CN114136778B - Aviation gear grinding burn stress detection method and device - Google Patents
Aviation gear grinding burn stress detection method and device Download PDFInfo
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- CN114136778B CN114136778B CN202111416643.0A CN202111416643A CN114136778B CN 114136778 B CN114136778 B CN 114136778B CN 202111416643 A CN202111416643 A CN 202111416643A CN 114136778 B CN114136778 B CN 114136778B
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- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/08—Investigating strength properties of solid materials by application of mechanical stress by applying steady tensile or compressive forces
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- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L5/00—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/02—Details not specific for a particular testing method
- G01N2203/06—Indicating or recording means; Sensing means
- G01N2203/067—Parameter measured for estimating the property
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Abstract
The invention discloses a method and a device for detecting grinding burn stress of an aircraft gear, wherein a static load tensile test is carried out under the yield strength of the aircraft gear, a convex surface on the side of a probe is tightly attached to the center of a tensile test block, corresponding MBN signals are measured and recorded at intervals of 1kN, the obtained MBN signals are subjected to wavelet packet transformation, a feature vector formed by MBN singular values is used as an input sample, training optimization is carried out, and a stress prediction model is finally obtained; and inputting a stress prediction model to obtain a corresponding stress value. The magnetic yoke is provided with an exciting coil, a magnetic shielding body is arranged below the magnetic yoke, a receiving coil is arranged at the center position right below the magnetic shielding body, the receiving coil is connected with a signal amplifier through a signal line, and the signal amplifier is sequentially connected with a filter circuit and a PC. The MBN probe is suitable for detecting the gear tooth surface and can be used for detecting grinding burn of aviation gears with smaller sizes. The detection sensitivity is high, the detection speed is high, no coupling agent is needed, and the portability is good.
Description
Technical Field
The invention relates to a stress detection technology in nondestructive detection, in particular to a method and a device for detecting grinding burn stress of an aviation gear.
Background
The aviation gear is widely applied to the fields of aviation, war industry and the like in China. In the process of preparing and serving the aviation gear for a long time, stress concentration can occur due to factors such as grinding burn, working environment, using method and the like, so that potential safety hazards are caused, and even casualties are caused. In the grinding burn detection of aviation gears, high requirements are generally put on the structure and the size of a detection probe due to the size limitation between teeth of the gears. And the stress detection is carried out by using a conventional method, the step of extracting characteristic values from MBN signals is complicated, and meanwhile, the accuracy is insufficient when the data set is fitted.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method and a device for detecting the grinding burn stress of an aviation gear, aiming at improving the detection precision and solving the problem of stress concentration caused by grinding burn in the machining process of the aviation gear.
In order to achieve the purpose, the invention adopts the following technical scheme. A method for detecting aviation gear grinding burn stress comprises the following steps:
step 1: preparing a tensile test block and a detection probe;
and 2, step: carrying out a static load tensile test under the yield strength, closely attaching a lateral convex surface of the probe to the center of the tensile test block, and measuring and recording corresponding MBN signals at intervals of 1 kN;
and step 3: and (3) carrying out wavelet packet transformation on the obtained MBN signal f (t):
wherein i =1,2, \8230, n,
is wavelet packet transform coefficient; i is the band order; j is a decomposition level; k is a translation parameter; />
Is a function of the wavelet packet, is greater than or equal to>
Wavelet packet coefficients of each frequency band order of the MBN signal are obtained by wavelet packet transformation, and a wavelet packet coefficient matrix A can be constructed:
wherein n =2 j The total number of frequency bands obtained by decomposing j layers of wavelet packets; m is the wavelet packet coefficient length of each frequency band; let A be an element of R m×n Then there is an orthogonal matrix U ∈ R m×n And the orthogonal matrix B ∈ R m×n So that the following holds:
A＝USV T
wherein S = diag [ σ ] 1 ，σ 2 ，…，σ p ]P = min (m, n); the non-zero elements on the diagonal of the matrix S are the singular values of the matrix A, and σ 1 ≥σ 2 ≥…≥σ p Not less than 0; singular values are eigenvalues of matrix A, thus MBN eigenvectors { σ } consisting of elements of matrix S that are non-zero on the diagonal i There is a link between surface stress and (i =1,2, \8230;, q);
and 4, step 4: feature vector [ sigma ] formed by MBN singular values i The method comprises the following steps of (i =1,2, \8230;, q) serving as an input sample, taking an actual stress value as an output sample, setting initial parameters, inputting the initial parameters into a BP neural network, and then carrying out training optimization to finally obtain a stress prediction model;
and 5: and (3) detecting the aviation gear by using the detection method in the step 1, recording an MBN signal of the tooth surface to be detected, and inputting the MBN signal into a stress prediction model after the MBN signal is processed in the steps 2 and 3 to obtain a corresponding stress value.
