CN113094816A - Method for constructing comprehensive working condition vibration spectrum and long-life test spectrum of armored vehicle - Google Patents
Method for constructing comprehensive working condition vibration spectrum and long-life test spectrum of armored vehicleDownload PDFInfo
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Abstract
Description
Technical Field
The invention relates to the technical field of armored vehicle vibration spectrum data processing methods, in particular to a method for constructing an armored vehicle comprehensive working condition vibration spectrum and a long service life test spectrum.
Background
The armored vehicle belongs to military vehicles, can run on various road surfaces, is mainly used for transportation, investigation, maneuverability attack and the like, and therefore the vibration of the armored vehicle not only affects the fatigue degree and the working efficiency of fighters, but also affects the attack precision of weapons arranged on the vehicle. If the whole vehicle vibrates excessively, the service life of the device can be shortened, and even the device is damaged.
In order to analyze the vibration performance of armored vehicles, a test method is mainly adopted at present to test the structural strength, durability and the like of armored vehicle frames and the like. For this reason, long-life simulation tests are often used for quality inspection to determine the vibration performance and safety and reliability of the entire armored vehicle.
However, the random vibration environment measurement data induction method specified in the GJB/Z126-99 vibration and impact environment test data induction method can only perform data induction on random vibration under single working condition to obtain a random vibration standard spectrum. And the armored vehicle can have different road surfaces and different vehicle speeds and other comprehensive working conditions when actually running, so the method aiming at the single working condition can not be directly used for the comprehensive working conditions, and can not be directly used for the long-life simulation test.
Disclosure of Invention
Therefore, in order to overcome the defects, the embodiment of the invention provides a method for constructing a comprehensive working condition vibration spectrum and a long-life test spectrum of an armored vehicle, which can realize reconstruction of a comprehensive working condition vibration actual measurement spectrum and a standard spectrum, and construct the long-life test spectrum suitable for the comprehensive working condition based on the comprehensive working condition vibration standard spectrum.
Therefore, the method for constructing the comprehensive working condition vibration spectrum of the armored vehicle comprises the following steps:
obtaining the road surface occupation ratio b of the armored vehicle under the ith road condition under the test comprehensive working conditioniAnd the vehicle speed of the jth vehicle speed used for driving on the ith road condition accounts for aijWherein, in the step (A),
1, p is the total number of road surface types, and q is the total number of vehicle speed types;
respectively obtaining the single-working-condition actual measurement spectrum of the mth sample under the road condition i and the vehicle speed j
From the measured spectrum of the simplex condition
First reserve factor cij(1)Obtaining the comprehensive working condition actual measurement spectrum FGS of all vehicle speeds and road conditions of the measuring points by induction, and obtaining the single working condition actual measurement spectrum
First reserve factor cij(1)And a first relation between the measured spectrum FGS and the comprehensive working condition;
from the measured spectrum of the simplex condition
And a first reserve coefficient cij(1)Calculating to obtain comprehensive working condition actual measurement spectrum FGS of all vehicle speeds and road conditions of the measuring points, and obtaining single working condition actual measurement spectrum
First reserve factor cij(1)And a second relation between the measured spectrum FGS and the comprehensive working condition;
single working condition actual measurement spectrum
First reserve factor cij(1)Substituting a second relation between the measured spectrum and the comprehensive working condition actual measurement spectrum FGS into the single working condition actual measurement spectrum
First reserve factor cij(1)Obtaining a first storage coefficient c according to a first relation between the measured spectrum and the comprehensive working condition FGSij(1)And the relation between the measured spectrum FGS and the comprehensive working condition;
