A helical gear detection method and gear detection system based on waviness information

Abstract

The present invention relates to a helical gear detection method and gear detection system based on waviness information. The helical gear detection method based on waviness information comprises the following steps: extracting the full tooth waviness information of the helical gear; processing the full tooth waviness information to obtain a frequency spectrum; calculating the angle α of the waviness on any single tooth of the helical gear; judging whether the gear meets the noise requirement based on the angle α of the waviness on the single tooth and the frequency spectrum. The present invention calculates and extracts the important factors affecting the gear meshing noise by analyzing the full tooth order of the involute helical gear and the three-dimensional angle of the single tooth topology. It can effectively detect the tooth surface waviness information, identify and discover ghost frequencies, and analyze the angle of the corrugation line. Compared with the existing detection method, it greatly improves the fitting quality of the data, thereby improving the detection accuracy of the gear, so that it meets the noise detection requirements, and provides a new detection method for the production of high-speed and low-noise gears.

Description

Technical Field

[0001] The present invention relates to the technical field of helical gear detection, and in particular to a helical gear detection method and a gear detection system based on waviness information. Background Art

[0002] The current new energy market is developing rapidly. Due to the operating characteristics of pure electric motors, their reducers operate over a wide speed range. During normal vehicle operation, the motor typically operates within a relatively high speed range, and the reducer's operating speed is also relatively high. Furthermore, due to the high input torque of the motor and the lack of a transmission to further increase torque, the reducers of pure electric vehicles operate in a high-torque environment for a long period of time. This high-speed, high-torque operating characteristic exacerbates the reducer's radiated noise, resulting in a medium-to-high-frequency, whistling-like noise, forcing higher overall NVH performance requirements for pure electric vehicles. High-speed, low-noise gears, a critical component of high-performance transmission systems, cannot meet these noise requirements through traditional precision testing methods. Summary of the Invention

[0003] Based on this, it is necessary to provide a helical gear detection method and gear detection system based on waviness information to address the problem that the existing helical gear accuracy detection method cannot meet the gear noise requirements.

[0004] In a first aspect, the present invention proposes a helical gear detection method based on waviness information, which comprises the following steps:

[0005] Extract the full tooth waviness information of helical gears;

[0006] Process the full tooth waviness information to obtain a spectrum diagram;

[0007] Calculate the angle α of the waviness on any single tooth of the helical gear;

[0008] Determine whether the gear meets the noise requirements based on the α and spectrum diagram on a single tooth.

[0009] The method for calculating α on a single tooth includes the following steps:

[0010] S301, obtaining I tooth profile shape data C11 on a certain gear tooth;

[0011] S302, performing Fourier transform on I C11 to extract I tooth profile waviness curve B11 in the length domain;

[0012] Among them, B11 represents the waviness information in the tooth profile direction of a single tooth;

[0013] S303, fitting one B11 using a fitting model to obtain a fitted tooth profile waviness curve B12;

[0014] Among them, each fitted B12 includes K frequency components;

[0015] S304. Calculate α based on I B12. The calculation formula is:

[0016]

[0017] Where x i is the coordinate of the i-th tooth direction; i∈[1,I],φ k (x i ) is the kth frequency component at coordinate x i The phase of k∈[1,K],A k (x i ) is the kth frequency component at coordinate x i Amplitude;

[0018] Among them, the method of judging whether the gear meets the noise requirements based on the α and spectrum diagram on a single tooth includes:

[0019] If ghost orders are identified on the spectrum graph and α is greater than the threshold, the gear is considered to not meet the noise requirements.

[0020] In a second aspect, the present invention further proposes a waviness-based gear detection system, which utilizes the waviness-based helical gear detection method described in the first aspect. The waviness-based gear detection system includes a multi-tooth information extraction module, a spectrum generation module, a single-tooth information extraction module, and a judgment module.

[0021] The multi-tooth information extraction module extracts the waviness information of all teeth. The spectrum generation module processes the waviness information of all teeth to generate a spectrum. The single-tooth information extraction module calculates the waviness angle α on a single tooth. The judgment module determines whether the gear meets the noise requirements based on the waviness angle α and the spectrum.

