CN115292801A - Method, device and equipment for evaluating and predicting abnormal sound of shock absorber and readable storage medium - Google Patents

Method, device and equipment for evaluating and predicting abnormal sound of shock absorber and readable storage medium Download PDF

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CN115292801A
CN115292801A CN202210677362.9A CN202210677362A CN115292801A CN 115292801 A CN115292801 A CN 115292801A CN 202210677362 A CN202210677362 A CN 202210677362A CN 115292801 A CN115292801 A CN 115292801A
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张�浩
高小清
周副权
刘浩
叶永威
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Abstract

The invention provides a method, a device and equipment for evaluating and predicting abnormal sound of a shock absorber and a readable storage medium. The method comprises: obtaining the average value P of the time domain peak value of the vibration signal at the upper end of the piston rod of the vibration absorber bench test in each period max Frequency f i And frequency f j RMS value P within the interval RMS Dynamic stiffness curve K of upper supporting piece of shock absorber d Noise transfer function curve F of shock absorber mounting point to human ear ntf And the subjective evaluation score value of the abnormal sound of the shock absorber on the real vehicle; aiming at each vibration damper, the step of acquiring data is respectively executed to obtain K corresponding to each vibration damper d 、P max 、P RMS 、F ntf And a subjective evaluation score value; based on each damper corresponding K d 、P max 、P RMS 、F ntf Training the neural network model by the subjective evaluation score value to obtain a trained neural network model; and obtaining the value of the predictive subjective evaluation score of the vibration absorber to be tested through the trained neural network model. By the aid of the method and the device, the problem that the cost is high when the shock absorber is adjusted in the prior art is solved.

Description

Method, device and equipment for evaluating and predicting abnormal sound of shock absorber and readable storage medium
Technical Field
The invention relates to the field of vehicle NVH (Noise, vibration and Harshness) performance, in particular to a method, a device and equipment for evaluating and predicting abnormal sound of a shock absorber and a readable storage medium.
Background
The shock absorber is an important part in a vehicle suspension system, has important influence on the smoothness and the comfort of an automobile, can be repeatedly stretched and compressed when the automobile passes through an uneven road surface, can generate impact and vibration in the reversing process, and can cause the vibration of an automobile body through the transmission of a shock absorber piston rod, so that noise is generated, and the noise is abnormal sound of the shock absorber.
The influence factors for generating the abnormal sound of the shock absorber are numerous, and at present, the shock absorber is generally tested through two ways of a shock absorber rack test or a whole vehicle road test. However, the impact of the whole vehicle carrying factor is not considered in the shock absorber bench test, the abnormal sound condition of the shock absorber on the vehicle cannot be accurately predicted, the whole vehicle road test can only adjust the shock absorber in the later stage of the project development, the efficiency is low, and the cost is high.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a readable storage medium for evaluating and predicting abnormal sound of a shock absorber, and aims to solve the problems that in the prior art, the abnormal sound condition generated after the shock absorber is carried on a whole vehicle cannot be predicted in the early stage of project development, and the shock absorber can only be adjusted in the later stage of the project development, so that the test efficiency is low and the cost is high.
In a first aspect, the present invention provides a method for evaluating and predicting abnormal sound of a shock absorber, the method comprising:
s10, obtaining a dynamic stiffness curve K of the upper supporting piece of the shock absorber d ；
S20, acquiring a time domain peak value of the vibration signal at the upper end of the piston rod of the vibration damper bench test in each period, and calculating an average value P of the time domain peak values of the vibration signal at the upper end of the piston rod of the vibration damper bench test in each period max ；
S30, obtaining the frequency f through calculation according to the vibration signal of the vibration damper i And frequency f j RMS value P within the interval RMS ；
S40, coupling the finite element model of the whole vehicle structure with the sound cavity model in the vehicle to obtain a CAE model of the whole vehicle;
s50, acting force is applied to a shock absorber mounting point on the CAE model of the whole vehicle, and a noise transfer function curve F from the shock absorber to human ears is obtained ntf ；
S60, mounting the shock absorber on the real vehicle, and obtaining a subjective evaluation score value for scoring abnormal sound of the shock absorber when the real vehicle runs on a road surface;
s70, respectively executing the steps S10 to S60 to obtain K corresponding to each shock absorber d 、P max 、P RMS 、F ntf And a subjective evaluation score value;
s80, using K corresponding to each vibration damper d 、P max 、P RMS 、F ntf The subjective evaluation score value is used as a group of training data, and the neural network model is trained on the basis of the plurality of groups of training data to obtain a trained neural network model;
s90, damping vibration to be measuredK corresponding to device d 、P max 、P RMS And F ntf And inputting the trained neural network model to obtain the predicted subjective evaluation score value of the shock absorber to be tested.