the measured spectrum FGS of the comprehensive working condition is used as the input of the first RBF neural network and is based on the first reserve coefficient cij(1)A first reserve coefficient c obtained by the relation between the measured spectrum FGS and the comprehensive working conditionij(1)As expected output of the first RBF neural network, training the first RBF neural network by utilizing input and expected output, and inputting the actually measured spectrum of the current comprehensive working condition into the trained first RBF neural network to obtain a current first reserve coefficient;
respectively obtaining the standard spectrum of the single working condition of the mth sample under the conditions of the road condition i and the vehicle speed j
According to a single working condition standard spectrum
Second reserve factor cij(2)Obtaining the comprehensive working condition standard spectrum FGG of all vehicle speeds and road conditions of the measuring points by induction, and obtaining the single working condition standard spectrum
Second reserve factor cij(2)And a first relation between the combined working condition normalized spectrum FGG;
according to a single working condition standard spectrum
And a second reserve coefficient cij(2)Calculating to obtain comprehensive condition standard spectrum FGG of all vehicle speeds and road conditions of the measured points, and obtaining single-condition standard spectrum
Second reserve factor cij(2)And a second relationship between the combined operating mode normalized spectrum FGG;
standard spectrum of single working condition
Second reserve factor cij(2)Substituting a second relation between the comprehensive working condition standard spectrum FGG and the single working condition standard spectrum
Second reserve factor cij(2)And obtaining a second reserve coefficient c according to a first relation between the comprehensive working condition normalized spectrum FGGij(2)And the relation between the comprehensive working condition standard spectrum FGG;
taking the comprehensive working condition standard spectrum FGG as the input of a second RBF neural network, and according to a second reserve coefficient cij(2)Second reserve coefficient c obtained by relation with comprehensive working condition standard spectrum FGGij(2)As expected output of the second RBF neural network, training the second RBF neural network by utilizing input and expected output, and inputting the current comprehensive working condition standard spectrum into the trained second RBF neural network to obtain a current second reserve coefficient;
calculating to obtain a reserve coefficient c according to the current first reserve coefficient and the current second reserve coefficientij；
According to the reserve coefficient cijActual measurement spectrum G of single working condition under road condition i and vehicle speed jshice(i, j) and the simplex condition normalized spectrum Gguifan(i, j), respectively calculating and obtaining comprehensive working condition actual measurement spectrums FGS and comprehensive working condition standard spectrums FGG of all vehicle speeds and road conditions of the measured points.
Preferably, the measured spectrum is based on a single-working condition
And a first reserve coefficient cij(1)The step of summarizing and obtaining the comprehensive working condition actual measurement spectrum FGS of all vehicle speeds and road conditions of the measuring points comprises the following steps:
from the measured spectrum of the simplex condition
And a first reserve coefficient cij(1)Calculating to obtain the mean value and the variance under the road condition i and the vehicle speed j, wherein the calculation formula is as follows:
wherein N isi，jThe number of samples under a single working condition;
calculating to obtain weighted mean value and variance according to the mean value and variance under the road condition i and the vehicle speed j, wherein the calculation formula is as follows:
and calculating to obtain the comprehensive working condition actual measurement spectrum FGS of all vehicle speeds and road conditions of the measuring points according to the weighted mean value and the variance, wherein the calculation formula is as follows:
wherein, F11The first tolerance upper limit coefficient is calculated by the following formula:
wherein Z isβTo satisfy Prob [ Z.ltoreq.Zβ]A normally distributed quantile of beta,
is a degree of freedom of (N-1) ×2Distribution of alpha quantilesPoint, tN-1；(1-α)1-alpha quantile distributed at the center t with the degree of freedom (N-1), N is the total number of samples,
preferably, the measured spectrum is based on a single-working condition
And a first reserve coefficient cij(1)The calculation formula of the comprehensive working condition actual measurement spectrum FGS for calculating and obtaining all vehicle speeds and road conditions of the measuring points is as follows:
wherein N isi,jIs the number of samples in the simplex case.
