CN114429060A - Method for assessing structure dislocation failure and service life prediction in fatigue vibration - Google Patents

Method for assessing structure dislocation failure and service life prediction in fatigue vibration Download PDF

Info

Publication number
CN114429060A
CN114429060A CN202111459422.1A CN202111459422A CN114429060A CN 114429060 A CN114429060 A CN 114429060A CN 202111459422 A CN202111459422 A CN 202111459422A CN 114429060 A CN114429060 A CN 114429060A
Authority
CN
China
Prior art keywords
vibration
product
equation
establishing
fatigue
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202111459422.1A
Other languages
Chinese (zh)
Other versions
CN114429060B (en
Inventor
李一舒
张澧桐
曹贺全
王佰超
高永亮
陈巍
赵岩
陈达宇
宗铎
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Weapon Science Academy Ningbo Branch
Changchun University of Science and Technology
Original Assignee
China Weapon Science Academy Ningbo Branch
Changchun University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China Weapon Science Academy Ningbo Branch, Changchun University of Science and Technology filed Critical China Weapon Science Academy Ningbo Branch
Priority to CN202111459422.1A priority Critical patent/CN114429060B/en
Publication of CN114429060A publication Critical patent/CN114429060A/en
Application granted granted Critical
Publication of CN114429060B publication Critical patent/CN114429060B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M7/00—Vibration-testing of structures; Shock-testing of structures
    • G01M7/02—Vibration-testing by means of a shake table
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computer Hardware Design (AREA)
  • General Engineering & Computer Science (AREA)
  • Geometry (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

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 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; 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 product design and maintenance easier.