[0022] In a third aspect, the present invention further provides a software program product comprising program instructions, which, when executed on an electronic device, causes the electronic device to execute the steps of the helical gear detection method based on waviness information in the first aspect.

[0023] The beneficial effects of the present invention are:

[0024] 1. The present invention calculates and extracts the important factors affecting gear meshing noise through full tooth order analysis and single tooth topology three-dimensional angle analysis of involute helical gears. It can effectively detect tooth surface waviness information, identify and discover ghost frequencies, and analyze waviness line angles. Compared with existing detection methods, it greatly improves the data fitting quality, thereby improving the gear detection accuracy, making it meet the noise detection requirements, and providing a new detection method for high-speed and low-noise gear production.

[0025] 2. Based on the principle of gear meshing, the present invention solves the problem of connecting adjacent tooth data in multi-tooth or full-tooth waviness detection, and uses grid search to optimize the initial parameters of the fitting model to further improve the meshing quality of the data. At the same time, the present invention calculates the angle of the gear's corrugation line, revealing the influence of the angle between the corrugation line and the meshing trace line on the noise of the gear's high-speed operation, providing an important basis for gear quality evaluation and of great significance for guiding gear production. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 This is a flow chart of the helical gear detection method based on waviness information proposed by the present invention;

[0028] Figure 2 Schematic diagram of multi-tooth or full-tooth detection positions in Example 1;

[0029] Figure 3 Schematic diagram of the connection of tooth profile shape data in Example 1;

[0030] Figure 4 This is a schematic diagram of the connection of the tooth shape data in Example 1;

[0031] Figure 5 This is the frequency spectrum corresponding to the tooth profile shape data in Example 1;

[0032] Figure 6 This is the frequency spectrum corresponding to the tooth shape data in Example 1;

[0033] Figure 7 Schematic diagram of a single tooth detection position in Example 1;

[0034] Figure 8 Schematic diagram of the angle between the corrugation line and the meshing line on the tooth surface in Example 1;

[0035] Figure 9 This is a schematic diagram of residual data obtained by removing the correction amount in Example 2. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0038] Please refer to Figure 1 The present invention provides a helical gear detection method based on waviness information, the main route of which includes the following steps:

[0039] Step 1: Extract the full tooth waviness information of the helical gear;

[0040] Step 2: Process the full tooth waviness information to obtain a spectrum diagram;

[0041] Step 3: Calculate the angle α of the waviness on any single tooth of the helical gear;

[0042] Step 4: Determine whether the gear is qualified based on the angle α and frequency spectrum of the waviness on a single tooth.

[0043] It should be noted that there is no strict order between the above steps 1 and 3. For example, step 1 can be performed first, or step 3 can be performed first, or steps 1 and 3 can be performed simultaneously. However, for ease of description, the step sequence process in Example 1 is specifically described.

[0044] Example 1

[0045] The present invention provides a helical gear detection method based on waviness information. In this embodiment, the gear detection method involves extracting the full tooth waviness of an involute helical gear and calculating the waviness angle of a single tooth. Specifically, for involute helical gears, the helical gear detection method based on waviness information includes the following steps:

[0046] 1. Extract the full tooth waviness information of the helical gear.

[0047] S101, obtain the geometric shape data of all N gear teeth on the helical gear. The geometric shape data of the nth gear tooth includes: the tooth profile shape data C1 of the nth gear tooth along the tooth profile direction on the tooth surface n , tooth shape data C2 of the nth tooth along the tooth direction n ; n∈[1,N].

