CN114429060B - Method for examining structure dislocation failure and service life prediction in fatigue vibration - Google Patents
Method for examining structure dislocation failure and service life prediction in fatigue vibration Download PDFInfo
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Abstract
The invention relates to a method for assessing structure dislocation failure and service life prediction in fatigue vibration, which comprises the following steps of S100, establishing a three-dimensional geometric structure model of a product to be examined; s200, establishing a finite element structure model of the product to be examined on the basis of the three-dimensional geometric structure model; s300, performing free modal calculation in a solver based on the finite element structure model; s400, performing uninterrupted fatigue vibration response calculation on a product to be examined based on vibration schemes in different directions and different amplitudes, and forming a data sample; s500, fitting a product based on a neural network to evaluate a mathematical function of the structure displacement, and establishing a limit state equation of the structure dislocation; s600, selecting the data samples in the S400 to perform test verification on the fitted mathematical function; and S700, establishing a conversion relation among frequency, resolution and a time domain based on the mathematical function verified in the S600, obtaining a connection coefficient equation of the reliability prediction model based on the service life, and predicting the fatigue life of the product in the specified vibration environment according to the extreme state equation. The method makes the product design and maintenance easier.
Description
Technical Field
The invention relates to the field of product reliability research, in particular to a method for assessing structure dislocation failure and predicting service life in fatigue vibration.
Background
The vibration load is widely existed in the transportation or working environment of various products, the product structure generates vibration response under the excitation of the vibration load, the structural dislocation of each group of components can be caused, the product structure is in the fatigue vibration working environment for a long time, the tiny dislocation can be accumulated along with the time to form large dislocation, the service performance and the service life of the product are reduced to a great extent, the prediction of the service life of the product has important significance for guiding the product design and maintenance, and the assessment of the structural dislocation failure and the service life prediction in the fatigue vibration is significant.
However, the fatigue vibration environment is characterized by small vibration amplitude, random vibration frequency, small influence on the performance of the product in a short time and large accumulated damage to the product in a long time. The fatigue vibration response performance of the product is evaluated at the present stage mainly by loading a random vibration frequency spectrum in an actual working environment or a fatigue vibration test bed, and the problems that the evaluation time is long, the internal vibration response damage of the product cannot be observed in real time and the like exist, so that the adverse factors of the internal structure of the product to the fatigue vibration cannot be effectively improved when the product is designed and maintained.
Therefore, a method for rapidly checking the structure dislocation failure and the service life prediction in the fatigue vibration is needed.
Disclosure of Invention
In view of the above problems, the present invention provides a method for rapidly checking the structure dislocation failure and life prediction in fatigue vibration.
In order to achieve the purpose, the technical scheme of the invention is as follows: a method for assessing structure dislocation failure and service life prediction in fatigue vibration is characterized by comprising the following steps: the method comprises the following steps of,
s100, establishing a three-dimensional geometric structure model of a product to be checked;
s200, establishing a finite element structure model of the product to be examined on the basis of the three-dimensional geometric structure model;
s300, performing free mode calculation in a solver based on the finite element structure model;
s400, performing uninterrupted fatigue vibration response calculation on a product to be examined based on vibration schemes in different directions and different amplitudes, and forming a data sample;
s500, fitting a mathematical function of product assessment structure displacement based on a neural network, and establishing a structural dislocation limit state equation; s600, selecting the data samples in the S400 to perform test verification on the fitted mathematical function;
s700, establishing a conversion relation among frequency, resolution and time domain based on the mathematical function verified in the S600, obtaining a connection coefficient equation of the reliability prediction model based on the service life, and predicting the fatigue life of the product in the specified vibration environment according to the extreme state equation.
Further, the S300 includes the steps of,
s3100, calculating power spectral density of road excitation according to actual application working conditions and narrow-band random vibration program data of goods fastened by a tracked vehicle;
s3200, calculating a mean value, a variance and a peak acceleration based on excitation energy and spectral characteristics reflected by power spectral density;
s3300, determining a frequency range of free mode calculation according to the mean value, the variance and the peak acceleration, performing free mode calculation in a nanostran solver on the basis of a finite element structure model to obtain inherent properties of free vibration of the product structure, and analyzing states of all groups of parts of the structure during vibration.