Preferably, the normalized spectrum according to single-working-condition
And a second reserve coefficient cij(2)The step of summarizing and obtaining the comprehensive working condition standard spectrum FGG of all vehicle speeds and road conditions of the measured points comprises the following steps:
according to a single working condition standard spectrum
And a second reserve coefficient cij(2)Calculating to obtain the mean value and the variance of each spectral line under the road condition i and the vehicle speed j, wherein the calculation formula is as follows:
wherein N isi，jThe number of samples under a single working condition;
calculating to obtain a weighted mean value and a weighted variance according to the mean value and the variance of each spectral line under the road condition i and the vehicle speed j, wherein the calculation formula is as follows:
obtaining a statistic F by weighted mean and variance calculationnAnd tnThe calculation formula is as follows:
wherein N is the total number of samples,
according to statistic FnAnd tnFrequency division is carried out under the confidence coefficient (1-alpha) to obtain H1A frequency band, wherein the spectral line number of two end points of the h-th frequency band is kh1、kh2，h＝1,2,…,H1The number of spectral lines is Nh＝kh2-kh1+1；
And calculating to obtain a weighted mean value and a weighted variance in the h frequency band according to the weighted mean value and the weighted variance, wherein the calculation formula is as follows:
calculating according to the weighted mean and the variance in the h frequency band to obtain the upper limit estimation of the straight spectral tolerance in the h frequency band, wherein the calculation formula is as follows:
wherein, F12The second tolerance upper limit coefficient with the confidence coefficient of (1-alpha) and the quantile point of beta is calculated by the following formula:
wherein Z isβTo satisfy Prob [ Z.ltoreq.Zβ]A normally distributed quantile of beta,
with a degree of freedom of (N.N)hX of-1)2The distribution of the alpha quantile points is,
with a degree of freedom of (N.N)h-1) central t distribution 1-a quantiles;
and taking the upper limit estimation of the tolerance of the straight spectrum in the h-th frequency band as the amplitude of the frequency band, and connecting each adjacent straight spectrum by using a straight line under a log-log coordinate to obtain a comprehensive working condition standard spectrum FGG of all vehicle speeds and road conditions of the measured point.
Preferably, the normalized spectrum according to single-working-condition
And a second reserve coefficient cij(2)The calculation formula of the comprehensive working condition standard spectrum FGG for calculating and obtaining all vehicle speeds and road conditions of the measured points is as follows:
wherein N isi,jIs the number of samples in the simplex case.
Preferably, the reserve coefficient cijThe root mean square values of the current first reserve coefficient and the current second reserve coefficient.
Preferably, said reserve coefficient c is used for determining a reserve valueijActual measurement spectrum G of single working condition under road condition i and vehicle speed jshice(i, j) and the simplex condition normalized spectrum Gguifan(i, j), respectively calculating and obtaining the calculation formulas of the comprehensive working condition actual measurement spectrum FGS and the comprehensive working condition standard spectrum FGG of all vehicle speeds and road conditions of the measured points, wherein the calculation formulas are respectively as follows:
the method for constructing the long-life test spectrum of the armored vehicle comprises the following steps:
obtaining the time T of the jth vehicle speed used for driving under the ith road conditionijI is 1,2, …, p, j is 1,2, …, q, p is the total number of road surface types, q is the total number of vehicle speed types;
time T of jth vehicle speed used for driving according to ith road conditionijAnd calculating to obtain a segment acceleration proportional coefficient beta under the road condition i and the vehicle speed jij；
Obtaining comprehensive working condition standard spectrum according to the method for constructing comprehensive working condition vibration spectrum of armored vehicle
And a segment acceleration proportionality coefficient betaijAnd calculating to obtain a long-life test spectrum LF.
Preferably, the piecewise acceleration proportionality coefficient betaijThe calculation formula of (2) is as follows:
wherein, TsFor the actual run time, m is an index.
Preferably, the long-life test spectrum LF is calculated by the formula:
the technical scheme of the embodiment of the invention has the following advantages:
1. according to the method for constructing the armored vehicle comprehensive working condition vibration spectrum, the measured spectrum and the standard spectrum of the comprehensive working condition are respectively deduced by using the induction method and the formula calculation method, the relationship between the first storage coefficient and the measured spectrum of the comprehensive working condition and the relationship between the second storage coefficient and the standard spectrum of the comprehensive working condition are respectively obtained by using the conclusions of the two methods, so that the RBF neural network is respectively trained based on the relationships to obtain the first storage coefficient estimation value and the second storage coefficient estimation value, and then the storage coefficient adopted when the vibration spectrum is actually constructed is calculated according to the estimation values, so that the construction precision is improved.
