CN105136454A - Wind turbine gear box fault recognition method - Google Patents
Wind turbine gear box fault recognition method Download PDFInfo
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- CN105136454A CN105136454A CN201510672350.7A CN201510672350A CN105136454A CN 105136454 A CN105136454 A CN 105136454A CN 201510672350 A CN201510672350 A CN 201510672350A CN 105136454 A CN105136454 A CN 105136454A
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Abstract
The invention discloses a wind turbine gear box fault recognition method. The method comprises the following steps: historical wind turbine gear box operation data in a certain time range are acquired; autocorrelation analysis is adopted for carrying out wavelet de-noising processing on the historical data; through fast Fourier transform, time domain and frequency domain characteristic parameters in the historical data after de-noising are extracted; a kernel principal component analysis method is adopted to carry out dimensionality reduction on the characteristic parameters, and several nonlinear principal elements with the maximum variance cumulative contribution rate are extracted; the nonlinear principal elements extracted by the historical gear box normal operation data are used for building a normal model, a support vector machine is used for training to guide the nonlinear principal elements extracted by later gear box operation historical data to the model after training, and thus, the gear box fault is recognized. The vibration signal processing ability is improved, and an important role is played in gear box fault recognition.
Description
Technical field
The present invention relates to wind turbine generator drive system malfunction monitoring and fault diagnosis field, particularly relate to a kind of gearbox of wind turbine fault recognition method based on fast fourier transform, core pivot element analysis and support vector machine.
Background technology
Along with the fast development of wind energy, the factors such as a large amount of Wind turbines puts into operation, and is arranged on remote districts due to most of Wind turbines, and load is unstable, China has many Wind turbines to occur operation troubles, and this directly will affect security and the economy of wind-power electricity generation.In order to make wind-power electricity generation have more competitiveness, guarantee that blower fan continues Effec-tive Function, the importance of the maintenance service such as status monitoring, fault diagnosis, maintenance of Wind turbines is subject to common concern.This is wherein particularly serious to the damage of Wind turbines critical mechanical parts, and according to statistics, the wind field gear case spoilage of China, up to 40% ~ 50%, is the parts that in Wind turbines mechanical part, failure rate is the highest.
When gearbox of wind turbine breaks down, owing to there is multi-part coupled vibrations, and when working, vibration noise interference is huge, and the signal of vibration is presented as non-gaussian, non-stationary, non-linear.Conventional time domain, frequency domain character information extracting method often comprise many redundant informations, so that the precision of signal is not high, are difficult to the internal characteristics that accurate evaluation discloses running status of wind generator, can not effectively reflect current device status.Wavelet analysis technology based on time-frequency domain can meet above-mentioned requirements, but the signal of institute's extraction equipment often exists very strong noise background in actual applications, how to be further processed these fault-signals, is a large obstacle of signal analysis.
Given this, need to propose a kind of gearbox of wind turbine fault recognition method, it can be analyzed the historical data after gear case of blower de-noising by core pivot element analysis and contribution rate of accumulative total of variance, and carry out training test by support vector machine, be conducive to the processing power improving vibration signal, to gearbox fault identification important in inhibiting.
Summary of the invention
For overcoming the deficiency that above-mentioned prior art exists, the object of the present invention is to provide a kind of gearbox of wind turbine fault recognition method, it fully takes into account in gear case of blower fault exists a large amount of nonlinear properties, the basis of carrying out failure modes utilizing support vector machine adds core pivot element analysis method, the basis taking into account failure modes effect improve fault recognition rate, particularly having stronger adaptability for there are a large amount of nonlinear properties in gear case of blower fault.
For reaching above-mentioned and other object, the present invention proposes a kind of gearbox of wind turbine fault recognition method, comprises the steps:
Step one, obtains the historical data that the gearbox of wind turbine within the scope of certain hour runs;
Step 2, adopts autocorrelation analysis to carry out wavelet noise process to historical data;
Step 3, by fast fourier transform, extracts the time domain in the historical data after de-noising and frequency domain character parameter;
Step 4, adopts core pivot element analysis method characteristic parameter to be carried out to the dimensionality reduction of dimension, extracts several nonlinear principal components that contribution rate of accumulative total of variance is maximum;
Step 5, the nonlinear principal component that the historical data normally run with gear case is extracted sets up normal model, and utilizes support vector machine to train
Step 6, the nonlinear principal component that the historical data run by later stage gear case is extracted imports the model after training, identifies thus to the fault of gear case.