Optionally, the frequency f is obtained by calculation according to the vibration signal of the vibration damper i And frequency f j RMS value P within the interval RMS The method comprises the following steps:
carrying out Fourier transform on the vibration signal of the vibration absorber to obtain a frequency domain result;
substituting the frequency domain result into a first preset formula, and calculating to obtain the frequency f i And frequency f j RMS value P within the interval RMS Wherein, the first preset formula is as follows:
Figure BDA0003695281490000021
wherein, P RMS Is a frequency f i And frequency f j The RMS value within the interval is,
Figure BDA0003695281490000022
for the result of the ith frequency domain,
Figure BDA0003695281490000023
for the result of the k-th frequency domain,
Figure BDA0003695281490000024
the jth frequency domain result.
Optionally, the acting force is applied to the mounting point of the shock absorber on the finished vehicle CAE model to obtain a noise transfer function curve F from the shock absorber to the human ear ntf Comprises the following steps:
applying an acting force at a shock absorber mounting point on a finished automobile CAE model, calculating sound pressure response at the ear position in the automobile through preset software, and obtaining a noise transfer function curve F from the shock absorber to the ear ntf 。
Optionally, the subjective evaluation score value includes subjective evaluation score values of the shock absorber under different environments, and one environment is determined by a complete vehicle CAE model and a road surface.
Optionally, the step of training the neural network model based on the plurality of sets of training data to obtain a trained neural network model includes:
selecting a plurality of groups of training data according to a preset proportion to train and verify the neural network model;
and if the verification is passed, obtaining the trained neural network model.
In a second aspect, the present invention also provides a shock absorber abnormal sound evaluation and prediction device, including:
a first acquisition module for acquiring a dynamic stiffness curve K of the upper support piece of the shock absorber d ；
The first calculation module is used for acquiring a time domain peak value of the vibration signal at the upper end of the piston rod of the shock absorber bench test in each period and calculating an average value P of the time domain peak values of the vibration signal at the upper end of the piston rod of the shock absorber bench test in each period max ；
A second calculation module for calculating the frequency f according to the vibration signal of the vibration damper i And frequency f j RMS value P within the interval RMS ；
The coupling module is used for coupling the finite element model of the whole vehicle structure and the sound cavity model in the vehicle to obtain a CAE model of the whole vehicle;
the second acquisition module is used for applying acting force at the mounting point of the shock absorber on the CAE model of the whole vehicle to acquire a noise transfer function curve F from the shock absorber to human ears ntf ；
The third acquisition module is used for installing the shock absorber on the real vehicle and acquiring the subjective evaluation score value of the real vehicle for scoring the abnormal sound of the shock absorber when the real vehicle runs on the road surface;
an execution module, configured to obtain, for each shock absorber, K corresponding to each shock absorber through the first obtaining module, the first calculating module, the second calculating module, the coupling module, the second obtaining module, and the third obtaining module d 、P max 、P RMS 、F ntf And a subjective evaluation score value;
training module for K for each shock absorber d 、P max 、P RMS 、F ntf The subjective evaluation score value is used as a group of training data, and the neural network model is trained based on a plurality of groups of training data to obtain a trained neural network model;
a fourth acquisition module for acquiring the K corresponding to the vibration damper to be tested d 、P max 、P RMS And F ntf And inputting the trained neural network model to obtain the value of the predictive subjective evaluation score of the shock absorber to be tested.
Optionally, the second calculating module is specifically configured to:
carrying out Fourier transform on the vibration signal of the vibration absorber to obtain a frequency domain result;
substituting the frequency domain result into a first preset formula, and calculating to obtain the frequency f i And frequency f j RMS value P within the interval RMS Wherein, the first preset formula is as follows:
Figure BDA0003695281490000031
wherein, P RMS Is a frequency f i And frequency f j The RMS value within the interval is,
Figure BDA0003695281490000041
for the result of the ith frequency domain,
Figure BDA0003695281490000042
for the result of the k-th frequency domain,
Figure BDA0003695281490000043
the jth frequency domain result.
Optionally, the second obtaining module is configured to:
applying an acting force at a shock absorber mounting point on a finished automobile CAE model, calculating sound pressure response at the ear position in the automobile through preset software, and obtaining a noise transfer function curve F from the shock absorber to the ear ntf 。
In a third aspect, the present invention further provides a shock absorber abnormal sound evaluation and prediction device, which comprises a processor, a memory, and a shock absorber abnormal sound evaluation and prediction program stored on the memory and executable by the processor, wherein when the shock absorber abnormal sound evaluation and prediction program is executed by the processor, the steps of the shock absorber abnormal sound evaluation and prediction method are realized.
In a fourth aspect, the present invention further provides a readable storage medium, wherein the readable storage medium stores a shock absorber abnormal sound evaluation and prediction program, and when the shock absorber abnormal sound evaluation and prediction program is executed by a processor, the shock absorber abnormal sound evaluation and prediction program implements the steps of the shock absorber abnormal sound evaluation and prediction method described above.
