阅读说明：本技术 一种基于振动谱分析的电主轴轴承结构参数推断方法 (Electric main shaft bearing structure parameter inference method based on vibration spectrum analysis ) 是由 周昕 李梦梅 陶文坚 李连玉 熊虎山 吉勇 于 2021-07-27 设计创作，主要内容包括：本发明公开了一种基于振动谱分析的电主轴轴承结构参数推断方法,依次进行信号采集、滤波预处理、FFT变换、基频搜索、比例因子计算、调制系数序列构建的操作,最终获得的电主轴轴承结构参数包括：比例因子、滚子个数、轴承内外径尺寸,利于实现轴承的状态评估和故障预警。本发明基于电主轴振动信号频谱与理论的一致性开展振动谱分析,实现电主轴轴承结构参数的逆向推断,并通过实际生产验证了本发明的实用性和有效性。(The invention discloses an electric main shaft bearing structure parameter inference method based on vibration spectrum analysis, which sequentially carries out the operations of signal acquisition, filtering pretreatment, FFT transformation, fundamental frequency search, scale factor calculation and modulation coefficient sequence construction, and finally obtains the electric main shaft bearing structure parameters, wherein the operation comprises the following steps: and the scale factor, the number of rollers and the inner and outer diameter sizes of the bearing are beneficial to realizing the state evaluation and the fault early warning of the bearing. The vibration spectrum analysis is carried out based on the consistency of the electric spindle vibration signal frequency spectrum and theory, the reverse inference of the electric spindle bearing structure parameters is realized, and the practicability and effectiveness of the vibration spectrum analysis method are verified through actual production.)

1. An electric main shaft bearing structure parameter inference method based on vibration spectrum analysis is characterized by comprising the following steps:

step S100: carrying out band-pass filtering on a vibration signal x (t) of the electric spindle by adopting a digital filter to eliminate the interference of low-frequency and high-frequency components on the frequency spectrum analysis process;

step S200: reading and verifying electric spindle frequency conversion f_rCarrying out maximum value search in a specified frequency spectrum interval to determine a fundamental frequency f_bAnd correcting through harmonic frequency information of the harmonic wave;

step S300: the contact angle of the electric spindle bearing is 15 degrees or 25 degrees, and a candidate set theta of the scale factor lambda is constructed on the basis of the contact angle of the electric spindle bearing, wherein the candidate set theta is { theta ═ theta { (theta) } theta { (theta) } of the scale factor lambda { (λ)_15°,θ_25°Determining a scale factor lambda through calculation and logic judgment;

step S400: defining a modulation factor rho, specifying a central modulation frequency f_cAnd carrier frequency f_eSliding calculation on frequency spectrum forms modulation coefficient sequence P^(κ)And κ ═ 1,2, plot P^(κ)Constructing index sequence numbers of the stem leaf graph to form a set I, J, and then Z equals to I and equals to J;

step S500: roughly estimated shaft diameter d_estAnd looking up a bearing manual by taking the scale factor lambda and the roller quantity Z as constraint conditions, and determining the outer diameter D and the inner diameter D of the bearing.

2. The vibration spectrum analysis-based electric spindle bearing structure parameter inference method according to claim 1, wherein in step S100Acquiring a vibration signal x (t) of idle running and stable state of the electric spindle by a sensor to obtain a conversion frequency f_r(ii) a The acquisition position of the sensor is a bearing shell at the front end of the electric spindle.

3. The vibration spectrum analysis-based electric spindle bearing structure parameter inference method according to claim 2, wherein a digital filter is adopted to perform band-pass filtering on the vibration signal x (t) with a filter passband of [10,20f ]_r]。

4. The vibration spectrum analysis-based electric spindle bearing structure parameter inference method according to claim 1, wherein in step S200, FFT transformation obtains amplitude spectrum x (f) of steady-state vibration signal x (t), and verifies frequency conversion f according to harmonic frequency information_r(ii) a At [0.4f_r,0.47f_r]Interval local maximum search for fundamental frequency f_b。

5. The vibration spectrum analysis-based electric spindle bearing structure parameter inference method according to claim 4, wherein in step S200, harmonic frequency information is used for f_bAnd (3) carrying out matching correction: calculating nf_bN 1,2, …,10, checking if the sequence x (f) has a local extremum falling at nf_bWithin a small neighborhood range ([ nf)_b-nf_Δ,nf_b+nf_Δ]，f_ΔFor spectral resolution, the extreme frequency f is used if the range is exceeded_mTo f_bMake a correction f_b＝f_m/n。