Further, step 2 comprises further:
Step 2.1, selects a suitable small echo and sets the level N of wavelet decomposition, then carrying out N layer wavelet decomposition to Noise signal;
Step 2.2, arrives each layer of high frequency coefficient of n-th layer, selects a threshold value to carry out soft-threshold quantification treatment to the 1st;
Step 2.3, according to the low frequency coefficient of the n-th layer of wavelet decomposition and after quantification treatment the 1st layer to the high frequency coefficient of n-th layer, carries out the wavelet reconstruction of one-dimensional signal.
Further, step 3 comprises further:
Step 3.1, carries out Fast Fourier Transform (FFT) pre-service, extracts time domain charactreristic parameter and frequency domain character parameter;
Step 3.2, calculates 8 time domain indexes had the greatest impact;
Step 3.3, calculates 6 frequency-domain index;
Step 3.4; Time domain index after calculating and frequency-domain index are formed gear condition primitive character collection.
Further, the average of this time domain index involving vibrations signal, peak value, root-mean-square value, variance, kurtosis and the dimensionless group kurtosis factor, the pulse Summing Factor nargin factor.
Further, turn frequency amplitude, meshing frequency amplitude, meshing frequency double frequency amplitude, gravity frequency, all square frequency, the frequency variance of this frequency-domain index involving vibrations signal.
Further, step 4 comprises further:
Step 4.1, chooses suitable kernel function;
Step 4.2, calculate nuclear matrix K;
Step 4.3, carries out eigendecomposition to this nuclear matrix K, and nuclear matrix K centralization is obtained K ';
Step 4.4, chooses normal sample notebook data, calculates its average and standard deviation, and to sample data standardization, builds training matrix X;
Step 4.5, calculates eigenwert and proper vector;
Step 4.6, calculates contribution rate of accumulative total of variance, determines pivot number, extracts maximum several eigenwert characteristic of correspondence vector V;
Step 4.7, calculates the projection t of proper vector V on feature space
k, i.e. the nonlinear principal component of matrix X.
Further, in step 4.6, in data set X, i-th component xi variance contribution ratio is:
The contribution rate of accumulative total of variance of a front k component is:
Wherein λ
ifor the eigenwert of covariance matrix Σ, and λ
1>=λ
2>=λ
m>=0.
Further, if the contribution rate of accumulative total of variance of current k core pivot reaches more than 90%, then think that required core pivot can characterize primitive character information comprehensively, now definite kernel pivot number is k.
Further, in step one, from data memory module, obtain the gearbox of wind turbine input shaft within the scope of certain hour, historical data that output shaft amounts to 8 measuring points.
Further, in step one, the historical data of acquisition is input shaft, the axis of output shaft and the historical data of radial measuring point.
Compared with prior art, a kind of gearbox of wind turbine fault recognition method of the present invention is according to the fault characteristic of gearbox of wind turbine, by core pivot element analysis and contribution rate of accumulative total of variance, the historical data after gear case of blower de-noising is analyzed, be conducive to processing nonlinear properties, recycling support vector machine carries out failure modes, be conducive to the processing power improving vibration signal, the basis taking into account failure modes effect improves fault recognition rate, particularly there is stronger adaptability for there are a large amount of nonlinear properties in gear case of blower fault, to gearbox fault identification important in inhibiting, simultaneously also significant to the maintenance optimization cost of gearbox of wind turbine.
Accompanying drawing explanation
Fig. 1 is the flow chart of steps of a kind of gearbox of wind turbine fault recognition method of the present invention.
Embodiment
Below by way of specific instantiation and accompanying drawings embodiments of the present invention, those skilled in the art can understand other advantage of the present invention and effect easily by content disclosed in the present specification.The present invention is also implemented by other different instantiation or is applied, and the every details in this instructions also can based on different viewpoints and application, carries out various modification and change not deviating under spirit of the present invention.
Fig. 1 is the flow chart of steps of a kind of gearbox of wind turbine fault recognition method of the present invention.As shown in Figure 1, a kind of gearbox of wind turbine fault recognition method of the present invention, comprises the steps:
Step 101, obtains the historical data that the gearbox of wind turbine within the scope of certain hour runs.
Step 102, adopts autocorrelation analysis to carry out wavelet noise process to historical data.
Step 103, by fast fourier transform, extracts the time domain in the historical data after de-noising and frequency domain character parameter.
