OPAX method based on pre-noise-reduction MMV compressed sensing

Abstract

The invention provides an OPAX method based on pre-noise reduction MMV compressed sensing. The method comprises the following steps: S1, establishing an excavator vibration transmission path analysis model from a power assembly to a cab steering wheel and a seat guide rail according to actual application requirements; and S2, performing denoising processing on the sample data through a wavelet denoising method. And S3, based on the de-noised sample data, constructing a learning dictionary through a K-SVD algorithm. And S4, constructing a pre-noise-reduction MMV compressed sensing framework suitable for excavator data based on the learning dictionary constructed in the step S3 and an MMV compressed sensing theory. And S5, obtaining working condition data of the excavator based on the pre-noise-reduction MMV compressed sensing frame in the fourth step. S6, based on a transmission path analysis model, establishing and improving an OPAX analysis model by using an OPAX method and the working condition data obtained in the step S5; the method can accurately find out the contribution of each path from the power assembly of the excavator to the steering wheel of the cab and the guide rail of the seat to noise reduction and the path which has the greatest influence on the noise reduction.

Description

Technical Field

[0001] The invention relates to the technical field of engineering machinery vibration noise, and in particular to an OPAX method based on pre-noise reduction MMV compressed sensing. Background Art

[0002] As competition in the excavator industry becomes increasingly fierce and user demands continue to increase, the NVH (Noise, Vibration, Harshness) performance requirements for excavators are becoming increasingly higher. Excessive vibration and noise will not only affect the service life and operating comfort of the excavator, but will also significantly damage the physical and mental health of the driver. Therefore, it is particularly important to reduce the vibration and noise levels of the excavator.

[0003] Due to the complex structure of excavators, the large number of vibration sources, and the diverse operating conditions, it is of great significance to determine the vibration sources and the main transfer paths. Transfer Path Analysis (TPA) can effectively solve the above problems. After years of development, many TPA methods have emerged, such as OTPA (Operational Transfer Path Analysis) and OPAX. Among them, OPAX takes into account both efficiency and accuracy and is widely used in engineering and scientific research fields.

[0004] OPAX establishes a parametric model to find out the contribution of each path from the vibration source to the target point and the path with the greatest impact, which can provide a certain reference for subsequent improvement and optimization. However, OPAX parametric modeling requires a large amount of working condition data, and the collection of working condition data is often mixed with noise signals, which affects the analysis accuracy of OPAX and makes it impossible to determine the exact contribution of each transmission path.

[0005] The traditional OPAX method is difficult to accurately find the contribution of each path from the excavator powertrain to the steering wheel and seat rails of the cab and the path with the greatest impact. Summary of the invention

[0006] The present invention proposes an OPAX method based on pre-noise reduction MMV compressed sensing, which can accurately find out the contribution of each path from the excavator powertrain to the steering wheel and seat rails of the cab to noise reduction and the path with the greatest impact.

[0007] The present invention adopts the following technical solutions.

[0008] An OPAX method based on pre-denoising MMV compressed sensing is used for an OPAX analysis model of an excavator, the method comprising the following steps;

[0009] Step S1: Establish an excavator vibration transmission path analysis model from the powertrain to the cab steering wheel and seat rails according to actual application requirements.

[0010] Step S2: De-noising the sample data using a wavelet de-noising method.

[0011] Step S3: Based on the denoised sample data, a learning dictionary is constructed using the K-SVD algorithm.

[0012] Step S4: Based on the learning dictionary constructed in step S3 and the MMV compressed sensing theory, a pre-denoising MMV compressed sensing framework suitable for excavator data is constructed.

[0013] Step S5: Based on the pre-noise reduction MMV compressed sensing framework in the fourth step, the working condition data of the excavator is obtained.

[0014] Step S6: Based on the transfer path analysis model, the OPAX analysis model is established and improved using the OPAX method and the operating condition data obtained in step S5.

[0015] In step S1, the transfer path of the transfer path analysis model is powertrain-suspension system-upper frame-cab steering wheel and seat rails.

[0016] In step S1, the powertrain and the suspension system are flexibly connected through the engine bracket and the suspension element, the suspension system and the upper frame are flexibly connected through the suspension element, and the cab and the upper frame are flexibly connected through the shock absorber pad. The transfer path analysis model is as follows Figure 1 shown.

[0017] The specific method of step S2 includes: establishing a wavelet function and setting corresponding parameters according to the wavelet denoising method theory, and applying it to the sample data for constructing the learning dictionary to complete the sample data denoising.

[0018] In step S4, the experimental conditions of the excavator are first determined in detail, and then a pre-denoising MMV compressed sensing framework suitable for the excavator experimental data is constructed based on the learning dictionary constructed in step S3 and the MMV compressed sensing theory.