An aviation gear grinding burn stress detection device comprises an excitation module 1 connected with an excitation coil 2 and a magnetic yoke 5 arranged on a detection platform 6, wherein the excitation coil 2 is arranged on the magnetic yoke 5, a magnetic shielding body 3 is arranged below the magnetic yoke 5, a detection coil 4 is arranged at the center position right below the magnetic shielding body 3, the detection coil 4 is connected with a signal amplifier 7 through a signal line, and the signal amplifier 7 is sequentially connected with a filter circuit 8 and a PC 9;
the excitation module 1 is used for generating a sine signal with fixed frequency, the sine signal flows through the excitation coil 2 to generate a magnetic field, and the magnetic field is transmitted to the detection platform 6 through a high-permeability material used in the magnetic yoke 5;
the Barkhausen noise signal generated by the detection platform 6 is received by the detection coil 4, and the interference of external noise is reduced through the magnetic shielding body 3;
the signal amplifier 7 is used for amplifying an initial barkhausen noise signal;
the filter circuit 8 is used for filtering out interference noise signals;
the PC 9 is configured to convert the analog signal into a digital signal and display the obtained barkhausen noise signal.
The invention develops an MBN probe suitable for detecting the gear tooth surface aiming at the gear tooth surface, and the MBN probe can be used for detecting the grinding burn of an aviation gear with a smaller size. The detection sensitivity is high, the detection speed is high, no coupling agent is needed, and the portability is good.
Drawings
FIG. 1 is a flow chart of a stress detection method of the present invention;
FIG. 2 is a schematic structural diagram of the stress detection apparatus of the present invention;
FIG. 3 is a schematic representation of the Barkhausen signal taken by the apparatus of the present invention under various stress conditions;
FIG. 4 shows the results of the neural network stress prediction model after parameter optimization;
in the figure: 1. the device comprises an excitation module, 2 excitation coils, 3 magnetic shielding bodies, 4 detection coils, 5 magnetic yokes, 6 detection platforms, 7 signal amplifiers, 8 filter circuits and 9 PC machines.
Detailed Description
The invention will be further explained with reference to the drawings and the embodiments.
The invention relates to a grinding burn detection method of an aviation gear (as shown in figure 1), which comprises the following steps:
1. a tensile test block and a test probe S1 were prepared. The excitation coil 2, the magnetic shield 3, the detection coil 4 and the magnetic yoke 5 jointly form a Barkhausen detection probe (as shown in FIG. 2). Wherein, a high-permeability material is selected as a material of the side convex magnetic yoke 5, the exciting coil 2 is formed by uniformly winding a copper enameled wire on the side convex magnetic yoke 5, and the number of turns is 200-300; the detection coil 4 is formed by uniformly winding a copper enameled wire on a magnetic core, the number of turns is 1000-1300 turns, and the detection coil is placed in the center position right below the magnetic yoke 5. The material of the tensile test block is the same as that of the aviation gear.
2. And (3) acquiring original data S2 in a static load stretching experiment, attaching a side convex surface of the probe to the center of the stretching test block, and sequentially acquiring MBN signals every 1 kN. The barkhausen noise signals (as shown in fig. 3) at different stress levels are acquired by using the cantilever beam method, the ordinate represents the voltage value of the obtained barkhausen signal, the abscissa σ represents the maximum value of the stress, and the barkhausen noise signals are gradually reduced in the process that the stress is gradually reduced from 87% σ to 17% σ.