2. According to the method for constructing the long-life test spectrum of the armored vehicle, provided by the embodiment of the invention, the acceleration proportional coefficient is segmented according to the road condition and the vehicle speed, so that the method is more consistent with the actual vibration data measurement test condition, the accuracy is improved, the real vibration environment can be more effectively simulated, and the test result is more real and reliable.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a specific example of a method for constructing an armored vehicle combination behavior vibration spectrum according to embodiment 1 of the present invention;
FIG. 2 is a flow chart of another specific example of a method for constructing an armored vehicle combination vibration spectrum according to embodiment 1 of the present invention;
FIG. 3 is a flow chart of yet another specific example of a method for constructing a comprehensive working condition vibration spectrum of an armored vehicle in embodiment 1 of the present invention;
fig. 4 is a flowchart of a specific example of the method of constructing the long life test spectrum of the armored vehicle in embodiment 2 of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In describing the present invention, it is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises" and/or "comprising," when used in this specification, are intended to specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The term "and/or" includes any and all combinations of one or more of the associated listed items.
Furthermore, certain drawings in this specification are flow charts illustrating methods. It will be understood that each block of the flowchart illustrations, and combinations of blocks in the flowchart illustrations, can be implemented by computer program instructions. These computer program instructions may be loaded onto a computer or other programmable apparatus to produce a machine, such that the instructions which execute on the computer or other programmable apparatus create means for implementing the functions specified in the flowchart block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block or blocks. The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.
Accordingly, blocks of the flowchart illustrations support combinations of means for performing the specified functions and combinations of steps for performing the specified functions. It will also be understood that each block of the flowchart illustrations, and combinations of blocks in the flowchart illustrations, can be implemented by special purpose hardware-based computer systems which perform the specified functions or steps, or combinations of special purpose hardware and computer instructions.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
The embodiment provides a method for constructing a vibration spectrum of comprehensive working conditions of an armored vehicle, wherein the vibration spectrum comprises an actually measured spectrum and a standard spectrum, and as shown in figure 1, the method comprises the following steps:
102-1, actually measuring the spectrum according to the simplex condition
And a first reserve coefficient cij(1)Calculating to obtain the mean value and the variance under the road condition i and the vehicle speed j, wherein the calculation formula is as follows:
wherein N isi，jThe number of samples under a single working condition;
102-2, calculating to obtain a weighted mean value and a weighted variance according to the mean value and the variance under the road condition i and the vehicle speed j, wherein the calculation formula is as follows:
102-3, calculating and obtaining comprehensive working condition actual measurement spectrums FGS of all vehicle speeds and road conditions of the measured points according to the weighted mean value and the variance, wherein the calculation formula is as follows:
wherein, F11The first tolerance upper limit coefficient is calculated by the following formula:
wherein Z isβTo satisfy Prob [ Z.ltoreq.Zβ]A normally distributed quantile of beta,
is a degree of freedom of (N-1) ×2Distribution of alpha quantites, tN-1；(1-α)1-alpha quantile distributed at the center t with the degree of freedom (N-1), N is the total number of samples,
obtaining single working condition actual measurement spectrum
First reserve factor cij(1)And a first relation between the measured spectrum FGS and the combined operating condition.
103, actually measuring the spectrum according to the single working condition
And a first reserve coefficient cij(1)Calculating to obtain comprehensive working condition actual measurement spectrum FGS of all vehicle speeds and road conditions of the measuring points, and obtaining single working condition actual measurement spectrum
First reserve factor cij(1)And the second relation between the measured spectrum FGS under the comprehensive working condition, the calculation formula is as follows:
104, single working condition actual measurement spectrum
First reserve factor cij(1)Substituting a second relation between the measured spectrum and the comprehensive working condition actual measurement spectrum FGS into the single working condition actual measurement spectrum
First reserve factor cij(1)Obtaining a first storage coefficient c according to a first relation between the measured spectrum and the comprehensive working condition FGSij(1)And comprehensive working conditionsThe relationship between the spectral FGS is measured.
105, taking the measured spectrum FGS of the comprehensive working condition as the input of the first RBF neural network, and according to the first reserve coefficient cij(1)A first reserve coefficient c obtained by the relation between the measured spectrum FGS and the comprehensive working conditionij(1)And as the expected output of the first RBF neural network, training the first RBF neural network by utilizing the input and the expected output, and inputting the actually measured spectrum of the current comprehensive working condition into the trained first RBF neural network to obtain a current first reserve coefficient.