Description

Method for assessing structure dislocation failure and service life prediction in fatigue vibration
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 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;
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;
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.
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＝Sb/TSthe method comprises the following steps of A, judging whether MTBF is a continuous fault-free working time in a limit state, Sb is a minimum fault-free mileage of a product driven by a vehicle, and Ts is a vehicle speed corresponding to a vibration frequency spectrum in 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 misalignment evaluation mathematical function r ═ f (Δ x, Δ y, Δ z) according to a structure position relationship between the group components in the product, where r is a relative displacement between the group components, and Δ x, Δ y, Δ z are change values of the group components after displacement in X, Y, Z directions from an initial position;
s5200, inputting all groups of component influence factors under X, Y, Z three-direction vibration acceleration excitation and structural tolerance as input quantities into a three-layer neural network, and training the neural network 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:
Figure BDA0003389302430000031
wherein i ═ X, Y, Z, D1，D2，...，DnINPUT when i takes value of X, Y, ZiRespectively representing the vibration acceleration in the direction of X, Y, Z, and INPUT when the value of i is DniRepresents all sets of component influences under structural tolerances;
the first layer of the three-layer neural network is
Figure BDA0003389302430000032
i takes a value of 1-15; the second layer of neural network is
Figure BDA0003389302430000033
i takes a value of 1-5; the third layer of neural network is
Figure BDA0003389302430000034
i takes a value of 1-5; the normalized processing equation of the output layer is
Figure BDA0003389302430000035
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, establishing a limit state function of product structure dislocation failure according to a fault mode and a fault influence analysis structure of a product, wherein the limit state function is g (x) ═ N (x) -place, the place is a displacement value of product structure dislocation in a fatigue vibration process but not causing product function failure, 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 a time domain environment of a continuous excitation working state, a frequency spectrum range is 5 Hz-500 Hz, a maximum resolution fn is 0.2S and is used as a vibration period, and a conversion equation of working time and vibration times is established as follows:
t＝T/fn
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:
Figure BDA0003389302430000036
in the formula, s is the dislocation displacement between two adjacent parts, and sigma is the variance of the maximum displacement of the reference national military standard loading capacity;
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:
Rn＝Φ(Z)N
the service life reliability equation after using n hours of vibration is:
Figure BDA0003389302430000041
the probability of failure F (t) after t hours can be expressed as:
F(t)＝1-R(t)
the failure rate after t hours λ (t) is expressed as:
Figure BDA0003389302430000042
the equations for the reliable life, median life and characteristic life are then expressed as follows:
t＝R-1(t)
Figure BDA0003389302430000043
Figure BDA0003389302430000044
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 GJB 150.16A-2009 military equipment laboratory environment test method part 16.
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 diagram showing the results of free mode calculations for a product according to an embodiment of the present invention in the frequency range of 0-600 Hz.
Fig. 5 is a diagram of a neural network computational model scenario in accordance with an embodiment of the present invention.
FIG. 6 is a graph of a fit of the structural displacement of the product set components 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 characteristic data of excitation normal distribution 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 or similar reference numerals refer to the same or similar elements or elements having the same or similar function 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 a 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, carrying out meshing on the finite element model, and then loading the 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 mode calculation to be 0-600Hz according to key parameter values such as mean value, variance and peak acceleration obtained by calculation; based on the finite element structure model, performing free mode calculation in a nanostran solver, wherein the calculation result is shown in FIG. 4; and obtaining the inherent property of 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 based on vibration schemes in different directions and different amplitudes, calculating the uninterrupted fatigue vibration response of the product.
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＝Sb/TSthe method comprises the following steps that MTBF is continuous fault-free working time in a limit state, Sb is the minimum fault-free mileage of a product in vehicle running, and Ts is the vehicle speed corresponding to a vibration frequency spectrum in 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 mathematical function of product assessment structure displacement based on a neural network, and establishing a limit state equation of 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 product assessment structure displacement, and establishing a structural dislocation extreme state equation;
preferably, step S500 includes the steps of:
s5100, establishing a structure misalignment evaluation mathematical function r ═ f (Δ x, Δ y, Δ z) according to a structure position relationship between the group components in the product, where r is a relative displacement between the group components, and Δ x, Δ y, Δ z are change values of the group components after displacement in X, Y, Z directions from an initial position;
s5200, inputting all groups of component influence factors under X, Y, Z three-direction vibration acceleration excitation and structural tolerance as input quantities into a three-layer neural network, and training the neural network 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:
Figure BDA0003389302430000061
wherein i ═ X, Y, Z, D1，D2，...，DnINPUT when i takes value of X, Y, ZiRespectively representing the vibration acceleration in the direction of X, Y, Z, and INPUT when the value of i is DniRepresents all sets of component influences under structural tolerances;
the first layer of the three-layer neural network is
Figure BDA0003389302430000071
i takes a value of 1-15; the second layer of neural network is
Figure BDA0003389302430000072
i takes a value of 1-5; the third layer of neural network is
Figure BDA0003389302430000073
i takes a value of 1-5; the normalized processing equation of the output layer is
Figure BDA0003389302430000074
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, establishing a limit state function of product structure dislocation failure according to a fault mode and a fault influence analysis structure of a product, wherein the limit state function is g (x) ═ N (x) -place, the place is a displacement value of product structure dislocation in a fatigue vibration process but not causing product function failure, and N (x) is used for representing the limit state function of a test point in a system structure.
Step S600: performing experimental 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 midline of the part 7, and 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 deviates 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 (3) establishing a conversion relation between parameters such as frequency and resolution and a time domain based on the product structure dislocation mathematical fitting function established in the step (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.
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 core based on the service life is to establish the relation between the excitation times and the time. Since the whole vibration test is a continuous excitation working state in a time domain environment, and the frequency spectrum ranges from 5Hz to 500Hz, the maximum resolution fn is 0.2s as a vibration period, and the conversion equation of the working time and the vibration frequency is as follows:
t＝T/fn
wherein T is the working time.
According to the input (acceleration excitation) -output (displacement) database obtained after the calculation in the step S4300, a connection coefficient equation of the reliability prediction model based on the service life is calculated as follows:
Figure BDA0003389302430000081
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 load;
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:
Rn＝Φ(Z)N
the service life reliability equation after using n hours of vibration is:
Figure BDA0003389302430000082
the probability of failure F (t) after t hours can be expressed as:
F(t)＝1-R(t)
the failure rate after t hours λ (t) is expressed as:
Figure BDA0003389302430000083
equations of the reliable life, the median life and the characteristic life are respectively expressed as follows:
t＝R-1(t)
Figure BDA0003389302430000084
Figure BDA0003389302430000085
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 present 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 (5)

