CN114674420A - Method for identifying vibration of gearbox - Google Patents
Method for identifying vibration of gearbox Download PDFInfo
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- CN114674420A CN114674420A CN202210514733.1A CN202210514733A CN114674420A CN 114674420 A CN114674420 A CN 114674420A CN 202210514733 A CN202210514733 A CN 202210514733A CN 114674420 A CN114674420 A CN 114674420A
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- 238000013016 damping Methods 0.000 claims description 3
- 238000013461 design Methods 0.000 abstract description 4
- 238000005457 optimization Methods 0.000 abstract description 4
- 238000001228 spectrum Methods 0.000 description 10
- 230000005540 biological transmission Effects 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000000354 decomposition reaction Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
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- 230000002159 abnormal effect Effects 0.000 description 1
- 230000003213 activating effect Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M7/00—Vibration-testing of structures; Shock-testing of structures
- G01M7/02—Vibration-testing by means of a shake table
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
Abstract
The invention discloses a method for identifying the vibration of a gear box, which is characterized in that a vibration sensor is arranged on the gear box to obtain the vibration signal of the gear box, the vibration signal of the gear box is input into a harmonic Gaussian random resonance system to obtain an optimal parameter pair of the harmonic Gaussian random resonance system, the obtained optimal parameter pair is substituted into the harmonic Gaussian random resonance system, the optimal signal of the resonance system is numerically solved, and finally the vibration source position of the gear box is identified from the optimal signal; the method has the advantages that the weak vibration signal of the gearbox vibration source is enhanced by establishing the harmonic Gaussian random resonance system, and four parameters of the harmonic Gaussian random resonance system are optimized based on the multilayer coding genetic algorithm, so that the accurate identification of the complex gearbox vibration source is realized, and the feedback optimization design of the power-assisted gearbox is realized.
Description
Technical Field
The invention relates to vibration detection of a transmission part in mechanical equipment, in particular to a method for identifying vibration of a gearbox.
Background
The gear box is a key basic part of mechanical equipment and a transmission part in an automobile, and the vibration of the gear box is related to the service safety of the whole part. Because installation, design and machining errors are inevitably generated in the manufacturing process of the gear box, the errors inevitably excite abnormal vibration in the whole component operation process, and how to identify the vibration source of the gear box and lock the position of the vibration source is of great importance to the safe and efficient service of the gear box manufacturing and components thereof.
The whole gear box is complex in structure and consists of multi-stage gear train transmission, and the vibration transmission path is long and tortuous, so that the attenuated vibration signal transmitted from a source to a sensor measuring point is extremely weak; moreover, the gearbox is only a small part of the whole mechanical equipment or a production chain, the vibration signal of the gearbox is submerged in the vibration signals of other parts and components and is difficult to separate, and strong background noise in the working environment causes difficulty in identifying the vibration source of the gearbox.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for identifying the vibration of the gearbox, which enhances the weak vibration signal of the vibration source of the gearbox by activating the harmonic Gaussian random resonance of the weak vibration signal of the vibration source of the gearbox and the background noise in the vibration signal so as to realize the vibration source identification of the gearbox.
The technical scheme adopted by the invention for solving the technical problems is as follows: a method for identifying vibration of a gearbox comprises the following specific steps:
(1) mounting a vibration sensor on the gearbox, and acquiring a vibration signal of the gearbox, wherein the vibration signal is represented as F (t);
(2) inputting a vibration signal F (t) of the gearbox into a harmonic Gaussian stochastic resonance system:
wherein: x (t) is an output vibration signal of the harmonic Gaussian stochastic resonance system; gamma is the damping factor of the system; k.
、
are all system parameters; t is a sampling time variable of the vibration sensor;
(3) obtaining the optimal parameter pair of the harmonic Gaussian stochastic resonance system as (alpha)opt，βopt，γopt，kopt）；
(4) And (3) obtaining the optimal parameter pair (alpha) in the step (3)opt，βopt，γopt，kopt) Substituting the harmonic gaussian stochastic resonance system, namely:
numerically solving the optimal output vibration signal of the resonant system
And finally, identifying the vibration source position of the gearbox from the optimal output vibration signal.
