CN103901111A - Nondestructive detection system and method of blades of wind turbine generator system - Google Patents
Nondestructive detection system and method of blades of wind turbine generator system Download PDFInfo
- Publication number
- CN103901111A CN103901111A CN201410111073.8A CN201410111073A CN103901111A CN 103901111 A CN103901111 A CN 103901111A CN 201410111073 A CN201410111073 A CN 201410111073A CN 103901111 A CN103901111 A CN 103901111A
- Authority
- CN
- China
- Prior art keywords
- acoustic emission
- signal
- unit
- blade
- data acquisition
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
Abstract
The invention provides a nondestructive detection system and a nondestructive detection method of blades of a wind turbine generator system. An acoustic emission sensor unit is used for detecting internal deformation of a fan blade and a destruction signal of crack expansion when a fan blade material is loaded, and used for transmitting a detected signal to an acoustic emission pre-amplification and filtering unit; the acoustic emission pre-amplification and filtering unit is used for amplifying and filtering the detected signal, and used for subsequently transmitting the amplified and filtered signal to an acoustic emission data acquisition unit; the acoustic emission data acquisition unit is used for acquiring a signal transmitted from the acoustic emission pre-amplification and filtering unit so as to generate an acoustic emission signal, and used for transmitting the acoustic emission signal to a data processing display unit; the data processing and display unit is used for performing denoising treatment on the acoustic emission signal by using a wavelet analysis method, used for reconstructing the processed signal through wavelet transform, and used for analyzing the reconstructed signal so as to extract characteristic values and a frequency spectrum.
Description
Technical field
The present invention relates to state-detection and fault diagnosis field in wind-powered electricity generation generating, more particularly, the present invention relates to a kind of nondestructive detection system and method for wind generator set blade.
Background technology
Wind-powered electricity generation is fastest-rising new forms of energy in the world, and fan blade is the first link that wind-powered electricity generation unit carries out energy conversion, is also important step, and its performance directly has influence on the performance of whole wind power system.Blade working is in high-altitude, and environment is very severe, and blade is suffering strict test almost all the time all bearing and corroding, and spring, summer, autumn and winter, scorching summer and freezing winter, thunder and lightning, hail, sleet, sand and dust all likely produce damage to fan blade at any time.According to statistics, the accident that blade injury produces will account for 1/3rd of total accident, and when blade generation damage accident, electric field must stop generating, start repairing, the serious also necessary blade of changing, this will cause the maintenance cost of great number, also brings very large economic loss to wind energy turbine set.
Therefore, if can detect in time the damage of blade and safeguard, must effectively cause machine halt trouble by lacking fan blade, effectively avoid the generation of huge loss.
Summary of the invention
Technical matters to be solved by this invention is for there being above-mentioned defect in prior art, and a kind of nondestructive detection system and method for the wind generator set blade based on calibrate AE sensor is provided.
In order to realize above-mentioned technical purpose, according to a first aspect of the invention, the nondestructive detection system that a kind of wind generator set blade is provided, it comprises: calibrate AE sensor unit, the preposition amplification of acoustic emission and filter unit, acoustic emission data acquisition unit and data processing display unit;
Calibrate AE sensor unit is connected with the preposition amplification of acoustic emission and filter unit; The preposition amplification of acoustic emission and filter unit are connected with acoustic emission data acquisition unit; Acoustic emission data acquisition unit is connected with data processing display unit;
Calibrate AE sensor unit is used for detecting the internal modification of fan blade and the damage signal of Crack Extension at fan blade material in stand under load process, and the signal detecting is passed to the preposition amplification of acoustic emission and filter unit;
The preposition amplification of acoustic emission and filter unit, for the signal detecting is amplified and filtering, then pass to acoustic emission data acquisition unit by amplification and filtered signal;
Acoustic emission data acquisition unit produces acoustic emission signal for the signal collection that the preposition amplification of acoustic emission and filter unit are passed over, and acoustic emission signal is passed to data processing display unit;
Data processing display unit is used for utilizing wavelet analysis method to carry out Denoising disposal to acoustic emission signal, and utilizes wavelet transformation will cause signal after treatment to be reconstructed, and the signal analysis after reconstruct is extracted to eigenwert and frequency spectrum.