In the invention, S10, a dynamic stiffness curve K of an upper supporting piece of the shock absorber is obtained d (ii) a S20, acquiring a time domain peak value of the vibration signal at the upper end of the piston rod of the vibration absorber bench test in each period, and calculating an average value P of the time domain peak values of the vibration signal at the upper end of the piston rod of the vibration absorber bench test in each period max (ii) a S30, obtaining the frequency f through calculation according to the vibration signal of the vibration damper i And frequency f j RMS value P within the interval RMS (ii) a S40, coupling the finite element model of the whole vehicle structure with the sound cavity model in the vehicle to obtain a CAE model of the whole vehicle; s50, acting force is applied to a shock absorber mounting point on the CAE model of the whole vehicle, and a noise transfer function curve F from the shock absorber to human ears is obtained ntf (ii) a S60, mounting the shock absorber on the real vehicle, and obtaining a subjective evaluation score value for scoring abnormal sound of the shock absorber when the real vehicle runs on a road surface; s70, respectively executing the steps S10 to S60 to obtain K corresponding to each shock absorber d 、P max 、P RMS 、F ntf And a subjective evaluation score value; s80, with K corresponding to each vibration damper d 、P max 、P RMS 、F ntf The subjective evaluation score value is used as a group of training data, and the neural network model is trained based on a plurality of groups of training data to obtain a trained neural network model; s90, enabling the vibration absorber to be tested to correspond to K d 、P max 、P RMS And F ntf And inputting the trained neural network model to obtain the predicted subjective evaluation score value of the shock absorber to be tested. By the invention, K corresponding to each shock absorber is obtained d 、P max 、P RMS 、F ntf After subjective evaluation of score value, K corresponding to each vibration damper d 、P max 、P RMS 、F ntf And taking the subjective evaluation score value as a group of training data, training the neural network model based on the plurality of groups of training data to obtain the trained neural network model, and determining K corresponding to the shock absorber to be tested d 、P max 、P RMS And F ntf The method has the advantages that the trained neural network model is input, so that the value of the predicted subjective evaluation score of the shock absorber to be tested can be obtained, the abnormal sound condition generated after the shock absorber is carried on the whole vehicle can be predicted in the early stage of project development, the shock absorber can be adjusted, and the problems that in the prior art, the shock absorber can only be adjusted in the later stage of the project development, the abnormal sound condition of the shock absorber on the vehicle cannot be accurately predicted in the early stage of the project development, and the cost is high are solved.
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Fig. 1 is a schematic hardware configuration diagram of a shock absorber abnormal sound evaluation prediction apparatus according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating an embodiment of the method for evaluating and predicting abnormal sound of the shock absorber according to the present invention;
FIG. 3 is a schematic diagram of a shock absorber rack test according to the shock absorber abnormal sound evaluation prediction method of the present invention;
FIG. 4 is a detailed flowchart of step S30 in FIG. 2;
fig. 5 is a functional block diagram of an embodiment of the abnormal sound evaluation and prediction device for the shock absorber according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In a first aspect, an embodiment of the present invention provides an abnormal vibration damper response evaluation prediction apparatus.
Referring to fig. 1, fig. 1 is a schematic diagram of a hardware configuration of a shock absorber abnormal sound evaluation prediction apparatus according to an embodiment of the present invention. In the embodiment of the present invention, the apparatus for evaluating and predicting abnormal vibration of a vibration damper may include a processor 1001 (e.g., a Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used for realizing connection communication among the components; the user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard); the network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WI-FI interface, WI-FI interface); the memory 1005 may be a Random Access Memory (RAM) or a non-volatile memory (non-volatile memory), such as a magnetic disk memory, and the memory 1005 may optionally be a storage device independent of the processor 1001. Those skilled in the art will appreciate that the hardware configuration shown in fig. 1 is not intended to limit the present invention, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
With continued reference to fig. 1, a memory 1005, which is one type of computer storage medium in fig. 1, may include an operating system, a network communication module, a user interface module, and a shock absorber abnormal sound evaluation prediction program. The processor 1001 may call the shock absorber abnormal sound evaluation and prediction program stored in the memory 1005, and execute the shock absorber abnormal sound evaluation and prediction method provided by the embodiment of the present invention.
In a second aspect, an embodiment of the invention provides a method for evaluating and predicting abnormal sound of a shock absorber.
In an embodiment, referring to fig. 2, fig. 2 is a schematic flow chart of an embodiment of the method for evaluating and predicting abnormal sound of a shock absorber according to the present invention. As shown in fig. 2, the method for evaluating and predicting abnormal vibration of a shock absorber includes:
step S10, obtaining a dynamic stiffness curve K of the upper support piece of the shock absorber d ；
In this embodiment, the movable steelDynamic stiffness curve K of upper supporting piece of shock absorber is obtained through degree test d The shock absorber upper supporting piece topcount is used for connecting the shock absorber and a car body, and a buffer rubber sleeve is arranged inside the shock absorber upper supporting piece topcount and has a vibration isolation effect. Because the dynamic stiffness value of the rubber part is greatly influenced by factors such as temperature, the influence of the temperature can be considered when the upper supporting part of the shock absorber performs the dynamic stiffness test, the dynamic stiffness curve under different temperature conditions is measured, and the dynamic stiffness curve is matched with the temperature condition of the shock absorber bench test.