Further, the S400 includes the steps of,
s4100, designing a random full combination scheme for narrow-band random vibration program data of goods fastened by the tracked vehicle based on different directions and different normal distribution characteristics;
s4200, obtaining continuous non-fault working time based on a conversion relation between kilometers and time agreed in narrow-band random vibration program data of goods fastened by the crawler, wherein the corresponding relation is as follows:
MTBF＝S b /T S wherein MTBF is a continuous non-fault working time in a limit state, S b The vehicle-mounted driving fault-free minimum mileage of the product is represented, and Ts is the vehicle speed corresponding to the vibration frequency spectrum in the program data;
and S4300, for each vibration scheme obtained in S4100, calculating a displacement value of a system target fault point in the vibration scheme by using a finite element mode-based forced response durability simulation method, wherein the loading time is a continuous non-fault working time MTBF, and obtaining data samples between the continuous non-fault working time MTBF and the vibration response, wherein the data samples comprise vibration acceleration in an X/Y/Z direction, accumulated displacement error of the system fault point and MTBF.
Further, the S500 includes the steps of,
s5100, establishing a structure dislocation evaluation mathematical function r = f (delta X, delta Y, delta Z) according to a structure position relation between the group components in the product, wherein r is relative displacement between the group components, and delta X, delta Y, and delta Z are change values of the group components in X, Y and Z directions of coordinate axes compared with an initial position after displacement;
s5200, inputting three layers of neural networks by taking vibration acceleration excitations in X, Y and Z directions and all component influence factors under structural tolerance as input quantities, and training the neural networks to obtain a neural network model, wherein the input quantities are subjected to normalization processing during input, and the mathematical expression of the input quantities is as follows:
wherein i = X, Y, Z, D 1 ,D 2 ,…,D n INPUT when i takes X, Y and Z values i Respectively representing vibration acceleration in X, Y and Z directions, and INPUT when the value of i is Dn i Represents all sets of component contributions to structural tolerances;
the first layer of the three-layer neural network is
i takes a value of 1-15; the second layer of neural network is
i takes a value of 1-5; the third layer of neural network is
i takes a value of 1-5; the normalized processing equation of the output layer is
S5300, performing fitting training on the neural network model, wherein the fitting training times are more than or equal to 300, obtaining a fitting curve of output structure displacement under the influence of different groups of component structures after multidirectional fatigue stress loading, and obtaining a fitted mathematical function based on the fitting curve;
s5400, according to a fault mode and a fault influence analysis structure of a product, establishing a limit state function of product structure dislocation failure, wherein the limit state function is g (x) = N (x) -place, the place is a displacement value of the product structure dislocation in a fatigue vibration process but not enabling the product function to fail, and N (x) is used for representing the limit state function of a test point in a system structure.
Further, the S700 includes the steps of,
s7100, under the time domain environment of a continuous excitation working state, the frequency spectrum range is between 5Hz and 500Hz,at maximum resolution f n Setting up a conversion equation of working time and vibration times N as follows, wherein the frequency of the vibration is set as 0.2 s:
N＝t/f n
in the formula, t is the working time.
S7200, calculating a connection coefficient equation of the reliability prediction model based on the acceleration excitation-displacement data sample obtained in the S400, wherein the connection coefficient equation is as follows:
in the formula, s is the dislocation displacement between two adjacent parts, and sigma is the variance of the maximum displacement of the military standard loading capacity of the reference country;
s7300, obtaining a reliability calculation equation of single excitation according to a connection coefficient equation of the reliability prediction model based on the service life of S7200, wherein the reliability calculation equation comprises the following steps:
R＝Φ(Z)
the service life reliability calculation equation after N times of vibration is as follows:
R n ＝Φ(Z)
the service life reliability equation after using t hours vibration is as follows:
F(t)＝1-R(t)
the expression for the failure rate after t hours λ (t) is:
the reliable life L, the median life L 0.5 And characteristic lifetime L 1/e Are respectively expressed as follows:
L＝R -1 (t)
compared with the prior art, the invention has the advantages that:
the invention solves the technical problems that the product fatigue vibration test in the prior art is long in test period and the structure dislocation in the product can not be observed in real time, solves the problem that the reliability of the product is reduced and can not be quantified due to the structure dislocation under the vehicle-mounted condition, solves the problem of random matching in three directions, and is more scientific and effective for the limit test of the fatigue vibration.