Preferably, described calibrate AE sensor unit comprises with root of blade, at a distance of the position at length of blade 20% to 40% place, calibrate AE sensor is arranged as to distributed sensor array.
Preferably, the preposition amplification of acoustic emission and filter unit are connected with acoustic emission data acquisition unit by coaxial cable for high frequency.
According to a second aspect of the invention, provide a kind of lossless detection method of wind generator set blade, it comprises:
In stand under load process, utilize the internal modification of calibrate AE sensor unit inspection fan blade and the damage signal of Crack Extension at fan blade material, and the signal detecting is passed to the preposition amplification of acoustic emission and filter unit;
Utilize the preposition amplification of acoustic emission and filter unit to amplify and filtering the signal detecting, then amplification and filtered signal are passed to acoustic emission data acquisition unit;
The signal collection of utilizing acoustic emission data acquisition unit to pass over the preposition amplification of acoustic emission and filter unit produces acoustic emission signal, and acoustic emission signal is passed to data processing display unit;
Utilize data processing display unit to utilize wavelet analysis method to carry out Denoising disposal to acoustic emission signal, and utilize wavelet transformation will cause signal after treatment to be reconstructed, the signal analysis after reconstruct is extracted to eigenwert and frequency spectrum.
Preferably, described calibrate AE sensor unit comprises with root of blade, at a distance of the position at length of blade 20% to 40% place, calibrate AE sensor is arranged as to distributed sensor array.
Brief description of the drawings
By reference to the accompanying drawings, and by reference to detailed description below, will more easily there is more complete understanding to the present invention and more easily understand its advantage of following and feature, wherein:
Fig. 1 schematically shows the nondestructive detection system of wind generator set blade according to the preferred embodiment of the invention.
Fig. 2 schematically shows kayser (Kaiser) effect.
Fig. 3 schematically shows burst parameter.
It should be noted that, accompanying drawing is used for illustrating the present invention, and unrestricted the present invention.Note, the accompanying drawing that represents structure may not be to draw in proportion.And in accompanying drawing, identical or similar element indicates identical or similar label.
Embodiment
In order to make content of the present invention more clear and understandable, below in conjunction with specific embodiments and the drawings, content of the present invention is described in detail.
Fig. 1 schematically shows the nondestructive detection system of wind generator set blade according to the preferred embodiment of the invention.
As shown in Figure 1, the nondestructive detection system of wind generator set blade comprises according to the preferred embodiment of the invention: calibrate AE sensor unit 1, the preposition amplification of acoustic emission and filter unit 2, acoustic emission data acquisition unit 3 and data processing display unit 4.
Calibrate AE sensor unit 1 is connected with the preposition amplification of acoustic emission and filter unit 2; The preposition amplification of acoustic emission and filter unit 2(for example pass through coaxial cable for high frequency) be connected with acoustic emission data acquisition unit 3; Acoustic emission data acquisition unit 3(is for example by Electricity Federation mode) be connected with data processing display unit 4.
At fan blade material, in stand under load process, calibrate AE sensor unit 1 can detect the internal modification of fan blade and the damage signal of Crack Extension, and the signal detecting is passed to the preposition amplification of acoustic emission and filter unit 2.
The preposition amplification of acoustic emission and filter unit 2 amplify and filtering the signal detecting, thus acoustic emission signal are carried out to rough handling, then amplification and filtered signal are passed to acoustic emission data acquisition unit 3.Such as, the signal after prime amplifier amplifies, some are less than the mechanical noise of 100KHz, carry out frequency discriminating, and then pre-filtering is transferred to data acquisition unit through coaxial cable for high frequency.
The signal collection that acoustic emission data acquisition unit 3 passes over the preposition amplification of acoustic emission and filter unit 2 produces acoustic emission signal, and acoustic emission signal is passed to data processing display unit 4.Preferably, for data acquisition unit 4, can be according to the suitable data sampling frequency of the efficiently sampling frequency selection purposes of selected calibrate AE sensor, then choose suitable data collecting card, at supporting corresponding interface circuit, be embedded in master controller, then master controller be connected with computer, thereby realize collection and the digital-to-analog conversion of simulating signal.
