CN202793793U - Large wind generation set bearing fault diagnosis system - Google Patents
Large wind generation set bearing fault diagnosis system Download PDFInfo
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- CN202793793U CN202793793U CN 201220436345 CN201220436345U CN202793793U CN 202793793 U CN202793793 U CN 202793793U CN 201220436345 CN201220436345 CN 201220436345 CN 201220436345 U CN201220436345 U CN 201220436345U CN 202793793 U CN202793793 U CN 202793793U
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Abstract
The utility model discloses a large wind generation set bearing fault diagnosis system which is characterized in that vibration signals of a bearing of a spindle of a wind generation set are measured, a resonance frequency band of bearing fault is automatically extracted by aid of the self-adapting spectral kurtosis technology, on the basis, fault characteristic frequency is obtained by aid of the narrow-band filtering technology and the envelope demodulation technology, and finally the fault characteristic frequency is compared with the theoretical calculated value to recognize the bearing fault type in positioning mode. The large wind generation set bearing fault diagnosis system is not influenced by unstable working conditions and working condition noise of the wind generation set, and is suitable for practical wind generation set practical operation conditions; can automatically recognize the resonance frequency band caused by bearing faults without manual interference; and can fast and automatically recognize the bearing fault type.
Description
Technical field
The utility model belongs to the physical construction fault diagnosis field, is specifically related to a kind of large scale wind unit bearing failure diagnosis system.
Background technology
Along with green energy resource is more and more paid attention to, world's wind-powered electricity generation obtains high speed development in recent years.The new installed capacity of wind-powered electricity generation of China in 2009 has occupied the whole world first, total installation of generating capacity only falls behind the U.S. and occupies the whole world second, but the normal working hours of China's blower fan and generated energy are all disproportionate with installed capacity, far below world average level, reason is exactly that primary clustering such as the failure rates such as gear case, bearing of fan transmission system are higher.Recent years, Wind turbine developed towards the large type of MW class, and in a single day they break down and will cause larger economic loss.Therefore, carry out the monitoring and diagnosis to large scale wind unit kinematic train, improve the reliability of unit, reduce failure rate and reduce maintenance cost very urgent.
The utility model content
Technical problem to be solved in the utility model is to propose a kind of large scale wind unit bearing failure diagnosis system, and it can be to diagnosing the bearing fault of large scale wind unit variable parameter operation efficiently, reliably.
For addressing the above problem, the utility model is realized by following scheme:
A kind of large scale wind unit bearing failure diagnosis system comprises acceleration transducer, Fast Fourier Transform (FFT) unit, spectrum kurtosis analytic unit, filter unit, Envelope Analysis unit, property data base and pattern recognition unit; Wherein acceleration transducer is installed on the bearing seat of Wind turbine to be measured, the output terminal of acceleration transducer is connected to the input end of spectrum kurtosis analytic unit through the Fast Fourier Transform (FFT) unit, the output terminal of spectrum kurtosis analytic unit connects the input end of Envelope Analysis unit through filter unit, the output terminal of Envelope Analysis unit and property data base are connected on the input end of pattern recognition unit jointly, and the output terminal of pattern recognition unit is the fault diagnosis output terminal.
In the such scheme, under the bearing seat of described Wind turbine to be measured respectively installation shaft to 2 piezoelectric acceleration vibration transducers radially, the output terminal of these 2 piezoelectric acceleration vibration transducers all is connected to spectrum kurtosis analytic unit.
In the such scheme, also be connected to AD conversion unit between described acceleration transducer and the Fast Fourier Transform (FFT) unit.
Compared with prior art, the utlity model has following features:
(1) the utility model overcomes non-stationary operating mode and the operating mode noise effect of wind-powered electricity generation unit, is suitable for actual wind-powered electricity generation unit actual motion condition; Do not need artificial participation can automatically identify the resonance bands that bearing fault causes; And can automatically identify the bearing fault type.
(2) the damage of the bearing speed of the utility model identification is fast, is suitable under the wind-powered electricity generation unit duty real time fail and patrols and examines and on-line monitoring; Avoid sudden accident to occur.
(3) the utility model does not rely on concrete bearing designation, can according to basic geometric parameters and the rotation rotating speed of different model wind generator set main shaft bearing, provide in advance the theoretical computation of characteristic values of bearing fault to use.Therefore, can in wind-powered electricity generation unit bearing failure diagnosis, extensively promote the use of.