106, respectively obtaining the standard spectrum of the single working condition of the mth sample under the road condition i and the vehicle speed j
According to a single working condition standard spectrum
And a second reserve coefficient cij(2)Obtaining the comprehensive working condition standard spectrum FGG of all vehicle speeds and road conditions of the measuring points by induction, and obtaining the single working condition standard spectrum
Second reserve factor cij(2)And the behaviour-by-wire normalization spectrum FGG, as shown in fig. 3, the generalization procedure is as follows:
step 106-1, standardizing the spectrum according to the single working condition
And a second reserve coefficient cij(2)Calculating to obtain the mean value and the variance of each spectral line under the road condition i and the vehicle speed j, wherein the calculation formula is as follows:
wherein N isi，jThe number of samples under a single working condition;
106-2, calculating to obtain a weighted mean value and a weighted variance according to the mean value and the variance of each spectral line under the road condition i and the vehicle speed j, wherein the calculation formula is as follows:
step 106-3, calculating to obtain statistic F according to weighted mean and variancenAnd tnThe calculation formula is as follows:
wherein N is the total number of samples,
step 106-4, according to the statistic FnAnd tnFrequency division is carried out under the confidence coefficient (1-alpha) to obtain H1A frequency band, wherein the spectral line number of two end points of the h-th frequency band is kh1、kh2，h＝1，2，…，H1The number of spectral lines is Nh＝kh2-kh1+1；
Preferably, step 106-4 comprises:
judging whether the frequency bands are the same under a given confidence coefficient (1-alpha), wherein the judgment conditions are as follows:
F(N-1，N-1)；α/2≤Fn(k，k+1)≤F(N-1，N-1)；(1-α/2)
|tn(k，k+1)|≤t2(N-1)；(1-α/2)
wherein, t2(N-1)；(1-α/2)The central t with the degree of freedom of 2(N-1) is distributed with 1-alpha/2 quantile points, F(N-1，N-1)；α/2F with degree of freedom of (N-1) is distributed at alpha/2 quantile, if Fn(k, k +1) obeys F distribution with degree of freedom (N-1), tnAnd (k, k +1) obeys the central t distribution with the degree of freedom of 2(N-1), and when the discrimination condition is met, the Power Spectral Densities (PSD) of adjacent spectral lines k and k +1 of the characteristic sample belong to the same frequency band, otherwise, the power spectral densities do not belong to the same frequency band.
Adjacent ones belong to the sameThe spectral lines of a frequency band are merged in the same frequency band to form H1A frequency band, wherein the spectral line number of two end points of the h-th frequency band is kh1、kh2，h＝1，2，…，H1The number of spectral lines is Nh＝kh2-kh1+1。
106-5, calculating to obtain a weighted mean value and a weighted variance in the h frequency band according to the weighted mean value and the weighted variance, wherein the calculation formula is as follows:
106-6, calculating according to the weighted mean and the variance in the h frequency band to obtain the straight spectral tolerance upper limit estimation in the h frequency band, wherein the calculation formula is as follows:
wherein, F12The second tolerance upper limit coefficient with the confidence coefficient of (1-alpha) and the quantile point of beta is calculated by the following formula:
wherein Z isβTo satisfy Prob [ Z.ltoreq.Zβ]A normally distributed quantile of beta,
with a degree of freedom of (N.N)hX of-1)2The distribution of the alpha quantile points is,
with a degree of freedom of (N.N)h-1) central t distribution 1-a quantiles;
106-7, taking the upper limit estimation of the tolerance of the straight spectrum in the h-th frequency band as the amplitude of the frequency band, and connecting each adjacent straight spectrum by straight lines under the double logarithmic coordinates to obtain the comprehensive working condition of all vehicle speeds and road conditions of the measured pointA normalized spectrum FGG, thereby obtaining a single-operating-condition normalized spectrum
Second reserve factor cij(2)And a first relation between the behaviour-synthesis specification spectrum FGG.