Data processing display unit 4 utilizes wavelet analysis method to carry out Denoising disposal to acoustic emission signal, and utilizes wavelet transformation will cause signal after treatment to be reconstructed, and the signal analysis after reconstruct is extracted to eigenwert and frequency spectrum.The signal obtaining through acoustic emission data acquisition unit 3, sends into data processing display unit 4.Data processing display unit 4, using intelligent computer as hardware foundation, utilizes intelligent computer, to signal process, analyze, judgement etc., and by processing, analysis, judgment result displays on screen.
In fact, the signal collecting through acoustic emission data acquisition unit 3, it is a kind of astable burst, need to adopt software or the module such as matlab data analysis software, utilize wavelet analysis to carry out making an uproar to signal, namely noise is eliminated, the signal after noise is eliminated, by signal reconstruction, be beneficial to analyze extraction eigenwert and frequency thereof at process wavelet transformation.
Thereby the crackle that can utilize eigenwert and frequency spectrum to describe fan blade can be expanded destruction situation, realize the real-time detection diagnosis to blade of wind-driven generator, the monitoring while work to realizing fan blade has great meaning.
As the calibrate AE sensor of the concrete example of calibrate AE sensor unit 1, it is a kind of sensor that stress signal is converted to simulating signal.Can, according to different blower fan different leaves, choose applicable calibrate AE sensor; And, can for example, at the high position of blade loss percentage (: with the position of root of blade at a distance of length of blade 20% to 40% place), adopt reasonably intensive sensor arrangement (preferably, adopt distributed sensor array), at the low position of blade loss percentage, can adopt single-sensor to arrange.And, can calmodulin binding domain CaM localization method determine damaged scope.
The acoustic emission signal that each calibrate AE sensor gathers can have a circuit-switched data line to be transferred to acoustic emission preamplifier, and signal is amplified to processing.The voltage signal of calibrate AE sensor output is low to moderate several microvolts sometimes, faint like this signal, if enter the transmission of long-distance, intensity must reduce, so transmit after original sensor signal must being amplified to several times, conventional enlargement factor has 34,40 and 60Db, is being transferred to data acquisition unit through coaxial cable for high frequency.A key technical indexes of acoustic emission preamplifier is noise level, generally should be less than 10 microvolts.Prime amplifier need to have the function of impedance matching and conversion, impacts for preventing that input signal is excessive, also should have the protective capability of reactance voltage impact and the recovery capability of antiblocking phenomenon, and have larger out-put dynamic range.
In wavelet analysis, in the face of this numerous wavelet basis, choose suitable wavelet basis as the instrument of analyzing acoustic emission signal, there is very important effect for the noise taking-up in signal analysis, the extraction of key message, for acoustic emission signal, choosing of small echo need to be considered several aspects:
(1) discrete wavelet analysis is compared continuous wavelet analysis and is more suitable for the processing in acoustic emission signal.
(2) acoustic emission signal is generally all demblee form signal, has the characteristic of momentary signal, so the wavelet basis of choosing need to have the property tightening in time domain, needs wavelet basis on the frequency band of frequency domain, to have the character of quick decay.
(3) can be learnt by the formula of wavelet analysis, the character of the wavelet basis of choosing preferably and measured signal be similar to.
(4) for the analysis of signal, preferably choose symmetrical wavelet basis, or the symmetrical wavelet basis of trying one's best is to reduce the distortion of signal.
After the eigenwert and frequency thereof of extracting acoustic emission signal through wavelet analysis, in conjunction with kayser (Kaiser) effect and Felicity (Felicity) effect, and utilize time-of-arrival loaction and regional mapping method, determine damaged position and size.