Step S20, acquiring a time domain peak value of the vibration signal at the upper end of the piston rod of the shock absorber bench test in each period, and calculating an average value P of the time domain peak values of the vibration signal at the upper end of the piston rod of the shock absorber bench test in each period max ；
In the embodiment, referring to fig. 3, fig. 3 is a schematic diagram of a test of a shock absorber rack of the method for evaluating and predicting abnormal sound of a shock absorber. As shown in figure 3, the vibration damper is fixed on a test bench for bench test, displacement excitation with fixed frequency and amplitude, such as signals with frequency of 13Hz and amplitude of 5mm, is applied below the vibration damper test bench, and a vibration signal { x ] at the upper end of a piston rod of the vibration damper bench test is obtained through a vibration sensor n Time domain peaks in each period. After the time domain peak value of the vibration signal at the upper end of the piston rod of the vibration absorber bench test in each period is obtained, the average value P of the time domain peak value of the vibration signal at the upper end of the piston rod of the vibration absorber bench test in each period is obtained through calculation max 。
Further, aiming at the problem of shock abnormal sound deterioration of the shock absorber under the low-temperature condition, a temperature sensor can be pasted on the wall of the shock absorber cylinder, and the vibration response under different temperature conditions can be tested in an environment cabin.
Step S30, obtaining the frequency f by calculation according to the vibration signal of the vibration damper i And frequency f j RMS value P within the interval RMS ；
In the embodiment, the vibration sensor is used for acquiring the vibration signal { x) at the upper end of the piston rod of the vibration damper bench test n According to vibration signal { x } of the vibration damper n Get the frequency f by calculation i And frequency f j Within a regionRMS value P of RMS 。
Further, in an embodiment, referring to fig. 4, fig. 4 is a schematic detailed flow chart of step S30 in fig. 2. As shown in fig. 4, step S30 includes:
step S301, carrying out Fourier transform on vibration signals of the vibration absorber to obtain frequency domain results;
step S302, substituting the frequency domain result into a first preset formula, and calculating to obtain the frequency f i And frequency f j RMS value P within the interval RMS Wherein, the first preset formula is as follows:
Figure BDA0003695281490000071
wherein, P RMS Is a frequency f i And frequency f j The RMS value in the interval is such that,
Figure BDA0003695281490000072
for the result of the ith frequency domain,
Figure BDA0003695281490000073
for the result of the k-th frequency domain,
Figure BDA0003695281490000074
the jth frequency domain result.
In this embodiment, the vibration signal of the vibration absorber is substituted into the formula
Figure BDA0003695281490000075
In the method, fourier transform is carried out to obtain a frequency domain result x f And N is the vibration signal length of the vibration absorber, and f is the vibration signal frequency of the vibration absorber.
Substituting the frequency domain result into a first preset formula, and calculating to obtain the frequency f i And frequency f j RMS value P within the interval RMS Wherein, the first preset formula is as follows:
Figure BDA0003695281490000076
wherein, P RMS Is a frequency f i And frequency f j The RMS value in the interval is such that,
Figure BDA0003695281490000077
for the result of the ith frequency domain,
Figure BDA0003695281490000078
for the result of the k-th frequency domain,
Figure BDA0003695281490000079
the jth frequency domain result. Frequency f i And frequency f j The main frequency interval for reversing the shock abnormal sound of the shock absorber is referenced to 300Hz-600Hz.
S40, coupling the finite element model of the whole vehicle structure with the sound cavity model in the vehicle to obtain a CAE model of the whole vehicle;
in this embodiment, a complete vehicle structure finite element model and an internal acoustic cavity model are established by Hypermesh and other commercial software except chassis parts, and the complete vehicle structure finite element model and the internal acoustic cavity model are coupled to obtain a complete vehicle CAE model. The CAE is short for Computer aid Engineering, and is an approximate numerical analysis method for solving the problems of analysis and calculation of mechanical properties such as complex Engineering and product structural strength, rigidity, buckling stability, dynamic response, heat conduction, three-dimensional multi-body contact, elastoplasticity and the like, optimization design of structural properties and the like with the assistance of a Computer.
Step S50, acting force is applied to the installation point of the shock absorber on the CAE model of the whole vehicle, and a noise transfer function curve F from the shock absorber to the human ear is obtained ntf ；
In this embodiment, based on the entire car CAE model, 1N excitation is applied to the shock absorber mounting point, that is, an acting force is applied to the shock absorber mounting point on the entire car CAE model, and a noise transfer function curve F from the shock absorber to the human ear is obtained ntf . Wherein, NTF (noise transfer function) is an english abbreviation of noise transfer function, and the method is a transfer function of an analysis and calculation structure.
