CN105784353A - Fault diagnosis method for gear case of aerogenerator - Google Patents
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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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- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention provides a fault diagnosis method for a gear case of an aerogenerator. The method comprises the following steps that a vibration sensor obtains a vibration signal when the gear box runs; a wavelet packet analysis method is used to carry out three-layer decomposition analysis on the collected vibration signal; empirical mode decomposition is carried out on the vibration signal after wavelet packet analysis, and a first component of the signal is extracted; characteristic values are extracted from the extracted first signal component, and serve as a characteristic value used for fault diagnosis; a characteristic vector sample of historical fault data of the gear case is obtained; a support vector machine is used to train the characteristic vector sample, and a group of highest classification accuracy is used as parameters for fault diagnosis later; real-time operation data of the gear case is obtained, and a characteristic vector is obtained; and the support vector machine is used to classify the characteristic vector, and a diagnosis result is output. According to the invention, whether the operation state of the gear box is normal can be analyzed, and a fault part can be determined in a fault state.
Description
Technical Field
The invention relates to the field of wind generating set fault detection, in particular to a wind driven generator gearbox fault diagnosis method.
Background
Because of the problems of the shortage and safety of the previous energy sources, the world is looking at green, pollution-free and renewable wind energy, the wind energy is taken as a clean and renewable energy source and is increasingly valued by countries all over the world, the accumulated amount of the wind energy is huge, and the wind energy of the world is about 2.74 × 109MW, in which the available wind power is 2 × 107MW is 10 times larger than the total amount of water energy which can be developed and utilized on the earth. The kinetic energy of wind is converted into mechanical kinetic energy, and then the mechanical energy is converted into electric kinetic energy, namely wind power generation. The principle of wind power generation is that wind power drives windmill blades to rotate, and then the rotating speed is increased through a speed increaser, so that a generator is promoted to generate electricity. The devices required for wind power generation are called wind generating sets. The wind generating set can be divided into three parts of a wind wheel (including a tail vane), a generator and a tower. Because the rotating speed ratio of the wind wheel is lower, and the size and the direction of wind power are changed frequently, the rotating speed is unstable; therefore, before driving the generator, a gear box for increasing the rotating speed to the rated rotating speed of the generator must be added, a speed regulating mechanism is added for keeping the rotating speed stable, and then the speed regulating mechanism is connected to the power generationAnd 4, the machine is on board.
As the installed capacity of fans continues to increase, failure of fans also occurs frequently. The slight fault needs to carry out maintenance detection on the fan; major failures require shutdown for maintenance, which not only causes serious economic loss, but also causes a series of potential safety hazards. The gearbox is statistically used as an important transmission component of the fan, and the failure rate of the component is very high. Therefore, in order to ensure the safe and stable operation of the wind turbine and the durable and effective power generation, the fault diagnosis work of the gearbox is very necessary. However, although the conventional methods such as spectrum analysis can detect a single and simple fault, they cannot be satisfied with the identification of complex faults. Therefore, it is necessary to provide a fault diagnosis method with high efficiency and good performance to solve this problem.
Disclosure of Invention
In order to overcome the defects of the existing fault diagnosis technology, the invention aims to provide a method for diagnosing the fault of the gearbox of the wind driven generator, which can analyze whether the running state of the gearbox is normal or not and determine a fault component in the fault state.
In order to achieve the above object, the present invention provides a wind turbine gearbox fault diagnosis method, comprising the following steps:
acquiring a vibration signal of a gearbox during operation through a vibration sensor arranged on a gearbox body of the fan;
carrying out three-layer decomposition analysis on the acquired vibration signals by adopting a wavelet packet analysis method;
carrying out empirical mode decomposition on the vibration signal subjected to wavelet packet analysis, and extracting a first component of the signal;
extracting a characteristic value of the extracted first signal component to be used as a characteristic vector used in fault diagnosis;
acquiring a feature vector sample of historical fault data of the gearbox;
training the characteristic vector samples by using a support vector machine, and taking the group with the highest classification accuracy as a parameter used in the subsequent fault diagnosis;
acquiring real-time operation data of a gearbox, and acquiring a characteristic vector;
and classifying the feature vectors by using a support vector machine, and outputting a diagnosis result.
Furthermore, the mounting points of the vibration sensors are respectively distributed at the axial position and the radial position of the low-speed end, the middle end and the high-speed end of the gearbox body of the fan.
Further, the wavelet packet three-layer decomposition analysis method comprises the following steps:
establishing a wavelet packet change three-layer decomposition relational expression;
selecting a corresponding threshold according to the soft threshold function to carry out threshold quantization processing on the high-frequency coefficient under each decomposition scale;
and performing one-dimensional wavelet reconstruction on the decomposed lowest layer low-frequency coefficient and high-frequency coefficient.