6. The vibration spectrum analysis-based electric spindle bearing structural parameter inference method according to claim 1, wherein in step S300, a spindle bearing scale factor λ is calculated:

constructing a scale factor lambda candidate set theta ═ theta_15°,θ_25°In which θ_τIs calculated by the following formula:

if | round (theta)_15°)-θ_15°|≤|round(θ_25°)-θ_25°I, then the scaling factor λ is round (θ)_15°) Otherwise, λ is round (θ)_25°)。

7. The vibration spectrum analysis-based electric spindle bearing structural parameter inference method according to claim 1, wherein in step S400, the number of spindle bearing rollers Z is calculated:

step S401: defining a modulation coefficient ρ:

ρ＝sig<δ(f)·X(f)> (2)

wherein the content of the first and second substances,

is a bounded nonlinear transformation function;

wherein: f. of_cThe frequency is modulated for the center; f. of_eIs the carrier frequency;

step S402: respectively take (f)_c,f_e)＝(if_b,f_r) And (jf)_r-jf_b,f_r) Sequentially taking 1,2, …,50 and 1,2, …,35 as the i and the j, sliding on x (f) to calculate the modulation coefficient sequence

Step S403: drawing P⁽¹⁾And P⁽²⁾Identifying local extreme values of the stem and leaf images of the sequence, and forming a set I and J by using index sequence numbers corresponding to the extreme values, wherein Z is equal to I and N is equal to J;

step S404: deducing bearing scale factor lambda and roller number upper limit Z from geometric relation_maxHas a definite quantitative relation of Z_maxFloor (λ pi), calculate and verify Z<Z_max。

Technical Field

The invention belongs to the technical field of bearing structure parameter inference, and particularly relates to an electric spindle bearing structure parameter inference method based on vibration spectrum analysis.

Background

The electric spindle of the machine tool usually works under the scenes of high rotating speed and low load, and has high machining and assembling precision. The bearing is used as a key part for connecting the electric main shaft rotor and the stator, is a weak and rigid link of an electric main shaft system, and can cause bearing faults caused by poor lubrication, dynamic abrasion, impact collision and the like in the long-term production service process of a machine tool, thereby threatening the production safety and the quality of parts. The bearing fault information forms coupling feedback with the environment through acoustic emission, thermal radiation, vibration and other forms, and modern fault diagnosis technology can realize state evaluation and fault early warning of the bearing by collecting the feedback information and stripping the fault information by adopting an advanced signal processing means. The vibration signal is the most common form, has the characteristics of visual feeling, easy acquisition and the like, and is widely applied in the manufacturing industry.

However, the fault diagnosis technology generally requires structural parameters of the bearing as support conditions, and due to the technical secrecy of manufacturers and the modular packaging of the motorized spindle, the structural parameters of the bearing are difficult to directly obtain, and the application of the fault diagnosis technology is greatly limited. Due to the ultrahigh manufacturing and assembling precision of the electric spindle, the frequency spectrum of the vibration signal of the electric spindle is highly consistent with the theory even in a high-speed running state, and the structural parameters of the bearing can be reliably deduced by a spectrum analysis means.

The contact angle is one of important bases of bearing parameter inference, the electric spindle is similar to a mechanical spindle, and bears radial and axial loads in the working process, but due to the high-speed application scene, the axial and radial loads are greatly reduced compared with the mechanical spindle, and the structure of the electric spindle does not involve complicated mechanical transmission, so that the angular contact ball bearing with the contact angle of 15 degrees or 25 degrees is widely selected. Furthermore, ISO standardization of bearing height also facilitates parameter inference.

Disclosure of Invention

The invention aims to provide a method for deducing structural parameters of an electric main shaft bearing based on vibration spectrum analysis, and aims to solve the problems.