Further, in an embodiment, the step S50 includes:
applying acting force at the installation point of the shock absorber on the CAE model of the whole vehicle, calculating sound pressure response at the ear in the vehicle through preset software, and obtaining a noise transfer function curve F from the shock absorber to the ear ntf 。
In the embodiment, the preset software is Hypermesh, acting force is applied to the mounting points of the shock absorbers on the CAE model of the whole vehicle, sound pressure response of the ears in the vehicle is calculated through the preset software Hypermesh, and a noise transfer function curve F from the shock absorbers to the ears at each mounting point is obtained ntf . The Hypermesh software is a CAE application software package with strong functions, is also an innovative and open enterprise-level CAE platform, integrates various tools required by design and analysis, and has unrivaled performance, high openness, flexibility and a friendly user interface.
Step S60, installing the shock absorber on the real vehicle, and obtaining the subjective evaluation score value of scoring the abnormal sound of the shock absorber when the real vehicle runs on the road surface;
in this embodiment, the road surface is a typical abnormal sound road surface, such as belgium road surface, and professional engineers perform subjective evaluation and scoring on the abnormal sound of the shock absorber. The shock absorber is installed on an actual vehicle, and the subjective evaluation score value of the real vehicle for scoring the abnormal sound of the shock absorber when the actual vehicle runs on the Belgium road surface is obtained.
Step S70, aiming at each vibration damper, respectively executing the steps S10 to S60 to obtain K corresponding to each vibration damper d 、P max 、P RMS 、F ntf And a subjective evaluation score value;
in this embodiment, steps S10 to S60 are respectively executed for each shock absorber, and K corresponding to each shock absorber is obtained d 、P max 、P RMS 、F ntf And subjective evaluation score values. Specifically, for 4 dampers, step S10 to step S60 are respectively executed, and K corresponding to damper 1 is obtained d 、P max 、P RMS 、F ntf And the value of the subjective evaluation score, K corresponding to the vibration absorber 2 d 、P max 、P RMS 、F ntf And subjective evaluation scoreValue, K for damper 3 d 、P max 、P RMS 、F ntf And the value of the subjective evaluation score and K corresponding to the shock absorber 4 d 、P max 、P RMS 、F ntf And subjective evaluation score values. It is easy to think that the number of the dampers in the present embodiment is only for reference and is not limited herein.
Step S80, using K corresponding to each vibration damper d 、P max 、P RMS 、F ntf The subjective evaluation score value is used as a group of training data, and the neural network model is trained based on a plurality of groups of training data to obtain a trained neural network model;
in the present embodiment, K corresponds to the vibration absorber 1 d 、P max 、P RMS 、F ntf And the subjective evaluation score value is the first group of data, and K corresponding to the shock absorber 2 d 、P max 、P RMS 、F ntf And the subjective evaluation score value is K corresponding to the second group of data and the shock absorber 3 d 、P max 、P RMS 、F ntf And K corresponding to the shock absorber 4 and taking the subjective evaluation score value as the third group of data d 、P max 、P RMS 、F ntf And the subjective evaluation score value is the fourth group of data. And training the neural network model based on the four groups of training data to obtain the trained neural network model. It is easy to understand that the number of sets of training data in this embodiment is only used for reference and is not limited herein.
Further, in an embodiment, the step of training the neural network model based on the plurality of sets of training data to obtain a trained neural network model includes:
selecting a plurality of groups of training data according to a preset proportion to train and verify the neural network model;
and if the verification is passed, obtaining the trained neural network model.
In this embodiment, according to a preset ratio, multiple sets of training data are selected to train and verify the neural network model, and in this embodiment, if the preset ratio is 1: and 3, selecting 3 groups of training data to train the neural network model, verifying the trained neural network model by the rest 1 group of training data, finishing the training of the neural network model if the training passes the verification, obtaining the trained neural network model, and adding new training data or adjusting the preset proportion or the fitting precision of the neural network if the training does not pass the verification. It is easy to understand that the preset ratio in the embodiment is only for reference and is not limited herein.
Step S90, corresponding K of the vibration damper to be tested d 、P max 、P RMS And F ntf And inputting the trained neural network model to obtain the value of the predictive subjective evaluation score of the shock absorber to be tested.
In this embodiment, the vibration absorber to be tested corresponds to K d 、P max 、P RMS And F ntf The method is used for predicting the shock absorber to be tested and inputting the result into the trained neural network model to obtain the value of the predicted subjective evaluation score of the shock absorber to be tested, which is output by the trained neural network model, so that the advance prediction of the real vehicle evaluation effect is realized, and the abnormal sound development work of the shock absorber is preposed.