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 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;
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.
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, further comprising: 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＝Sb/TSthe method comprises the following steps that MTBF is continuous fault-free working time in a limit state, Sb is the minimum fault-free mileage of a product in vehicle running, and Ts is the vehicle speed corresponding to a vibration frequency spectrum in 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.
4. The method of claim 3, wherein: the S500 includes the steps of,
s5100, establishing a structure dislocation evaluation mathematical function r which is 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, Z three-direction coordinate axes compared with an initial position after the group components are displaced;
s5200, inputting all groups of component influence factors under X, Y, Z three-direction vibration acceleration excitation and structural tolerance as input quantities into a three-layer neural network, and training the neural network 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:
Figure FDA0003389302420000021
wherein i ═ X, Y, Z, D1，D2，...，DnINPUT when i takes value of X, Y, ZiRespectively representing the vibration acceleration in the direction of X, Y, Z, and INPUT when the value of i is DniRepresents all sets of component influences under structural tolerances;
the first layer of the three-layer neural network is
Figure FDA0003389302420000022
i takes a value of 1-15; the second layer of neural network is
Figure FDA0003389302420000023
i takes a value of 1-5; the third layer of neural network is
Figure FDA0003389302420000024
i takes a value of 1-5; the normalized processing equation of the output layer is
Figure FDA0003389302420000025
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, establishing a limit state function of product structure dislocation failure according to a fault mode and a fault influence analysis structure of a product, wherein the limit state function is g (x) ═ N (x) -place, the place is a displacement value of product structure dislocation in a fatigue vibration process but not causing product function failure, and N (x) is used for representing the limit state function of a test point in a system structure.
5. The method of claim 4, further comprising: the S700 includes the steps of,
s7100, under a time domain environment of a continuous excitation working state, a frequency spectrum range is 5 Hz-500 Hz, a maximum resolution fn is 0.2S and is used as a vibration period, and a conversion equation of working time and vibration times is established as follows:
t＝T/fn
in the formula, T is 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:
Figure FDA0003389302420000031
in the formula, s is the dislocation displacement between two adjacent parts, and sigma is the variance of the maximum displacement of the reference national military standard loading capacity;
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:
Rn＝Φ(Z)N
the service life reliability equation after using n hours of vibration is:
Figure FDA0003389302420000032
the probability of failure F (t) after t hours can be expressed as:
F(t)＝1-R(t)
the failure rate after t hours λ (t) is expressed as:
Figure FDA0003389302420000033
the equations for the reliable life, median life and characteristic life are then expressed as follows:
t＝R-1(t)
Figure FDA0003389302420000034
Figure FDA0003389302420000035
CN202111459422.1A 2021-12-02 2021-12-02 Method for examining structure dislocation failure and service life prediction in fatigue vibration Active CN114429060B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111459422.1A CN114429060B (en) 2021-12-02 2021-12-02 Method for examining structure dislocation failure and service life prediction in fatigue vibration

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111459422.1A CN114429060B (en) 2021-12-02 2021-12-02 Method for examining structure dislocation failure and service life prediction in fatigue vibration

Publications (2)

Publication Number Publication Date
CN114429060A true CN114429060A (en) 2022-05-03
CN114429060B CN114429060B (en) 2022-12-27

Family

ID=81312123

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111459422.1A Active CN114429060B (en) 2021-12-02 2021-12-02 Method for examining structure dislocation failure and service life prediction in fatigue vibration

Country Status (1)

Country Link
CN (1) CN114429060B (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115096378A (en) * 2022-08-29 2022-09-23 宇一精工科技(南通)有限公司 Intelligent recognition method for dislocation fault of rubber pad
CN116629078A (en) * 2023-07-21 2023-08-22 成都航空职业技术学院 Method and system for predicting fatigue life durability of automobile
CN116663320A (en) * 2023-06-27 2023-08-29 中国工程物理研究院研究生院 Interlayer slip failure probability prediction method for multi-layer heterogeneous pre-tightening interlayer structure
CN116842812A (en) * 2023-09-01 2023-10-03 国网浙江省电力有限公司电力科学研究院 Hydrogen storage bottle fatigue failure prediction method and system considering material distribution non-uniformity
CN117191311A (en) * 2023-08-14 2023-12-08 暨南大学 Accelerated vibration test method for product under non-stationary and non-Gaussian vibration of logistics

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2500836A2 (en) * 2011-03-16 2012-09-19 Airbus Operations Limited Stresses induced by random loading
CN104268335A (en) * 2014-09-23 2015-01-07 工业和信息化部电子第五研究所 Vibration fatigue life predication method and system for micro-packaging assembly
CN104392073A (en) * 2014-12-12 2015-03-04 中国航空综合技术研究所 Electronic product reliability accelerated test method based on failure physics
CN108763611A (en) * 2018-03-28 2018-11-06 北京航空航天大学 A kind of wing structure random eigenvalue analysis method based on probabilistic density evolution
CN109492345A (en) * 2019-01-10 2019-03-19 西安交通大学 A kind of turbine blade high-cycle fatigue life method based on SENet
CN110532723A (en) * 2019-09-06 2019-12-03 北京航空航天大学 A kind of turbine disk multi-invalidation mode reliability optimization method based on EGRA
CN110750875A (en) * 2019-09-26 2020-02-04 东南大学 Structure dynamic and static parameter uncertainty quantitative analysis system only using output response
CN112257320A (en) * 2020-10-26 2021-01-22 西安石油大学 Method for analyzing vibration fatigue characteristics of airborne case