Further, the wavelet packet change three-layer decomposition relation is expressed as:
S＝AAA3+DAA3+ADA3+DDA3+AAD3+DAD3+ADD3+DDD3，
where A is the low frequency portion of the signal and D is the high frequency portion of the signal.
Further, the soft threshold function is:
wherein, sgn (W)j,k) Is a return shaping function, Wj,kDenotes the k threshold of the j layer if Wj,kIf > 0, sgn returnsReturning to 1; if Wj,kWhen the value is 0, sgn returns to 0; if Wj,kIf the value is less than 0, sgn returns to-1, and lambda represents the value range (lambda is more than or equal to 0 and less than or equal to 1).
Further, the wavelet reconstruction function is:
wherein,the wavelet coefficient of the nth node in the jth layer is represented, x (t) represents a denoised signal, and h and g are respectively low-frequency and high-frequency coefficients obtained after soft threshold processing.
Further, the characteristic values are respectively obtained from a time domain and a frequency domain, the time domain characteristic values comprise a peak index, a kurtosis index, a skewness index and a margin index, and the frequency domain characteristic values comprise a frequency center of gravity, a frequency standard deviation and a root mean square frequency.
The prior diagnosis method mainly has the problems of low diagnosis speed, inaccurate diagnosis result and the like. Experiments have shown that samples of operational characteristics of gearboxes play a crucial role in fault diagnosis work. Therefore, in order to improve the diagnosis effect, the invention utilizes a method of combining wavelet packet decomposition and empirical mode decomposition to reduce the noise of the vibration signal. Firstly, vibration sensors are arranged on the low-speed shaft, the intermediate shaft and the high-speed shaft of the gearbox in the axial direction and the radial direction, and vibration signals generated when the gearbox runs are collected through the vibration sensors. And secondly, carrying out wavelet packet decomposition and reconstruction on the acquired vibration signal line. After the signal is reconstructed, the signal is processed again by using empirical mode decomposition. The method can effectively eliminate the interference of noise on the signal.
The characteristic values extracted from the vibration signals after the two times of processing can better represent the running state of the equipment, and the characteristic values are provided for a support vector machine to carry out sample training, so that the accuracy of the classification result can be improved. The method can diagnose the fault type of the gear box, can determine the fault position, and has the advantages of accurate diagnosis result, clear result display and the like.
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FIG. 1 is a flow chart of a method for diagnosing a gearbox fault of a wind turbine generator according to a preferred embodiment of the present invention.
Fig. 2 is an exploded view of a three-layer wavelet packet according to a preferred embodiment of the present invention.
Detailed Description
The following description will be given with reference to the accompanying drawings, but the present invention is not limited to the following embodiments. Advantages and features of the present invention will become apparent from the following description and from the claims. It is noted that the drawings are in greatly simplified form and that non-precision ratios are used for convenience and clarity only to aid in the description of the embodiments of the invention.
Referring to fig. 1, fig. 1 is a flow chart illustrating a method for diagnosing a gearbox fault of a wind turbine according to a preferred embodiment of the invention. The invention provides a wind driven generator gearbox fault diagnosis method, which comprises the following steps of:
step S100: acquiring a vibration signal of a gearbox during operation through a vibration sensor arranged on a gearbox body of the fan;
step S200: carrying out three-layer decomposition analysis on the acquired vibration signals by adopting a wavelet packet analysis method;
step S300: carrying out empirical mode decomposition on the vibration signal subjected to wavelet packet analysis, and extracting a first component of the signal;
step S400: extracting a characteristic value of the extracted first signal component to be used as a characteristic vector used in fault diagnosis;
step S500: acquiring a feature vector sample of historical fault data of the gearbox;
step S600: training the characteristic vector samples by using a support vector machine, and taking the group with the highest classification accuracy as a parameter used in the subsequent fault diagnosis;
step S700: acquiring real-time operation data of a gearbox, and acquiring a characteristic vector;
step S800: and classifying the feature vectors by using a support vector machine, and outputting a diagnosis result.
According to the preferred embodiment of the invention, the mounting points of the vibration sensors are respectively distributed at the axial position and the radial position of the low-speed end, the middle end and the high-speed end of the gearbox body of the fan.
The wavelet packet three-layer decomposition analysis method comprises the following steps:
establishing a wavelet packet change three-layer decomposition relational expression;
selecting a corresponding threshold according to the soft threshold function to carry out threshold quantization processing on the high-frequency coefficient under each decomposition scale;
and performing one-dimensional wavelet reconstruction on the decomposed lowest layer low-frequency coefficient and high-frequency coefficient.
