinking coefficient graded load spectrum and the actual rolling coefficient graded load spectrum corresponding to the new wheel in test example 1 of the present invention.
Fig. 6 is a graph showing an actual load spectrum of the float coefficient and an actual load spectrum of the roll coefficient corresponding to the abrasion wheel in the test example 1 of the present invention.
Fig. 7 shows the actual sinking-floating equivalent load coefficient based on the standard load spectrum shape corresponding to the new wheel in the test example 1 of the present invention.
Fig. 8 shows the actual roll equivalent load factor based on the standard load spectrum shape for the new wheel in test example 1 of the present invention.
FIG. 9 is a graph of the probability density function of the distribution of the measured random sink-float coefficient in test example 1 of the present invention.
FIG. 10 is a graph of the cumulative probability function of the empirical distribution of the heave coefficient in test example 1 of the present invention.
Labeled in the figure as: a vehicle body 1 and a frame 2. The unit "g" in the figure is the acceleration of gravity, g =9.8m/s 2.
Detailed Description
The invention will be described more fully hereinafter with reference to the accompanying drawings. Those skilled in the art will be able to implement the teachings of the present invention based on these teachings. Before describing the present invention in detail with reference to the accompanying drawings, it is to be noted that: in the present specification, the technical solutions and the technical features provided in the respective portions including the following description may be combined with each other without conflict.
Reference in the following description will generally be made to only some embodiments, but not all embodiments, of the present invention, and therefore all other embodiments that can be obtained by one of ordinary skill in the art without inventive faculty based on the embodiments of the present invention shall fall within the scope of the present invention.
The terms "comprising," "including," "having," and any variations thereof in this specification and claims and in any related parts thereof, are intended to cover non-exclusive inclusions.
In addition, it should be noted that the correlation mathematical formula in the present invention adopts a linear expression, wherein the symbol "^" represents "superscript".
The method for evaluating the fatigue load coefficient of the railway vehicle bogie provided by the embodiment of the invention is used for evaluating the standard sink-float coefficient beta and the standard side rolling coefficient alpha of the vertical fatigue load of the railway vehicle bogie frame main body, and comprises the following steps:
step S1: acquiring actual measurement random heave coefficient time domain data and actual measurement random roll coefficient time domain data of a vertical fatigue load of a railway vehicle bogie frame main body, wherein the actual measurement random heave coefficient time domain data reflect the time variation condition of an actual measurement random heave coefficient beta 'and the time variation condition of an actual measurement random roll coefficient alpha', the actual measurement random heave coefficient beta '= (A1 (z) + A2 (z))/2, and the actual measurement random roll coefficient alpha' = (A1 (z) -A2 (z))/2, wherein A1 (z) and A2 (z) are respectively vertical accelerations acquired by acceleration sensors on the left side and the right side of an air spring of a vehicle body sleeper beam, and the unit of the vertical acceleration is g (gravity acceleration), and g =9.8m/s ^2.
Fig. 1 is a schematic diagram showing arrangement positions of left and right acceleration sensors of air springs of body bolster in a method for evaluating fatigue load coefficient of railway vehicle bogie according to an embodiment of the present invention. Fig. 2 is a schematic diagram of calculating an actually measured random heave coefficient β' by acquiring vertical acceleration through left and right acceleration sensors of air springs of a body bolster in the method for evaluating a fatigue load coefficient of a railway vehicle bogie according to the embodiment of the present invention. Fig. 3 is a schematic diagram of calculating an actually measured random roll coefficient α' by acquiring vertical acceleration through left and right acceleration sensors of air springs of a body bolster in the method for evaluating a fatigue load coefficient of a railway vehicle bogie according to the embodiment of the present invention. As shown in fig. 1, the coordinate axis X represents the horizontal longitudinal direction of the railway vehicle body 1, and the coordinate axis Y represents the horizontal width direction of the railway vehicle body 1. The left and right acceleration sensors of the air spring of the sleeper beam of the vehicle body 1 are distributed at the marked parts of a measuring point 1 and a measuring point 2 shown in figure 1. As shown in fig. 2 and 3, the body bolster of the vehicle body 1 is supported on the bogie frame 2 by left and right side air springs, respectively.
As shown in fig. 1 and fig. 2, the inventor has recognized based on the understanding of the standard EN13749 that the heave coefficient reflects the influence of the heave motion of the upper vehicle body on the left and right air springs (here, the air springs refer to the air springs), on the vertical dynamic load of the bogie frame, and therefore, the heave motion of the vehicle body can be reflected by the average value of the left and right vertical accelerations at the positions where the air springs of the vehicle body are loaded, so that the measured random heave coefficient β 'can be expressed as β' = (A1 (z) + A2 (z))/2. A1 (z) and A2 (z) are vertical accelerations acquired by acceleration sensors on the left side and the right side of an air spring of a sleeper beam of the vehicle body respectively.