As shown in Figure 2, so-called kayser (Kaiser) effect and Felicity (Felicity) effect, be two important characteristics of acoustic emission signal.The acoustic emission that same test specimen produces under identical conditions for once, it is exactly so-called kayser (Kaiser) effect, in the time adding load for the first time, blade construction can release energy with the form of acoustic emission, but while reloading after recovering, in the time that not exceeding a front load, load capacity do not produce acoustic emission, only have blade construction in the time exceeding a front maximum load amount that acoustic emission just can occur, as Fig. 2.
Felicity (Felicity) effect refers in the time that damage has occurred blade construction, replys the front maximum load amount of after load amount light rain, and blade construction still has acoustic emission and produces.These two characteristics are to differentiate the Important Theoretic Foundation whether blade occurs damage, in the time that blade adds load, if after load capacity once when increasing progressively but being no more than a front load capacity, to not there is not or occur a small amount of acoustic emission, now meet kayser (Kaiser) effect, think that blade is healthy; Otherwise acoustic emission signal meets Felicity (Felicity) effect, can think blade occur damage, as ripple hits technology, Ring-down count, amplitude, energy, rise time and duration etc.Ripple hits the instantaneous acoustic emission signal of the mistake threshold value that refers to that a certain passage detects, in Fig. 3, crossing the large-signal that the envelope of threshold value forms is exactly that a ripple hits.Ripple is hit and counted, can reflect the total amount of acoustic emission activity, the foundation that occurs and expand as damage.
In the time having judged damage generation or expansion, in conjunction with time-of-arrival loaction and regional mapping method, determine acoustic emission source, obtain corresponding blade injury position, realize the Non-Destructive Testing of genset blade.
In another preferred embodiment of the present invention, also provide the lossless detection method of corresponding wind generator set blade.
The present invention at least tool has the following advantages:
1, acoustic emission is a kind of dynamic lossless detection method, is applicable to wind-powered electricity generation detection field, can reduce wind-power electricity generation maintenance cost, increases the service life and guarantee safe power supply.
2, acoustic emission detection is subject to the restriction of material and member geometric configuration hardly, and applicability is very strong.No matter which kind of design specification fan blade is, acoustic emission detection method can be utilized.
3, the susceptibility of acoustic emission is high, can detect accurately that out of order a situation arises.
4, the method can communicate with the pitch-controlled system of wind power generating set and brake system, under risk status, controls blower fan, stoppage protection equipment at blade.
In addition, it should be noted that, unless stated otherwise or point out, otherwise the descriptions such as term " first " in instructions, " second ", " the 3rd " are only for distinguishing each assembly, element, step of instructions etc., instead of for representing logical relation or the ordinal relation etc. between each assembly, element, step.
Be understandable that, although the present invention discloses as above with preferred embodiment, but above-described embodiment is not in order to limit the present invention.For any those of ordinary skill in the art, do not departing from technical solution of the present invention scope situation, all can utilize the technology contents of above-mentioned announcement to make many possible variations and modification to technical solution of the present invention, or be revised as the equivalent embodiment of equivalent variations.Therefore, every content that does not depart from technical solution of the present invention,, all still belongs in the scope of technical solution of the present invention protection any simple modification made for any of the above embodiments, equivalent variations and modification according to technical spirit of the present invention.
Claims (5)
1. a nondestructive detection system for wind generator set blade, is characterized in that comprising: calibrate AE sensor unit, the preposition amplification of acoustic emission and filter unit, acoustic emission data acquisition unit and data processing display unit;
Calibrate AE sensor unit is connected with the preposition amplification of acoustic emission and filter unit; The preposition amplification of acoustic emission and filter unit are connected with acoustic emission data acquisition unit; Acoustic emission data acquisition unit is connected with data processing display unit;
Calibrate AE sensor unit is used for detecting the internal modification of fan blade and the damage signal of Crack Extension at fan blade material in stand under load process, and the signal detecting is passed to the preposition amplification of acoustic emission and filter unit;
The preposition amplification of acoustic emission and filter unit, for the signal detecting is amplified and filtering, then pass to acoustic emission data acquisition unit by amplification and filtered signal;
Acoustic emission data acquisition unit produces acoustic emission signal for the signal collection that the preposition amplification of acoustic emission and filter unit are passed over, and acoustic emission signal is passed to data processing display unit;
Data processing display unit is used for utilizing wavelet analysis method to carry out Denoising disposal to acoustic emission signal, and utilizes wavelet transformation will cause signal after treatment to be reconstructed, and the signal analysis after reconstruct is extracted to eigenwert and frequency spectrum.