[0048] In this step, multiple teeth or all teeth of the gear are inspected to obtain geometric shape data. A gear measuring center (such as Klingelnberg p26) can be used to inspect the gear tooth profile direction and tooth direction. The inspection position is as follows: Figure 2 When using the gear measurement center for detection, the detection point position in the tooth profile direction is divided into points by the gear rotation angle, and then the angle value of the detection point position is converted into the corresponding involute length and recorded in the form of coordinate data results to form the tooth profile shape data C1 n , the horizontal axis is the involute length, and the vertical axis is the amplitude. Specifically, according to the conversion formula, the tooth profile shape data C1 n The horizontal coordinate is converted from the gear angle value to the corresponding involute length value, and the conversion formula is:

[0049]

[0050] Where λ is the angle value corresponding to the detection point. dθ represents the derivative of θ. θ is the length of the involute corresponding to the detection point. R b is the base circle radius of the helical gear.

[0051] The detection point position in the tooth direction is evenly divided in the tooth direction of the gear teeth and recorded in the form of coordinate data results to form the tooth shape data C2 n , the horizontal axis is the position (length) in the tooth direction.

[0052] Furthermore, for the N tooth profile shape data C1 measured n , Tooth shape data C2 n , extract the effective evaluation data. In this embodiment, remove N tooth profile shape data C1 n , Tooth shape data C2 n The first 5% and the last 5% of the tooth profile data C1 are taken from the middle 90% n , Tooth shape data C2 n As effective evaluation data, it can serve as the data source for subsequent processing.

[0053] S102, calculate C1 first n and C1 n+1 Tooth profile overlap ratio e p , then based on e p C11~C1N The ends of the tooth profile are overlapped and spliced ​​to obtain the spliced ​​tooth profile shape data H1, and then H1 is fitted to obtain the tooth profile shape curve C1'.

[0054] Calculate C2 first n and C2 n+1 Tooth overlap ratio e f , then based on e f C21~C2 N The ends of the tooth shape data H2 are overlapped and spliced ​​to obtain the spliced ​​tooth shape data H2, and then the tooth shape curve C2' is obtained by fitting H2.

[0055] Due to problems with the tool and tool installation, improper cutting parameters, machine vibration and other situations, vibrations are caused during processing, which in turn causes fluctuations in cutting forces during the cutting process, resulting in periodic irregular step-shaped defects on the surface. Gears with defects are prone to excessive vibration, howling and other situations during the transmission process. This problem is often difficult to detect on a single tooth, and requires detection of multiple teeth or all teeth. This requires associating the data of each tooth for a complete analysis. According to the formation principle of the involute, the involute length measured by the single tooth profile has a corresponding arc length equal to it on the gear base circle. The complete base circle circumference describes a complete rotation of the gear, and the sum of the unilateral involute lengths of the gear is greater than the base circle, indicating that the arc lengths corresponding to the involutes of each tooth on the base circle are partially overlapping, which also means that the overlapping parts of the data of adjacent teeth should be calculated when processing. In this step, the N tooth profile shape data C1 n , Tooth shape data C2 n Record in the form of a corrugated curve. This step requires the tooth profile shape data C1 n and adjacent tooth profile shape data C1 n+1 Between, tooth shape data C2 n and adjacent tooth shape data C2 n+1 The tooth profile shape curve C1' and tooth direction shape curve C2' are finally fitted. Figure 3 and Figure 4 As shown, Figure 3 is the tooth profile data C1 n The horizontal axis is the involute length / mm and the vertical axis is the amplitude / μm. Figure 4 C2 is the tooth shape data n The horizontal axis is the length in the tooth direction / mm, and the vertical axis is the amplitude / μm.

[0056] Among them, the tooth profile overlap rate e p The calculation formula is:

[0057]

[0058] Where λ is the involute length of a single tooth, p bt is the base circle pitch of the helical gear, L is the base circle circumference of the helical gear, d a is the tip diameter of the helical gear, d f is the root diameter of the helical gear, and z is the number of teeth on the helical gear.

[0059] Tooth overlap rate e f The calculation formula is:

[0060]

[0061] Where b is the tooth width of the helical gear, h is the sum of the leads of the gear teeth, z is the number of teeth of the helical gear, β is the helix angle of the pitch circle of the helical gear, b is the tooth width of the helical gear, and m is the module of the helical gear.