As shown in fig. 1 and fig. 3, since the vertical vibration acceleration at the vehicle body sleeper air spring (here, the air spring is also referred to as the air spring) is mainly caused by vehicle body sinking and vehicle body rolling, theoretically, the influence of vehicle body rolling on the vertical vibration acceleration can be obtained by making a difference between the vertical vibration acceleration at the left and right air springs of the sleeper beam, that is, the actually measured random rolling coefficient α 'can be expressed as α' = (A1 (z) -A2 (z))/2. A1 (z) and A2 (z) are vertical accelerations acquired by acceleration sensors on the left side and the right side of an air spring of a sleeper beam of the vehicle body respectively.
A1 and a2 marked in fig. 2 and fig. 3 are vertical acceleration vectors respectively acquired by the acceleration sensors above the air springs on the left side and the right side of the body sleeper beam.
Step S2: and performing low-pass filtering on the actually measured random heave coefficient time domain data and the actually measured random roll coefficient time domain data, wherein the low-pass filtering is used for filtering an actually measured random heave coefficient beta 'corresponding to vertical acceleration caused by local elastic vibration of a vehicle body in the actually measured random heave coefficient time domain data and an actually measured random roll coefficient alpha' corresponding to vertical acceleration caused by local elastic vibration of the vehicle body in the actually measured random roll coefficient time domain data, and is used for keeping an actually measured random heave coefficient beta 'corresponding to the local rigidity acceleration of the vehicle body in the actually measured random heave coefficient time domain data and an actually measured random roll coefficient alpha' corresponding to the rigidity acceleration of the vehicle body in the actually measured random roll coefficient time domain data.
Preferably, the low-pass filtering the actually measured random floating and sinking coefficient time domain data and the actually measured random rolling coefficient time domain data includes: and filtering data with the frequency more than 3Hz in the actually measured random floating and sinking coefficient time domain data and the actually measured random rolling coefficient time domain data. Experience shows that the translational acceleration of the train body of the main Chinese high-speed train is mainly determined by 6 rigid body modes of the floating and sinking of the train body, the transverse movement of the train body, the longitudinal direction of the train body, the side rolling of the train body, the shaking of the train body and the nodding of the train body, the distribution frequency range of the translational acceleration is mainly within 2Hz, and the modes such as the shaking of the train body and the like can reach about 2.5Hz under the influence of a coupler and a windshield after being connected. The frequency of more than 10Hz is caused by the elastic vibration of the vehicle body, and the rigid motion of the vehicle body caused by the passing frequency of the bridge is also within 2.5 Hz. In order to consider only the influence of the rigid motion of the vehicle body as far as possible and avoid the non-conservative result caused by filtering some rigid body modal frequency components of the vehicle body in a filtering range as far as possible, the filtering is preferably performed by using 3 Hz.
The time domain data of the actually measured random sinking coefficient and the time domain data of the actually measured random rolling coefficient obtained in the above steps belong to random data, and the standard sinking coefficient beta and the standard rolling coefficient alpha are based on a standard load spectrum, so that the invention establishes a random load spectrum for the random data and converts the random load spectrum into the shape of the standard load spectrum to obtain the actual sinking equivalent load coefficient and the actual rolling equivalent load coefficient, and then compares the actual sinking equivalent load coefficient and the actual rolling equivalent load coefficient with the standard sinking coefficient beta and the standard rolling coefficient alpha.
And step S3: and carrying out statistical analysis on the actual measurement random floating coefficient time domain data and the actual measurement random roll coefficient time domain data after low-pass filtering by a rain flow counting method, and then respectively obtaining an actual floating coefficient grading load spectrum and an actual roll coefficient grading load spectrum, wherein the actual floating coefficient grading load spectrum is used for reflecting the corresponding relation between the actual floating coefficient amplitude and each stage of cycle times, and the actual roll coefficient grading load spectrum is used for reflecting the corresponding relation between the actual roll coefficient amplitude and each stage of cycle times.
And step S4: and calculating by utilizing the actual sinking coefficient graded load spectrum and the actual side rolling coefficient graded load spectrum and by using an equal damage principle to obtain an actual sinking equivalent load coefficient and an actual side rolling equivalent load coefficient based on the shape of the standard load spectrum.