2. the nondestructive detection system of wind generator set blade according to claim 1, it is characterized in that, described calibrate AE sensor unit comprises with root of blade, at a distance of the position at length of blade 20% to 40% place, calibrate AE sensor is arranged as to distributed sensor array.
3. the nondestructive detection system of wind generator set blade according to claim 1 and 2, is characterized in that, the preposition amplification of acoustic emission and filter unit are connected with acoustic emission data acquisition unit by coaxial cable for high frequency.
4. a lossless detection method for wind generator set blade, is characterized in that comprising:
In stand under load process, utilize the internal modification of calibrate AE sensor unit inspection fan blade and the damage signal of Crack Extension at fan blade material, and the signal detecting is passed to the preposition amplification of acoustic emission and filter unit;
Utilize the preposition amplification of acoustic emission and filter unit to amplify and filtering the signal detecting, then amplification and filtered signal are passed to acoustic emission data acquisition unit;
The signal collection of utilizing acoustic emission data acquisition unit to pass over the preposition amplification of acoustic emission and filter unit produces acoustic emission signal, and acoustic emission signal is passed to data processing display unit;
Utilize data processing display unit to utilize wavelet analysis method to carry out Denoising disposal to acoustic emission signal, and utilize wavelet transformation will cause signal after treatment to be reconstructed, the signal analysis after reconstruct is extracted to eigenwert and frequency spectrum.
5. the lossless detection method of wind generator set blade according to claim 4, it is characterized in that, described calibrate AE sensor unit comprises with root of blade, at a distance of the position at length of blade 20% to 40% place, calibrate AE sensor is arranged as to distributed sensor array.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410111073.8A CN103901111A (en) | 2014-03-24 | 2014-03-24 | Nondestructive detection system and method of blades of wind turbine generator system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410111073.8A CN103901111A (en) | 2014-03-24 | 2014-03-24 | Nondestructive detection system and method of blades of wind turbine generator system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103901111A true CN103901111A (en) | 2014-07-02 |
Family
ID=50992570
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410111073.8A Pending CN103901111A (en) | 2014-03-24 | 2014-03-24 | Nondestructive detection system and method of blades of wind turbine generator system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103901111A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104101652A (en) * | 2014-07-10 | 2014-10-15 | 南京航空航天大学 | Audio signal based wind power blade damage monitoring method and system |
| CN104391045A (en) * | 2014-10-28 | 2015-03-04 | 邢涛 | Sound-wave-based square wood hole-defect recognition system and method |
| CN104677623A (en) * | 2015-03-16 | 2015-06-03 | 西安交通大学 | On-site acoustic diagnosis method and monitoring system for wind turbine blade failure |
| CN105004363A (en) * | 2015-07-01 | 2015-10-28 | 长安大学 | Sensor performance on-line test device and method based on multi-threshold wavelet under strong interference |
| CN106353406A (en) * | 2016-08-26 | 2017-01-25 | 北京普华亿能风电技术有限公司 | Wind turbine generation set bolt breakage monitoring device |
| CN106645424A (en) * | 2016-12-09 | 2017-05-10 | 四川西南交大铁路发展股份有限公司 | Method and system for filtering online monitored noise of steel rail cracks and judging cracks |
| CN110208848A (en) * | 2019-06-20 | 2019-09-06 | 中国长江电力股份有限公司 | Detection device of metal foreign body between a kind of generator stator-rotator |
| CN110967251A (en) * | 2019-12-02 | 2020-04-07 | 湘潭大学 | Method for identifying damage mode of wind power blade |