[0062] S103. Perform a Fourier transform on the tooth profile shape curve C1' to convert it from the length domain to the frequency domain, and separate it in the frequency domain to obtain a tooth profile waviness curve B1'. Perform a Fourier transform on the tooth direction shape curve C2' to separate it in the frequency domain to obtain a tooth direction waviness curve B2'. Then, perform an inverse Fourier transform on the tooth profile waviness curve B1' and the tooth direction waviness curve B2' to obtain the tooth profile waviness curve B1 and the tooth direction waviness curve B2 in the length domain. B1 represents the waviness information in the tooth profile direction of the entire tooth, and B2 represents the waviness information in the tooth direction of the entire tooth.

[0063] Geometric shape errors are generally composed of waviness, roughness, and form and position errors. Waviness is generally a periodic surface unevenness with a wavelength between that of roughness and form and position errors. In this step, the waviness component is separated from the form and position errors and roughness components in the frequency domain using the Fourier transform method. An inverse Fourier transform is then performed to obtain the waviness component (data) in the length domain, resulting in the tooth profile waviness curve B1 and the tooth direction waviness curve B2.

[0064] 2. Process the full tooth waviness information to obtain a spectrum diagram.

[0065] S201 , respectively fitting the tooth profile waviness curve B1 and the tooth direction waviness curve B2 through a fitting model to generate corresponding sinusoidal curves Q1 and Q2 .

[0066] According to the core concept of Fourier analysis, any periodic waveform can be viewed as a superposition of multiple sine waves (or cosine waves) of different frequencies. Therefore, by fitting the sine waves, the extracted ripple component is separated into sine waves of different frequencies, phases, and amplitudes for further analysis.

[0067] Set an initial fitting model. Due to the complex data components, the initial fitting model with conventional parameters has a poor fitting effect, resulting in inaccurate calculation results. Therefore, the grid search method is used to optimize the parameters in the initial fitting model: iterative calculation is performed by setting a reasonable range and step size for the initial prediction parameters. The final fitting model has a good fitting effect and high result accuracy. Among them, the fitting model is a sine curve fitting model, which is:

[0068] y=p0+p1sin(2πf*x+p2)

[0069] Where p0, p1, p2, and f are the DC component, amplitude, initial phase, and frequency, respectively. y and x are two variables.

[0070] Specifically, multiple points can be evenly collected on the tooth profile waviness curve B1 to form point set D1. Multiple points can be evenly collected on the tooth direction waviness curve B2 to form point set D2. Point sets D1 and D2 are substituted into the variables in the fitting model to obtain the sinusoidal curves Q1 and Q2 of the tooth profile waviness curve B1 and B2, respectively.

[0071] S202 , performing order analysis on the sinusoidal curves Q1 and Q2 to generate corresponding frequency spectra pic1 and pic2 (order graphs), respectively; wherein the abscissas of the frequency spectra pic1 and pic2 are order / HZ and the ordinates are amplitude / μm.

[0072] Specifically, take the tooth profile waviness curve B1 as an example. Record the amplitude and frequency of the sine curve after fitting the tooth profile waviness curve B1. Calculate the number of complete cycles of the fitted sine curve on the full tooth data to correspond to the order of the spectrum, and then generate the spectrum diagram pic1 corresponding to the tooth profile waviness curve B1, as shown in Figure 5 Similarly, generate the spectrum diagram pic2 corresponding to the tooth waviness curve B2, as shown in Figure 6 shown.

[0073] 3. Calculate the angle α of the waviness on any single tooth of the helical gear.

[0074] S301, such as Figure 7 As shown in FIG. 1 , I paths in the tooth profile direction are evenly arranged on a certain tooth along the tooth direction, and I tooth profile shape data C11 corresponding to the I paths are obtained.

[0075] S302: Perform a Fourier transform on the tooth profile shape data C11 to obtain a tooth profile waviness curve B11' in the frequency domain. Perform an inverse Fourier transform on the tooth profile waviness curve B11' to obtain a tooth profile waviness curve B11 in the length domain. B11 represents the waviness information of a single tooth along the tooth profile direction.