Optionally, the standard heave factor β and the standard roll factor α are respectively for standard "EN13749-2011 railway applications, wheel set and bogie specifications: standard heave coefficient beta and standard roll coefficient alpha specified in the requirements of bogie frame; the standard load spectrum is for standard "EN13749-2011 railway applications, wheel set and bogie specifications: bogie frame fatigue load spectrum as specified in "requirements for bogie frame" (as shown in figure 4); then, the step of calculating by using the actual sinking coefficient graded load spectrum and the actual side rolling coefficient graded load spectrum and through an equal damage principle to obtain an actual sinking equivalent load coefficient and an actual side rolling equivalent load coefficient based on a standard load spectrum shape comprises: respectively substituting the related data of the actual sinking coefficient grading load spectrum and the related data of the actual rolling coefficient grading load spectrum into a formula F eq =｛[L t /(f d L 1 (N 1 +N 2 (1.2)^m+N 3 (1.4)^m))]∑(n i ∆F i M) and (1 \8260m) to obtain a float-sink equivalent load coefficient and a side-roll equivalent load coefficient, wherein L t For safe operation kilometers, 1500 kilometers are taken 1 The number of kilometers of the actual load spectrum or the actual roll coefficient graded load spectrum is the actual sinking coefficient graded load spectrum, and N is the number of equivalent load cycles of 1 multiplied by 10 7 ，∆F i Lam is the actual heave coefficient amplitude of each level in the actual heave coefficient graded load spectrum or the actual roll coefficient amplitude of each level in the actual roll coefficient graded load spectrum, n i F is the circulation times of each level in the actual float-sink coefficient graded load spectrum or the circulation times of each level in the actual side-roll coefficient graded load spectrum d Taking 1, 0.5 and 0.3 as damage coefficient and corresponding welding structure parent metal and welding line respectively, taking 3 as welding line and 5 as parent metal, and calculating N when sinking and floating equivalent load coefficient 1 、N 2 And N 3 Respectively taking 600 ten thousand times, 200 ten thousand times and 200 ten thousand times, and calculating N when the side rolling equivalent load coefficient is calculated 1 、N 2 And N 3 30 ten thousand times, 10 ten thousand times and 10 ten thousand times respectively.
Fig. 4 is a standard "EN13749-2011 railway application, wheel set and truck specification: the requirements of the truck frame "a specified standard load spectrum shape. As shown in fig. 4, for the EN13749 bogie frame fatigue load spectrum, which is generally defined as a three-stage load spectrum, the total cycle number of the vertical load is 1000 ten thousand, the first stage is 600 ten thousand, the second stage load coefficient is increased to 1.2 times and 200 ten thousand times, and the third stage load coefficient is increased to 1.4 times and 200 ten thousand times. The EN13749 standard specifies that the floating and sinking load is circulated for 10-20 times and the side rolling load is reversed for 1 time; therefore, in the total cycle times of 1000 ten thousand, the side rolling load is circulated for 50-100 ten thousand times, and the floating and sinking load is circulated for 1000 ten thousand times.
If it is required to be stated that eq =｛[Lt/(f d L1(N 1 +N 2 (1.2)^m+N 3 (1.4)^m))]∑(n i ∆F i M) obtained by the following method.
The design of the railway vehicle body and the bogie frame based on the EN12663 standard and the EN13749 standard belongs to infinite life design; under the action of a standard load spectrum, the stress cycle amplitude of a key position is smaller than the fatigue limit of a material; for fatigue testing, it is required that the structure does not crack under the standard load spectrum. When the above requirements are met, the structure is deemed to meet the infinite life design requirements. The infinite life herein specifically corresponds to the number of years of service life, and the standard and the existing operation experience are not strictly defined. Only in the experience of the industry and the total procurement specifications of iron, the service life of the vehicle is required to be 30 years, so the industry corresponds the time of infinite life to 30 years.
This default specification was followed in the study of the project, namely that the design requirements are met under the design load spectrum, i.e. that it has a service life of at least 30 years. Meanwhile, the service life of the vehicle is considered to be 30 years, and the operating mileage of the vehicle is 1500 kilometers. Therefore, the actual measurement service load spectrum needs to be expanded to 1500 kilometers, and then the equivalent load coefficient based on the design load spectrum shape is obtained by using the equal damage principle.
For the random load spectrum obtained in the operating range L1, the damage it causes can be expressed as:
D 1 =∑ i (n i /N i )=∑ i [(n i ∆F i ^m)/C]
in the above formula: n is a radical of an alkyl radical i The number of load cycles at each level; c and m are parameters of the S-N curve (the values of m and C can be determined by referring to the IIW standard).