The invention is mainly realized by the following technical scheme:

an electric main shaft bearing structure parameter inference method based on vibration spectrum analysis comprises the following steps:

step S100: carrying out band-pass filtering on a vibration signal x (t) of the electric spindle by adopting a digital filter to eliminate the interference of low-frequency and high-frequency components on the frequency spectrum analysis process;

step S200: reading and verifying electric spindle frequency conversion f_rCarrying out maximum value search in a specified frequency spectrum interval to determine a fundamental frequency f_bAnd correcting through harmonic frequency information of the harmonic wave;

step S300: the contact angle of the electric spindle bearing is 15 degrees or 25 degrees, and a candidate set theta of the scale factor lambda is constructed on the basis of the contact angle of the electric spindle bearing, wherein the candidate set theta is { theta ═ theta { (theta) } theta { (theta) } of the scale factor lambda { (λ)_15°,θ_25°Determining a scale factor lambda through calculation and logic judgment;

step S400: defining a modulation factor rho, specifying a central modulation frequency f_cAnd carrier frequency f_eSliding calculation on frequency spectrum forms modulation coefficient sequence P^(κ)And κ ═ 1,2, plot P^(κ)Constructing index sequence numbers of the stem leaf graph to form a set I, J, and then Z equals to I and equals to J;

step S500: roughly estimated shaft diameter d_estAnd looking up a bearing manual by taking the scale factor lambda and the roller quantity Z as constraint conditions, and determining the outer diameter D and the inner diameter D of the bearing.

In order to better implement the present invention, in step S100, a sensor is used to acquire a vibration signal x (t) of the electric spindle in idle running and steady state, and a frequency conversion f is obtained_r(ii) a The acquisition position of the sensor is a bearing shell at the front end of the electric spindle.

In order to better implement the present invention, further, the pass band of the digital filter for band-pass filtering the vibration signal x (t) is [10,20f ]_r]。

To better implement the present invention, further, in step S200, the FFT transform acquires an amplitude spectrum x (f) of the steady-state vibration signal x (t),and verifying the conversion frequency f according to the harmonic frequency information_r(ii) a At [0.4f_r,0.47f_r]Interval local maximum search for fundamental frequency f_b。

To better implement the present invention, further, in step S200, the harmonic frequency information pair f is utilized_bAnd (3) carrying out matching correction: calculating nf_bN 1,2, …,10, checking if the sequence x (f) has a local extremum falling at nf_bWithin a small neighborhood range ([ nf)_b-nf_Δ,nf_b+nf_Δ]，f_ΔFor spectral resolution, the extreme frequency f is used if the range is exceeded_mTo f_bMake a correction f_b＝f_m/n。

To better implement the present invention, further, in step S300, a spindle bearing scale factor λ is calculated:

constructing a scale factor lambda candidate set theta ═ theta_15°,θ_25°In which θ_τIs calculated by the following formula:

if | round (theta)_15°)-θ_15°|≤|round(θ_25°)-θ_25°I, then the scaling factor λ is round (θ)_15°) Otherwise, λ is round (θ)_25°)。

To better implement the present invention, further, in step S400, the number Z of spindle bearing rollers is calculated:

step S401: defining a modulation coefficient ρ:

ρ＝sig<δ(f)·X(f)> (2)

wherein the content of the first and second substances,

is a bounded nonlinear transformation function;

wherein: f. of_cThe frequency is modulated for the center; f. of_eIs the carrier frequency;

step S402: respectively take (f)_c,f_e)＝(if_b,f_r) And (jf)_r-jf_b,f_r) Sequentially taking 1,2, …,50 and 1,2, …,35 as the i and the j, sliding on x (f) to calculate the modulation coefficient sequence

Step S403: drawing P⁽¹⁾And P⁽²⁾Identifying local extreme values of the stem and leaf images of the sequence, and forming a set I and J by using index sequence numbers corresponding to the extreme values, wherein Z is equal to I and N is equal to J;

step S404: deducing bearing scale factor lambda and roller number upper limit Z from geometric relation_maxHas a definite quantitative relation of Z_maxFloor (λ pi), calculate and verify Z<Z_max。

The invention has the beneficial effects that:

the invention carries out vibration spectrum analysis based on the consistency of the frequency spectrum of the vibration signal of the electric spindle and theory, and realizes the reverse inference of the structural parameters of the electric spindle bearing. The method sequentially performs operations such as signal acquisition, filtering pretreatment, FFT (fast Fourier transform), fundamental frequency search, scale factor calculation, modulation coefficient sequence construction and the like, and finally obtains the structural parameters of the electric main shaft bearing, wherein the structural parameters comprise: the practicability and effectiveness of the method are verified in the embodiment of producing and processing the central electric spindle of a certain aviation structural part, and the method has better practicability.

Drawings

FIG. 1 is a flow chart of the present invention;

FIG. 2 is a schematic representation of a bearing scale factor λ;

FIG. 3 is a time domain vibration signal and a frequency domain amplitude spectrum of the motorized spindle;

FIG. 4 is P⁽¹⁾Sequence stem and leaf diagram;

FIG. 5 is P⁽²⁾Sequence stem and leaf diagram.