Further, in the step (1), the vibration sensor is installed on a bearing end cover of the input end of the gearbox, the sampling frequency of the vibration sensor is set to be 1-2 times of the maximum value in the vibration frequency range of the gearbox, and the sampling time is 1-2 s.
Further, in the step (3), the method for obtaining the optimal parameter pair of the resonance system includes: initializing the optimizing ranges of four parameters of gamma, k, alpha and beta, wherein the optimizing ranges are respectively gamma epsilon (0, 1)]，k∈[-5，5]，α∈[-5，5]，β∈[-5，5]And calculating a corrected kurtosis index WKC:
wherein: c represents the cross correlation coefficient between the output vibration signal x (t) of the resonance system and the vibration signal F (t) of the gear box, K represents the dimensionless kurtosis index of the output vibration signal x (t) of the resonance system, WKC is taken as the target function of the multilayer coding genetic algorithm, then the maximum value of the corrected kurtosis index WKC is taken as the target, and the optimal parameter pair (alpha) is obtained through the multilayer coding genetic algorithmopt，βopt，γopt，kopt）。
Further, the modified kurtosis index
In the above-mentioned relation, the first and second,
，
，
wherein: m is the number of sampling points, n is a natural number, and the value range is n from the element [1, M]；
For output vibration signals of harmonic Gaussian stochastic resonance systems at a certain sampling point, FnThe vibration signal of the gearbox is obtained by vibrating the sensor at a certain sampling point;
and
represents the average of signals x (t) and f (t), respectively.
Compared with the prior art, the method has the advantages that the weak vibration signal of the gearbox vibration source is enhanced by establishing the harmonic Gaussian random resonance system, and four parameters of the harmonic Gaussian random resonance system are optimized based on the multilayer coding genetic algorithm, so that the accurate identification of the complex gearbox vibration source is realized, and the feedback optimization design of the power-assisted gearbox is realized.
Drawings
FIG. 1a is a raw vibration signal of a gearbox obtained experimentally for the present invention;
FIG. 1b is a frequency spectrum corresponding to the original vibration signal of FIG. 1 a;
FIG. 1c is an envelope spectrum corresponding to the original vibration signal of FIG. 1 a;
FIG. 2a is a graph of resonance enhanced output vibration signal after treatment by the method of the present invention;
FIG. 2b is a partially amplified frequency spectrum corresponding to the output vibration signal of FIG. 2 a;
FIG. 3a is a vibration signal obtained by a robust local mean decomposition method;
fig. 3b is a partially enlarged envelope spectrum corresponding to the vibration signal of fig. 3 a.
Detailed Description
The invention is described in further detail below with reference to the accompanying examples.
As shown in the figure, the method for identifying the vibration of the gearbox comprises the following specific steps:
(1) selecting a vibration sensor with corresponding frequency response according to design parameters (input rotating speed and vibration signal frequency band) of the gearbox, then installing the vibration sensor on a bearing end cover at the input end of the gearbox, setting the sampling frequency of the vibration sensor to be 1-2 times of the maximum value in the vibration frequency range of the gearbox, and setting the sampling time to be 1-2 s, and obtaining a vibration signal of the gearbox, wherein the vibration signal is expressed as F (t);
(2) inputting a vibration signal F (t) of the gearbox into a harmonic Gaussian stochastic resonance system:
wherein: x (t) is an output vibration signal of the harmonic Gaussian stochastic resonance system; gamma is the damping factor of the system; k.
