Bevel gear detection method and gear detection system based on waviness information

Abstract

The invention relates to a helical gear detection method based on waviness information and a gear detection system. The helical gear detection method based on waviness information comprises the following steps: extracting full-tooth waviness information of a helical gear; processing the full-tooth waviness information to obtain a spectrogram; calculating an angle alpha of waviness on any single tooth of the helical gear; and according to the angle alpha of the waviness on the single tooth and the spectrogram, judging whether the gear has noise requirements or not. According to the method, through full-tooth order analysis and single-tooth topology three-dimensional angle analysis of the involute helical gear, important factors influencing gear meshing noise are calculated and extracted, tooth surface waviness information can be effectively detected, ghost frequency can be effectively identified and found, and a ripple line angle can be effectively analyzed; the method greatly improves the fitting quality of data, thereby improving the detection precision of the gear, enabling the gear to meet the noise detection requirements, and providing a brand new detection method for high-speed and low-noise gear production.

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, and due to the working characteristics of pure electric motors, the working speed range of their reducers is very wide. When the vehicle is operating normally, the motor is usually in a higher speed range, and the working speed of the reducer is also high. In addition, due to the large input torque of the motor and the lack of a transmission to further increase the torque, the reducer of pure electric vehicles has been operating in a high-torque environment for a long time. This high-speed, high-torque working characteristic aggravates the radiation noise of the reducer, and at the same time, it is destined that this noise is a medium-high frequency whistle-like noise, forcing the overall NVH performance requirements of pure electric vehicles to be higher. As an important component of high-performance transmission systems, high-speed and low-noise gears cannot meet their noise requirements through traditional precision detection 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 whole 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 I B11 through a fitting model to obtain I fitted tooth profile waviness curve B12;

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

[0015] S304, calculate α according to I B12, and the calculation formula is:

[0016]

[0017] In the formula, 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 of

[0018] Among them, the method of judging whether the gear meets the noise requirement 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, it is considered that the gear does not meet the noise requirements.

[0020] In a second aspect, the present invention also proposes a gear detection system based on waviness information, which uses the helical gear detection method based on waviness information as described in the first aspect. 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.

[0021] Among them, the multi-tooth information extraction module is used to extract the whole tooth waviness information. The spectrum generation module is used to process the whole tooth waviness information to obtain a spectrum diagram. The single tooth information extraction module 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.

[0022] In a third aspect, the present invention further proposes a software program product, which includes program instructions. When the program instructions are executed on an electronic device, the electronic device executes 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 the gear meshing noise through full tooth order analysis and single tooth topology three-dimensional angle analysis of the involute helical gear, and 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 methods, the data fitting quality is greatly improved, 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.

[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 corrugation 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 corrugation line of the gear, and reveals the influence of the angle between the corrugation line and the meshing trace line on the high-speed operation noise of the gear, which provides an important basis for the quality evaluation of the gear and is of great significance for guiding the production of gears. 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative labor.

[0027] Figure 1 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 It is a schematic diagram of the connection of the 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 It 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] Fig. 9 This is a schematic diagram of the residual data obtained by removing the modification amount in Example 2. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work 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 those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "or / and" used herein includes any and all combinations of one or more of the related 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 whole 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 spectrum of the waviness on a single tooth.

[0043] It should be noted that there is no strict order relationship between the above steps 1 and 3, such as step 1 can be performed first, or step 3 can be performed first, or step 1 and step 3 can be performed at the same time. However, for the sake of convenience, a specific description is given based on the step sequence process in Example 1.

[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 single tooth waviness angle. Specifically, for an involute helical gear, 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, obtaining geometric shape data of all N gear teeth on the helical gear. The geometric shape data of the nth gear tooth includes: 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 gear 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 abscissa of is converted from the angle value of the gear 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 shape 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 The 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 The 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 H2 is fitted to obtain the tooth shape curve C2'.