| CN111058996A (en) * | 2019-11-25 | 2020-04-24 | 上海电机学院 | Brake energy storage device and control method thereof |
| WO2020228386A1 (en) * | 2019-05-13 | 2020-11-19 | 青岛理工大学 | Method for identifying crack initiation stress of rock using acoustic emission technology |
| CN112945531A (en) * | 2021-02-03 | 2021-06-11 | 西人马(西安)测控科技有限公司 | Method, device and equipment for detecting cracks of fan blade and computer storage medium |
| CN113406200A (en) * | 2021-05-27 | 2021-09-17 | 北京京能能源技术研究有限责任公司 | Wind turbine blade damage positioning detection device |
| CN115166032A (en) * | 2022-05-23 | 2022-10-11 | 东南大学 | Device and method for detecting cracks of fan blade |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008119350A2 (en) * | 2007-03-29 | 2008-10-09 | Vestas Wind Systems A/S | Method for inspecting at least one rotor blade of a wind turbine and inspection system for at least one rotor blade of a wind turbine |
| CN101435799A (en) * | 2008-12-19 | 2009-05-20 | 清华大学 | Failure diagnosis method and apparatus of hydroturbine based on acoustic emission technology |
| CN101949895A (en) * | 2010-08-20 | 2011-01-19 | 江苏大学 | Method for identifying fine crack impact signal of metal deep drawing part |
| CN102109498A (en) * | 2009-12-28 | 2011-06-29 | 天津工业大学 | Nondestructive testing system and testing analysis method for three-dimensional braided composite material |
| US20130191040A1 (en) * | 2010-08-17 | 2013-07-25 | Korea Research Institute Of Standards And Science | Method and apparatus for locating a source of damage in a large composite material structure |
| CN103389341A (en) * | 2012-05-10 | 2013-11-13 | 沈阳工业大学 | Windmill blade crack detection method |
-
2014
- 2014-03-24 CN CN201410111073.8A patent/CN103901111A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008119350A2 (en) * | 2007-03-29 | 2008-10-09 | Vestas Wind Systems A/S | Method for inspecting at least one rotor blade of a wind turbine and inspection system for at least one rotor blade of a wind turbine |
| CN101435799A (en) * | 2008-12-19 | 2009-05-20 | 清华大学 | Failure diagnosis method and apparatus of hydroturbine based on acoustic emission technology |
| CN102109498A (en) * | 2009-12-28 | 2011-06-29 | 天津工业大学 | Nondestructive testing system and testing analysis method for three-dimensional braided composite material |
| US20130191040A1 (en) * | 2010-08-17 | 2013-07-25 | Korea Research Institute Of Standards And Science | Method and apparatus for locating a source of damage in a large composite material structure |
| CN101949895A (en) * | 2010-08-20 | 2011-01-19 | 江苏大学 | Method for identifying fine crack impact signal of metal deep drawing part |
| CN103389341A (en) * | 2012-05-10 | 2013-11-13 | 沈阳工业大学 | Windmill blade crack detection method |
Non-Patent Citations (1)
| Title |
|---|
| 杜文超: "基于声发射技术的大型风力机叶片材料的损伤研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》, no. 2, 15 February 2012 (2012-02-15) * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104101652A (en) * | 2014-07-10 | 2014-10-15 | 南京航空航天大学 | Audio signal based wind power blade damage monitoring method and system |
| CN104391045A (en) * | 2014-10-28 | 2015-03-04 | 邢涛 | Sound-wave-based square wood hole-defect recognition system and method |
| CN104677623A (en) * | 2015-03-16 | 2015-06-03 | 西安交通大学 | On-site acoustic diagnosis method and monitoring system for wind turbine blade failure |
| CN105004363A (en) * | 2015-07-01 | 2015-10-28 | 长安大学 | Sensor performance on-line test device and method based on multi-threshold wavelet under strong interference |
| CN106353406A (en) * | 2016-08-26 | 2017-01-25 | 北京普华亿能风电技术有限公司 | Wind turbine generation set bolt breakage monitoring device |
| CN106645424A (en) * | 2016-12-09 | 2017-05-10 | 四川西南交大铁路发展股份有限公司 | Method and system for filtering online monitored noise of steel rail cracks and judging cracks |
| CN106645424B (en) * | 2016-12-09 | 2020-01-17 | 四川西南交大铁路发展股份有限公司 | Steel rail crack online monitoring noise filtering and crack judging method |