Step 104, adopts core pivot element analysis method characteristic parameter to be carried out to the dimensionality reduction of dimension, extracts several nonlinear principal components that contribution rate of accumulative total of variance is maximum.
Step 105, the nonlinear principal component that the historical data normally run with gear case is extracted sets up normal model, and trains by support vector machine.
Step 106, the nonlinear principal component that the historical data run by later stage gear case is extracted imports the model after training, can identify thus to the fault of gear case.
Further, step 102 is further comprising the steps of:
Step 2.1 wavelet decomposition.Select a suitable small echo and set the level N of wavelet decomposition, then N layer wavelet decomposition being carried out to Noise signal.
The threshold value quantizing of step 2.2 wavelet decomposition high frequency coefficient.Arrive each layer of high frequency coefficient of n-th layer to the 1st, select a threshold value to carry out soft-threshold quantification treatment.
The reconstruct of step 2.3 one dimension small echo.According to the low frequency coefficient of the n-th layer of wavelet decomposition and after quantification treatment the 1st layer to the high frequency coefficient of n-th layer, carry out the wavelet reconstruction of one-dimensional signal.
Further, step 103 is further comprising the steps of:
Step 3.1 Fast Fourier Transform (FFT) pre-service, extracts time domain charactreristic parameter and frequency domain character parameter.
Step 3.2 calculates 8 time domain indexes had the greatest impact, and time domain index is the average of vibration signal, peak value, root-mean-square value, variance, kurtosis and the dimensionless group kurtosis factor, the pulse Summing Factor nargin factor.
Step 3.3 calculates 6 frequency-domain index, and frequency-domain index is turn frequency amplitude, meshing frequency amplitude, meshing frequency double frequency amplitude, gravity frequency, all square frequency, the frequency variance of vibration signal
Time domain index after calculating and frequency-domain index are formed gear condition primitive character collection by step 3.4.
Further, step 104 is further comprising the steps of:
Step 4.1: choose suitable kernel function;
Step 4.2: calculate nuclear matrix K;
Step 4.3: carry out eigendecomposition to K, obtains K ' by nuclear matrix K centralization;
Step 4.4: choose normal sample notebook data, calculates its average and standard deviation, and to sample data standardization, builds training matrix X;
Step 4.5: calculate eigenwert and proper vector;
Step 4.6: calculate contribution rate of accumulative total of variance, determine pivot number, extracts maximum several eigenwert characteristic of correspondence vectors;
Step 4.7: calculate the projection t of proper vector V on feature space
k, i.e. the nonlinear principal component of matrix X.
Below by specific embodiment, each step of the present invention will be described:
Step 1, obtains historical data.Namely from data memory module, obtain the gearbox of wind turbine input shaft within the scope of certain hour, historical data that output shaft (comprise axially and radial) amounts to 8 measuring points.
Step 2, adopts autocorrelation analysis to carry out wavelet noise process to historical data.By tentatively comparing, the data of output shaft radial direction more can react the change of vibration signal, so select the data of the radial measuring point of output shaft to carry out subsequent analysis
Step 3, by fast fourier transform, extracts the time domain in the historical data after de-noising and frequency domain character parameter.Fast Fourier Transform (FFT) pre-service, extracts time domain charactreristic parameter and frequency domain character parameter.Calculate 8 time domain indexes had the greatest impact, time domain index is the average of vibration signal, peak value, root-mean-square value, variance, kurtosis and the dimensionless group kurtosis factor, the pulse Summing Factor nargin factor.Calculate 6 frequency-domain index, frequency-domain index is turn frequency amplitude, meshing frequency amplitude, meshing frequency double frequency amplitude, gravity frequency, all square frequency, the frequency variance of vibration signal.Time domain index after calculating and frequency-domain index are formed gear condition primitive character collection.
Step 4, adopts core pivot element analysis method characteristic parameter to be carried out to the dimensionality reduction of dimension, extracts several nonlinear principal components that contribution rate of accumulative total of variance is maximum.First choose suitable kernel function: gaussian radial basis function kernel function, calculate nuclear matrix K, eigendecomposition is carried out to K, nuclear matrix K centralization is obtained K ', choose normal sample notebook data, calculate its average and standard deviation, and to sample data standardization, build training matrix X, calculate eigenwert and proper vector, calculate contribution rate of accumulative total of variance, determine pivot number, extract maximum several eigenwert characteristic of correspondence vectors, calculate the projection tk of proper vector V on feature space, i.e. the nonlinear principal component of matrix X.