[0019] In step S6, the OPAX method theoretical formula of the OPAX analysis model is as follows:

[0020]

[0021] In the formula, Y(ω) represents the total contribution of a target point, ω is the frequency, n is the number of structural paths, and H i (ω) is the frequency response function of the structure path at a certain frequency, F i (ω) is the structural load.

[0022] The OPAX analysis model is derived based on the evolution of TPA. When the excavator's working condition data is used for parametric modeling and load solving, the parameters to be solved in the parametric model are independent of each other in frequency and simplified into a function of three parameters, which can be expressed as follows:

[0023]

[0024] K i (ω)=-m i ω 2 +jc i ω+k i Formula 3;

[0025] In the formula, K i (ω) is the dynamic stiffness of the elastic element, a ai (ω) is the vibration acceleration of the active end, a pi (ω) is the vibration acceleration of the passive end, m i , c i and k i are the dynamic mass, damping and static stiffness of the elastic element respectively.

[0026] In the OPAX analysis model, the structural response of the target point of the excavator transfer path is expressed as

[0027] In the formula,

[0028] When the OPAX analysis model performs parametric modeling and solves the load based on the working condition data of the excavator, the method adopted is: under a certain working condition, the collected data is sliced ​​into orders, and the parameter variables are solved according to the number of speed sampling points in each order slice and the corresponding vibration acceleration and frequency values.

[0029] When solving for parameter variables, Formula 4 is transformed into:

[0030] [A q ][X]=[B q ]Formula 5;

[0031]

[0032] In view of the difficulty of the traditional OPAX method in meeting the requirement of accurately finding out the contribution of each path from the excavator powertrain to the steering wheel and seat rails of the cab and the path with the greatest impact, the present invention proposes an OPAX method based on pre-noise reduction MMV compressed sensing, which solves the problems existing in the existing traditional OPAX method and can accurately find out the contribution of each path from the excavator powertrain to the steering wheel and seat rails of the cab to noise reduction and the path with the greatest impact, which can help to reduce and optimize the working noise of the excavator equipment and reduce the vibration and noise level of the excavator when it is working. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0034] Attached Figure 1 It is a schematic diagram of the transmission path analysis model of excavator vibration;

[0035] Attached Figure 2 It is a pseudo code diagram of K-SVD algorithm;

[0036] Attached Figure 3 It is a schematic diagram of the process of pre-noise reduction MMV compressed sensing provided by an embodiment of the present invention;

[0037] Attached Figure 4 is a schematic diagram of a field experiment of an embodiment of the present invention;

[0038] Attached Figure 5 It is a schematic diagram comparing the effects of the method of the present invention and other methods. DETAILED DESCRIPTION

[0039] As shown in the figure, an OPAX method based on pre-noise reduction MMV compressed sensing is used for the OPAX analysis model of the excavator, and the method includes the following steps;

[0040] Step S1: Establish an excavator vibration transmission path analysis model from the powertrain to the cab steering wheel and seat rails according to actual application requirements.

[0041] Step S2: De-noising the sample data using a wavelet de-noising method.

[0042] Step S3: Based on the denoised sample data, a learning dictionary is constructed using the K-SVD algorithm.

[0043] Step S4: Based on the learning dictionary constructed in step S3 and the MMV compressed sensing theory, a pre-denoising MMV compressed sensing framework suitable for excavator data is constructed.

[0044] Step S5: Based on the pre-noise reduction MMV compressed sensing framework in the fourth step, the working condition data of the excavator is obtained.

[0045] Step S6: Based on the transfer path analysis model, the OPAX analysis model is established and improved using the OPAX method and the operating condition data obtained in step S5.

[0046] In step S1, the transfer path of the transfer path analysis model is powertrain-suspension system-upper frame-cab steering wheel and seat rails.

[0047] In step S1, the powertrain and the suspension system are flexibly connected through the engine bracket and the suspension element, the suspension system and the upper frame are flexibly connected through the suspension element, and the cab and the upper frame are flexibly connected through the shock absorber pad. The transfer path analysis model is as follows Figure 1 shown.

[0048] The specific method of step S2 includes: establishing a wavelet function and setting corresponding parameters according to the wavelet denoising method theory, and applying it to the sample data for constructing the learning dictionary to complete the sample data denoising.

[0049] In step S4, the experimental conditions of the excavator are first specifically determined, and then a pre-denoising MMV compressed sensing framework suitable for the excavator experimental data is constructed based on the learning dictionary constructed in step S3 and the MMV compressed sensing theory.

[0050] In step S6, the OPAX method theoretical formula of the OPAX analysis model is as follows:

[0051]

[0052] In the formula, Y(ω) represents the total contribution of a target point, ω is the frequency, n is the number of structural paths, and H i (ω) is the frequency response function of the structure path at a certain frequency, F i (ω) is the structural load.