108, normalizing the spectrum under the single working condition
Second reserve factor cij(2)Substituting a second relation between the comprehensive working condition standard spectrum FGG and the single working condition standard spectrum
Second reserve factor cij(2)And obtaining a second reserve coefficient c according to a first relation between the comprehensive working condition normalized spectrum FGGij(2)And the behaviour-wide specification spectrum FGG.
according to the method for constructing the armored vehicle comprehensive working condition vibration spectrum, the measured spectrum and the standard spectrum of the comprehensive working condition are respectively deduced by using an induction method and a formula calculation method, the relationship between the first storage coefficient and the measured spectrum of the comprehensive working condition and the relationship between the second storage coefficient and the standard spectrum of the comprehensive working condition are respectively obtained by using the conclusions of the two methods, so that the RBF neural network is respectively trained based on the relationships to obtain the first storage coefficient estimation value and the second storage coefficient estimation value, and then the storage coefficient adopted when the vibration spectrum is actually constructed is calculated according to the estimation values, so that the construction precision is improved.
Preferably, the process of training the first/second RBF neural network using the inputs and the desired outputs is:
definition of X ═ (X)1,x2,…,xn)TFor the network input vector, Y ═ Y1,y2,…,ys)TIs output by the network, [ phi ]i(. is a radial basis of the ith hidden layer nodeA function. The distribution function of the RBF neural network is:
where m is the number of hidden layer neuron nodes, i.e. the number of radial basis function centers, and the coefficient wiIs a connection weight;
wherein φ (#) is a radial basis function, | | x-ciI is the Euclidean norm, ciIs the ith center, xi, of the RBFiFor the ith radius of the RBF, the available network outputs are:
thus, the matrix expression for an RBF network can be expressed as:
D＝HW+E，
wherein the desired output vector is D ═ D (D)1,d2,…,dp)TThe error vector between the desired output and the network output is E ═ E (E)1,e2,…,ep)TThe weight vector is W ═ W1,w2,…,wm)TThe regression matrix is H ═ H1,h2,…,hm)T；
Taking into account the influence of all training samples, ci、ξiAnd wiThe adjustment amounts of (a) and (b) are:
wherein phi isi(xj) For the ith implicit node pair xjInput of η1、η2、η3Respectively corresponding learning rates, ci(t) and ci(t +1) c at the t-th and t + 1-th iterations, respectivelyi，ξi(t) and xii(t +1) is ξ for the t-th and t + 1-th iterations, respectivelyi，wi(t) and wi(t +1) w at the t-th and t + 1-th iterations, respectivelyi(ii) a Obtaining a mean square error according to the cost function E so as to finish the training condition; when the mean square error of the actual output and the expected output is less than the set threshold, the network is considered to be trained.
Example 2
The embodiment provides a method for constructing a long life test spectrum of an armored vehicle, as shown in FIG. 4, comprising the following steps:
wherein, TsSetting the actual running time according to the actual service life requirement of the armored vehicle, wherein m is an index and is generally 3-9;
according to the method for constructing the long-life test spectrum of the armored vehicle, the acceleration proportional coefficient is segmented according to the road condition and the vehicle speed, so that the method is more consistent with the actual vibration data measurement test condition, the accuracy is improved, the real vibration environment can be more effectively simulated, and the test result is more real and reliable.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. A method for constructing a comprehensive working condition vibration spectrum of an armored vehicle is characterized by comprising the following steps:
obtaining the road surface occupation ratio b of the armored vehicle under the ith road condition under the test comprehensive working conditioniAnd the vehicle speed of the jth vehicle speed used for driving on the ith road condition accounts for aijWherein, in the step (A),
p is the total number of road surface types, and q is the total number of vehicle speed types;
respectively obtaining the single-working-condition actual measurement spectrum of the mth sample under the road condition i and the vehicle speed j
From the measured spectrum of the simplex condition
And a first reserve coefficient cij(1)Obtaining the comprehensive working condition actual measurement spectrum FGS of all vehicle speeds and road conditions of the measuring points by induction, and obtaining the single working condition actual measurement spectrum
First reserve factor cij(1)And a first relation between the measured spectrum FGS and the comprehensive working condition;
from the measured spectrum of the simplex condition
And a first reserve coefficient cij(1)Calculating to obtain comprehensive working condition actual measurement spectrum FGS of all vehicle speeds and road conditions of the measuring points, and obtaining single working condition actual measurement spectrum
First reserve factor cij(1)And a second relation between the measured spectrum FGS and the comprehensive working condition;
single working condition actual measurement spectrum