Then L for the operating range t =1500 kilometres, the damage can be expressed as:
D=(L t /L 1 )D 1
the random load spectrum can be converted into the equivalent load amplitude F according to the principle that the damage is consistent eq Acting for N times, and generating D damage to the structure, then:
D=(N∆F eq ^m)/C
to this end, it can be expressed as:
(N∆F eq ^m)/C=(L t /L 1 )∑ i [(n i ∆F i ^m)/C]
the equivalent load can be calculated as:
∆F eq =｛[L t /(NL 1 )]∑(n i ∆F i ^m)}^(1⁄m)
in the existing FKM standard (FKM guide ANALYTICAL STEENGTH ASSESSMENT OF COMPONENTS) and IIW standard (XIII-1539-07/XV-1254 r4-07 IIW document Recommendations for manufacturing purposes) in order to take account OF the influence OF uncertainty such as processing, it is generally considered that the total damage to the weld joint is 0.5 and the total damage to the base material is 0.3. Therefore, in order to take this uncertainty into account, a damage factor f is introduced d Then the equivalent load can be expressed as:
∆F eq =｛[L t /(f d NL 1 )]∑(n i ∆F i ^m)}^(1⁄m)
in the above formula:
L t for safe operation kilometers, safe operation is considered for 30 years, and the operation mileage is 1500 kilometers;
L 1 the number of kilometers of an actually measured load spectrum;
n is equivalent load cycle number 1 x 10 7 ；
∆F i The ^ m is the load amplitude of each stage; n is a radical of an alkyl radical i The stress cycle times of each stage;
f d taking 1, 0.5 and 0.3 corresponding to the base metal and the welding line of the welding structure respectively as damage coefficients;
m is S-N curve parameter, the welding seam is 3, and the parent metal is 5.
For the heave and roll coefficients, then 3 phases of the fatigue test need to be considered:
(N 1 ∆F eq ^m)/C+[N 2 (1.2∆F eq )^m)]/C+[N 2 (1.2∆F eq )^m)]/C=[L t /(f d L 1 )]∑ i [(n i ∆F i ^m)/C]
from this, the following equation is further obtained:
∆F eq =｛[L t /(f d L 1 (N 1 +N 2 (1.2)^m+N 3 (1.4)^m))]∑(n i ∆F i ^m)}^(1⁄m)
wherein, for the sinking-floating coefficient, N 1 、N 2 And N 3 600, 200 and 200 ten thousand times respectively; considering that the roll load changes direction once per cycle of 20 times of sink and float load, for the roll coefficient N 1 、N 2 And N 3 30, 10 and 10 ten thousand times respectively.
Step S5: comparing the actual float-sink equivalent load coefficient with the standard float-sink equivalent load coefficient beta, evaluating the standard float-sink coefficient beta according to the ratio of the standard float-sink equivalent load coefficient beta to the actual float-sink equivalent load coefficient, comparing the actual roll equivalent load coefficient with the standard roll coefficient alpha, and evaluating the standard roll coefficient alpha according to the ratio of the standard roll coefficient alpha to the actual roll equivalent load coefficient.
Optionally, the method for evaluating the fatigue load coefficient of the railway vehicle bogie may further include predicting reliability of the standard heave coefficient β and the standard roll coefficient α, and evaluating reliability of the standard heave coefficient β and the standard roll coefficient α includes: and carrying out statistical analysis on the actual measurement random float coefficient time domain data and the actual measurement random roll coefficient time domain data after low-pass filtering by a rain flow counting method, then respectively obtaining an actual measurement random float coefficient distribution probability density function and an actual measurement random roll coefficient distribution probability density function, then respectively obtaining a float coefficient experience distribution cumulative probability function and a roll coefficient experience distribution cumulative probability function by the actual measurement random float coefficient distribution probability density function and the actual measurement random roll coefficient distribution probability density function, and finally respectively predicting the credibility of the standard float coefficient beta and the standard roll coefficient alpha by the float coefficient experience distribution cumulative probability function and the roll coefficient experience distribution cumulative probability function.
Optionally, in the method for evaluating the fatigue load coefficient of the railway vehicle bogie, if a ratio of the standard heave coefficient β to the actual heave equivalent load coefficient is lower than a set threshold and/or a ratio of the standard roll coefficient α to the actual roll equivalent load coefficient is lower than a set threshold, a modification suggestion for improving the standard heave coefficient β and/or the standard roll coefficient α is given.
Optionally, in the method for evaluating the fatigue load coefficient of the railway vehicle bogie, a plurality of different railway lines are selected, time domain data of actual measurement random sink coefficient and time domain data of actual measurement random side roll coefficient of the vertical fatigue load of the main body of the railway vehicle bogie frame running on each railway line in the plurality of different railway lines are respectively obtained, actual sink and float equivalent load coefficients and actual side roll equivalent load coefficients corresponding to each railway line in the plurality of different railway lines one to one are obtained through calculation, the ratio of the standard sink and float coefficient β to the maximum value in the actual sink and float equivalent load coefficients corresponding to each railway line in the plurality of different railway lines one to one is defined as a sink and float coefficient safety margin, the ratio of the standard side roll coefficient α to the maximum value in the actual side roll equivalent load coefficients corresponding to each railway line in the plurality of different railway lines one to one is defined as a side roll coefficient safety margin, the standard sink and float coefficient β is evaluated through the side roll coefficient safety margin, and the standard side roll coefficient α is evaluated through the side roll coefficient safety margin.
