CN107144426A - A kind of utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress - Google Patents
A kind of utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress Download PDFInfo
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- CN107144426A CN107144426A CN201710291403.XA CN201710291403A CN107144426A CN 107144426 A CN107144426 A CN 107144426A CN 201710291403 A CN201710291403 A CN 201710291403A CN 107144426 A CN107144426 A CN 107144426A
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- wind turbines
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- gray relative
- relative analysis
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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
- G01M13/00—Testing of machine parts
- G01M13/02—Gearings; Transmission mechanisms
- G01M13/021—Gearings
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- General Physics & Mathematics (AREA)
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Abstract
A kind of utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress, it is characterized in that：Step one：Fault signature based on angular domain signal dimensionless factor trend is extracted；Step 2：By the degree of association between gray relative analysis method analytical parameters and fault mode feature, so as to obtain accurate analysis result.The present invention proposes rank proportion sampling method, detects that data, from time domain to angular domain conversion processing, complete data characteristics transition from non-linear to stable by unit, so as to fluctuate the influence brought to data in view of gear-box center shafting.
Description
Technical field
The present invention relates to the diagnostic method that a kind of utilization gray relative analysis method detects Wind turbines gear distress, belong to event
Hinder detection field.
Background technology
As outdoor generating equipment, the operating condition of wind power generating set is influenceed fairly obvious by natural cause.Due to wind
The change at any time of speed, shaft system of unit rotating speed also changes accordingly, causes running of wind generating set operating mode to have more obvious
Fluctuation.
Gear-box is the important speed increasing mechanism of double-fed wind generator, and its compact conformation, part is various, is turned for a long time by change
Speed, the influence of varying load, easily occur the failures such as gear surface abrasion, spot corrosion, equipment failure can be caused when serious.Although gear
The ratio that case failure accounts for machine failure does not include height, but the downtime that its failure is caused is longer, electric field economic loss very
Greatly.
For such issues that, domestic and foreign scholars propose many analysis methods for being directed to Fault Diagnosis of Gear Case, wherein having
The methods such as small wavelength-division, neutral net, SVMs.Although these methods can realize the extraction to gear-box data characteristics with
Diagnosis, but during whole analysis, the difference between characteristic parameter under different faults pattern is generally only concerned, seldom consider
Because the influence that the fluctuation of gear-box center shafting is brought to data.Meanwhile, in failure diagnostic process, lack to participating in each of calculating
The further investigation contacted between individual characteristic parameter and fault mode, is caused in actual applications, can not be obtained very good
Effect.By air speed influence, the rotating speed moment of gearbox of wind turbine shafting changes, and Monitoring Data, operational factor are with operating mode
Change had a significant effect for final failure analysis result.As can be seen here, the status monitoring for Wind turbines equipment and event
The research for hindering diagnosis content is particularly significant.For this Wind turbines tooth is detected the invention provides one kind using gray relative analysis method
Take turns the diagnostic method of failure.
The content of the invention
It is an object of the invention to provide the diagnosis that a kind of utilization gray relative analysis method detects Wind turbines gear distress
Method.
To achieve the above object, the present invention is adopted the technical scheme that：One kind detects wind-powered electricity generation using gray relative analysis method
The diagnostic method of unit gear distress, it is characterized in that：Step one：Fault signature based on angular domain signal dimensionless factor trend is carried
Take；Step 2：By the degree of association between gray relative analysis method analytical parameters and fault mode feature, so as to be relatively defined
True analysis result.
A kind of described utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress, described step
One includes rank than the pretreatment of resampling signal and two parts of feature extraction of set gear box fault-signal.
A kind of described utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress, described rank ratio
Time-domain signal is changed into angular domain signal by method for resampling, to improve the stationarity of signal.
A kind of described utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress, described failure
Signal characteristic abstraction and by the extraction to a variety of dimensionless groups of angular domain signal, in this, as analysis just opinion case run according to
According to, and due to the certainty of failure, its malfunction is also classified into n kinds.
A kind of described utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress, described grey
The essence of correlation method is to compare the degree of closeness of data and curves geometries, and data and curves geometry closer to, association
Degree is bigger, and actually detected data are just closer to reference case.
Brief description of the drawings
Accompanying drawing 1 is the principle schematic of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the present invention is clearly and completely retouched
State, it is clear that described embodiment is only a part of embodiment of invention, rather than whole embodiments, based in the present invention
Embodiment, the every other embodiment that those of ordinary skill in the art are obtained under the premise of creative work is not made,
Belong to the scope of protection of the invention.
The invention discloses the diagnostic method that a kind of utilization gray relative analysis method detects Wind turbines gear distress, it is special
Levying is：Step one：Fault signature based on angular domain signal dimensionless factor trend is extracted；Step 2：Pass through grey correlation analysis
The degree of association between method analytical parameters and fault mode feature, so as to obtain accurate analysis result.