First reserve factor cij(1)Substituting a second relation between the measured spectrum and the comprehensive working condition actual measurement spectrum FGS into the single working condition actual measurement spectrum
First reserve factor cij(1)Obtaining a first storage coefficient c according to a first relation between the measured spectrum and the comprehensive working condition FGSij(1)And the relation between the measured spectrum FGS and the comprehensive working condition;
the measured spectrum FGS of the comprehensive working condition is used as the input of the first RBF neural network and is based on the first reserve coefficient cij(1)A first reserve coefficient c obtained by the relation between the measured spectrum FGS and the comprehensive working conditionij(1)As the desired output of the first RBF neural network, utilizeInputting and expecting to output a trained first RBF neural network, inputting the current measured spectrum of the comprehensive working condition into the trained first RBF neural network, and obtaining a current first storage coefficient;
respectively obtaining the standard spectrum of the single working condition of the mth sample under the conditions of the road condition i and the vehicle speed j
According to a single working condition standard spectrum
And a second reserve coefficient cij(2)Obtaining the comprehensive working condition standard spectrum FGG of all vehicle speeds and road conditions of the measuring points by induction, and obtaining the single working condition standard spectrum
Second reserve factor cij(2)And a first relation between the combined working condition normalized spectrum FGG;
according to a single working condition standard spectrum
And a second reserve coefficient cij(2)Calculating to obtain comprehensive condition standard spectrum FGG of all vehicle speeds and road conditions of the measured points, and obtaining single-condition standard spectrum
Second reserve factor cij(2)And a second relationship between the combined operating mode normalized spectrum FGG;
standard spectrum of single working condition
Second reserve factor cij(2)Substituting a second relation between the comprehensive working condition standard spectrum FGG and the single working condition standard spectrum
Second reserve factor cij(2)And obtaining a second reserve coefficient c according to a first relation between the comprehensive working condition normalized spectrum FGGij(2)And the relation between the comprehensive working condition standard spectrum FGG;
taking the comprehensive working condition standard spectrum FGG as the input of a second RBF neural network, and according to a second reserve coefficient cij(2)Second reserve coefficient c obtained by relation with comprehensive working condition standard spectrum FGGij(2)As expected output of the second RBF neural network, training the second RBF neural network by utilizing input and expected output, and inputting the current comprehensive working condition standard spectrum into the trained second RBF neural network to obtain a current second reserve coefficient;
calculating to obtain a reserve coefficient c according to the current first reserve coefficient and the current second reserve coefficientij；
According to the reserve coefficient cijActual measurement spectrum G of single working condition under road condition i and vehicle speed jshice(i, j) and the simplex condition normalized spectrum Gguifan(i, j), respectively calculating and obtaining comprehensive working condition actual measurement spectrums FGS and comprehensive working condition standard spectrums FGG of all vehicle speeds and road conditions of the measured points.
2. The method of claim 1, wherein the measured spectra are based on single-site conditions
And a first reserve coefficient cij(1)The step of summarizing and obtaining the comprehensive working condition actual measurement spectrum FGS of all vehicle speeds and road conditions of the measuring points comprises the following steps:
from the measured spectrum of the simplex condition
And a first reserve coefficient cij(1)Calculating to obtain the mean value and the variance under the road condition i and the vehicle speed j, wherein the calculation formula is as follows:
wherein N isi,jThe number of samples under a single working condition;
calculating to obtain weighted mean value and variance according to the mean value and variance under the road condition i and the vehicle speed j, wherein the calculation formula is as follows:
and calculating to obtain the comprehensive working condition actual measurement spectrum FGS of all vehicle speeds and road conditions of the measuring points according to the weighted mean value and the variance, wherein the calculation formula is as follows:
wherein, F11The first tolerance upper limit coefficient is calculated by the following formula:
wherein Z isβTo satisfy Prob [ Z.ltoreq.Zβ]A normally distributed quantile of beta,
is a degree of freedom of (N-1) ×2Distribution of alpha quantites, tN-1；(1-α)1-alpha quantile distributed at the center t with the degree of freedom (N-1), N is the total number of samples,
3. method according to claim 1 or 2, wherein the measured spectrum is measured from a single working condition
And a first reserve coefficient cij(1)All vehicle speeds and roads of measuring points are obtained through calculationThe calculation formula of the actual measurement spectrum FGS of the comprehensive working condition is as follows:
wherein N isi,jIs the number of samples in the simplex case.