Drawings
FIG. 1 is a flow chart of a method for accelerating structural dislocation failure in assessment of fatigue vibration and a service life prediction method provided by the invention.
Fig. 2 is a data diagram of a narrow-band random vibration program of goods fastened by a crawler in the GJB 150.16A-2009 military equipment laboratory environment test method part 16 vibration test.
Fig. 3 is a diagram of road excitation power spectral density obtained by a crawler running according to an embodiment of the present invention.
FIG. 4 is a graph of the free mode calculation results of the product according to the embodiment of the present invention in the frequency range of 0-600 Hz.
Fig. 5 is a diagram of a neural network computational model according to an embodiment of the present invention.
FIG. 6 is a fit curve of the structural displacement of the product group component after multidirectional fatigue stress loading in accordance with embodiments of the present invention.
Fig. 7 is a schematic diagram of a product fatigue vibration test arrangement according to an embodiment of the present invention.
FIG. 8 is a graph of product reliability versus time according to an embodiment of the present invention.
Fig. 9 is normal distribution characteristic data of excitation of roads in different directions according to an embodiment of the present invention.
Fig. 10 is a diagram illustrating part of the input-output database created in step S400 according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
The invention provides a method for accelerating the examination of structural dislocation failure in fatigue vibration and a service life prediction method, which can be used for accelerating the examination of the function loss of a certain product arranged on a tracked vehicle caused by structural dislocation and can effectively solve the technical problems that the test period of a product fatigue vibration test is long and the structural dislocation in the product can not be observed in real time; the reliability of the product is reduced and the service life of the product cannot be predicted due to structural dislocation under the vehicle-mounted condition; the existing testing method is divided into the technical problems of X-axis, Y-axis and Z-axis unidirectional respective testing and poor operability.
Referring to fig. 1, the invention provides a method for accelerating the structural dislocation failure in the examination of fatigue vibration and a service life prediction method, comprising the following steps:
step S100: establishing a three-dimensional geometric structure model of the assessment product;
and (3) carrying out equal-proportion modeling according to the component structure of the product, assembling the component of the product according to the machining tolerance, and completing the modeling of the three-dimensional geometric structure model of the product, wherein the structure meets the static strength standard.
Step S200: establishing a product structure finite element model;
establishing a finite element model of the product based on the three-dimensional geometric structure model, meshing the finite element model, and then loading a material model.
Step S300: calculating a mode;
and based on the finite element structure model, performing free modal calculation in a nanostran solver.
Preferably, step S300 includes the steps of:
step S3100: according to the actual application working condition of the product, narrow-band random vibration program data (see figure 2) of goods fastened by the tracked vehicle in GJB 150.16A-2009 military equipment laboratory environment test method part 16 vibration test is selected as a basis; the power spectral density of the road excitation is calculated based on narrowband random vibration program data of the tracked vehicle securing cargo, as shown in fig. 3.
Step S3200: based on the excitation energy and the spectral characteristics reflected by the power spectral density, the power spectral density meets the normal distribution, and key parameter values such as a mean value, a variance and a peak acceleration are calculated and obtained, as shown in table 1;
step S3300: determining the frequency range of free modal calculation to be 0-600Hz according to key parameter values such as the mean value, the variance and the peak acceleration obtained by calculation; based on the finite element structure model, performing free mode calculation in a nanostran solver, wherein a calculation result is shown in fig. 4; and obtaining the inherent attribute of the free vibration of the product structure according to the calculation result, and analyzing the states of all groups of parts of the structure during vibration.
Step S400: calculating vibration response;
and carrying out calculation of uninterrupted fatigue vibration response of the product based on vibration schemes in different directions and different amplitudes.