Description of drawings
Fig. 1 is a kind of large scale wind unit bearing failure diagnosis schematic diagram.
Embodiment
Referring to Fig. 1, a kind of large scale wind unit bearing failure diagnosis system comprises acceleration transducer, AD conversion unit, Fast Fourier Transform (FFT) unit, spectrum kurtosis analytic unit, filter unit, Envelope Analysis unit, property data base and pattern recognition unit.Wherein acceleration transducer is installed on the bearing seat of Wind turbine to be measured, the output terminal of acceleration transducer is connected on the input end of AD conversion unit, the output terminal of AD conversion unit is connected to the input end of spectrum kurtosis analytic unit through the Fast Fourier Transform (FFT) unit, the output terminal of spectrum kurtosis analytic unit connects the input end of Envelope Analysis unit through filter unit, the output terminal of Envelope Analysis unit and property data base are connected on the input end of pattern recognition unit jointly, and the output terminal of pattern recognition unit is the fault diagnosis output terminal.In the utility model, above-mentioned modules adopts the known hardware capability module of prior art, and the interconnected relationship between its module and the module is core improvement of the present utility model.By the formation of above-mentioned each known function module, so that the wind-powered electricity generation unit does not need artificial participation just can automatically identify the resonance bands that bearing fault causes, and can automatically identify the bearing fault type.
Acceleration transducer resamples by typical non-stationary run signal being carried out angular domain for the original vibration signal of picking up main shaft bearing, obtains steady-state signal.In this example, the wind power generating set model is NEG-MiconNM1000/60, and rated power is 1070kw, and maximum rotative speed is 1500rpm.Be subjected to the impact of working environment, wind power generating set often is operated in the non-stationary that presents operation under the impact of alternate load, the characteristic such as non-linear; These extract bearing fault characteristics and bring many difficulties.In this example under the bearing seat of bearing respectively installation shaft to 2 piezoelectric acceleration vibration transducers radially, to gather the vibration signal of main shaft bearing.The output terminal of 2 piezoelectric acceleration vibration transducers all links to each other with the Fast Fourier Transform (FFT) unit by AD conversion unit.
AD conversion unit is carried out the pre-service of analog to digital conversion, amplification and anti-aliasing filter to the original vibration signal that collects.
The Fast Fourier Transform (FFT) unit carries out Fast Fourier Transform (FFT) to original vibration signal.
The vibration signal of spectrum kurtosis analytic unit after to Fast Fourier Transform (FFT) composed the kurtosis analysis, seeks the corresponding window function of maximum spectrum kurtosis value.
Filter unit constructs an optimum bandpass filter according to the window function that spectrum kurtosis analytic unit searches out.
The Envelope Analysis unit to filtered signal carry out Envelope Analysis and and analysis of spectrum, obtain the envelope signal spectrum, in envelope spectrum, extract bearing fault characteristics frequency and each harmonic composition thereof.
Pattern recognition unit compares the bearing fault characteristics frequency that extracts and pre-stored known bearing fault characteristic frequency in the computer characteristic database; When the bearing fault characteristics frequency that extracts was identical with known bearing fault characteristic frequency, Computer Automatic Recognition went out the concrete fault mode of this bearing to be measured; When the bearing fault characteristics frequency that extracts is not identical with known bearing fault characteristic frequency, computing machine is stored this bearing fault characteristics frequency that can't differentiate, under the storage can't automatic discrimination the concrete fault mode of gear case need to adopt artificial investigation mode could progressively go out to judge the concrete fault mode of bearing.
Claims (3)
1. large scale wind unit bearing failure diagnosis system is characterized in that: comprise acceleration transducer, Fast Fourier Transform (FFT) unit, spectrum kurtosis analytic unit, filter unit, Envelope Analysis unit, property data base and pattern recognition unit; Wherein acceleration transducer is installed on the bearing seat of Wind turbine to be measured, the output terminal of acceleration transducer is connected to the input end of spectrum kurtosis analytic unit through the Fast Fourier Transform (FFT) unit, the output terminal of spectrum kurtosis analytic unit connects the input end of Envelope Analysis unit through filter unit, the output terminal of Envelope Analysis unit and property data base are connected on the input end of pattern recognition unit jointly, and the output terminal of pattern recognition unit is the fault diagnosis output terminal.