4. A method according to any of claims 1-3, characterized in that the spectrum is normalized according to a single regime
And a second reserve coefficient cij(2)The step of summarizing and obtaining the comprehensive working condition standard spectrum FGG of all vehicle speeds and road conditions of the measured points comprises the following steps:
according to a single working condition standard spectrum
And a second reserve coefficient cij(2)Calculating to obtain the mean value and the variance of each spectral line under the road condition i and the vehicle speed j, wherein the calculation formula is as follows:
wherein N isi,jThe number of samples under a single working condition;
calculating to obtain a weighted mean value and a weighted variance according to the mean value and the variance of each spectral line under the road condition i and the vehicle speed j, wherein the calculation formula is as follows:
obtaining a statistic F by weighted mean and variance calculationnAnd tnThe calculation formula is as follows:
wherein N is the total number of samples,
according to statistic FnAnd tnFrequency division is carried out under the confidence coefficient (1-alpha) to obtain H1A frequency band, wherein the spectral line number of two end points of the h-th frequency band is kh1、kh2，h＝1,2,…,H1The number of spectral lines is Nh＝kh2-kh1+1；
And calculating to obtain a weighted mean value and a weighted variance in the h frequency band according to the weighted mean value and the weighted variance, wherein the calculation formula is as follows:
calculating according to the weighted mean and the variance in the h frequency band to obtain the upper limit estimation of the straight spectral tolerance in the h frequency band, wherein the calculation formula is as follows:
wherein, F12The second tolerance upper limit coefficient with the confidence coefficient of (1-alpha) and the quantile point of beta is calculated by the following formula:
wherein Z isβTo satisfy Prob [ Z.ltoreq.Zβ]A normally distributed quantile of beta,
with a degree of freedom of (N.N)hX of-1)2The distribution of the alpha quantile points is,
with a degree of freedom of (N.N)h-1) central t distribution 1-a quantiles;
and taking the upper limit estimation of the tolerance of the straight spectrum in the h-th frequency band as the amplitude of the frequency band, and connecting each adjacent straight spectrum by using a straight line under a log-log coordinate to obtain a comprehensive working condition standard spectrum FGG of all vehicle speeds and road conditions of the measured point.
5. Method according to any of claims 1-4, characterized in that the spectrum is normalized according to a single regime
And a second reserve coefficient cij(2)The calculation formula of the comprehensive working condition standard spectrum FGG for calculating and obtaining all vehicle speeds and road conditions of the measured points is as follows:
wherein N isi，jIs the number of samples in the simplex case.
6. Method according to any of claims 1 to 5, characterized in that the reserve coefficient cijThe root mean square values of the current first reserve coefficient and the current second reserve coefficient.
7. Method according to any one of claims 1 to 6, characterized in that said reserve coefficient c is determined according to saidijActual measurement spectrum G of single working condition under road condition i and vehicle speed jshice(i, j) and the simplex condition normalized spectrum Gguifan(i, j), respectively calculating and obtaining the calculation formulas of the comprehensive working condition actual measurement spectrum FGS and the comprehensive working condition standard spectrum FGG of all vehicle speeds and road conditions of the measured points, wherein the calculation formulas are respectively as follows:
8. a method for constructing a long life test spectrum of an armored vehicle is characterized by comprising the following steps:
obtaining the time T of the jth vehicle speed used for driving under the ith road conditionijI is 1,2, …, p, j is 1,2, …, q, p is the total number of road surface types, q is the total number of vehicle speed types;
time T of jth vehicle speed used for driving according to ith road conditionijAnd calculating to obtain a segment acceleration proportional coefficient beta under the road condition i and the vehicle speed jij；
A specification spectrum of work conditions obtained by the method for constructing a vibration spectrum of a work condition of an armored vehicle according to any one of claims 1-7
And a segment acceleration proportionality coefficient betaijAnd calculating to obtain a long-life test spectrum LF.
9. The method of claim 8, wherein the piecewise acceleration scaling factor βijThe calculation formula of (2) is as follows:
wherein, TsFor the actual run time, m is an index.
10. Method according to claim 8 or 9, characterized in that said long life test spectrum LF is calculated by the formula:
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