、
are all system parameters; t is the sampling time variable of the vibration sensor, t belongs to [0 ] and the sampling time length]；
(3) And initializing optimization ranges of the four parameters of gamma, k, alpha and beta, wherein the optimization ranges are respectively gamma belongs to (0, 1), k belongs to-5, alpha belongs to-5, and beta belongs to-5, 5), and calculating a corrected kurtosis index WKC:
wherein: c represents the cross-correlation coefficient between the output vibration signal x (t) of the resonance system and the vibration signal F (t) of the gear box, K represents the dimensionless kurtosis index of the output vibration signal x (t) of the resonance system, M is the number of sampling points, n is a natural number, and the value range is n epsilon [1, M]；
For output vibration signals of harmonic Gaussian stochastic resonance systems at a certain sampling point, FnTo at a certain miningA vibration signal of the gearbox is obtained by a vibration sensor during sampling;
and
represents the average of signals x (t) and f (t), respectively; then, the WKC is used as a target function of the multilayer coding genetic algorithm, then the maximum value of the corrected kurtosis index WKC is used as a target, and the optimal parameter pair (alpha) is obtained through the multilayer coding genetic algorithmopt，βopt，γopt，kopt）；
(4) And (3) obtaining the optimal parameter pair (alpha) in the step (3)opt，βopt，γopt，kopt) Substituting the harmonic gaussian stochastic resonance system, namely:
numerically solving the optimal output vibration signal of the resonant system
And finally, identifying the vibration source position of the gearbox from the optimal output vibration signal.
The experimental verification process for vibration source identification of the gearbox is as follows.
Firstly, a complex gearbox experiment table is utilized to verify the identification method of the invention, a vibration sensor is arranged at a bearing end cover at the input end of the gearbox, the sampling frequency of the vibration sensor is set to be 20kHz, the sampling time is set to be 1s, and a multi-source strong coupling vibration signal F (t) of the complex gearbox is obtained, as shown in figures 1 a-1 c, the noise in the signal is strong, the multi-source vibration signal is strongly coupled, no obvious vibration component can be seen, the frequency spectrum and the envelope spectrum of the multi-source vibration signal can not see the obvious vibration component, the spectrum and the envelope spectrum are basically submerged by background noise, although an obvious characteristic spectrum peak exists at 1000Hz, the identification method is found to be irrelevant to the vibration source of the gearbox through comparison with the theoretical calculation frequency of each vibration part of the gearbox.
Then the method of the invention is used for processing the vibration signal F (t) of the complex gearbox, and the result is shown in figures 2 a-2 b, and the frequency f is shownouterAnd 2fouterClear spectral peaks exist, and the characteristic frequency and the second harmonic component of the fault of the outer ring of the bearing at the driving end of the gear box are found by comparing the frequency with the theoretical calculation frequency of each vibration part of the gear box, which shows that the outer ring of the bearing at the driving end of the gear box has a wear fault. It is inferred that the cause of the increased vibration of the gearbox is the failure of the drive end bearing.
For comparison, fig. 3a to 3b show the result of the robust local mean decomposition method, and it can be seen that there is no obvious periodic impact feature in the decomposed component signal, and the clear spectral peaks in the local amplification envelope spectrum are not the drive end bearing outer ring fault feature and its harmonic, so it can be seen that the method cannot identify the vibration source of the gearbox.
In summary, according to the gear box vibration identification method, the harmonic gaussian stochastic resonance system is established, the corrected kurtosis index is used as the target function of the multilayer coding genetic algorithm, four parameters of the harmonic gaussian stochastic resonance system are optimized, the cooperative resonance of noise and the gear box weak vibration signal is optimally activated, the weak vibration signal excited by the gear box vibration source is enhanced, and therefore the vibration source position of the gear box is identified.
The scope of the present invention includes, but is not limited to, embodiments, which are subject to the appended claims, and any alterations, modifications, and improvements that may occur to those skilled in the art are intended to be within the scope of the present invention.
Claims (4)
1. A method for identifying vibration of a gearbox is characterized by comprising the following specific steps:
(1) mounting a vibration sensor on the gearbox, and acquiring a vibration signal of the gearbox, wherein the vibration signal is represented as F (t);
(2) inputting a vibration signal F (t) of the gearbox into a harmonic Gaussian stochastic resonance system:
wherein: x (t) is an output vibration signal of the harmonic Gaussian stochastic resonance system; gamma is the damping factor of the system; k.
、
are all system parameters; t is a sampling time variable of the vibration sensor;
(3) obtaining the optimal parameter pair of the harmonic Gaussian stochastic resonance system as (alpha)opt，βopt，γopt，kopt）；
(4) And (3) obtaining the optimal parameter pair (alpha) in the step (3)opt，βopt，γopt，kopt) Substituting the harmonic gaussian stochastic resonance system, namely:
numerically solving the optimal output vibration signal of the resonant system
And finally, identifying the vibration source position of the gearbox from the optimal output vibration signal.