2. large scale wind unit bearing failure diagnosis system according to claim 1, it is characterized in that: under the bearing seat of described Wind turbine to be measured respectively installation shaft to 2 piezoelectric acceleration vibration transducers radially, the output terminal of these 2 piezoelectric acceleration vibration transducers all is connected to spectrum kurtosis analytic unit.
3. large scale wind unit bearing failure diagnosis system according to claim 1 is characterized in that: also be connected to AD conversion unit between described acceleration transducer and the Fast Fourier Transform (FFT) unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201220436345 CN202793793U (en) | 2012-08-30 | 2012-08-30 | Large wind generation set bearing fault diagnosis system |
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| Application Number | Priority Date | Filing Date | Title |
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| CN 201220436345 CN202793793U (en) | 2012-08-30 | 2012-08-30 | Large wind generation set bearing fault diagnosis system |
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| CN202793793U true CN202793793U (en) | 2013-03-13 |
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| CN 201220436345 Expired - Fee Related CN202793793U (en) | 2012-08-30 | 2012-08-30 | Large wind generation set bearing fault diagnosis system |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102798529A (en) * | 2012-08-30 | 2012-11-28 | 桂林电子科技大学 | Method and system for diagnosing bearing faults of large-size wind turbine bearing |
| CN103575523A (en) * | 2013-11-14 | 2014-02-12 | 哈尔滨工程大学 | Rotating machine fault diagnosis method based on Fast ICA-spectrum kurtosis-envelope spectrum analysis |
| CN103792086A (en) * | 2014-02-26 | 2014-05-14 | 徐可君 | Rolling bearing fault diagnostic method based on spectral kurtosis algorithm and quantum genetic algorithm |
| CN104198186A (en) * | 2014-08-29 | 2014-12-10 | 南京理工大学 | Method and device for diagnosing gear faults based on combination of wavelet packet and spectral kurtosis |
| CN105954030A (en) * | 2016-06-29 | 2016-09-21 | 潍坊学院 | Envelopment analysis method based on intrinsic time scale decomposition and spectral kurtosis |
| CN107643181A (en) * | 2016-07-21 | 2018-01-30 | 北京航空航天大学 | A kind of rolling bearing variable working condition method for diagnosing faults based on image recognition |
| CN111769810A (en) * | 2020-06-29 | 2020-10-13 | 浙江大学 | Fluid mechanical modulation frequency extraction method based on energy kurtosis spectrum |
-
2012
- 2012-08-30 CN CN 201220436345 patent/CN202793793U/en not_active Expired - Fee Related
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102798529A (en) * | 2012-08-30 | 2012-11-28 | 桂林电子科技大学 | Method and system for diagnosing bearing faults of large-size wind turbine bearing |
| CN103575523A (en) * | 2013-11-14 | 2014-02-12 | 哈尔滨工程大学 | Rotating machine fault diagnosis method based on Fast ICA-spectrum kurtosis-envelope spectrum analysis |
| CN103792086A (en) * | 2014-02-26 | 2014-05-14 | 徐可君 | Rolling bearing fault diagnostic method based on spectral kurtosis algorithm and quantum genetic algorithm |
| CN104198186A (en) * | 2014-08-29 | 2014-12-10 | 南京理工大学 | Method and device for diagnosing gear faults based on combination of wavelet packet and spectral kurtosis |
| CN105954030A (en) * | 2016-06-29 | 2016-09-21 | 潍坊学院 | Envelopment analysis method based on intrinsic time scale decomposition and spectral kurtosis |
| CN105954030B (en) * | 2016-06-29 | 2018-03-23 | 潍坊学院 | It is a kind of based on it is interior grasp time scale decompose and spectrum kurtosis envelope Analysis Method |
| CN107643181A (en) * | 2016-07-21 | 2018-01-30 | 北京航空航天大学 | A kind of rolling bearing variable working condition method for diagnosing faults based on image recognition |
| CN107643181B (en) * | 2016-07-21 | 2019-11-12 | 北京航空航天大学 | A kind of rolling bearing variable working condition method for diagnosing faults based on image recognition |
| CN111769810A (en) * | 2020-06-29 | 2020-10-13 | 浙江大学 | Fluid mechanical modulation frequency extraction method based on energy kurtosis spectrum |
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| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20130313 Termination date: 20150830 |
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| EXPY | Termination of patent right or utility model |