Referring to fig. 2, fig. 2 is an exploded view of a three-layer wavelet packet according to a preferred embodiment of the present invention. The relationship of wavelet packet change three-layer decomposition is as follows:
S＝AAA3+DAA3+ADA3+DDA3+AAD3+DAD3+ADD3+DDD3(1)
where a is the low frequency portion of the signal and D is the high frequency portion of the signal, the number of layers of wavelet packet decomposition is indicated by a number.
The soft threshold function is:
wherein, sgn (W)j,k) Is a return shaping function, Wj,kDenotes the k threshold of the j layer if Wj,kIf the value is more than 0, sgn returns to 1; if Wj,kWhen the value is 0, sgn returns to 0; if Wj,kIf the value is less than 0, sgn returns to-1, and lambda represents the value range (lambda is more than or equal to 0 and less than or equal to 1). The general method of selecting the threshold is to compare the noise reduction effect by selecting different lambda values.
The wavelet reconstruction function is:
wherein,the wavelet coefficient of the nth node in the jth layer is represented, x (t) represents a denoised signal, and h and g are respectively low-frequency and high-frequency coefficients obtained after soft threshold processing.
And (3) carrying out characteristic value extraction on the first signal component extracted by Empirical Mode Decomposition (EMD). The eigenvalues are obtained from the time domain and the frequency domain, respectively. The time domain characteristic values comprise a peak index, a kurtosis index, a skewness index, a margin index and the like; the frequency domain characteristic values comprise frequency barycenter, frequency standard deviation, root mean square frequency and the like. These time-frequency domain feature quantities are used as feature vectors for failure diagnosis.
Compared with the existing diagnosis method, the method provided by the invention has the advantages that the method of combining wavelet packet decomposition and empirical mode decomposition is utilized to reduce the noise of the vibration signal, and the interference of the noise to the signal can be effectively eliminated. The characteristic value extracted from the vibration signal after the secondary processing can accurately represent the running state of the equipment and can improve the accuracy of the classification of the support vector machine. The method not only can diagnose the fault type of the gearbox, but also can determine the fault position, and can be applied to fault diagnosis of other large mechanical equipment.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.
Claims (7)
1. A wind driven generator gearbox fault diagnosis method is characterized by comprising the following steps:
acquiring a vibration signal of a gearbox during operation through a vibration sensor arranged on a gearbox body of the fan;
carrying out three-layer decomposition analysis on the acquired vibration signals by adopting a wavelet packet analysis method;
carrying out empirical mode decomposition on the vibration signal subjected to wavelet packet analysis, and extracting a first component of the signal;
extracting a characteristic value of the extracted first signal component to be used as a characteristic vector used in fault diagnosis;
acquiring a feature vector sample of historical fault data of the gearbox;
training the characteristic vector samples by using a support vector machine, and taking the group with the highest classification accuracy as a parameter used in the subsequent fault diagnosis;
acquiring real-time operation data of a gearbox, and acquiring a characteristic vector;
and classifying the feature vectors by using a support vector machine, and outputting a diagnosis result.
2. The wind turbine gearbox fault diagnosis method according to claim 1, wherein the vibration sensor mounting points are distributed at axial and radial positions of a low speed end, a middle end and a high speed end of a wind turbine gearbox casing respectively.
3. The wind turbine gearbox fault diagnosis method according to claim 1, characterized in that said wavelet packet three-layer decomposition analysis method employs the following steps:
establishing a wavelet packet change three-layer decomposition relational expression;
selecting a corresponding threshold according to the soft threshold function to carry out threshold quantization processing on the high-frequency coefficient under each decomposition scale;
and performing one-dimensional wavelet reconstruction on the decomposed lowest layer low-frequency coefficient and high-frequency coefficient.
4. The wind turbine gearbox fault diagnosis method according to claim 3, characterized in that said wavelet packet variation three-layer decomposition relation is expressed as:
S＝AAA3+DAA3+ADA3+DDA3+AAD3+DAD3+ADD3+DDD3，
where A is the low frequency portion of the signal and D is the high frequency portion of the signal.
5. The wind turbine gearbox fault diagnosis method according to claim 3, characterized in that said soft threshold function is:
wherein, sgn (W)j,k) Is a return shaping function, Wj,kDenotes the k threshold of the j layer if Wj,kIf the value is more than 0, sgn returns to 1; if Wj,kWhen the value is 0, sgn returns to 0; if Wj,kIf the value is less than 0, sgn returns to-1, and lambda represents the value range (lambda is more than or equal to 0 and less than or equal to 1).