In this embodiment, S10, a dynamic stiffness curve K of the upper support member of the shock absorber is obtained d (ii) a S20, acquiring a time domain peak value of the vibration signal at the upper end of the piston rod of the vibration damper bench test in each period, and calculating an average value P of the time domain peak values of the vibration signal at the upper end of the piston rod of the vibration damper bench test in each period max (ii) a S30, obtaining the frequency f through calculation according to the vibration signal of the vibration damper i And frequency f j RMS value P within the interval RMS (ii) a S40, coupling the finite element model of the whole vehicle structure with the sound cavity model in the vehicle to obtain a CAE model of the whole vehicle; s50, applying acting force at a shock absorber mounting point on the CAE model of the whole vehicle to obtain a noise transfer function curve F from the shock absorber to human ears ntf (ii) a S60, mounting the shock absorber on the real vehicle, and obtaining the subjective evaluation score value of the real vehicle for scoring the abnormal sound of the shock absorber when the real vehicle runs on the road surface; s70, respectively executing the steps S10 to S60 to obtain K corresponding to each shock absorber d 、P max 、P RMS 、F ntf And a subjective evaluation score value; s80, by each subtractionK corresponding to the vibrator d 、P max 、P RMS 、F ntf The subjective evaluation score value is used as a group of training data, and the neural network model is trained on the basis of the plurality of groups of training data to obtain a trained neural network model; s90, corresponding K of the vibration damper to be tested d 、P max 、P RMS And F ntf And inputting the trained neural network model to obtain the value of the predictive subjective evaluation score of the shock absorber to be tested. By the embodiment, K corresponding to each vibration damper is obtained d 、P max 、P RMS 、F ntf After the score value is subjectively evaluated, K corresponding to each shock absorber is used d 、P max 、P RMS 、F ntf And taking the subjective evaluation score value as a group of training data, training the neural network model based on the plurality of groups of training data to obtain the trained neural network model, and determining the K corresponding to the vibration absorber to be tested d 、P max 、P RMS And F ntf The method has the advantages that the trained neural network model is input, the value of the predicted subjective evaluation score of the shock absorber to be tested can be obtained, the abnormal sound condition generated after the shock absorber is carried on the whole vehicle can be predicted in the early stage of project development, and therefore the shock absorber can be adjusted, and the problems that in the prior art, the shock absorber can only be adjusted in the later stage of the project development, the abnormal sound condition of the shock absorber on the vehicle cannot be accurately predicted in the early stage of the project development, and the cost is high are solved.
Further, in one embodiment, the subjective evaluation score value includes subjective evaluation score values of the shock absorber under different environments, and one environment is determined by a vehicle CAE model and a road surface.
In this embodiment, the subjective evaluation score includes subjective evaluation score values of the shock absorber under different environments, wherein an environment is determined by a vehicle CAE model and a road surface. For example:
environment 1 is a finished vehicle CAE model 1 plus a road surface 1, environment 2 is a finished vehicle CAE model 1 plus a road surface 2, environment 3 is a finished vehicle CAE model 2 plus a road surface 1, environment 4 is a finished vehicle CAE model 2 plus a road surface 2, and so on. I.e. the complete car CAE model and/or the road surface differs between different environments.
In a third aspect, an embodiment of the present invention further provides a device for evaluating and predicting abnormal vibration of a shock absorber.
In an embodiment, referring to fig. 5, fig. 5 is a functional module schematic diagram of an embodiment of the abnormal sound evaluation and prediction apparatus for a shock absorber according to the present invention. As shown in fig. 5, the abnormal vibration damper noise evaluation and prediction device includes:
a first obtaining module 10 for obtaining a dynamic stiffness curve K of the upper support of the shock absorber d ；
The first calculating module 20 is configured to obtain a time-domain peak value of the vibration signal at the upper end of the piston rod of the shock absorber bench test in each period, and calculate an average value P of the time-domain peak values of the vibration signal at the upper end of the piston rod of the shock absorber bench test in each period max ；
A second calculation module 30 for calculating the frequency f according to the vibration signal of the vibration damper i And frequency f j RMS value P within the interval RMS ；
The coupling module 40 is used for coupling the finite element model of the whole vehicle structure and the sound cavity model in the vehicle to obtain a CAE model of the whole vehicle;
a second obtaining module 50, configured to apply an acting force at a shock absorber mounting point on the finished vehicle CAE model to obtain a noise transfer function curve F from the shock absorber to the human ear ntf ；
A third obtaining module 60, configured to install the shock absorber on the real vehicle, and obtain a subjective evaluation score value for scoring abnormal sound of the shock absorber when the real vehicle runs on a road surface;
an executing module 70, configured to obtain, for each shock absorber, K corresponding to each shock absorber through the first obtaining module 10, the first calculating module 20, the second calculating module 30, the coupling module 40, the second obtaining module 50, and the third obtaining module 60 respectively d 、P max 、P RMS 、F ntf And a subjective evaluation score value;
training module 80 for K for each shock absorber d 、P max 、P RMS 、F ntf And the subjective evaluation score value is used as a group of training data, and the neural network model is processed based on a plurality of groups of training dataPerforming training to obtain a trained neural network model;
a fourth obtaining module 90, configured to obtain K corresponding to the shock absorber to be tested d 、P max 、P RMS And F ntf And inputting the trained neural network model to obtain the value of the predictive subjective evaluation score of the shock absorber to be tested.
Further, in an embodiment, the second calculating module 30 is configured to:
carrying out Fourier transform on the vibration signal of the vibration absorber to obtain a frequency domain result;
substituting the frequency domain result into a first preset formula, and calculating to obtain the frequency f i And frequency f j RMS value P within the interval RMS Wherein, the first preset formula is as follows:
Figure BDA0003695281490000111
wherein, P RMS Is a frequency f i And frequency f j The RMS value within the interval is,
Figure BDA0003695281490000112
for the result of the ith frequency domain,
Figure BDA0003695281490000113
for the result of the k-th frequency domain,
Figure BDA0003695281490000114
the jth frequency domain result.
Further, in an embodiment, the second obtaining module 50 is configured to:
applying an acting force at a shock absorber mounting point on a finished automobile CAE model, calculating sound pressure response at the ear position in the automobile through preset software, and obtaining a noise transfer function curve F from the shock absorber to the ear ntf 。
Further, in one embodiment, the subjective evaluation score value includes subjective evaluation score values of the shock absorber under different environments, and one environment is determined by a vehicle CAE model and a road surface.
Further, in an embodiment, the training module 80 is configured to:
selecting a plurality of groups of training data according to a preset proportion to train and verify the neural network model;
and if the verification is passed, obtaining the trained neural network model.
The function implementation of each module in the device for evaluating and predicting the abnormal sound of the shock absorber corresponds to each step in the embodiment of the method for evaluating and predicting the abnormal sound of the shock absorber, and the function and the implementation process are not repeated herein.
In a fourth aspect, the embodiment of the present invention further provides a readable storage medium.
The readable storage medium of the invention stores the abnormal vibration damper sound evaluation and prediction program, wherein when the abnormal vibration damper sound evaluation and prediction program is executed by a processor, the steps of the abnormal vibration damper sound evaluation and prediction method are realized.
The method implemented when the shock absorber abnormal sound evaluation and prediction program is executed can refer to each embodiment of the shock absorber abnormal sound evaluation and prediction method of the present invention, and details are not repeated here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or system comprising the element.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for causing a terminal device to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. An abnormal vibration damper noise evaluation and prediction method, characterized by comprising:
s10, obtaining a dynamic stiffness curve K of an upper supporting piece of the shock absorber d ；
S20, acquiring a time domain peak value of the vibration signal at the upper end of the piston rod of the vibration damper bench test in each period, and calculating an average value P of the time domain peak values of the vibration signal at the upper end of the piston rod of the vibration damper bench test in each period max ；
S30, obtaining the frequency f through calculation according to the vibration signal of the vibration damper i And frequency f j RMS value P within the interval RMS ；
S40, coupling the finite element model of the whole vehicle structure with the sound cavity model in the vehicle to obtain a CAE model of the whole vehicle;
s50, applying acting force at a shock absorber mounting point on the CAE model of the whole vehicle to obtain a noise transfer function curve F from the shock absorber to human ears ntf ；
S60, mounting the shock absorber on the real vehicle, and obtaining the subjective evaluation score value of the real vehicle for scoring the abnormal sound of the shock absorber when the real vehicle runs on the road surface;
s70, respectively executing the steps S10 to S60 to obtain K corresponding to each shock absorber d 、P max 、P RMS 、F ntf And a subjective evaluation score value;
s80, using K corresponding to each vibration damper d 、P max 、P RMS 、F ntf The subjective evaluation score value is used as a group of training data, and the neural network model is trained based on a plurality of groups of training data to obtain a trained neural network model;
s90, enabling the vibration absorber to be tested to correspond to K d 、P max 、P RMS And F ntf And inputting the trained neural network model to obtain the value of the predictive subjective evaluation score of the shock absorber to be tested.
2. The method for evaluating and predicting abnormal vibration of a shock absorber according to claim 1, wherein the frequency f is obtained by calculation based on the vibration signal of the shock absorber i And frequency f j RMS value P within the interval RMS The method comprises the following steps:
carrying out Fourier transform on the vibration signal of the vibration absorber to obtain a frequency domain result;
substituting the frequency domain result into a first preset formula, and calculating to obtain the frequency f i And frequency f j RMS value P within the interval RMS Wherein, the first preset formula is as follows:
Figure FDA0003695281480000021
wherein, P RMS Is a frequency f i And frequency f j The RMS value in the interval is such that,
Figure FDA0003695281480000024
for the result of the ith frequency domain,
Figure FDA0003695281480000023
for the result of the k-th frequency domain,
Figure FDA0003695281480000022
the jth frequency domain result.