| WO2020228386A1 (en) * | 2019-05-13 | 2020-11-19 | 青岛理工大学 | Method for identifying crack initiation stress of rock using acoustic emission technology |
| CN110208848A (en) * | 2019-06-20 | 2019-09-06 | 中国长江电力股份有限公司 | Detection device of metal foreign body between a kind of generator stator-rotator |
| CN111058996A (en) * | 2019-11-25 | 2020-04-24 | 上海电机学院 | Brake energy storage device and control method thereof |
| CN110967251A (en) * | 2019-12-02 | 2020-04-07 | 湘潭大学 | Method for identifying damage mode of wind power blade |
| CN112945531A (en) * | 2021-02-03 | 2021-06-11 | 西人马(西安)测控科技有限公司 | Method, device and equipment for detecting cracks of fan blade and computer storage medium |
| CN113406200A (en) * | 2021-05-27 | 2021-09-17 | 北京京能能源技术研究有限责任公司 | Wind turbine blade damage positioning detection device |
| CN115166032A (en) * | 2022-05-23 | 2022-10-11 | 东南大学 | Device and method for detecting cracks of fan blade |
| CN115166032B (en) * | 2022-05-23 | 2024-04-19 | 东南大学 | Device and method for detecting cracks of fan blades |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103901111A (en) | Nondestructive detection system and method of blades of wind turbine generator system | |
| CN105136435B (en) | A kind of method and apparatus of wind generator set blade fault diagnosis | |
| CN102798529B (en) | Method and system for diagnosing bearing faults of large-size wind turbine bearing | |
| CN104677623B (en) | A kind of blade of wind-driven generator fault acoustics in place diagnostic method and monitoring system | |
| CN103698674A (en) | Flow cable partial discharge online monitoring system | |
| CN101458158A (en) | Steam turbine plain bearing failure diagnosis method based on acoustic emission detection and device thereof | |
| CN105738806B (en) | A kind of wind power generating set fault diagnosis system and method | |
| CN104101652B (en) | Audio signal based wind power blade damage monitoring method and system | |
| CN105021706B (en) | A kind of grinding wheel broken state early warning identification device and method | |
| CN104900013A (en) | Mud-rock flow monitoring and alarming system based on infrasonic wave | |
| CN103018601A (en) | Primary fault diagnosis method of converter in wind turbine system | |
| CN101776644B (en) | Double-spectrum analysis-based fatigue damage state characterization and quantitative evaluation system for in-service 16 manganese steel bearing member | |
| CN107064298B (en) | The laser detecting method of blower blade cracks in a kind of operation | |
| CN104198904A (en) | Insulation tube type bus partial discharge detecting device | |
| Ma et al. | An integrated monitoring scheme for wind turbine main bearing using acoustic emission | |
| CN203745428U (en) | Micro-crack nondestructive testing device | |
| Xu et al. | Caputo-Fabrizio fractional order derivative stochastic resonance enhanced by ADOF and its application in fault diagnosis of wind turbine drivetrain | |
| CN110596447A (en) | Electric field measurement-based charged equipment surrounding safety detection device and detection method thereof | |
| CN107064673A (en) | Electric arc detecting device and double-fed wind power generator electric brush slip ring protection system | |
| CN111833336A (en) | Hyperspectrum-based wind power blade surface sand hole fault detection system and method | |
| CN103344893A (en) | Distributed cable partial discharge measuring method based on frequency conversion series resonance high-voltage holding test | |
| CN203881539U (en) | Wind turbine generator state monitoring and fault diagnosis device | |
| CN202383244U (en) | Partial discharge detection device of gas insulated switchgear | |
| CN104777446B (en) | A kind of capacitive current transformer on-line fault diagnosis device and method | |
| CN101788536B (en) | Deformation damage state representing and quantitative evaluating system of in-service 16 manganese steel load-bearing member welding structure based on bispectrum analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20140702 |