2. A method of identifying gearbox vibrations as claimed in claim 1, wherein: in the step (1), the vibration sensor is installed on a bearing end cover at the input end of the gear box, the sampling frequency of the vibration sensor is set to be 1-2 times of the maximum value in the vibration frequency range of the gear box, and the sampling duration is 1-2 s.
3. An identification of gearbox vibrations as claimed in claim 1The method is characterized in that: in the step (3), the method for obtaining the optimal parameter pair of the resonance system comprises: initializing the optimizing ranges of four parameters of gamma, k, alpha and beta, wherein the optimizing ranges are respectively gamma epsilon (0, 1)]，k∈[-5，5]，α∈[-5，5]，β∈[-5，5]And calculating a corrected kurtosis index WKC:
wherein: c represents the cross correlation coefficient between the output vibration signal x (t) of the resonance system and the vibration signal F (t) of the gear box, K represents the dimensionless kurtosis index of the output vibration signal x (t) of the resonance system, WKC is taken as the target function of the multilayer coding genetic algorithm, then the maximum value of the corrected kurtosis index WKC is taken as the target, and the optimal parameter pair (alpha) is obtained through the multilayer coding genetic algorithmopt，βopt，γopt，kopt）。
4. A method of identifying gearbox vibrations as claimed in claim 3, wherein: the corrected kurtosis index
In the above-mentioned relation, the first and second,
，
，
wherein: m is the number of sampling points, n is a natural number, and the value range is n from the element [1, M]；
For output vibration signals of harmonic Gaussian stochastic resonance systems at a certain sampling point, FnThe vibration signal of the gearbox is obtained by vibrating the sensor at a certain sampling point;
and
represents the average of signals x (t) and f (t), respectively.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1141578A1 (en) * | 1998-12-23 | 2001-10-10 | Sikorsky Aircraft Corporation | Active vibration control system identification with enhanced noise reduction |
| CN105303181A (en) * | 2015-11-04 | 2016-02-03 | 燕山大学 | Stochastic resonance weak impact feature enhancement extraction method on the basis of sliding window |
| CN109855874A (en) * | 2018-12-13 | 2019-06-07 | 安徽大学 | A kind of accidental resonance filter of sound ancillary vibration small-signal enhancing detection |
| CN113052000A (en) * | 2021-02-04 | 2021-06-29 | 江苏科技大学 | Early weak fault signal characteristic diagnosis method for ship mechanical equipment |
| CN113447267A (en) * | 2021-06-22 | 2021-09-28 | 上海电机学院 | Gear box complete machine state evaluation method and system based on vibration signal analysis |
-
2022
- 2022-05-12 CN CN202210514733.1A patent/CN114674420A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1141578A1 (en) * | 1998-12-23 | 2001-10-10 | Sikorsky Aircraft Corporation | Active vibration control system identification with enhanced noise reduction |
| CN105303181A (en) * | 2015-11-04 | 2016-02-03 | 燕山大学 | Stochastic resonance weak impact feature enhancement extraction method on the basis of sliding window |
| CN109855874A (en) * | 2018-12-13 | 2019-06-07 | 安徽大学 | A kind of accidental resonance filter of sound ancillary vibration small-signal enhancing detection |
| CN113052000A (en) * | 2021-02-04 | 2021-06-29 | 江苏科技大学 | Early weak fault signal characteristic diagnosis method for ship mechanical equipment |
| CN113447267A (en) * | 2021-06-22 | 2021-09-28 | 上海电机学院 | Gear box complete machine state evaluation method and system based on vibration signal analysis |
Non-Patent Citations (2)
| Title |
|---|
| 张刚 等: "指数型随机共振微弱振动信号检测方法", 《振动与冲击》 * |
| 谭继勇 等: "冲击信号的随机共振自适应检测方法", 《机械工程学报》 * |
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Application publication date: 20220628 |