Optionally, the measured random sinking coefficient time domain data and the measured random rolling coefficient time domain data of the vertical fatigue load of the main body of the bogie frame of the railway vehicle in the new wheel state and the worn wheel state, which are operated on each railway line of the different railway lines, are respectively obtained, and the actual sinking equivalent load coefficient and the actual rolling equivalent load coefficient of each railway line in the different railway lines, which correspond to the new wheel state and the worn wheel state one by one, are obtained through calculation, the ratio of the standard sinking coefficient β to the maximum value in the actual sinking equivalent load coefficients of each railway line in the different railway lines, which correspond to the new wheel state and the worn wheel state one by one, is defined as a sinking coefficient safety margin, the ratio of the standard rolling coefficient α to the maximum value in the actual sinking equivalent load coefficients of each railway line in the different railway lines, which correspond to the new wheel state and the worn wheel state one by one is defined as a rolling coefficient safety margin, the standard sinking coefficient β is evaluated through the sinking coefficient safety margin, and the standard rolling coefficient α is evaluated through the rolling coefficient safety margin.
A computer device according to an embodiment of the present invention includes a processor coupled to a memory, the memory being configured to store a computer program or instructions, and the processor being configured to execute the computer program or instructions in the memory, so that the computer device executes the method for evaluating a fatigue load coefficient of a railway vehicle bogie.
The processor may include a Central Processing Unit (CPU), a Digital Signal Processor (DSP), a microprocessor, an Application Specific Integrated Circuit (ASIC), a Microcontroller (MCU), a Field Programmable Gate Array (FPGA), or one or more Integrated circuits for implementing logical operations. The processor may be used to implement the required functions for the above-mentioned computer device, for example to control the whole computer device, to execute software programs, to process data of software programs, etc. The software may be software for implementing the method for evaluating a fatigue load coefficient of a railway vehicle bogie according to the present invention.
The memory may include mass storage for data or instructions. By way of example, and not limitation, memory may include a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical disks, magneto-optical disks, magnetic tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. The memory may include removable or non-removable (or fixed) media, where appropriate. The memory may be internal or external to the processor, where appropriate. In a particular embodiment, the memory is non-volatile solid-state memory. In a particular embodiment, the memory includes Read Only Memory (ROM); where appropriate, the ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically Alterable ROM (EAROM), or flash memory, or a combination of two or more of these.
A computer-readable storage medium of an embodiment of the present invention stores a computer program or instructions that, when executed, cause the computer to execute the above-described railway vehicle bogie fatigue load coefficient evaluation method.
The computer device and the computer-readable storage medium realize computer software for implementing the method for evaluating a fatigue load coefficient of a railway vehicle bogie according to the present invention. In the application process, the evaluation results of the standard sink coefficient beta and the standard side rolling coefficient alpha of the vertical fatigue load of the bogie frame main body of the railway vehicle can be obtained by collecting the big data of the railway vehicle operated on the Chinese railway line and inputting the collected data into the computer equipment for data processing and analysis, so that the standard sink coefficient beta and the standard side rolling coefficient alpha can be more accurately and reliably evaluated and corrected in time.
Test example 1
Selecting a railway line 1, acquiring actually measured random heave coefficient time domain data and actually measured random roll coefficient time domain data of vertical fatigue loads of a railway vehicle steering frame main body in a new wheel state and a worn wheel state running on the railway line 1, performing low-pass filtering on the actually measured random heave coefficient time domain data and the actually measured random roll coefficient time domain data, performing statistical analysis on the actually measured random heave coefficient time domain data and the actually measured random roll coefficient time domain data after the low-pass filtering by a rainflow counting method, and respectively obtaining an actual heave coefficient graded load spectrum and an actual roll coefficient graded load spectrum (see figures 5 and 6 in detail, wherein a curve of a roll coefficient is an actual roll system, and a heave coefficient is an actual heave coefficient), and calculating by using the actual heave coefficient graded load spectrum and the actual roll coefficient graded load spectrum and an equal damage principle to obtain an actual heave equivalent load coefficient and an actual roll equivalent load coefficient based on the shape of a standard load spectrum (see figures 7 and 8 in detail).