[0053] The OPAX analysis model is derived based on the evolution of TPA. When the excavator's working condition data is used for parametric modeling and load solving, the parameters to be solved in the parametric model are independent of each other in frequency and simplified into a function of three parameters, which can be expressed as follows:

[0054]

[0055] K i (ω)=-m i ω 2 +jc i ω+k i Formula 3;

[0056] In the formula, K i (ω) is the dynamic stiffness of the elastic element, a ai (ω) is the vibration acceleration of the active end, api (ω) is the vibration acceleration of the passive end, m i , c i and k i are the dynamic mass, damping and static stiffness of the elastic element respectively.

[0057] In the OPAX analysis model, the structural response of the target point of the excavator transfer path is expressed as

[0058] In the formula,

[0059] When the OPAX analysis model performs parametric modeling and solves the load based on the working condition data of the excavator, the method adopted is: under a certain working condition, the collected data is sliced ​​into orders, and the parameter variables are solved according to the number of speed sampling points in each order slice and the corresponding vibration acceleration and frequency values.

[0060] When solving for parameter variables, Formula 4 is transformed into:

[0061] [A q ][X]=[B q ]Formula 5;

[0062]

Claims

1. An OPAX method based on pre-denoising MMV compressed sensing, used for the OPAX analysis model of excavators, characterized by: The method comprises the following steps: Step S1: Establish an excavator vibration transmission path analysis model from the powertrain to the cab steering wheel and seat rails according to actual application requirements. Step S2: De-noising the sample data using a wavelet de-noising method. Step S3: Based on the denoised sample data, a learning dictionary is constructed using the K-SVD algorithm. Step S4: Based on the learning dictionary constructed in step S3 and the MMV compressed sensing theory, a pre-denoising MMV compressed sensing framework suitable for excavator data is constructed. Step S5: Based on the pre-noise reduction MMV compressed sensing framework in the fourth step, the working condition data of the excavator is obtained. Step S6: Based on the transfer path analysis model, the OPAX analysis model is established and improved using the OPAX method and the operating condition data obtained in step S5.

2. The OPAX method based on pre-denoising MMV compressed sensing according to claim 1, characterized in that: In step S1, the transfer path of the transfer path analysis model is powertrain-suspension system-upper frame-cab steering wheel and seat rails.

3. The OPAX method based on pre-noise reduction MMV compressed sensing according to claim 2, characterized in that: In step S1, the powertrain and the suspension system are flexibly connected via the engine bracket and the suspension element, the suspension system and the upper frame are flexibly connected via the suspension element, and the cab and the upper frame are flexibly connected via the shock-absorbing pad. The transfer path analysis model is shown in Figure 1.

4. The OPAX method based on pre-noise reduction MMV compressed sensing according to claim 1, characterized in that: The specific method of step S2 includes: establishing a wavelet function and setting corresponding parameters according to the wavelet denoising method theory, and applying it to the sample data for constructing the learning dictionary to complete the sample data denoising.

5. The OPAX method based on pre-noise reduction MMV compressed sensing according to claim 1, characterized in that: In step S4, the experimental conditions of the excavator are first determined in detail, and then a pre-denoising MMV compressed sensing framework suitable for the excavator experimental data is constructed based on the learning dictionary constructed in step S3 and the MMV compressed sensing theory.

6. The OPAX method based on pre-noise reduction MMV compressed sensing according to claim 1, characterized in that: In step S6, the OPAX method theoretical formula of the OPAX analysis model is as follows: In the formula, Y(ω) represents the total contribution of a target point, ω is the frequency, n is the number of structural paths, and H i (ω) is the frequency response function of the structure path at a certain frequency, F i (ω) is the structural load.

7. The OPAX method based on pre-noise reduction MMV compressed sensing according to claim 6, characterized in that: The OPAX analysis model is derived based on the evolution of TPA. When the excavator's working condition data is used for parametric modeling and load solving, the parameters to be solved in the parametric model are independent of each other in frequency and simplified into a function of three parameters, which can be expressed as follows: K i (ω)=-m i ω 2 +jc i ω+k i Formula 3; In the formula, K i (ω) is the dynamic stiffness of the elastic element, a ai (ω) is the vibration acceleration of the active end, a pi (ω) is the vibration acceleration of the passive end, m i , c i and k i are the dynamic mass, damping and static stiffness of the elastic element respectively.

8. The OPAX method based on pre-noise reduction MMV compressed sensing according to claim 7, characterized in that: In the OPAX analysis model, the structural response of the target point of the excavator transfer path is expressed as In the formula, 9. The OPAX method based on pre-denoising MMV compressed sensing according to claim 8, characterized in that: When the OPAX analysis model performs parametric modeling and solves the load based on the working condition data of the excavator, the method adopted is: under a certain working condition, the collected data is sliced ​​into orders, and the parameter variables are solved according to the number of speed sampling points in each order slice and the corresponding vibration acceleration and frequency values.

10. The OPAX method based on pre-noise reduction MMV compressed sensing according to claim 9, characterized in that: When solving for parameter variables, Formula 4 is transformed into: [A q ][X]=[B q ]Formula 5;

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