Preferably, step S400 includes the steps of:
s4100, designing a random full combination scheme for narrow-band random vibration program data of goods fastened by the tracked vehicle based on different directions and different normal distribution characteristics;
s4200, obtaining continuous non-fault working time based on a conversion relation between kilometers and time agreed in narrow-band random vibration program data of goods fastened by the crawler, wherein the corresponding relation is as follows:
MTBF＝S b /T S wherein MTBF is a continuous non-fault working time in a limit state, S b The vehicle-mounted driving fault-free minimum mileage of the product is represented, and Ts is the vehicle speed corresponding to the vibration frequency spectrum in the program data;
and S4300, for each vibration scheme obtained in S4100, calculating a displacement value of a system target fault point in the vibration scheme by using a finite element mode-based forced response durability simulation method, wherein the loading time is a continuous non-fault working time MTBF, and obtaining data samples between the continuous non-fault working time MTBF and the vibration response, wherein the data samples comprise vibration acceleration in an X/Y/Z direction, accumulated displacement error of the system fault point and MTBF.
Step S500: fitting a product based on a neural network to examine a mathematical function of the structure displacement, and establishing a limit state equation of the structure dislocation;
determining a neural network computational model scheme based on the data samples established in step S400, the neural network computational model scheme being shown in fig. 5; training the neural network model for multiple times, setting a fitting program, fitting to obtain a mathematical polynomial of the product assessment structure displacement, and establishing a limit state equation of the structure dislocation;
preferably, step S500 includes the steps of:
s5100, establishing a structure dislocation evaluation mathematical function r = f (delta X, delta Y and delta Z) according to a structure position relation between the group components in the product, wherein r is relative displacement between the group components, and delta X, delta Y and delta Z are change values of the group components in X, Y and Z directions than an initial position after displacement;
s5200, inputting three layers of neural networks by taking vibration acceleration excitations in X, Y and Z directions and all component influence factors under structural tolerance as input quantities, and training the neural networks to obtain a neural network model, wherein the input quantities are subjected to normalization processing during input, and the mathematical expression of the input quantities is as follows:
wherein i = X, Y, Z, D 1 ,D 2 ,…,D n INPUT when i takes X, Y and Z values i Respectively representing vibration acceleration in X, Y and Z directions, and INPUT when the value of i is Dn i Represents all sets of component contributions to structural tolerances;
three-layer spiritThe first layer of neural network via the network is
i takes a value of 1-15; the second layer of neural network is
i takes a value of 1-5; the third layer of neural network is
i takes a value of 1-5; the normalized processing equation of the output layer is
S5300, carrying out fitting training on the neural network model, wherein the fitting training frequency is more than or equal to 300, obtaining a fitting curve of output structure displacement under the influence of different groups of component structures after multi-direction fatigue stress loading, and obtaining a fitted mathematical function based on the fitting curve;
s5400, establishing a limit state function of dislocation failure of the product structure according to a fault mode and a fault influence analysis structure of the product, wherein the limit failure state function is g (x) = N (x) -place, the place is a displacement value of the product structure in a fatigue vibration process, but the product function is not failed due to dislocation, and N (x) is used for representing the limit state function of a test point in a system structure.
Step S600: performing test verification on a mathematical fitting function;
based on the mathematical fitting function, selecting individual cases in the data sample of the step S400 for test verification; a vibration system is adopted, parameters such as vibration amplitude and the like are input, and the product structure dislocation failure time under individual conditions is obtained to verify the feasibility of a mathematical fitting function.
The product was subjected to a fatigue vibration test, the test layout being shown in fig. 7.
Firstly, designing a mounting bracket 2 according to the actual working condition of a product 3; thereafter, the mounting bracket 2 is fastened to the vibration table 1, and the product 3 is mounted on the bracket 2; the product 3 comprises a component 4 and a part 5, wherein the part 5 consists of a plurality of parts 6 and parts 7; the center of the part 6 is on the center line of the part 7, the structural dislocation of the part 6 and the part 7 can cause the product performance to be greatly reduced, and when the center of the part 6 is deviated from the boundary of the part 7, the product fails.