[0076] S303: Fit one tooth profile waviness curve B11 using a fitting model to obtain one fitted tooth profile waviness curve B12, wherein each fitted tooth profile waviness curve B12 includes K frequency components.

[0077] S304: Calculate the angle α of the waviness based on I B12. The calculation formula is:

[0078]

[0079] Where x i is the coordinate of the i-th tooth direction; i∈[1,I]. k (x i ) is the kth frequency component at coordinate x i The phase of A, k∈[1,K]. k (x i ) is the kth frequency component at coordinate x i Amplitude.

[0080] Although single-tooth detection has limitations in spectrum detection, the present invention utilizes it to calculate the angle α of the waviness. The angle α refers to the angle between the waviness generation direction (hereinafter referred to as the waviness line) and the contact line during gear fitting. The phase difference of the waviness line indicates the degree of offset of the waviness line along the tooth direction. If the waviness line is tilted, its different positions in the tooth direction will appear as phase changes in the time domain signal. The phase difference is caused by the tilt in the tooth direction and is directly related to the periodic frequency of the waviness line. The waviness line angle α can be calculated by performing amplitude weighting on the phase gradients of the K frequency components in B12 using the calculation formula of the angle α.

[0081] 4. Determine whether the gear meets the noise requirements based on the α and spectrum diagram on a single tooth.

[0082] In this step, the resulting spectrum is analyzed to determine whether the amplitude of the main order is abnormally large or ghost orders are present. Orders that are integer multiples of the number of gear teeth are considered main orders, while orders that are not integer multiples and have abnormally large amplitudes are considered ghost orders. (The definition of abnormally large main orders and ghost orders needs to be determined based on the data of the actual test object. In this embodiment, an amplitude greater than 2μm is considered abnormally large.) It should be noted that the frequency division range of the main order and ghost orders is not a fixed value and requires experimental determination based on data from a batch of gears. For example, the spectrum of a batch of gears can be compared with the noise data obtained from gear testing to further classify the corresponding main orders and ghost orders. Therefore, the classification of main orders and ghost orders needs to be based on the actual test object. The main order has the characteristic of corresponding to the number of teeth. In actual transmission system operation, vibration noise can be effectively avoided by simply avoiding abnormally large main orders and harmonic orders in the meshing order. Ghost orders, due to their uncertainty and irregularity, are the culprit of abnormal vibration noise in transmission systems. Therefore, gears detected to have abnormally large main orders or ghost orders may not meet the noise requirements of helical gears.

[0083] If only the main order and ghost order are detected, the detection results may be inaccurate or the analysis may be one-sided, and it is necessary to judge and analyze in combination with the angle α of the corrugation line. When the corrugation line and the contact line form a larger angle (a larger angle is an angle that exceeds the threshold, and the size of the threshold also needs to be determined according to different detection objects. In this embodiment, it is greater than 30°), the contact surface of the gear will produce a large local contact pressure fluctuation, resulting in an increase in the impact force between the tooth surfaces, thereby generating stronger noise. If the angle between the contact line and the corrugation line is small (less than or equal to 30° in this embodiment), the distribution of the contact force may be more uniform, reducing the local impact force, thereby reducing the generation of noise. Therefore, gears with an angle α greater than the threshold and an abnormally large main order or ghost order are detected are deemed to not meet the noise requirements. Gears with an angle α less than the threshold and no abnormally large main order or ghost order are detected are deemed to meet the noise requirements. As Figure 8 As shown, Figure 8 The solid line is the meshing line on the tooth surface, the dotted line is the wavy line on the tooth surface, and the angle between the two is angle α.

[0084] Visualization of geometric data, waviness angle α, and gear meshing traces enables more effective analysis of gears, waviness information, and angle α, which is of great significance for guiding gear production.