Detailed Description

Example 1:

an electric main shaft bearing structure parameter inference method based on vibration spectrum analysis comprises the following steps:

step S100: carrying out band-pass filtering on a vibration signal x (t) of the electric spindle by adopting a digital filter to eliminate the interference of low-frequency and high-frequency components on the frequency spectrum analysis process;

step S200: reading and verifying electric spindle frequency conversion f_rCarrying out maximum value search in a specified frequency spectrum interval to determine a fundamental frequency f_bAnd correcting through harmonic frequency information of the harmonic wave;

step S300: the contact angle of the electric spindle bearing is 15 degrees or 25 degrees, and a candidate set theta of the scale factor lambda is constructed on the basis of the contact angle of the electric spindle bearing, wherein the candidate set theta is { theta ═ theta { (theta) } theta { (theta) } of the scale factor lambda { (λ)_15°,θ_25°Determining a scale factor lambda through calculation and logic judgment;

step S400: defining a modulation factor rho, specifying a central modulation frequency f_cAnd carrier frequency f_eSliding calculation on frequency spectrum forms modulation coefficient sequence P^(κ)And κ ═ 1,2, plot P^(κ)Constructing index sequence numbers of the stem leaf graph to form a set I, J, and then Z equals to I and equals to J;

step S500: roughly estimated shaft diameter d_estAnd looking up a bearing manual by taking the scale factor lambda and the roller quantity Z as constraint conditions, and determining the outer diameter D and the inner diameter D of the bearing.

The invention carries out vibration spectrum analysis based on the consistency of the frequency spectrum of the vibration signal of the electric spindle and theory, and realizes the reverse inference of the structural parameters of the electric spindle bearing. The method sequentially performs operations such as signal acquisition, filtering pretreatment, FFT (fast Fourier transform), fundamental frequency search, scale factor calculation, modulation coefficient sequence construction and the like, and finally obtains the structural parameters of the electric main shaft bearing, wherein the structural parameters comprise: the practicability and effectiveness of the method are verified in the embodiment of producing and processing the central electric spindle of a certain aviation structural part, and the method has better practicability.

Example 2:

an electric main shaft bearing structure parameter inference method based on vibration spectrum analysis comprises the following steps:

1. arranging a sensor to acquire a steady-state vibration signal x (t) of idle running of the main shaft to acquire a frequency conversion f_r；

2. Designing a digital filter, and performing band-pass filtering on the signal x (t) with a filtering passband of [10,20f_r]；

3, obtaining the amplitude spectrum X (f) of x (t) by FFT transformation, and verifying the frequency conversion f according to the harmonic frequency information_r；

4. At [0.4f_r,0.47f_r]Interval local maximum search for fundamental frequency f_b；

5. Using harmonic frequency information pairs f_bAnd (3) carrying out matching correction: calculating nf_bN 1,2, …,10, checking if the sequence x (f) has a local extremum falling at nf_bWithin a small neighborhood range ([ nf)_b-nf_Δ,nf_b+nf_Δ]，f_ΔSpectral resolution) is exceeded, at the extreme frequency f_mTo f_bMake a correction f_b＝f_m/n；

6. Calculating a main shaft bearing scale factor lambda, wherein the physical meaning of the main shaft bearing scale factor lambda is shown in figure 2;

6.1. constructing a scale factor lambda candidate set theta ═ theta_15°,θ_25°In which θ_τIs calculated by the following formula:

6.2. if | round (theta)_15°)-θ_15°|≤|round(θ_25°)-θ_25°I, then the scaling factor λ is round (θ)_15°) Otherwise, λ is round (θ)_25°)；

7. Calculating the number Z of main shaft bearing rollers;

7.1. defining a modulation coefficient ρ:

ρ＝sig<δ(f)·X(f)> (2)

in the formula (I), the compound is shown in the specification,

is a bounded nonlinear transformation function;

f_cthe frequency is modulated for the center; f. of_eIs the carrier frequency.