[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 force during the cutting process, resulting in periodic irregular step-shaped defects on the surface. Defective gears are prone to excessive vibration, howling and other situations during the transmission process, and this problem is often difficult to detect on a single tooth. It is necessary to detect multiple teeth or all teeth, which 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 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 single-sided involute lengths of the gear is greater than the base circle, indicating that the arc length corresponding to the involute of each tooth on the base circle is partially overlapping, which also means that the overlapping parts of the adjacent teeth should be calculated when processing the data. In this step, the N tooth profile shape data C1 n , Tooth shape data C2 n 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 C1 is the tooth profile data n Schematic diagram of the connection, with the abscissa being the involute length / mm and the ordinate being the amplitude / μm. Figure 4 C2 is the tooth shape data n The connection diagram is shown in Figure 2, where 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 ratio 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 ratio 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 Fourier transformation on the tooth profile shape curve C1' so that the tooth profile shape curve C1' is converted from the length domain to the frequency domain, and separate in the frequency domain to obtain the tooth profile waviness curve B1'. Perform Fourier transformation on the tooth direction shape curve C2', and separate in the frequency domain to obtain the tooth direction waviness curve B2'. Then perform inverse Fourier transformation 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. 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 direction direction of the entire tooth.

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

[0064] 2. Process the whole tooth waviness information to obtain the 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 idea of ​​Fourier analysis, any periodic waveform can be regarded as the superposition of multiple sine waves (or cosine waves) of different frequencies. Therefore, the ripple component extracted above is separated into sine waves of different frequencies, phases, and amplitudes by fitting the sine wave for further analysis.

[0067] Set an initial fitting model. Due to the complex data components, the initial fitting model preset by conventional parameters has 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 of 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] In the formula, the parameters p0, p1, p2, and f are the DC component, amplitude, initial phase, and frequency respectively. y and x are two variables respectively.

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

[0071] S202, performing order analysis on the sinusoidal curves Q1 and Q2, and generating corresponding frequency spectrum diagrams pic1 and pic2 (order diagrams) respectively; wherein the abscissa of the frequency spectrum diagrams pic1 and pic2 is order / HZ, and the ordinate is 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, I paths in the tooth profile direction are evenly arranged on a certain gear tooth along the tooth direction, and I tooth profile shape data C11 corresponding to the I paths are obtained.

[0075] S302, Fourier transform the tooth profile shape data C11, and separate it in the frequency domain to obtain a tooth profile waviness curve B11'. Perform 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 in the direction of the single tooth profile.

[0076] S303, fitting I tooth profile waviness curves B11 through a fitting model to obtain I fitted tooth profile waviness curves B12. Each fitted tooth profile waviness curve B12 includes K frequency components.

[0077] S304, calculating the angle α of the waviness according to I B12, the calculation formula is:

[0078]

[0079] In the formula, 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 corrugation. The angle α refers to the angle between the corrugation generation direction (hereinafter referred to as the corrugation line) and the contact line during gear fitting. The phase difference of the corrugation line indicates the degree of deviation of the corrugation line along the tooth direction. If the corrugation 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 corrugation line. The angle α of the corrugation line can be calculated by amplitude-weighting 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 obtained spectrum graph is analyzed to determine whether the amplitude of the main order is abnormally large or ghost orders appear. The order that is an integer multiple of the number of gear teeth is taken as the main order, and the order that is not an integer multiple and has an abnormally large amplitude is taken as the ghost order (the definition of abnormally large main order and ghost order needs to be determined according to the data of the actual detection object. In this embodiment, it is abnormally large if it is greater than 2μm). It should be noted that the frequency division range of the main order and the ghost order is not a fixed value, and it is necessary to conduct experiments based on the data of a batch of gears to determine it. For example, the spectrum graph of a batch of gears is compared with the noise data obtained by the gear test, and then the corresponding main order and ghost order are divided. Therefore, the main order and the ghost order need to be divided according to the actual detection object. The main order has the characteristics corresponding to the number of teeth. In the actual operation of the transmission system, it is only necessary to avoid the abnormally large main order and harmonious order in the meshing order to effectively avoid vibration noise. Due to its uncertainty and irregularity, the ghost order has become the culprit of abnormal vibration noise in the transmission system. Therefore, gears with abnormally large main orders or ghost orders may not meet the noise requirements of helical gears.