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2500836A2 (en) * 2011-03-16 2012-09-19 Airbus Operations Limited Stresses induced by random loading
CN104268335A (en) * 2014-09-23 2015-01-07 工业和信息化部电子第五研究所 Vibration fatigue life predication method and system for micro-packaging assembly
CN104392073A (en) * 2014-12-12 2015-03-04 中国航空综合技术研究所 Electronic product reliability accelerated test method based on failure physics
CN108763611A (en) * 2018-03-28 2018-11-06 北京航空航天大学 A kind of wing structure random eigenvalue analysis method based on probabilistic density evolution
CN109492345A (en) * 2019-01-10 2019-03-19 西安交通大学 A kind of turbine blade high-cycle fatigue life method based on SENet
CN110532723A (en) * 2019-09-06 2019-12-03 北京航空航天大学 A kind of turbine disk multi-invalidation mode reliability optimization method based on EGRA
CN110750875A (en) * 2019-09-26 2020-02-04 东南大学 Structure dynamic and static parameter uncertainty quantitative analysis system only using output response
CN112257320A (en) * 2020-10-26 2021-01-22 西安石油大学 Method for analyzing vibration fatigue characteristics of airborne case

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
张亚卿: "中置轴轿运车车架结构疲劳寿命分析及优化", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》 *
董国疆等: "考虑低载荷强化效应的汽车转向节疲劳分析", 《汽车工程》 *
陆秋海等: "利用模态试验参数识别结构损伤的神经网络法", 《工程力学》 *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115096378A (en) * 2022-08-29 2022-09-23 宇一精工科技(南通)有限公司 Intelligent recognition method for dislocation fault of rubber pad
CN116663320A (en) * 2023-06-27 2023-08-29 中国工程物理研究院研究生院 Interlayer slip failure probability prediction method for multi-layer heterogeneous pre-tightening interlayer structure
CN116663320B (en) * 2023-06-27 2024-01-26 中国工程物理研究院研究生院 Interlayer slip failure probability prediction method for multi-layer heterogeneous pre-tightening interlayer structure
CN116629078A (en) * 2023-07-21 2023-08-22 成都航空职业技术学院 Method and system for predicting fatigue life durability of automobile
CN116629078B (en) * 2023-07-21 2023-10-13 成都航空职业技术学院 Method and system for predicting fatigue life durability of automobile
CN117191311A (en) * 2023-08-14 2023-12-08 暨南大学 Accelerated vibration test method for product under non-stationary and non-Gaussian vibration of logistics
CN117191311B (en) * 2023-08-14 2024-05-24 暨南大学 Accelerated vibration test method for product under non-stationary and non-Gaussian vibration of logistics
CN116842812A (en) * 2023-09-01 2023-10-03 国网浙江省电力有限公司电力科学研究院 Hydrogen storage bottle fatigue failure prediction method and system considering material distribution non-uniformity
CN116842812B (en) * 2023-09-01 2023-11-24 国网浙江省电力有限公司电力科学研究院 Hydrogen storage bottle fatigue failure prediction method and system considering material distribution non-uniformity

Also Published As

Publication number Publication date
CN114429060B (en) 2022-12-27

Similar Documents

Publication Publication Date Title
CN114429060B (en) Method for examining structure dislocation failure and service life prediction in fatigue vibration
US7006947B2 (en) Method and apparatus for predicting failure in a system
CN112100765B (en) High-low cycle composite fatigue test piece of turbine disc cold extrusion reinforced hole structure and design method
CN112784356B (en) Test method for static strength of airplane structure
CN113125888A (en) Aviation electromechanical product accelerated life test method based on fault behaviors
CN111191391B (en) Simulation piece design method based on local stress distribution weighting coefficient
Sun A new stress field intensity model and its application in component high cycle fatigue research
CN114254533B (en) Method for examining influence and prediction of fatigue vibration on fixed angle of product group component
CN113919078A (en) Notch structure fatigue analysis method for coupling stress gradient under size effect
Su A road load data processing technique for durability optimization of automotive products
CN112733399B (en) Failure evaluation method and device for bolt in random vibration
Lee et al. Development of the fatigue life model for plastic gears of automotive actuators under vibration
LIN Exhaust system reliability evaluation
CN115219216A (en) Service life evaluation method of exhaust system
CN109376476B (en) Assembly stress engineering calculation method
Ohri et al. Compressor wheel low cycle fatigue calculations for off highway applications—An approach to accurately calculate application duty cycle
Su et al. Cae virtual test of air intake manifolds using coupled vibration and pressure pulsation loads
Songsong et al. Research of the accelerated fatigue experiment method based on the particle filtering algorithm method and the theory of crack growth
Su Automotive structural durability design using dynamic simulation and fatigue damage sensitivity techniques
CN111581737B (en) Finite element simulation-based structural member reliability assessment method and system
Kuwar et al. Multi-Axial Road Simulation for Component Level Validation of Engine Mount Structure and Elastomer
CN116384003A (en) Implanted sensor type bolt reliability evaluation method, device and equipment
Puzyr et al. Determination of the relationship of structural and diagnostic parameters of locomotives diesel engines
CN117113583A (en) Quantitative assessment method for contact damage of aviation gas turbine connecting interface
CN115563703A (en) NVH performance analysis method of whole vehicle power assembly based on component parameters

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant