Shock absorber abnormal noise evaluation prediction method, device, equipment and readable storage medium

Abstract

The present invention provides a method, device, equipment and readable storage medium for evaluating and predicting abnormal noise of a shock absorber. The method comprises: obtaining the average value P of the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle max , frequency f i and frequency f j RMS value P in the interval RMS , Dynamic stiffness curve K of the upper support of the shock absorber d , Noise transfer function curve from the shock absorber installation point to the human ear F ntf and the subjective evaluation score of the abnormal noise of the shock absorber on the actual vehicle; for each shock absorber, perform the above data acquisition steps respectively to obtain the K corresponding to each shock absorber d , P max , P RMS 、F ntf And subjective evaluation score; based on the K corresponding to each shock absorber d , P max , P RMS 、F ntf The neural network model is trained based on the subjective evaluation score value to obtain a trained neural network model; the predicted subjective evaluation score value of the shock absorber to be tested is obtained through the trained neural network model. The present invention solves the problem of high cost in adjusting the shock absorber in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of automobile NVH (Noise, Vibration, Harshness) performance, and in particular to a shock absorber abnormal noise evaluation prediction method, device, equipment and readable storage medium. Background Art

[0002] The shock absorber is an important component in the vehicle suspension system and has a significant impact on the smoothness and comfort of the vehicle. When the vehicle passes over uneven roads, the shock absorber will repeatedly stretch and compress. This switching process will generate impact and vibration, which will be transmitted through the shock absorber piston rod to cause vehicle body vibration, and then generate noise. This noise is the abnormal sound of the shock absorber.

[0003] There are many factors that affect the abnormal noise of shock absorbers. At present, shock absorbers are generally tested through two methods: shock absorber bench test or vehicle road test. However, the shock absorber bench test does not take into account the influence of vehicle loading factors and cannot accurately predict the abnormal noise of shock absorbers on vehicles. The vehicle road test can only adjust the shock absorbers in the later stage of project development, which is inefficient and costly. Summary of the invention

[0004] The main purpose of the present invention is to provide a shock absorber abnormal noise evaluation prediction method, device, equipment and readable storage medium, aiming to solve the problem that the abnormal noise generated by the shock absorber after being installed on the whole vehicle cannot be predicted in the early stage of project development in the prior art, and the shock absorber can only be adjusted in the later stage of project development, resulting in low test efficiency and high cost.

[0005] In a first aspect, the present invention provides a method for evaluating and predicting abnormal noise of a shock absorber, the method comprising:

[0006] S10, obtaining the dynamic stiffness curve K of the upper support of the shock absorber d ;

[0007] S20, obtaining the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle, and calculating the average value P of the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle max ;

[0008] S30, obtaining the frequency f by calculation according to the vibration signal of the shock absorber i and frequency f j RMS value P in the interval RMS ;

[0009] S40, coupling the whole vehicle structure finite element model and the vehicle interior acoustic cavity model to obtain a whole vehicle CAE model;

[0010] S50, applying force to the shock absorber mounting point on the vehicle CAE model to obtain the noise transfer function curve F from the shock absorber to the human ear ntf ;

[0011] S60, installing the shock absorber on a real vehicle, and obtaining a subjective evaluation score of abnormal noise of the shock absorber when the real vehicle is driving on a road;

[0012] S70: for each shock absorber, execute the above S10 to S60 respectively to obtain the K corresponding to each shock absorber. d , P max , P RMS 、F ntf and subjective evaluation score values;

[0013] S80, K corresponding to each shock absorber d , P max , P RMS 、F ntf and subjective evaluation score values ​​as a set of training data, training the neural network model based on multiple sets of training data to obtain a trained neural network model;

[0014] S90, set the K corresponding to the shock absorber to be tested d , P max , P RMS and F ntf The trained neural network model is input to obtain the predicted subjective evaluation score of the shock absorber to be tested.

[0015] Optionally, the frequency f is obtained by calculation according to the vibration signal of the shock absorber i and frequency f j RMS value P in the interval RMS The steps include:

[0016] Perform Fourier transform on the vibration signal of the shock absorber to obtain the frequency domain result;

[0017] Substitute the frequency domain result into the first preset formula to calculate the frequency f i and frequency f j RMS value P in the interval RMS , where the first preset formula is as follows:

[0018]

[0019] Among them, P RMS is the frequency f i and frequency f j The RMS value within the interval, is the ith frequency domain result, is the kth frequency domain result, The jth frequency domain result.

[0020] Optionally, the force is applied to the shock absorber mounting point on the vehicle CAE model to obtain the noise transfer function curve F from the shock absorber to the human ear. ntf The steps include:

[0021] Apply force to the shock absorber mounting point on the vehicle CAE model, calculate the sound pressure response at the human ear in the vehicle through the preset software, and obtain the noise transfer function curve F from the shock absorber to the human ear ntf .

[0022] Optionally, the subjective evaluation score value includes subjective evaluation score values ​​of the shock absorber in different environments, where an environment is determined by a whole vehicle CAE model and a road surface.

[0023] Optionally, the step of training the neural network model based on multiple sets of training data to obtain a trained neural network model includes:

[0024] Select multiple sets of training data according to preset ratios to train and verify the neural network model;

[0025] If the verification passes, the trained neural network model is obtained.

[0026] In a second aspect, the present invention further provides a device for evaluating and predicting abnormal noise of a shock absorber, the device comprising:

[0027] The first acquisition module is used to obtain the dynamic stiffness curve K of the upper support of the shock absorber d ;

[0028] The first calculation module is used to obtain the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle, and calculate the average value P of the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle. max ;

[0029] The second calculation module is used to calculate the frequency f according to the vibration signal of the shock absorber i and frequency f j RMS value P in the interval RMS ;

[0030] The coupling module is used to couple the vehicle structure finite element model and the vehicle interior acoustic cavity model to obtain the vehicle CAE model;

[0031] The second acquisition module is used to apply force at the shock absorber installation point on the vehicle CAE model to obtain the noise transfer function curve F from the shock absorber to the human ear. ntf ;

[0032] The third acquisition module is used to install the shock absorber on the actual vehicle and obtain the subjective evaluation score of the abnormal noise of the shock absorber when the actual vehicle is driving on the road;

[0033] The execution module is used to obtain the K corresponding to each shock absorber through the first acquisition module, the first calculation module, the second calculation module, the coupling module, the second acquisition module and the third acquisition module. d , P max , P RMS 、F ntf and subjective evaluation score values;

[0034] The training module is used to train the K d , P max , P RMS 、F ntf and subjective evaluation score values ​​as a set of training data, training the neural network model based on multiple sets of training data to obtain a trained neural network model;

[0035] The fourth acquisition module is used to obtain the K corresponding to the shock absorber to be tested. d , P max , P RMS and F ntf The trained neural network model is input to obtain the predicted subjective evaluation score of the shock absorber to be tested.

[0036] Optionally, the second computing module is specifically configured to:

[0037] Perform Fourier transform on the vibration signal of the shock absorber to obtain the frequency domain result;

[0038] Substitute the frequency domain result into the first preset formula to calculate the frequency f i and frequency f j RMS value P in the interval RMS , where the first preset formula is as follows:

[0039]

[0040] Among them, P RMS is the frequency f i and frequency f j The RMS value within the interval, is the ith frequency domain result, is the kth frequency domain result, The jth frequency domain result.

[0041] Optionally, the second acquisition module is used to:

[0042] Apply force to the shock absorber mounting point on the vehicle CAE model, calculate the sound pressure response at the human ear in the vehicle through the preset software, and obtain the noise transfer function curve F from the shock absorber to the human ear ntf .

[0043] In a third aspect, the present invention also provides a shock absorber abnormal noise evaluation prediction device, which includes a processor, a memory, and a shock absorber abnormal noise evaluation prediction program stored in the memory and executable by the processor, wherein when the shock absorber abnormal noise evaluation prediction program is executed by the processor, the steps of the shock absorber abnormal noise evaluation prediction method as described above are implemented.

[0044] In a fourth aspect, the present invention further provides a readable storage medium, on which a shock absorber abnormal noise evaluation prediction program is stored, wherein when the shock absorber abnormal noise evaluation prediction program is executed by a processor, the steps of the shock absorber abnormal noise evaluation prediction method as described above are implemented.

[0045] In the present invention, S10, obtaining the dynamic stiffness curve K of the upper support of the shock absorber d ; S20, obtain the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle, and calculate the average value P of the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle max ; S30, obtain the frequency f by calculation according to the vibration signal of the shock absorber i and frequency f j RMS value P in the interval RMS ; S40, coupling the vehicle structure finite element model and the vehicle interior acoustic cavity model to obtain the vehicle CAE model; S50, applying a force at the shock absorber mounting point on the vehicle CAE model to obtain the noise transfer function curve F from the shock absorber to the human ear ntf ; S60, install the shock absorber on the actual vehicle, and obtain the subjective evaluation score of the shock absorber abnormal noise when the actual vehicle is driving on the road; S70, for each shock absorber, respectively execute the above S10 to S60 to obtain the K corresponding to each shock absorber d , P max , P RMS 、F ntf And subjective evaluation score; S80, K corresponding to each shock absorber d , P max , P RMS 、F ntf and subjective evaluation score values ​​as a set of training data, and the neural network model is trained based on the multiple sets of training data to obtain a trained neural network model; S90, the K corresponding to the shock absorber to be tested is d , P max , P RMS and Fntf The trained neural network model is input to obtain the predicted subjective evaluation score of the shock absorber to be tested. d , P max , P RMS 、F ntf And the subjective evaluation score, the K corresponding to each shock absorber d , P max , P RMS 、F ntf and subjective evaluation scores as a set of training data, and the neural network model is trained based on multiple sets of training data to obtain a trained neural network model. d , P max , P RMS and F ntf By inputting the trained neural network model, the predicted subjective evaluation score of the shock absorber to be tested can be obtained. It can predict the abnormal noise generated by the shock absorber after it is installed on the whole vehicle in the early stage of project development, so as to adjust the shock absorber. This solves the problem that the shock absorber can only be adjusted in the late stage of project development in the prior art, and it is impossible to accurately predict the abnormal noise of the shock absorber on the vehicle in the early stage of project development, resulting in high cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the hardware structure of the shock absorber abnormal noise evaluation and prediction device involved in the embodiment of the present invention;

[0047] Figure 2 It is a flow chart of an embodiment of a method for evaluating and predicting abnormal noise of a shock absorber according to the present invention;

[0048] Figure 3 It is a schematic diagram of a shock absorber bench test of the shock absorber abnormal noise evaluation prediction method of the present invention;

[0049] Figure 4 for Figure 2 Detailed flow chart of step S30;

[0050] Figure 5 The figure is a functional module diagram of an embodiment of a device for evaluating and predicting abnormal noise of a shock absorber according to the present invention.

[0051] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0052] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0053] In a first aspect, an embodiment of the present invention provides a device for evaluating and predicting abnormal noise of a shock absorber.

[0054] Reference Figure 1 , Figure 1 The hardware structure diagram of the shock absorber abnormal noise evaluation prediction device involved in the embodiment of the present invention. In the embodiment of the present invention, the shock absorber abnormal noise evaluation prediction device may include a processor 1001 (such as a central processing unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these 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 (such as a wireless fidelity WIreless-FIdelity, WI-FI interface); the memory 1005 may be a high-speed random access memory (random access memory, RAM), or a stable memory (non-volatile memory), such as a disk storage, and the memory 1005 may optionally be a storage device independent of the aforementioned processor 1001. Those skilled in the art will understand that Figure 1 The hardware structure shown in the figure does not constitute a limitation of the present invention, and may include more or less components than those shown in the figure, or combine certain components, or arrange the components differently.

[0055] Continue to refer to Figure 1 , Figure 1 The memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a shock absorber abnormal noise evaluation prediction program. The processor 1001 may call the shock absorber abnormal noise evaluation prediction program stored in the memory 1005 and execute the shock absorber abnormal noise evaluation prediction method provided in the embodiment of the present invention.

[0056] In a second aspect, an embodiment of the present invention provides a method for evaluating and predicting abnormal noise of a shock absorber.

[0057] In one embodiment, referring to Figure 2 , Figure 2 FIG. 1 is a flow chart of an embodiment of a method for evaluating and predicting abnormal noise of a shock absorber according to the present invention. Figure 2 As shown, the shock absorber abnormal noise evaluation prediction method includes:

[0058] Step S10, obtaining the dynamic stiffness curve K of the upper support of the shock absorber d ;

[0059] In this embodiment, the dynamic stiffness curve K of the upper support of the shock absorber is obtained by a dynamic stiffness test. dThe top mount of the shock absorber is used to connect the shock absorber and the vehicle body. It has a buffer rubber sleeve inside, which has a vibration isolation effect. Since the dynamic stiffness value of rubber parts is greatly affected by factors such as temperature, the influence of temperature can also be considered when testing the dynamic stiffness of the shock absorber top mount. The dynamic stiffness curves under different temperature conditions are measured to match the temperature conditions of the shock absorber bench test.

[0060] Step S20, obtaining the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle, and calculating the average value P of the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle max ;

[0061] In this embodiment, refer to Figure 3 , Figure 3 This is a schematic diagram of the shock absorber bench test of the shock absorber abnormal noise evaluation prediction method of the present invention. Figure 3 As shown, the shock absorber is fixed on the test bench for bench testing, and a displacement excitation with a fixed frequency and amplitude is applied below the shock absorber test bench, such as a signal with a frequency of 13 Hz and an amplitude of 5 mm. The vibration signal {x n After obtaining the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle, the average value P of the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle is obtained by calculation. max .

[0062] Furthermore, to address the problem of worsening shock noise of the shock absorber under low temperature conditions, a temperature sensor can be attached to the wall of the shock absorber tube, and the vibration response under different temperature conditions can be tested in an environmental chamber.

[0063] Step S30, obtaining the frequency f by calculation according to the vibration signal of the shock absorber i and frequency f j RMS value P in the interval RMS ;

[0064] In this embodiment, the vibration signal {x n}, according to the vibration signal of the shock absorber {x n}The frequency f can be obtained by calculation i and frequency f j RMS value P in the interval RMS .

[0065] Further, in one embodiment, referring to Figure 4 , Figure 4 for Figure 2 Detailed flow chart of step S30 in FIG. Figure 4 As shown, step S30 includes:

[0066] Step S301, performing Fourier transform on the vibration signal of the shock absorber to obtain a frequency domain result;

[0067] Step S302: Substitute the frequency domain result into the first preset formula to calculate the frequency f i and frequency f j RMS value P in the interval RMS , where the first preset formula is as follows:

[0068]

[0069] Among them, P RMS is the frequency f i and frequency f j The RMS value within the interval, is the ith frequency domain result, is the kth frequency domain result, The jth frequency domain result.

[0070] In this embodiment, the vibration signal of the shock absorber is substituted into the formula Perform Fourier transform to obtain the frequency domain result x f , where N is the length of the vibration signal of the shock absorber and f is the frequency of the vibration signal of the shock absorber.

[0071] Substitute the frequency domain result into the first preset formula to calculate the frequency f i and frequency f j RMS value P in the interval RMS , where the first preset formula is as follows:

[0072]

[0073] Among them, P RMS is the frequency f i and frequency f j The RMS value within the interval, is the ith frequency domain result, is the kth frequency domain result, The jth frequency domain result. Frequency f i and frequency f j This is the main frequency range of the shock absorber switching impact abnormal noise, reference 300Hz-600Hz.

[0074] Step S40, coupling the whole vehicle structure finite element model and the vehicle interior acoustic cavity model to obtain a whole vehicle CAE model;

[0075] In this embodiment, a finite element model of the whole vehicle structure except for chassis components and a model of the interior acoustic cavity are established through commercial software such as Hypermesh, and the finite element model of the whole vehicle structure and the interior acoustic cavity model are coupled to obtain a CAE model of the whole vehicle. CAE is the abbreviation of Computer Aided Engineering, which is an approximate numerical analysis method for solving complex engineering and product structural strength, stiffness, buckling stability, dynamic response, heat conduction, three-dimensional multi-body contact, elastic-plastic and other mechanical properties analysis and calculation, as well as structural performance optimization design and other problems with computer-aided solutions.

[0076] Step S50: applying a force to the shock absorber mounting point on the vehicle CAE model to obtain a noise transfer function curve F from the shock absorber to the human ear. ntf ;

[0077] In this embodiment, based on the vehicle CAE model, an excitation of 1N is applied at the shock absorber mounting point, that is, a force is applied at the shock absorber mounting point on the vehicle CAE model to obtain the noise transfer function curve F from the shock absorber to the human ear. ntf Among them, NTF (noise transfer function) is the abbreviation of noise transfer function, and this method is to analyze and calculate the transfer function of the structure.

[0078] Furthermore, in one embodiment, step S50 includes:

[0079] Apply force to the shock absorber mounting point on the vehicle CAE model, calculate the sound pressure response at the human ear in the vehicle through the preset software, and obtain the noise transfer function curve F from the shock absorber to the human ear ntf .

[0080] In this embodiment, the preset software is Hypermesh. A force is applied to the shock absorber installation point on the vehicle CAE model. The preset software Hypermesh is used to calculate the sound pressure response at the human ear in the vehicle, and the noise transfer function curve F from the shock absorber to the human ear at each installation point is obtained. ntf Among them, Hypermesh software is a powerful CAE application software package and an innovative, open enterprise-level CAE platform that integrates various tools required for design and analysis, with unparalleled performance and a high degree of openness, flexibility and a user-friendly interface.

[0081] Step S60, installing the shock absorber on a real vehicle, and obtaining a subjective evaluation score of abnormal noise of the shock absorber when the real vehicle is driving on a road;

[0082] In this embodiment, the road surface is a typical abnormal noise road surface, such as a Belgian road surface, and professional engineers conduct subjective evaluation and scoring of the abnormal noise of the shock absorber. The shock absorber is installed on a real vehicle, and the subjective evaluation score of the abnormal noise of the shock absorber is obtained when the real vehicle is driving on the Belgian road surface.

[0083] Step S70: for each shock absorber, execute the above steps S10 to S60 respectively to obtain the K corresponding to each shock absorber. d , P max , P RMS 、F ntf and subjective evaluation scores;

[0084] In this embodiment, for each shock absorber, steps S10 to S60 are performed respectively to obtain the K corresponding to each shock absorber. d , P max , P RMS 、F ntf Specifically, for the four shock absorbers, steps S10 to S60 are performed respectively to obtain the K corresponding to shock absorber 1. d , P max , P RMS 、F ntf And the subjective evaluation score, K corresponding to shock absorber 2 d , P max , P RMS 、F ntf And the subjective evaluation score, K corresponding to shock absorber 3 d , P max , P RMS 、F ntf And the subjective evaluation score and the K corresponding to the shock absorber 4 d , P max , P RMS 、F ntf As well as the subjective evaluation score value. It is easy to imagine that the number of shock absorbers in this embodiment is only for reference and is not limited here.

[0085] Step S80: using the K corresponding to each shock absorber d , P max , P RMS 、F ntf and subjective evaluation score values ​​as a set of training data, training the neural network model based on multiple sets of training data to obtain a trained neural network model;

[0086] In this embodiment, the K corresponding to the shock absorber 1 is d , P max , P RMS 、F ntf The subjective evaluation score is the first group of data, and the K corresponding to shock absorber 2 isd , P max , P RMS 、F ntf The subjective evaluation score is the second set of data, and the K corresponding to shock absorber 3 is d , P max , P RMS 、F ntf The subjective evaluation score is the third set of data, and the K corresponding to shock absorber 4 is d , P max , P RMS 、F ntf The subjective evaluation score is the fourth set of data. The neural network model is trained based on the four sets of training data to obtain a trained neural network model. It is easy to understand that the number of sets of training data in this embodiment is for reference only and is not limited here.

[0087] Furthermore, in one embodiment, the step of training the neural network model based on multiple sets of training data to obtain a trained neural network model includes:

[0088] Select multiple sets of training data according to preset ratios to train and verify the neural network model;

[0089] If the verification passes, the trained neural network model is obtained.

[0090] In this embodiment, multiple sets of training data are selected to train and verify the neural network model according to the preset ratio. In this embodiment, if the preset ratio is 1:3, 3 sets of training data are selected to train the neural network model, and the remaining 1 set of training data is used to verify the trained neural network model. If the verification is passed, the training of the neural network model is completed, and a trained neural network model is obtained. If the verification fails, additional training data is required or the preset ratio or neural network fitting accuracy needs to be adjusted. It is easy to understand that the preset ratio in this embodiment is for reference only and is not limited here.

[0091] Step S90: The K corresponding to the shock absorber to be tested is d , P max , P RMS and F ntf The trained neural network model is input to obtain the predicted subjective evaluation score of the shock absorber to be tested.

[0092] In this embodiment, the K corresponding to the shock absorber to be tested is d , P max , P RMS and F ntfBy inputting the trained neural network model, the predicted subjective evaluation score of the shock absorber to be tested output by the trained neural network model is obtained, which realizes the early prediction of the actual vehicle evaluation effect and pre-empts the abnormal noise development work of the shock absorber.

[0093] In this embodiment, S10, obtain the dynamic stiffness curve K of the upper support of the shock absorber d ; S20, obtain the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle, and calculate the average value P of the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle max ; S30, obtain the frequency f by calculation according to the vibration signal of the shock absorber i and frequency f j RMS value P in the interval RMS ; S40, coupling the vehicle structure finite element model and the vehicle interior acoustic cavity model to obtain the vehicle CAE model; S50, applying a force at the shock absorber mounting point on the vehicle CAE model to obtain the noise transfer function curve F from the shock absorber to the human ear ntf ; S60, install the shock absorber on the actual vehicle, and obtain the subjective evaluation score of the shock absorber abnormal noise when the actual vehicle is driving on the road; S70, for each shock absorber, respectively execute the above S10 to S60 to obtain the K corresponding to each shock absorber d , P max , P RMS 、F ntf And subjective evaluation score; S80, K corresponding to each shock absorber d , P max , P RMS 、F ntf and subjective evaluation score values ​​as a set of training data, and the neural network model is trained based on the multiple sets of training data to obtain a trained neural network model; S90, the K corresponding to the shock absorber to be tested is d , P max , P RMS and F ntf The trained neural network model is input to obtain the predicted subjective evaluation score of the shock absorber to be tested. Through this embodiment, the K corresponding to each shock absorber is obtained. d , P max , P RMS 、F ntf And the subjective evaluation score, the K corresponding to each shock absorber d , P max , P RMS 、F ntf and subjective evaluation scores as a set of training data, and the neural network model is trained based on multiple sets of training data to obtain a trained neural network model.d , P max , P RMS and F ntf By inputting the trained neural network model, the predicted subjective evaluation score of the shock absorber to be tested can be obtained. It can predict the abnormal noise generated by the shock absorber after it is installed on the whole vehicle in the early stage of project development, so as to adjust the shock absorber. This solves the problem that the shock absorber can only be adjusted in the late stage of project development in the prior art, and it is impossible to accurately predict the abnormal noise of the shock absorber on the vehicle in the early stage of project development, resulting in high cost.

[0094] Furthermore, in one embodiment, the subjective evaluation score value includes a subjective evaluation score value of the shock absorber in different environments, and an environment is determined by a whole vehicle CAE model and a road surface.

[0095] In this embodiment, the subjective evaluation score value includes the subjective evaluation score value of the shock absorber in different environments, wherein an environment is determined by a whole vehicle CAE model and a road surface. For example:

[0096] Environment 1 is the whole vehicle CAE model 1 plus road surface 1, environment 2 is the whole vehicle CAE model 1 plus road surface 2, environment 3 is the whole vehicle CAE model 2 plus road surface 1, environment 4 is the whole vehicle CAE model 2 plus road surface 2, and so on. That is, the whole vehicle CAE model and / or road surface are different between different environments.

[0097] In a third aspect, an embodiment of the present invention further provides a device for evaluating and predicting abnormal noise of a shock absorber.

[0098] In one embodiment, referring to Figure 5 , Figure 5 Schematic diagram of the functional modules of an embodiment of the shock absorber abnormal noise evaluation and prediction device of the present invention. Figure 5 As shown, the shock absorber abnormal noise evaluation prediction device comprises:

[0099] The first acquisition module 10 is used to obtain the dynamic stiffness curve K of the upper support of the shock absorber. d ;

[0100] The first calculation module 20 is used to obtain the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle, and calculate the average value P of the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle. max ;

[0101] The second calculation module 30 is used to calculate the frequency f according to the vibration signal of the shock absorber i and frequency f j RMS value P in the interval RMS ;

[0102] A coupling module 40 is used to couple the whole vehicle structure finite element model and the vehicle interior acoustic cavity model to obtain a whole vehicle CAE model;

[0103] The second acquisition module 50 is used to apply a force at the shock absorber installation point on the vehicle CAE model to obtain the noise transfer function curve F from the shock absorber to the human ear. ntf ;

[0104] The third acquisition module 60 is used to install the shock absorber on a real vehicle and obtain a subjective evaluation score of abnormal noise of the shock absorber when the real vehicle is driving on a road;

[0105] The execution module 70 is used to obtain the K corresponding to each shock absorber through the first acquisition module 10, the first calculation module 20, the second calculation module 30, the coupling module 40, the second acquisition module 50 and the third acquisition module 60. d , P max , P RMS 、F ntf and subjective evaluation scores;

[0106] The training module 80 is used to train the K corresponding to each shock absorber. d , P max , P RMS 、F ntf and subjective evaluation score values ​​as a set of training data, training the neural network model based on multiple sets of training data to obtain a trained neural network model;

[0107] The fourth acquisition module 90 is used to obtain the K corresponding to the shock absorber to be tested. d , P max , P RMS and F ntf The trained neural network model is input to obtain the predicted subjective evaluation score of the shock absorber to be tested.

[0108] Furthermore, in one embodiment, the second calculation module 30 is used to:

[0109] Perform Fourier transform on the vibration signal of the shock absorber to obtain the frequency domain result;

[0110] Substitute the frequency domain result into the first preset formula to calculate the frequency f i and frequency f j RMS value P in the interval RMS , where the first preset formula is as follows:

[0111]

[0112] Among them, P RMS is the frequency f i and frequency fj The RMS value within the interval, is the ith frequency domain result, is the kth frequency domain result, The jth frequency domain result.

[0113] Furthermore, in one embodiment, the second acquisition module 50 is used to:

[0114] Apply force to the shock absorber mounting point on the vehicle CAE model, calculate the sound pressure response at the human ear in the vehicle through the preset software, and obtain the noise transfer function curve F from the shock absorber to the human ear ntf .

[0115] Furthermore, in one embodiment, the subjective evaluation score value includes a subjective evaluation score value of the shock absorber in different environments, and an environment is determined by a whole vehicle CAE model and a road surface.

[0116] Furthermore, in one embodiment, the training module 80 is used to:

[0117] Select multiple sets of training data according to preset ratios to train and verify the neural network model;

[0118] If the verification passes, the trained neural network model is obtained.

[0119] Among them, the functional implementation of each module in the above-mentioned shock absorber abnormal noise evaluation and prediction device corresponds to the various steps in the above-mentioned shock absorber abnormal noise evaluation and prediction method embodiment, and its functions and implementation processes will not be repeated here one by one.

[0120] In a fourth aspect, an embodiment of the present invention further provides a readable storage medium.

[0121] The readable storage medium of the present invention stores a shock absorber abnormal noise evaluation prediction program, wherein when the shock absorber abnormal noise evaluation prediction program is executed by a processor, the steps of the shock absorber abnormal noise evaluation prediction method as described above are implemented.

[0122] Among them, the method implemented when the shock absorber abnormal noise evaluation prediction program is executed can refer to the various embodiments of the shock absorber abnormal noise evaluation prediction method of the present invention, and will not be repeated here.

[0123] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0124] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0125] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present invention.

[0126] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for evaluating and predicting abnormal noise of a shock absorber, characterized in that: The shock absorber abnormal noise evaluation prediction method comprises: S10, obtaining the dynamic stiffness curve K of the upper support of the shock absorber d ; S20, obtaining the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle, and calculating the average value P of the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle max ; S30, obtaining the frequency f by calculation according to the vibration signal of the shock absorber i and frequency f j RMS value P in the interval RMS ; S40, coupling the whole vehicle structure finite element model and the vehicle interior acoustic cavity model to obtain a whole vehicle CAE model; S50, applying force to the shock absorber mounting point on the vehicle CAE model to obtain the noise transfer function curve F from the shock absorber to the human ear ntf ; S60, installing the shock absorber on a real vehicle, and obtaining a subjective evaluation score of abnormal noise of the shock absorber when the real vehicle is driving on a road; S70: for each shock absorber, execute the above S10 to S60 respectively to obtain the K corresponding to each shock absorber. d , P max , P RMS 、F ntf and subjective evaluation scores; S80, K corresponding to each shock absorber d , P max , P RMS 、F ntf and subjective evaluation score values ​​as a set of training data, training the neural network model based on multiple sets of training data to obtain a trained neural network model; S90, the shock absorber to be tested K d , P max , P RMS and F ntf Input the trained neural network model to obtain the predicted subjective evaluation score of the shock absorber to be tested; The frequency f is obtained by calculation according to the vibration signal of the shock absorber i and frequency f j RMS value P in the interval RMS The steps include: Perform Fourier transform on the vibration signal of the shock absorber to obtain the frequency domain result; Substitute the frequency domain result into the first preset formula to calculate the frequency f i and frequency f j RMS value P in the interval RMS , where the first preset formula is as follows: Among them, P RMS is the frequency f i and frequency f j The RMS value within the interval, is the ith frequency domain result, is the kth frequency domain result, The jth frequency domain result.

2. The shock absorber abnormal noise evaluation prediction method according to claim 1, characterized in that: The force is applied to the shock absorber mounting point on the vehicle CAE model to obtain the noise transfer function curve F from the shock absorber to the human ear. ntf The steps include: Apply force to the shock absorber mounting point on the vehicle CAE model, calculate the sound pressure response at the human ear in the vehicle through the preset software, and obtain the noise transfer function curve F from the shock absorber to the human ear ntf .

3. The shock absorber abnormal noise evaluation prediction method according to claim 1, characterized in that: The subjective evaluation score values ​​include subjective evaluation score values ​​of the shock absorber in different environments, where an environment is determined by a whole vehicle CAE model and a road surface.

4. The shock absorber abnormal noise evaluation prediction method according to claim 1, characterized in that: The step of training the neural network model based on multiple sets of training data to obtain a trained neural network model includes: Select multiple sets of training data according to preset ratios to train and verify the neural network model; If the verification passes, the trained neural network model is obtained.

5. A device for evaluating and predicting abnormal noise of a shock absorber, characterized in that: The shock absorber abnormal noise evaluation and prediction device comprises: The first acquisition module is used to obtain the dynamic stiffness curve K of the upper support of the shock absorber d ; The first calculation module is used to obtain the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle, and calculate the average value P of the time domain peak value of the vibration signal of the upper end of the piston rod of the shock absorber bench test in each cycle. max ; The second calculation module is used to calculate the frequency f according to the vibration signal of the shock absorber i and frequency f j RMS value P in the interval RMS ; The coupling module is used to couple the vehicle structure finite element model and the vehicle interior acoustic cavity model to obtain the vehicle CAE model; The second acquisition module is used to apply force at the shock absorber installation point on the vehicle CAE model to obtain the noise transfer function curve F from the shock absorber to the human ear. ntf ; The third acquisition module is used to install the shock absorber on the actual vehicle and obtain the subjective evaluation score of the abnormal noise of the shock absorber when the actual vehicle is driving on the road; An execution module is used to obtain the K corresponding to each shock absorber through the first acquisition module, the first calculation module, the second calculation module, the coupling module, the second acquisition module and the third acquisition module for each shock absorber. d , P max , P RMS 、F ntf and subjective evaluation scores; The training module is used to train the K d , P max , P RMS 、F ntf and subjective evaluation score values ​​as a set of training data, training the neural network model based on multiple sets of training data to obtain a trained neural network model; The fourth acquisition module is used to obtain the shock absorber K d , P max , P RMS and F ntf Input the trained neural network model to obtain the predicted subjective evaluation score of the shock absorber to be tested; The second calculation module is specifically used for: Perform Fourier transform on the vibration signal of the shock absorber to obtain the frequency domain result; Substitute the frequency domain result into the first preset formula to calculate the frequency f i and frequency f j RMS value P in the interval RMS , where the first preset formula is as follows: Among them, P RMS is the frequency f i and frequency f j The RMS value within the interval, is the ith frequency domain result, is the kth frequency domain result, The jth frequency domain result.

6. The vibration absorber abnormal noise evaluation prediction device according to claim 5, characterized in that: The second acquisition module is used to: Apply force to the shock absorber mounting point on the vehicle CAE model, calculate the sound pressure response at the human ear in the vehicle through the preset software, and obtain the noise transfer function curve F from the shock absorber to the human ear ntf .

7. A shock absorber abnormal noise evaluation prediction device, characterized in that: The shock absorber abnormal noise evaluation prediction device includes a processor, a memory, and a shock absorber abnormal noise evaluation prediction program stored in the memory and executable by the processor, wherein when the shock absorber abnormal noise evaluation prediction program is executed by the processor, the steps of the shock absorber abnormal noise evaluation prediction method according to any one of claims 1 to 4 are implemented.

8. A readable storage medium, characterized in that: The readable storage medium stores a shock absorber abnormal noise evaluation prediction program, wherein when the shock absorber abnormal noise evaluation prediction program is executed by a processor, the steps of the shock absorber abnormal noise evaluation prediction method according to any one of claims 1 to 4 are implemented.

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