3. The method for evaluating and predicting the abnormal sound of the shock absorber according to claim 1, wherein the acting force is applied to the mounting point of the shock absorber on the CAE model of the whole vehicle to obtain a noise transfer function curve F from the shock absorber to the human ear ntf The method comprises the following steps:
applying an acting force at a shock absorber mounting point on a finished automobile CAE model, calculating sound pressure response at the ear position in the automobile through preset software, and obtaining a noise transfer function curve F from the shock absorber to the ear ntf 。
4. The method for evaluating and predicting abnormal vibration damper response according to claim 1, wherein the subjective evaluation score values include subjective evaluation score values of the vibration damper under different environments, and one environment is determined by a vehicle CAE model and a road surface.
5. The method for evaluating and predicting abnormal vibration damper response according to claim 1, wherein the step of training the neural network model based on the plurality of sets of training data to obtain a trained neural network model comprises:
selecting a plurality of groups of training data according to a preset proportion to train and verify the neural network model;
and if the verification is passed, obtaining the trained neural network model.
6. An abnormal vibration damper evaluation and prediction device, characterized by comprising:
a first obtaining module for obtaining a dynamic stiffness curve K of the upper support piece of the shock absorber d ；
The first calculation module is used for acquiring a time domain peak value of the vibration signal at the upper end of the piston rod of the shock absorber bench test in each period and calculating an average value P of the time domain peak values of the vibration signal at the upper end of the piston rod of the shock absorber bench test in each period max ；
A second calculation module for calculating a vibration signal of the vibration damperThe frequency f is obtained by calculation i And frequency f j RMS value P within the interval RMS ；
The coupling module is used for coupling the finite element model of the whole vehicle structure and the sound cavity model in the vehicle to obtain a CAE model of the whole vehicle;
the second acquisition module is used for applying acting force at the mounting point of the shock absorber on the CAE model of the whole vehicle to acquire a noise transfer function curve F from the shock absorber to human ears ntf ；
The third acquisition module is used for installing the shock absorber on the real vehicle and acquiring the subjective evaluation score value of the real vehicle for scoring the abnormal sound of the shock absorber when the real vehicle runs on the road surface;
an execution module, configured to obtain, for each shock absorber, K corresponding to each shock absorber through the first obtaining module, the first calculating module, the second calculating module, the coupling module, the second obtaining module, and the third obtaining module d 、P max 、P RMS 、F ntf And a subjective evaluation score value;
a training module for K corresponding to each shock absorber d 、P max 、P RMS 、F ntf The subjective evaluation score value is used as a group of training data, and the neural network model is trained based on a plurality of groups of training data to obtain a trained neural network model;
a fourth acquisition module for acquiring the K corresponding to the vibration damper to be tested d 、P max 、P RMS And F ntf And inputting the trained neural network model to obtain the value of the predictive subjective evaluation score of the shock absorber to be tested.
7. The abnormal vibration damper response evaluation and prediction apparatus according to claim 6, wherein the second calculation module is specifically configured to:
carrying out Fourier transform on the vibration signal of the vibration absorber to obtain a frequency domain result;
substituting the frequency domain result into a first preset formula, and calculating to obtain the frequency f i And frequency f j RMS value P within the interval RMS Wherein, the first preset formula is as follows:
Figure FDA0003695281480000031
wherein, P RMS Is a frequency f i And frequency f j The RMS value within the interval is,
Figure FDA0003695281480000032
for the result of the ith frequency domain,
Figure FDA0003695281480000033
for the result of the k-th frequency domain,
Figure FDA0003695281480000034
the jth frequency domain result.
8. The abnormal sound evaluation and prediction device of a shock absorber according to claim 6, wherein the second acquisition module is configured to:
applying an acting force at a shock absorber mounting point on a finished automobile CAE model, calculating sound pressure response at the ear position in the automobile through preset software, and obtaining a noise transfer function curve F from the shock absorber to the ear ntf 。
9. An abnormal vibration damper response evaluation and prediction apparatus characterized by comprising a processor, a memory, and an abnormal vibration damper response evaluation and prediction program stored on the memory and executable by the processor, wherein the abnormal vibration damper response evaluation and prediction program when executed by the processor implements the steps of the abnormal vibration damper response evaluation and prediction method according to any one of claims 1 to 5.
10. A readable storage medium, characterized in that the readable storage medium has stored thereon an abnormal vibration damper response evaluation prediction program, wherein the abnormal vibration damper response evaluation prediction program, when executed by a processor, realizes the steps of the abnormal vibration damper response evaluation prediction method according to any one of claims 1 to 5.
CN202210677362.9A 2022-06-15 2022-06-15 Method, device and equipment for evaluating and predicting abnormal sound of shock absorber and readable storage medium Pending CN115292801A (en)

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