6. The wind turbine gearbox fault diagnosis method according to claim 3, characterized in that said wavelet reconstruction function is:
wherein,the wavelet coefficient of the nth node in the jth layer is represented, x (t) represents a denoised signal, and h and g are respectively low-frequency and high-frequency coefficients obtained after soft threshold processing.
7. The wind turbine gearbox fault diagnosis method according to claim 1, wherein the characteristic values are obtained from time domain and frequency domain respectively, the time domain characteristic values comprise a peak index, a kurtosis index, a skewness index, a margin index, and the frequency domain characteristic values comprise a frequency center of gravity, a frequency standard deviation, a root mean square frequency.
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106292622A (en) * | 2016-07-21 | 2017-01-04 | 中国人民解放军军械工程学院 | Fault distinguishing method based on the simulation of wavelet character value tolerance threshold value random statistical |
| CN106596116A (en) * | 2016-11-29 | 2017-04-26 | 西安理工大学 | Vibration fault diagnosis method of wind generating set |
| CN106769142A (en) * | 2016-12-23 | 2017-05-31 | 潘敏 | A kind of metallurgic fan machinery method for diagnosing faults |
| CN107525671A (en) * | 2017-07-28 | 2017-12-29 | 中国科学院电工研究所 | A kind of wind-powered electricity generation driving-chain combined failure character separation and discrimination method |
| CN107560844A (en) * | 2017-07-25 | 2018-01-09 | 广东工业大学 | A kind of fault diagnosis method and system of gearbox of wind turbine |
| CN107907324A (en) * | 2017-10-17 | 2018-04-13 | 北京信息科技大学 | A kind of Fault Diagnosis of Gear Case method composed based on DTCWT and order |
| CN108318249A (en) * | 2018-01-24 | 2018-07-24 | 广东石油化工学院 | A kind of method for diagnosing faults of bearing in rotating machinery |
| CN108710889A (en) * | 2018-04-02 | 2018-10-26 | 天津大学 | A kind of scarce cylinder method for diagnosing faults of automobile engine |
| CN109029996A (en) * | 2018-09-11 | 2018-12-18 | 温州大学苍南研究院 | A kind of hub bearing method for diagnosing faults |
| CN109063782A (en) * | 2018-08-16 | 2018-12-21 | 中国水利水电科学研究院 | A kind of adaptive pumping plant intelligent fault diagnosis method |
| CN109323860A (en) * | 2018-10-31 | 2019-02-12 | 广东石油化工学院 | A kind of rotating machinery gearbox fault data set optimization method |
| CN111397901A (en) * | 2019-03-12 | 2020-07-10 | 上海电机学院 | Rolling bearing fault diagnosis method based on wavelet and improved PSO-RBF neural network |
| CN111562105A (en) * | 2020-03-25 | 2020-08-21 | 浙江工业大学 | Wind turbine generator gearbox fault diagnosis method based on wavelet packet decomposition and convolutional neural network |
| CN111721528A (en) * | 2020-05-18 | 2020-09-29 | 浙江工业大学 | Wind generating set gear box fault early warning method based on CMS system big data |
| CN114239640A (en) * | 2021-11-15 | 2022-03-25 | 国网江西省电力有限公司吉安供电分公司 | Transformer substation secondary loop signal detection method based on wavelet decomposition rolling learning |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1997043729A1 (en) * | 1996-05-14 | 1997-11-20 | Csi Technology, Inc. | Vibration data analysis based on time waveform parameters |
| CN102944416A (en) * | 2012-12-06 | 2013-02-27 | 南京匹瑞电气科技有限公司 | Multi-sensor signal fusion technology-based fault diagnosis method for wind turbine blades |
| CN103033362A (en) * | 2012-12-31 | 2013-04-10 | 湖南大学 | Gear fault diagnosis method based on improving multivariable predictive models |
| CN103900816A (en) * | 2014-04-14 | 2014-07-02 | 上海电机学院 | Method for diagnosing bearing breakdown of wind generating set |
| CN104392082A (en) * | 2014-07-10 | 2015-03-04 | 中山火炬职业技术学院 | Diagnosis method for initial failure of gearbox of wind generating set based on vibration monitoring |
| CN104792520A (en) * | 2015-04-09 | 2015-07-22 | 中山火炬职业技术学院 | Fault diagnosis method for gear case of wind turbine generator system |