Secondly, according to the calculation result of the step S4300, selecting the vibration frequency and amplitude which have serious influence on the structure dislocation in the simulation vibration scheme, inputting the vibration frequency and amplitude into a control program of the vibration table, and starting the vibration program. If the vibration test time is too long, methods such as improving the vibration amplitude and the like can be adopted to shorten the vibration time.
Thirdly, comparing the difference between the structure dislocation displacement and the simulation vibration result after the vibration program is finished, analyzing errors possibly existing in the simulation, and correcting.
And finally, according to the corrected data, obtaining a mathematical function of the product assessment structure displacement based on neural network fitting.
Step S700: according to the model, the fatigue life of the product in a specified vibration environment is predicted.
And based on the product structure dislocation mathematical fitting function established in the step S600, establishing a conversion relation between parameters such as frequency and resolution and a time domain to obtain a connection coefficient equation of the reliability prediction model based on the service life, and predicting to obtain the fatigue life of the product in the specified vibration environment according to the extreme state equation.
According to the road spectrum requirement in the GJB 150.16A part 16 vibration test table D.1 (shown in figure 2), the reliability prediction model based on the service life has the core of establishing the relationship between the excitation times and the time. The whole vibration test is a continuous excitation working state in a time domain environment, and the frequency spectrum range is between 5Hz and 500Hz, so the maximum resolution f is realized n If =0.2s is regarded as one vibration cycle, the conversion equation of the operating time and the number of vibrations N is as follows:
N＝t/f n
in the formula, t is the working time.
According to the input (acceleration excitation) -output (displacement) database obtained after calculation in step S4300, the connection coefficient equation of the reliability prediction model based on the service life is calculated as follows:
in the formula, s is the dislocation displacement of the part 6 and the part 7, and sigma is the variance of the maximum displacement of the reference national military standard loading;
the reliability calculation equation for a single excitation is then:
R＝Φ(Z)
the service life reliability calculation equation after N times of vibration is as follows:
R n ＝Φ(Z) N
the service life reliability equation after using t hours vibration is:
F(t)＝1-R(t)
the failure rate after t hours λ (t) is expressed as:
its reliable life L, median life L 0.5 And characteristic lifetime L 1/e Are expressed as follows:
L＝R -1 (t)
by combining the above relational expressions, the relationship between the reliability of the product and the operating time can be obtained as shown in fig. 8, and the reliability life, the median life and the characteristic life of the product can be calculated.
While embodiments of the invention have been shown and described, it will be understood by those skilled in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (4)
1. A method for assessing structure dislocation failure and service life prediction in fatigue vibration is characterized by comprising the following steps: the method comprises the following steps of,
s100, establishing a three-dimensional geometric structure model of a product to be examined;
s200, establishing a finite element structure model of the product to be examined on the basis of the three-dimensional geometric structure model;
s300, performing free mode calculation in a solver based on the finite element structure model;
s400, performing uninterrupted fatigue vibration response calculation on a product to be examined based on vibration schemes in different directions and different amplitudes, and forming a data sample;
the S400 includes the steps of,
s4100, designing a random full-combination scheme for narrow-band random vibration program data of goods fastened by the tracked vehicle based on different directions and different normal distribution characteristics;
s4200, obtaining continuous non-fault working time based on a conversion relation between kilometers and time agreed in narrow-band random vibration program data of goods fastened by the crawler, wherein the corresponding relation is as follows:
MTBF＝S b /T S wherein MTBF is a continuous non-fault working time in a limit state, S b The minimum mileage is the failure-free running distance of the product on the vehicle, and Ts is the vehicle speed corresponding to the vibration frequency spectrum in the program data;
s4300, for each vibration scheme obtained in S4100, calculating a displacement value of a system target fault point in the vibration scheme by adopting a finite element durability simulation method based on modal-based forced response, wherein the loading time is continuous non-fault working time MTBF, and obtaining data samples between the continuous non-fault working time MTBF and the vibration response, wherein the data samples comprise vibration acceleration in an X/Y/Z direction, accumulated displacement error of the system fault point and the MTBF;
s500, fitting a mathematical function of product assessment structure displacement based on a neural network, and establishing a structural dislocation limit state equation;
s600, selecting the data samples in the S400 to perform test verification on the fitted mathematical function;
s700, establishing a conversion relation among frequency, resolution and a time domain based on the mathematical function verified in the S600 to obtain a connection coefficient equation of a reliability prediction model based on the service life, and predicting the fatigue life of a product in a specified vibration environment according to a limit state equation;
the connection coefficient equation of the reliability prediction model based on the service life is as follows:
in the formula, s is the dislocation displacement between two adjacent parts, and sigma is the variance of the maximum displacement of the military standard loading of the reference country; the display is a displacement value of the product structure dislocation in the fatigue vibration process without the product function failure.