[0083] If only the main step and ghost step detection are performed, the detection result may be inaccurate or the analysis may be one-sided, and it is necessary to judge and analyze in combination with the corrugation line angle α. 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 smaller (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 detected abnormally large main steps or ghost steps are determined to not meet the noise requirements. Gears with an angle α less than the threshold and no abnormally large main steps or ghost steps are detected are determined to meet the noise requirements. Figure 8 As shown, Figure 8 The solid line is the meshing line on the tooth surface, the dotted line is the corrugation line on the tooth surface, and the angle between the two is angle α.

[0084] Visualization of the geometric data, the angle α of the waviness, and the meshing trace of the gear enables a more effective analysis of the gear, waviness information, and angle α, which is of great significance in 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 preprocessed to remove the gear shaping amount. For some gears, they may have undergone a shaping 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 shaping curves are generally simple parabolas. Therefore, during preprocessing, a second-order polynomial is first used to fit the gear shaping amount, and then the tooth profile shape data C11 is subtracted from the fitting shaping amount to obtain the residual data Y1. The geometric shape data is subtracted from the fitting shaping amount to obtain the residual data Y2. Fig. 9 As shown, it is a schematic diagram of obtaining Y2. In the figure, "original data" is the geometric shape data, and "fitting data" is the fitting modification amount. Using the residual data Y1 to replace the tooth profile shape data C11 and using the residual data Y2 to replace the geometric shape data as the data source for the subsequent steps can eliminate the adverse effects caused by the modification amount, thereby improving the extraction accuracy of the waviness and the detection accuracy of the gear.

[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 is used to extract the whole tooth waviness information. The spectrum generation module is used to process the whole tooth waviness information to obtain a spectrum diagram. The single tooth information extraction module 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.

[0090] Example 4

[0091] The present embodiment provides a gear detection system based on waviness information, which uses the helical gear detection method based on waviness information in Embodiment 2. Compared with Embodiment 4, the present embodiment is different in that a preprocessing module is additionally provided, which is used to preprocess the tooth profile shape data C11 and the geometric shape data to remove the gear modification amount, so as to obtain the residual data Y1 and Y2.

[0092] In some other embodiments, an electronic device is also 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, which stores a computer program, which, when executed by a processor, implements the steps of the helical gear detection method based on waviness information in the above embodiment.

[0094] Some other embodiments also provide a software program product, which includes program instructions, and when the software program product is run on an electronic device, the electronic device executes the steps of the helical gear detection method based on waviness information in the above embodiment.

[0095] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described 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-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached 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 the helical gear; Process the whole 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 α and spectrum diagram on a single tooth; The method for calculating α 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 I B11 through a fitting model to obtain I fitted tooth profile waviness curve B12; Among them, each fitted B12 includes K frequency components; S304, calculate α according to I B12, and the calculation formula is: In the formula, 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 of Among them, the method of judging whether the gear meets the noise requirement 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, it is considered that the gear does 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 the whole tooth waviness information comprises 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 gear tooth along the tooth direction n ; n∈[1,N]; S102, calculate C1 first n and C1 n+1 The tooth profile overlap ratio e p , then based on e p C11~C1 N The ends of 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'; Calculate C2 first n and C2 n+1 The tooth overlap ratio e f , then based on e f C21~C2 N The ends of are overlapped and spliced ​​to obtain spliced ​​tooth shape data H2, and then H2 is fitted to obtain tooth shape curve C2'; S103, performing Fourier transform on C1' to extract the tooth profile waviness curve B1 in the length domain; Perform Fourier transformation 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 comprises 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, and generating 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: 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; Among them, 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: Preprocess C11 and geometric shape data to remove the gear modification amount and obtain 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 ratio 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: The grid search method is used to optimize the parameters in the fitting model; The fitting model is: y=p0+p1sin(2πf*x+p2) In the formula, 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 all-tooth waviness information; A spectrum generation module, which is used to process the whole tooth waviness information to obtain a spectrum diagram; A 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 requirement 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, and when the software program product is run on an electronic device, the electronic device executes 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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