7.2. Respectively take (f)_c,f_e)＝(if_b,f_r) And (jf)_r-jf_b,f_r) Sequentially taking 1,2, …,50 and 1,2, …,35 as the i and the j, sliding on x (f) to calculate the modulation coefficient sequence

7.3. Drawing P⁽¹⁾And P⁽²⁾Identifying local extreme values of the stem and leaf images of the sequence, and forming a set I and J by using index sequence numbers corresponding to the extreme values, wherein Z is equal to I and N is equal to J;

7.4. deducing bearing scale factor lambda and roller number upper limit Z from geometric relation_maxHas a definite quantitative relation of Z_maxFloor (λ pi), calculate and verify Z<Z_max；

8. Roughly estimating the shaft diameter d according to the contour dimension of the main shaft_estAnd looking up a bearing manual by taking the scale factor lambda and the roller quantity Z as constraint conditions, and determining the outer diameter D and the inner diameter D of the bearing.

The invention carries out vibration spectrum analysis based on the consistency of the frequency spectrum of the vibration signal of the electric spindle and theory, and realizes the reverse inference of the structural parameters of the electric spindle bearing. The method sequentially performs operations such as signal acquisition, filtering pretreatment, FFT (fast Fourier transform), fundamental frequency search, scale factor calculation, modulation coefficient sequence construction and the like, and finally obtains the structural parameters of the electric main shaft bearing, wherein the structural parameters comprise: the practicability and effectiveness of the method are verified in the embodiment of producing and processing the central electric spindle of a certain aviation structural part, and the method has better practicability.

Example 3:

a vibration spectrum analysis-based electric main shaft bearing structure parameter inference method is characterized in that a certain aviation structural component production and processing center is of a horizontal structure, a main shaft of the aviation structural component production and processing center is an integral electric main shaft,the model is as follows: MFWS-2307/24. Under the condition of limited parameters, the bearing structure parameter inference based on vibration spectrum analysis can be carried out, so that the bearing structure parameter of the electric spindle of the type can be reliably obtained, and a foundation is laid for carrying out bearing state evaluation and fault early warning subsequently. An acceleration sensor is arranged to acquire vibration signals of a spindle in an unloaded and steady running state, as shown in fig. 3(a), the acquisition position is a bearing shell at the front end of the electric spindle, the sampling frequency is 25600Hz, the rotating speed of the spindle is 15000rpm, namely 250Hz, and the time domain signal RMS is 6.857m/s^-2Filtering and FFT transformation to obtain the amplitude spectrum as shown in fig. 3 (b).

In FIG. 3(b), the spectral lines of 250Hz, 500Hz, 750Hz, 1000Hz, and 1500Hz can be precisely identified, and the spectral lines are consistent with the spindle rotation speed information set on the panel of the machining center, so that f is determined_r＝250Hz。f_bSearch interval is [100,117.3 ]]Hz, the maximum value is obtained at 113.05Hz in the interval, and f can be judged by verifying that the local extreme values at the first 10 th harmonic of 113.05Hz fall in the neighborhood range_b113.05 Hz. The candidate set Θ of scaling factors λ is calculated as {10.10,9.48}, and λ is easily determined to be 10 according to the criteria provided in step 6.2.

Selecting (f)_c,f_e) For (i × 113.05,250), P is plotted⁽¹⁾As in fig. 4, the index set I ═ {25,42,45,50} can be constructed from the graph. Selecting (f)_c,f_e) For (j × 139.95,250), P is plotted⁽²⁾As shown in fig. 5, we obtain the index set J ═ {25,28}, and further Z ═ I ═ J ═ 25. Z_max＝floor(10π)＝31，Z<Z_max。

The overall dimension d of the electric spindle is provided according to the drawing_estThe bearing parameter manual is consulted under the constraint conditions of lambda being 10 and Z being 25, the bearing outer diameter D being 100mm and the inner diameter D being 70mm are determined, and the SKF brand angular contact bearing reference model is 71914CD/P4A (hybrid ceramic ball bearing).

The invention carries out vibration spectrum analysis based on the consistency of the frequency spectrum of the vibration signal of the electric spindle and theory, and realizes the reverse inference of the structural parameters of the electric spindle bearing. The method sequentially performs operations such as signal acquisition, filtering pretreatment, FFT (fast Fourier transform), fundamental frequency search, scale factor calculation, modulation coefficient sequence construction and the like, and finally obtains the structural parameters of the electric main shaft bearing, wherein the structural parameters comprise: the practicability and effectiveness of the method are verified in the embodiment of producing and processing the central electric spindle of a certain aviation structural part, and the method has better practicability.

The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications and equivalent variations of the above embodiments according to the technical spirit of the present invention are included in the scope of the present invention.