The results show that: under the states of a new wheel and a worn wheel, the actual floating and sinking equivalent load coefficient is less than the standard floating and sinking coefficient 0.2 defined in the EN13749 standard; the actual roll equivalent load factor is less than the standard roll factor of 0.1 as defined in the EN13749 standard.
In addition, test example 1 further includes: statistical analysis is carried out on actual measurement random floating coefficient time domain data and actual measurement random side rolling coefficient time domain data after low-pass filtering through a rain flow counting method, then an actual measurement random floating coefficient distribution probability density function (shown in figure 9) and an actual measurement random side rolling coefficient distribution probability density function are obtained respectively, a floating coefficient empirical distribution cumulative probability function (shown in figure 10) and a side rolling coefficient empirical distribution cumulative probability function are obtained through the actual measurement random floating coefficient distribution probability density function and the actual measurement random side rolling coefficient distribution probability density function, and finally the credibility of the standard floating coefficient beta and the standard side rolling coefficient alpha is predicted through the floating coefficient empirical distribution cumulative probability function and the side rolling coefficient empirical distribution cumulative probability function respectively.
Test example 2
Selecting a railway line 2 (the railway line 2 belongs to a line with severe conditions), acquiring actually measured random sinking coefficient time domain data and actually measured random rolling coefficient time domain data of vertical fatigue loads of a railway vehicle bogie frame main body which runs on the railway line 2 and is in a new wheel state and a worn wheel state, performing low-pass filtering on the actually measured random sinking coefficient time domain data and the actually measured random rolling coefficient time domain data, performing statistical analysis on the actually measured random sinking coefficient time domain data and the actually measured random rolling coefficient time domain data after the low-pass filtering by a rainflow meter counting method to respectively obtain an actual sinking coefficient graded load spectrum and an actual rolling coefficient graded load spectrum, and calculating by using the actual sinking coefficient graded load spectrum and the actual rolling coefficient graded load spectrum and using an equal damage principle to obtain an actual sinking equivalent load coefficient and an actual rolling equivalent load coefficient based on the shape of a standard load spectrum.
The results show that: in the case of a new wheel, the actual rolling equivalent load factor (specifically, the actual rolling equivalent load factor with a damage factor of 1 is 0.13, the actual rolling equivalent load factor with a damage factor of 0.5 is 0.17, and the actual rolling equivalent load factor with a damage factor of 0.3 is 0.1) corresponding to the railway track 2 is significantly greater than the state of the worn wheel. And taking an actual roll equivalent load coefficient 0.17 with a damage coefficient of 0.5, and setting the ratio of a standard roll coefficient 0.1 defined in the EN13749 standard to 0.17 as a safety margin S, wherein the safety margin S is smaller than 1. The equivalent rolling coefficient is reduced under the condition of the worn wheel, which is mainly because the wheel track matching equivalent taper is low under the condition of a new wheel, and the vehicle is easy to have low-frequency vehicle shaking phenomenon on certain road sections, thereby leading to a larger rolling load coefficient. Therefore, the side rolling load coefficient has larger correlation with the vehicle state, and the side rolling load coefficient is properly adjusted according to the characteristic that the vehicle dynamic performance needs to be considered in the design of the vehicle so as to improve the safety margin of the side rolling load coefficient. The modification proposal is embodied in the specification of the standard "EN13749-2011 railway application, wheel set and bogie: the standard roll coefficient α specified in the truck frame requirements "was adjusted from 0.1 to 0.15.
The contents of the present invention have been explained above. Those skilled in the art will be able to practice the invention based on these descriptions. All other embodiments, which can be derived by a person skilled in the art from the description above without inventive step, shall fall within the scope of protection of the present invention.