[0085] Example 2

[0086] The difference between this embodiment and embodiment 1 is that the tooth profile shape data C11 and the geometric shape data need to be pre-processed to remove the gear modification amount. For some gears, they may have undergone a modification step before leaving the factory, and the modified gears have an adverse effect on the detection method of the present invention. The teeth of involute helical gears are generally drum-shaped teeth, and their modification curves are generally simple parabolas. Therefore, during pre-processing, a second-order polynomial is first used to fit the gear modification amount, and then the tooth profile shape data C11 is subtracted from the modified amount after fitting to obtain the residual data Y1. The geometric shape data is subtracted from the modified amount after fitting to obtain the residual data Y2. As shown in FIG. Figure 9 Figure 1 shows a schematic diagram of how to obtain Y2. In the figure, "Original Data" represents the geometric shape data, and "Fitted Data" represents the modified amount. Replacing the tooth profile shape data C11 with residual data Y1 and the geometric shape data with residual data Y2 as the data source for subsequent steps eliminates the adverse effects of the modified amount, thereby improving the accuracy of waviness extraction and gear inspection.

[0087] Example 3

[0088] This embodiment also proposes a gear detection system based on waviness information, which uses the helical gear detection method based on waviness information in embodiment 1. The gear detection system based on waviness information includes a multi-tooth information extraction module, a spectrum generation module, a single tooth information extraction module, and a judgment module.

[0089] The multi-tooth information extraction module extracts the waviness information for all teeth. The spectrum generation module processes the waviness information for all teeth to generate a spectrum. The single-tooth information extraction module calculates the waviness angle α on a single tooth. The judgment module determines whether the gear meets the noise requirements based on the waviness angle α and the spectrum.

[0090] Example 4

[0091] This embodiment provides a gear detection system based on waviness information, which utilizes the helical gear detection method based on waviness information described in Example 2. This embodiment differs from Example 4 in that it also includes a preprocessing module for preprocessing the tooth profile shape data C11 and geometric shape data to remove gear modification to obtain residual data Y1 and Y2.

[0092] In other embodiments, an electronic device is provided. The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of the helical gear detection method based on waviness information in the above embodiment are implemented.

[0093] Some other embodiments also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the helical gear detection method based on waviness information in the above-mentioned embodiment.

[0094] Some other embodiments also provide a software program product, which includes program instructions that, when executed on an electronic device, cause the electronic device to execute the steps of the helical gear detection method based on waviness information in the above-mentioned embodiment.

[0095] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0096] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A helical gear detection method based on waviness information, characterized in that: It includes the following steps: Extract the full tooth waviness information of helical gears; Process the full tooth waviness information to obtain a spectrum diagram; Calculate the angle α of the waviness on any single tooth of the helical gear; Determine whether the gear meets the noise requirements based on the angle α and spectrum of the waviness on a single tooth; The method for calculating the angle α of the waviness on a single tooth includes the following steps: S301, obtaining I tooth profile shape data C11 on a certain gear tooth; S302, performing Fourier transform on I C11 to extract I tooth profile waviness curve B11 in the length domain; Among them, B11 represents the waviness information in the tooth profile direction of a single tooth; S303, fitting one B11 using a fitting model to obtain a fitted tooth profile waviness curve B12; Among them, each fitted B12 includes K frequency components; S304. Calculate α based on I B12. The calculation formula is: Where x i is the coordinate of the i-th tooth direction; i∈[1,I],φ k (x i ) is the kth frequency component at coordinate x i The phase of A, k∈[1,K] k (x i ) is the kth frequency component at coordinate x i Amplitude; Among them, the method of judging whether the gear meets the noise requirements based on the α and spectrum diagram on a single tooth includes: If ghost orders are identified on the spectrum graph and α is greater than the threshold, the gear is considered to not meet the noise requirements.