3. The barkhausen signal received by the detection coil 4 passes through the signal amplifier 7 and the filter circuit 8, and then is collected into the PC 9 through the acquisition card. The signal amplifier 7 adopts an AD620 module, and the filter circuit 8 adopts an active filter. Inputting the obtained MBN signal into MATLAB 2020, wherein f (t) is subjected to wavelet packet transformation S3, and then wavelet packet transformation is utilized to obtain the wavelet packet coefficient of each frequency band order of the MBN signal, so as to construct a wavelet packet coefficient matrix A:
and reducing the dimension of the coefficient matrix A by using singular value decomposition. Let A be an element of R m×n Then there is an orthogonal matrix U ∈ R m×n And the orthogonal matrix V ∈ R m×n So that the following holds:
A＝USV T
wherein S = diag [ σ ] 1 ，σ 2 ，…，σ p ]P = min (m, n). The non-zero elements on the diagonal of the matrix S are the singular values of the matrix A, and σ 1 ≥σ 2 ≥…≥σ p Is more than or equal to 0. Singular values are eigenvalues of matrix A, thus MBN eigenvectors { σ } consisting of elements other than zero on the diagonal of matrix S i There is a link between i =1,2, \8230;, q) and the surface stress.
4. Feature vector [ sigma ] formed by MBN singular values i As input samples (i =1,2, \ 8230;, q) and the actual stress values as output samples, are input to the MATLAB neural netIn the collateral learning system, initial parameters are set and then training optimization is carried out, and finally, an optimized stress prediction model S4 is obtained. The Barkhausen signal on the steel plate measured using the three-point bending method (as shown in FIG. 4) has the abscissa representing the distance from the center of the probe to the left end of the test block and the ordinate representing the stress tensor. The test result shows that compared with the stress tensor actually measured, the stress tensor obtained after MATLAB neural network learning has high reliability, good fitting effect and error within an acceptable range, and can be used as a final stress prediction model.
5. The method comprises the steps of detecting the aviation gear by the same method, recording an MBN signal of a tooth surface to be detected, extracting a characteristic value, inputting a stress prediction model, and obtaining a corresponding stress value so as to realize stress detection of grinding burn of the aviation gear.
Although embodiments of the present invention have been shown and described, it is understood that the embodiments are illustrative and not restrictive, that various changes, modifications, substitutions and alterations may be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (2)
1. The method for detecting the grinding burn stress of the aviation gear is characterized by comprising the following steps:
step 1: preparing a tensile test block and a detection probe;
step 2: carrying out a static load tensile test under the yield strength, closely attaching a lateral convex surface of a probe to the center of a tensile test block, and measuring and recording corresponding MBN signals at intervals of 1 kN;
and step 3: and (3) carrying out wavelet packet transformation on the obtained MBN signal f (t):
wherein i =1,2, \8230;, n,
is wavelet packet transform coefficient; i is the band order; j is the decomposition level; k is a translation parameter; />
Is a function of the wavelet packet, is greater than or equal to>
Wavelet packet coefficients of each frequency band order of the MBN signal are obtained by wavelet packet transformation, and a wavelet packet coefficient matrix A can be constructed:
wherein n =2 j The total number of frequency bands obtained by decomposing j layers of wavelet packets; m is the wavelet packet coefficient length of each frequency band; let A be an element of R m×n Then there is an orthogonal matrix U ∈ R m×n And the orthogonal matrix V ∈ R m×n So that the following holds:
A＝USV T
wherein S = diag [ σ ] 1 ，σ 2 ，…,σ p ]P = min (m, n); the non-zero elements on the diagonal of the matrix S are the singular values of the matrix A, and σ is 1 ≥σ 2 ≥…≥σ p Not less than 0; singular values are eigenvalues of matrix A, thus MBN eigenvectors { σ } consisting of elements other than zero on the diagonal of matrix S i There is a link between surface stress and (i =1,2, \8230;, q);
and 4, step 4: feature vector [ sigma ] formed by MBN singular values i The method comprises the following steps of (i =1,2, \8230;, q) serving as an input sample, taking an actual stress value as an output sample, setting initial parameters, inputting the initial parameters into a BP neural network, and then carrying out training optimization to finally obtain a stress prediction model;
and 5: and (3) detecting the aviation gear by using the detection method in the step 1, recording an MBN signal of the tooth surface to be detected, and inputting the MBN signal into a stress prediction model after the MBN signal is processed in the steps 2 and 3 to obtain a corresponding stress value.