2. The method of claim 1, further comprising: the S300 includes the steps of,
s3100, calculating power spectral density of road excitation according to actual application working conditions and by taking narrow-band random vibration program data of goods fastened by a tracked vehicle as a basis;
s3200, calculating a mean value, a variance and a peak acceleration based on excitation energy and spectral characteristics reflected by power spectral density;
s3300, determining a frequency range of free mode calculation according to the mean value, the variance and the peak acceleration, performing free mode calculation in a nanostran solver on the basis of a finite element structure model to obtain inherent properties of free vibration of the product structure, and analyzing states of all groups of parts of the structure during vibration.
3. The method of claim 2, wherein: the S500 includes the steps of,
s5100, establishing a structure dislocation evaluation mathematical function r = f (delta X, delta Y, delta Z) according to a structure position relation between the group components in the product, wherein r is relative displacement between the group components, and delta X, delta Y, and delta Z are change values of the group components in X, Y and Z directions of coordinate axes compared with an initial position after displacement;
s5200, inputting three layers of neural networks by taking vibration acceleration excitations in X, Y and Z directions and all component influence factors under structural tolerance as input quantities, and training the neural networks to obtain a neural network model, wherein the input quantities are subjected to normalization processing during input, and the mathematical expression of the input quantities is as follows:
wherein i = X, Y, Z, D 1 ,D 2 ,…,D n INPUT when i takes values of X, Y, Z i Respectively representing vibration acceleration in X, Y and Z directions, and INPUT when the value of i is Dn i Represents all sets of component influences under structural tolerances;
the first layer of the three-layer neural network is
i takes a value of 1-15; the second layer of neural network is
i takes a value of 1-5; the third layer of neural network is
i takes a value of 1-5; the normalized processing equation of the output layer is
S5300, performing fitting training on the neural network model, wherein the fitting training times are more than or equal to 300, obtaining a fitting curve of output structure displacement under the influence of different groups of component structures after multidirectional fatigue stress loading, and obtaining a fitted mathematical function based on the fitting curve;
s5400, according to a fault mode and a fault influence analysis structure of a product, establishing a limit state function of product structure dislocation failure, wherein the limit state function is g (x) = N (x) -place, the place is a displacement value of the product structure dislocation in a fatigue vibration process but not enabling the product function to fail, and N (x) is used for representing the limit state function of a test point in a system structure.
4. The method of claim 3, wherein: the S700 includes the steps of,
s7100, under the time domain environment of a continuous excitation working state, the frequency spectrum range is 5 Hz-500 Hz, and the maximum resolution f n Setting up a conversion equation of working time and vibration times N as follows, wherein the frequency of the vibration is set as 0.2 s:
N＝t/f n
in the formula, t is working time;
s7200, calculating a connection coefficient equation of the reliability prediction model based on the service life based on the acceleration excitation-displacement data sample obtained in S400 as follows:
in the formula, s is the dislocation displacement between two adjacent parts, and sigma is the variance of the maximum displacement of the military standard loading capacity of the reference country;
s7300, obtaining a reliability calculation equation of single excitation according to a connection coefficient equation of the reliability prediction model based on the service life of S7200:
R＝Φ(Z)
the service life reliability calculation equation after N times of vibration is as follows:
R n ＝Φ(Z) N
the service life reliability equation after using t hours vibration is as follows:
the probability of failure F (t) after t hours is expressed as:
F(t)＝1-R(t)
the failure rate after t hours λ (t) is expressed as:
the reliable life L, the median life L 0.5 And characteristic lifetime L 1/e Are respectively expressed as follows:
L＝R -1 (t)
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