2. The helical gear detection method based on waviness information according to claim 1, characterized in that: The method for extracting full tooth waviness information includes the following steps: S101, obtaining geometric shape data of all N gear teeth on the helical gear; The geometric shape data of the nth gear tooth include: the tooth profile shape data C1 of the nth gear tooth along the tooth profile direction on the tooth surface n , tooth shape data C2 of the nth tooth along the tooth direction n ; n∈[1,N]; S102, calculate C1 first n and C1 n+1 Tooth profile overlap ratio e p , then based on e p C11~C1 N The ends of the tooth profile are overlapped and spliced ​​to obtain the spliced ​​tooth profile shape data H1, and then the tooth profile shape curve C1' is obtained by fitting H1; Calculate C2 first n and C2 n+1 Tooth overlap ratio e f , then based on e f C21~C2 N The ends of the tooth shape data H2 are overlapped and spliced ​​to obtain the spliced ​​tooth shape data H2, and then the tooth shape curve C2' is obtained by fitting H2; S103, performing Fourier transform on C1' to extract the tooth profile waviness curve B1 in the length domain; Perform Fourier transform on C2' to extract the tooth waviness curve B2 in the length domain; Among them, B1 represents the waviness information in the tooth profile direction of the entire tooth, and B2 represents the waviness information in the tooth strike direction of the entire tooth.

3. The helical gear detection method based on waviness information according to claim 2, characterized in that: The method for processing the full tooth waviness information to obtain a spectrum diagram includes the following steps: S201, fitting B1 and B2 respectively through a fitting model to generate corresponding sine curves Q1 and Q2; S202 , performing order analysis on Q1 and Q2 to generate corresponding frequency spectrum graphs pic1 and pic2 respectively; wherein the abscissa of pic1 and pic2 is the order and the ordinate is the amplitude.

4. The helical gear detection method based on waviness information according to claim 2, characterized in that: In S101, C1 n The detection point position is evenly divided into points according to the angle of gear rotation, and the angle value of the detection point position is converted into the corresponding involute length; C2 n The detection point position is evenly divided in the tooth direction of the gear teeth; The conversion formula for converting the angle value of the detection point position into the corresponding involute length is: Where λ is the angle value corresponding to the detection point; θ is the length of the involute corresponding to the detection point, R b is the base circle radius of the gear.

5. The helical gear detection method based on waviness information according to claim 2, characterized in that: Pre-process C11 and geometric shape data to remove the gear modification amount and obtain the corresponding residual data Y1 and residual data Y2; Y1 replaces C11 as the data source for subsequent steps; Y2 replaces the geometric shape data as the data source for subsequent steps; The pre-processing method comprises the following steps: Use second-order polynomial to fit the gear modification amount; Subtract the modified amount after fitting from C11 to obtain Y1; Subtract the modified amount after fitting from the geometric shape data to obtain Y2.

6. The helical gear detection method based on waviness information according to claim 5, characterized in that: The first 5% and the last 5% of the C11 and geometric shape data are removed, and the middle 90% of the C11 and geometric shape data are used as valid data.

7. The helical gear detection method based on waviness information according to claim 2, characterized in that: In S102, the tooth profile overlap ratio e p The calculation formula is: Where L is the base circle circumference of the gear, d a is the gear tooth tip diameter, d f is the root diameter of the gear, z is the number of teeth on the gear; Tooth overlap rate e f The calculation formula is: Where β is the pitch circle helix angle of the gear, b is the tooth width, and m is the module of the gear.

8. The helical gear detection method based on waviness information according to claim 1 or 3, characterized in that: Optimize the parameters in the fitting model using the grid search method; The fitted model is: y=p0+p1sin(2πf*x+p2) Where, parameters p0, p1, p2, and f are DC component, amplitude, initial phase, and frequency respectively; y and x are two variables respectively.

9. A gear detection system based on waviness information, characterized in that: It uses the helical gear detection method based on waviness information as described in any one of claims 1 to 8; The gear detection system based on waviness information includes: Multi-tooth information extraction module, which is used to extract the whole tooth waviness information; A spectrum generation module is used to process the full tooth waviness information to obtain a spectrum diagram; Single tooth information extraction module, which is used to calculate the angle α of the waviness on a single tooth; The judgment module is used to judge whether the gear meets the noise requirements based on the angle α of the waviness on a single tooth and the spectrum diagram.

10. A software program product, characterized in that The software program product includes program instructions, which, when running on an electronic device, enable the electronic device to execute the steps of the helical gear detection method based on waviness information as claimed in any one of claims 1 to 8.

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