2. A device for detecting the aviation gear grinding burn stress according to the claim 1, characterized by comprising an excitation module (1) connected with an excitation coil (2) and a magnetic yoke (5) arranged on a detection platform (6), wherein the excitation coil (2) is arranged on the magnetic yoke (5), a magnetic shielding body (3) is arranged below the magnetic yoke (5), a detection coil (4) is arranged at the center position right below the magnetic shielding body (3), the detection coil (4) is connected with a signal amplifier (7) through a signal line, and the signal amplifier (7) is sequentially connected with a filter circuit (8) and a PC (9);
the excitation module (1) is used for generating a sine signal with fixed frequency, the sine signal flows through the excitation coil (2) to generate a magnetic field, and the magnetic field is transmitted to the detection platform (6) through a high-permeability material used in the magnetic yoke (5);
the Barkhausen noise signal generated by the detection platform (6) is received by the detection coil (4), and the interference of external noise is reduced through the magnetic shielding body (3);
the signal amplifier (7) is used for amplifying an initial Barkhausen noise signal;
the filter circuit (8) is used for filtering out interference noise signals;
and the PC (9) is used for converting the analog signal into a digital signal and displaying the obtained Barkhausen noise signal.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5164669A (en) * | 1990-07-23 | 1992-11-17 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Method of characterizing residual stress in ferromagnetic materials using a pulse histogram of acoustic emission signals |
| CN101887048A (en) * | 2010-06-25 | 2010-11-17 | 南京航空航天大学 | Barkhausen detection system and method under condition of high-speed operations |
| CN104865309A (en) * | 2015-04-07 | 2015-08-26 | 江苏省特种设备安全监督检验研究院 | Method and sensor system for reducing Barkhausen noise detection errors |
| CN106052922A (en) * | 2016-05-24 | 2016-10-26 | 北京工业大学 | Multi-pole micro-magnetic sensor for steel belt residual stress nondestructive testing |
| CN107340162A (en) * | 2017-07-10 | 2017-11-10 | 北京工业大学 | The preparation method of calibration sample during a kind of Barkhausen Noise detection grinding burn |
| CN107576722A (en) * | 2017-08-30 | 2018-01-12 | 哈尔滨理工大学 | A kind of ferromagnetic material grinding burn detection method based on barkhausen |
| CN107655682A (en) * | 2016-07-25 | 2018-02-02 | 李志刚 | A kind of gear grinding burn automatic checkout system |
| CN109142057A (en) * | 2018-09-11 | 2019-01-04 | 北京工业大学 | The Barkhausen noise non-destructive measuring method and system of ferrimagnet local stress and strain field |
| CN109871956A (en) * | 2019-02-26 | 2019-06-11 | 北京工业大学 | The method of microscopic structure variation in quick predict burn gear |
| CN112683997A (en) * | 2020-12-15 | 2021-04-20 | 中国航发哈尔滨轴承有限公司 | Method for monitoring surface grinding quality of bearing ring by Barkhausen noise analysis method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109307568B (en) * | 2018-11-16 | 2021-03-02 | 北京理工大学 | Nondestructive testing method for welding residual stress and probe adopting same |
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- 2021-11-26 CN CN202111416643.0A patent/CN114136778B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5164669A (en) * | 1990-07-23 | 1992-11-17 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Method of characterizing residual stress in ferromagnetic materials using a pulse histogram of acoustic emission signals |
| CN101887048A (en) * | 2010-06-25 | 2010-11-17 | 南京航空航天大学 | Barkhausen detection system and method under condition of high-speed operations |
| CN104865309A (en) * | 2015-04-07 | 2015-08-26 | 江苏省特种设备安全监督检验研究院 | Method and sensor system for reducing Barkhausen noise detection errors |
| CN106052922A (en) * | 2016-05-24 | 2016-10-26 | 北京工业大学 | Multi-pole micro-magnetic sensor for steel belt residual stress nondestructive testing |
| CN107655682A (en) * | 2016-07-25 | 2018-02-02 | 李志刚 | A kind of gear grinding burn automatic checkout system |
| CN107340162A (en) * | 2017-07-10 | 2017-11-10 | 北京工业大学 | The preparation method of calibration sample during a kind of Barkhausen Noise detection grinding burn |
| CN107576722A (en) * | 2017-08-30 | 2018-01-12 | 哈尔滨理工大学 | A kind of ferromagnetic material grinding burn detection method based on barkhausen |
| CN109142057A (en) * | 2018-09-11 | 2019-01-04 | 北京工业大学 | The Barkhausen noise non-destructive measuring method and system of ferrimagnet local stress and strain field |
| CN109871956A (en) * | 2019-02-26 | 2019-06-11 | 北京工业大学 | The method of microscopic structure variation in quick predict burn gear |
| CN112683997A (en) * | 2020-12-15 | 2021-04-20 | 中国航发哈尔滨轴承有限公司 | Method for monitoring surface grinding quality of bearing ring by Barkhausen noise analysis method |
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