Claims (10)
1. A railway vehicle bogie fatigue load coefficient evaluation method is used for evaluating a standard heave coefficient beta and a standard side rolling coefficient alpha of a vertical fatigue load of a railway vehicle bogie frame main body, and is characterized by comprising the following steps:
acquiring actual measurement random heave coefficient time domain data and actual measurement random roll coefficient time domain data of a vertical fatigue load of a main body of a railway vehicle steering frame, wherein the actual measurement random heave coefficient time domain data reflect the time variation condition of an actual measurement random heave coefficient beta ', the actual measurement random roll coefficient time domain data reflect the time variation condition of an actual measurement random roll coefficient alpha', the actual measurement random heave coefficient beta '= (A1 (z) + A2 (z))/2, and the actual measurement random roll coefficient alpha' = (A1 (z) -A2 (z))/2, wherein A1 (z) and A2 (z) are vertical accelerations acquired by a vehicle body sleeper beam air spring left and right acceleration sensors respectively, and the unit of the vertical acceleration is g, and g =9.8m/s ^2;
low-pass filtering is carried out on the actually measured random sinking coefficient time domain data and the actually measured random rolling coefficient time domain data, wherein the low-pass filtering is used for filtering an actually measured random sinking coefficient beta 'corresponding to vertical acceleration caused by local elastic vibration of a vehicle body in the actually measured random sinking coefficient time domain data and an actually measured random rolling coefficient alpha' corresponding to vertical acceleration caused by local elastic vibration of the vehicle body in the actually measured random rolling coefficient time domain data, and the purposes of reserving the actually measured random sinking coefficient beta 'corresponding to rigidity acceleration of the vehicle body in the actually measured random sinking coefficient time domain data and the actually measured random rolling coefficient alpha' corresponding to rigidity acceleration of the vehicle body in the actually measured random rolling coefficient time domain data are achieved;
carrying out statistical analysis on actual measurement random float-sink coefficient time domain data and actual measurement random side rolling coefficient time domain data after low-pass filtering by a rain flow counting method, and then respectively obtaining an actual float-sink coefficient graded load spectrum and an actual side rolling coefficient graded load spectrum, wherein the actual float-sink coefficient graded load spectrum is used for reflecting the corresponding relation between the actual float-sink coefficient amplitude and the circulation times of each stage, and the actual side rolling coefficient graded load spectrum is used for reflecting the corresponding relation between the actual side rolling coefficient amplitude and the circulation times of each stage;
calculating by utilizing the actual sinking coefficient graded load spectrum and the actual side rolling coefficient graded load spectrum and an equal damage principle to obtain an actual sinking equivalent load coefficient and an actual side rolling equivalent load coefficient based on the shape of a standard load spectrum;
and comparing the actual float-sink equivalent load coefficient with the standard float-sink equivalent load coefficient beta, evaluating the standard float-sink coefficient beta according to the ratio of the standard float-sink coefficient beta to the actual float-sink equivalent load coefficient, comparing the actual roll equivalent load coefficient with the standard roll coefficient alpha, and evaluating the standard roll coefficient alpha according to the ratio of the standard roll coefficient alpha to the actual roll equivalent load coefficient.
2. The railway vehicle bogie fatigue load factor evaluation method according to claim 1, characterized in that: the standard heave factor β and the standard roll factor α are respectively for standard "EN13749-2011 railway applications, wheel set and bogie regulations: standard heave coefficient beta and standard roll coefficient alpha specified in the requirements of bogie frame;
the standard load spectrum is for standard "EN13749-2011 railway applications, wheel set and truck specifications: bogie fatigue load spectrum as specified in "requirements for bogie frame";
and calculating by utilizing the actual sinking coefficient graded load spectrum and the actual rolling coefficient graded load spectrum and by an equal damage principle to obtain the actual sinking and floating equal based on the shape of the standard load spectrumThe effective load factor and the actual roll equivalent load factor include: respectively substituting the related data of the actual sinking coefficient grading load spectrum and the related data of the actual rolling coefficient grading load spectrum into a formula F eq =｛[L t /(f d L 1 (N 1 +N 2 (1.2)^m+N 3 (1.4)^m))]∑(n i ∆F i The float-sink equivalent load coefficient and the side-roll equivalent load coefficient are obtained by the calculation of ^ m) } (1 \8260m), wherein L t For safe operation kilometers, and specifically 1500 kilometers L 1 The number of kilometers of the actual sinking coefficient graded load spectrum or the actual rolling coefficient graded load spectrum is N is 1 multiplied by 10 of the number of equivalent load cycles 7 ，∆F i The ^ m is the actual floating coefficient amplitude of each level in the actual floating coefficient grading load spectrum or the actual rolling coefficient amplitude of each level in the actual rolling coefficient grading load spectrum, n i F is the circulation times of each level in the actual float-sink coefficient graded load spectrum or the circulation times of each level in the actual side-roll coefficient graded load spectrum d Taking 1, 0.5 and 0.3 as damage coefficient and corresponding welding structure parent metal and welding line respectively, taking 3 as welding line and 5 as parent metal, and calculating N when sinking and floating equivalent load coefficient 1 、N 2 And N 3 Respectively taking 600 ten thousand times, 200 ten thousand times and 200 ten thousand times, and calculating N when the equivalent load coefficient of the side rolling is calculated 1 、N 2 And N 3 30 ten thousand times, 10 ten thousand times and 10 ten thousand times respectively.
3. The railway vehicle bogie fatigue load factor evaluation method according to claim 1, characterized in that: the low-pass filtering the actually measured random floating and sinking coefficient time domain data and the actually measured random rolling coefficient time domain data comprises: and filtering data with the frequency more than 3Hz in the actually measured random floating and sinking coefficient time domain data and the actually measured random rolling coefficient time domain data.