A kind of described utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress, it is characterized in that：
Described step one includes rank than the pretreatment of resampling signal and two parts of feature extraction of set gear box fault-signal.
A kind of described utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress, it is characterized in that：
Time-domain signal is changed into angular domain signal by described rank than method for resampling, to improve the stationarity of signal.
A kind of described utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress, it is characterized in that：
Described fault-signal feature extraction and by the extraction to a variety of dimensionless groups of angular domain signal, just case is discussed in this, as analysis
The foundation of operation, and due to the certainty of failure, its malfunction is also classified into n kinds.
A kind of described utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress, it is characterized in that：
The essence of described Grey Incidence is to compare the degree of closeness of data and curves geometries, and the geometry of data and curves is got over
Close, the degree of association is bigger, and actually detected data are just closer to reference case.
Claims (5)
1. a kind of utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress, it is characterized in that：
Step one：Fault signature based on angular domain signal dimensionless factor trend is extracted；
Step 2：By the degree of association between gray relative analysis method analytical parameters and fault mode feature, so as to obtain more
Accurate analysis result.
2. a kind of utilization gray relative analysis method according to claim 1 detects the diagnosis side of Wind turbines gear distress
Method, it is characterized in that：Described step one includes rank than the pretreatment of resampling signal and the feature of set gear box fault-signal
Extract two parts.
3. a kind of utilization gray relative analysis method according to claim 2 detects the diagnosis side of Wind turbines gear distress
Method, it is characterized in that：Time-domain signal is changed into angular domain signal by described rank than method for resampling, to improve the stationarity of signal.
4. a kind of utilization gray relative analysis method according to claim 2 detects the diagnosis side of Wind turbines gear distress
Method, it is characterized in that：Described fault-signal feature extraction and by the extraction to a variety of dimensionless groups of angular domain signal, is made with this
The foundation that case is run just is discussed for analysis, and due to the certainty of failure, its malfunction is also classified into n kinds.
5. a kind of utilization gray relative analysis method according to claim 1 detects the diagnosis side of Wind turbines gear distress
Method, it is characterized in that：The essence of described Grey Incidence is to compare the degree of closeness of data and curves geometries, and data and curves
Geometry closer to the degree of association is bigger, and actually detected data are just closer to reference case.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201710291403.XA CN107144426A (en) | 2017-04-28 | 2017-04-28 | A kind of utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress |
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| CN201710291403.XA CN107144426A (en) | 2017-04-28 | 2017-04-28 | A kind of utilization gray relative analysis method detects the diagnostic method of Wind turbines gear distress |
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| CN107144426A true CN107144426A (en) | 2017-09-08 |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112069626A (en) * | 2020-09-14 | 2020-12-11 | 哈尔滨理工大学 | Method for analyzing correlation between milling surface three-dimensional morphology parameters and abrasion loss based on grey correlation |
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| CN103983452A (en) * | 2014-04-18 | 2014-08-13 | 中国人民解放军国防科学技术大学 | Failure mode recognition method of epicyclic gearbox using mixed domain feature vector and grey correlation analysis |
| CN104330258A (en) * | 2014-10-23 | 2015-02-04 | 徐州隆安光电科技有限公司 | Method for identifying grey relational degree of rolling bearing fault based on characteristic parameters |
| CN105606360A (en) * | 2015-11-24 | 2016-05-25 | 国网内蒙古东部电力有限公司电力科学研究院 | Fault diagnosis method for condition-variable planetary gear box based on multi-sensor information fusion |
| CN106127136A (en) * | 2016-06-21 | 2016-11-16 | 上海电机学院 | Fault Diagnosis of Roller Bearings |
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2017
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Patent Citations (4)
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|---|---|---|---|---|
| CN103983452A (en) * | 2014-04-18 | 2014-08-13 | 中国人民解放军国防科学技术大学 | Failure mode recognition method of epicyclic gearbox using mixed domain feature vector and grey correlation analysis |
| CN104330258A (en) * | 2014-10-23 | 2015-02-04 | 徐州隆安光电科技有限公司 | Method for identifying grey relational degree of rolling bearing fault based on characteristic parameters |
| CN105606360A (en) * | 2015-11-24 | 2016-05-25 | 国网内蒙古东部电力有限公司电力科学研究院 | Fault diagnosis method for condition-variable planetary gear box based on multi-sensor information fusion |
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| CN112069626A (en) * | 2020-09-14 | 2020-12-11 | 哈尔滨理工大学 | Method for analyzing correlation between milling surface three-dimensional morphology parameters and abrasion loss based on grey correlation |
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Application publication date: 20170908 |