| CN104863840A (en) * | 2015-03-16 | 2015-08-26 | 北京化工大学 | Reciprocating compressor intelligent diagnosis method based on EMD-PCA |
-
2016
- 2016-03-25 CN CN201610178646.8A patent/CN105784353A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1997043729A1 (en) * | 1996-05-14 | 1997-11-20 | Csi Technology, Inc. | Vibration data analysis based on time waveform parameters |
| CN102944416A (en) * | 2012-12-06 | 2013-02-27 | 南京匹瑞电气科技有限公司 | Multi-sensor signal fusion technology-based fault diagnosis method for wind turbine blades |
| CN103033362A (en) * | 2012-12-31 | 2013-04-10 | 湖南大学 | Gear fault diagnosis method based on improving multivariable predictive models |
| CN103900816A (en) * | 2014-04-14 | 2014-07-02 | 上海电机学院 | Method for diagnosing bearing breakdown of wind generating set |
| CN104392082A (en) * | 2014-07-10 | 2015-03-04 | 中山火炬职业技术学院 | Diagnosis method for initial failure of gearbox of wind generating set based on vibration monitoring |
| CN104863840A (en) * | 2015-03-16 | 2015-08-26 | 北京化工大学 | Reciprocating compressor intelligent diagnosis method based on EMD-PCA |
| CN104792520A (en) * | 2015-04-09 | 2015-07-22 | 中山火炬职业技术学院 | Fault diagnosis method for gear case of wind turbine generator system |
Non-Patent Citations (2)
| Title |
|---|
| 伍雪冬: "《非线性时间序列在线预测建模与仿真》", 30 November 2015 * |
| 罗忠辉 等: "小波变换及经验模式分解方法在电机轴承早期故障诊断中的应用", 《中国电机工程学报》 * |
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| CN106292622A (en) * | 2016-07-21 | 2017-01-04 | 中国人民解放军军械工程学院 | Fault distinguishing method based on the simulation of wavelet character value tolerance threshold value random statistical |
| CN106596116A (en) * | 2016-11-29 | 2017-04-26 | 西安理工大学 | Vibration fault diagnosis method of wind generating set |
| CN106769142A (en) * | 2016-12-23 | 2017-05-31 | 潘敏 | A kind of metallurgic fan machinery method for diagnosing faults |
| CN107560844A (en) * | 2017-07-25 | 2018-01-09 | 广东工业大学 | A kind of fault diagnosis method and system of gearbox of wind turbine |
| CN107525671A (en) * | 2017-07-28 | 2017-12-29 | 中国科学院电工研究所 | A kind of wind-powered electricity generation driving-chain combined failure character separation and discrimination method |
| CN107907324A (en) * | 2017-10-17 | 2018-04-13 | 北京信息科技大学 | A kind of Fault Diagnosis of Gear Case method composed based on DTCWT and order |
| CN108318249A (en) * | 2018-01-24 | 2018-07-24 | 广东石油化工学院 | A kind of method for diagnosing faults of bearing in rotating machinery |
| CN108710889A (en) * | 2018-04-02 | 2018-10-26 | 天津大学 | A kind of scarce cylinder method for diagnosing faults of automobile engine |
| CN109063782B (en) * | 2018-08-16 | 2021-08-24 | 中国水利水电科学研究院 | Intelligent fault diagnosis method for self-adaptive pump station |
| CN109063782A (en) * | 2018-08-16 | 2018-12-21 | 中国水利水电科学研究院 | A kind of adaptive pumping plant intelligent fault diagnosis method |
| CN109029996A (en) * | 2018-09-11 | 2018-12-18 | 温州大学苍南研究院 | A kind of hub bearing method for diagnosing faults |
| CN109323860A (en) * | 2018-10-31 | 2019-02-12 | 广东石油化工学院 | A kind of rotating machinery gearbox fault data set optimization method |
| CN111397901A (en) * | 2019-03-12 | 2020-07-10 | 上海电机学院 | Rolling bearing fault diagnosis method based on wavelet and improved PSO-RBF neural network |
| CN111562105A (en) * | 2020-03-25 | 2020-08-21 | 浙江工业大学 | Wind turbine generator gearbox fault diagnosis method based on wavelet packet decomposition and convolutional neural network |
| CN111721528A (en) * | 2020-05-18 | 2020-09-29 | 浙江工业大学 | Wind generating set gear box fault early warning method based on CMS system big data |
| CN111721528B (en) * | 2020-05-18 | 2022-04-05 | 浙江工业大学 | Wind generating set gear box fault early warning method based on CMS system big data |
| CN114239640A (en) * | 2021-11-15 | 2022-03-25 | 国网江西省电力有限公司吉安供电分公司 | Transformer substation secondary loop signal detection method based on wavelet decomposition rolling learning |
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