4. The railway vehicle bogie fatigue load factor evaluation method according to claim 1, characterized in that: the method also comprises the step of predicting the credibility of the standard heave coefficient beta and the credibility of the standard roll coefficient alpha, wherein the step of predicting the credibility of the standard heave coefficient beta and the credibility of the standard roll coefficient alpha comprises the following steps: carrying out statistical analysis on actual measurement random floating coefficient time domain data and actual measurement random roll coefficient time domain data after low-pass filtering by a rain flow counting method, then respectively obtaining an actual measurement random floating coefficient distribution probability density function and an actual measurement random roll coefficient distribution probability density function, then respectively obtaining a floating coefficient empirical distribution cumulative probability function and a roll coefficient empirical distribution cumulative probability function by the actual measurement random floating coefficient distribution probability density function and the actual measurement random roll coefficient distribution probability density function, and finally respectively predicting the credibility of the standard floating coefficient beta and the standard roll coefficient alpha by the floating coefficient empirical distribution cumulative probability function and the roll coefficient empirical distribution cumulative probability function.
5. The railway vehicle bogie fatigue load factor evaluation method according to any one of claims 1 to 4, characterized in that: and if the ratio of the standard heave coefficient beta to the actual heave equivalent load coefficient is lower than a set threshold value and/or the ratio of the standard roll coefficient alpha to the actual roll equivalent load coefficient is lower than a set threshold value, giving a modification suggestion for improving the standard heave coefficient beta and/or the standard roll coefficient alpha.
6. The railway vehicle bogie fatigue load factor evaluation method according to claim 5, characterized in that: the modification proposal is specifically to specify the standard "EN13749-2011 railway application, wheelset and bogie: the standard roll coefficient α specified in the "requirements for bogie frame" was adjusted from 0.1 to 0.15.
7. The railway vehicle bogie fatigue load factor evaluation method according to any one of claims 1 to 4, characterized in that: selecting a plurality of different railway lines, respectively obtaining actual measurement random sink coefficient time domain data and actual measurement random side roll coefficient time domain data of the vertical fatigue load of the railway vehicle bogie frame main body running on each railway line in the plurality of different railway lines, calculating to obtain actual sink equivalent load coefficients and actual side roll equivalent load coefficients corresponding to each railway line in the plurality of different railway lines one by one, defining the ratio of the standard sink coefficient beta to the maximum value in the actual sink equivalent load coefficients corresponding to each railway line in the plurality of different railway lines one by one as a sink coefficient safety margin, defining the ratio of the standard side roll coefficient alpha to the maximum value in the actual side roll equivalent load coefficients corresponding to each railway line in the plurality of different railway lines one by one as a side roll coefficient safety margin, evaluating the standard sink coefficient beta through the sink coefficient safety margin, and evaluating the standard side roll coefficient alpha through the side roll coefficient safety margin.
8. The railway vehicle bogie fatigue load factor evaluation method according to any one of claims 1 to 4, characterized in that: selecting a plurality of different railway lines, respectively obtaining actual measurement random float coefficient time domain data and actual measurement random side roll coefficient time domain data of vertical fatigue load of a railway vehicle bogie frame main body in a new wheel state and a worn wheel state which run on each railway line in the plurality of different railway lines, calculating to obtain actual float coefficient equivalent load coefficients and actual side roll coefficient equivalent load coefficients of the new wheel state and the worn wheel state of each railway line in the plurality of different railway lines in a one-to-one correspondence manner, defining the ratio of the standard float coefficient beta to the maximum value in the actual float coefficient equivalent load coefficients of the new wheel state and the worn wheel state of each railway line in the plurality of different railway lines in a one-to-one correspondence manner as a float coefficient safety margin, defining the ratio of the standard side roll coefficient alpha to the maximum value in the actual side roll coefficient equivalent load coefficients of the new wheel state and the worn wheel state of each railway line in the plurality of different railway lines in a one-to-one manner as a side roll coefficient safety margin, evaluating the standard float coefficient beta through the float coefficient safety margin, and evaluating the standard side roll coefficient safety margin through the side roll coefficient alpha.
9. A computer device, characterized by: comprising a processor coupled to a memory for storing a computer program or instructions, the processor being configured to execute the computer program or instructions in the memory to cause the computer device to perform the method of evaluating a fatigue load coefficient of a railway vehicle bogie as claimed in any one of claims 1 to 6.
10. A computer-readable storage medium characterized by: for storing a computer program or instructions which, when executed, cause the computer to carry out the railway vehicle bogie fatigue load coefficient evaluation method of any one of claims 1 to 6.
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