Adopt core pivot element analysis method to reduce Con trolling index number, wherein sample variance reflects the size of carrying data message.Therefore the determination of index number according to certain criterion, should must be avoided data message to lose, effectively reduce the dimension of parameter again.Pivot is determined according to contribution rate of accumulative total of variance method at this.
In data set X, i-th component xi variance contribution ratio is:
The contribution rate of accumulative total of variance of a front k component is:
Wherein λ
ifor the eigenwert of covariance matrix Σ, and λ
1>=λ
2>=λ
m>=0.Contribute this component larger more important, the contribution rate of accumulative total of variance of current k core pivot reaches more than 90%, and think that required core pivot can characterize primitive character information comprehensively, now definite kernel pivot number is k.
Step 5 nonlinear principal component that the historical data that gear case normally runs is extracted sets up normal model, and trains by support vector machine.
The nonlinear principal component that the historical data that later stage gear case runs by step 6 is extracted imports the model after training, can identify thus, draw identification conclusion to the fault of gear case.
Visible, a kind of gearbox of wind turbine fault recognition method of the present invention passes through autocorrelation analysis, wavelet noise process is carried out to historical data, pass through fast fourier transform, extract the time domain in the historical data after de-noising and frequency domain character parameter, then core pivot element analysis method is adopted characteristic parameter to be carried out to the dimensionality reduction of dimension, extract several nonlinear principal components that contribution rate of accumulative total of variance is maximum, the nonlinear principal component that the historical data using gear case normally to run is extracted sets up normal model, and train by support vector machine, finally the nonlinear principal component that the historical data that later stage gear case runs is extracted is imported the model after training, can identify the fault of gear case thus.
In sum, the gearbox of wind turbine fault recognition method of the present invention is according to the fault characteristic of gearbox of wind turbine, namely there are a large amount of nonlinear properties in gear case of blower fault, by core pivot element analysis and contribution rate of accumulative total of variance, the historical data after gear case of blower de-noising is analyzed, be conducive to processing nonlinear properties, recycling support vector machine carries out failure modes, be conducive to the processing power improving vibration signal, the basis taking into account failure modes effect improves fault recognition rate, particularly there is stronger adaptability for there are a large amount of nonlinear properties in gear case of blower fault, to gearbox fault identification important in inhibiting, simultaneously also significant to the maintenance optimization cost of gearbox of wind turbine.
Above-described embodiment is illustrative principle of the present invention and effect thereof only, but not for limiting the present invention.Any those skilled in the art all without prejudice under spirit of the present invention and category, can carry out modifying to above-described embodiment and change.Therefore, the scope of the present invention, should listed by claims.
Claims (10)
1. a gearbox of wind turbine fault recognition method, comprises the steps:
Step one, obtains the historical data that the gearbox of wind turbine within the scope of certain hour runs;
Step 2, adopts autocorrelation analysis to carry out wavelet noise process to historical data;
Step 3, by fast fourier transform, extracts the time domain in the historical data after de-noising and frequency domain character parameter;
Step 4, adopts core pivot element analysis method characteristic parameter to be carried out to the dimensionality reduction of dimension, extracts several nonlinear principal components that contribution rate of accumulative total of variance is maximum;
Step 5, the nonlinear principal component that the historical data normally run with gear case is extracted sets up normal model, and utilizes support vector machine to train
Step 6, the nonlinear principal component that the historical data run by later stage gear case is extracted imports the model after training, identifies thus to the fault of gear case.
2. a kind of gearbox of wind turbine fault recognition method as claimed in claim 1, it is characterized in that, step 2 comprises further:
Step 2.1, selects a suitable small echo and sets the level N of wavelet decomposition, then carrying out N layer wavelet decomposition to Noise signal;
Step 2.2, arrives each layer of high frequency coefficient of n-th layer, selects a threshold value to carry out soft-threshold quantification treatment to the 1st;
Step 2.3, according to the low frequency coefficient of the n-th layer of wavelet decomposition and after quantification treatment the 1st layer to the high frequency coefficient of n-th layer, carries out the wavelet reconstruction of one-dimensional signal.
3. a kind of gearbox of wind turbine fault recognition method as claimed in claim 1, it is characterized in that, step 3 comprises further:
Step 3.1, carries out Fast Fourier Transform (FFT) pre-service, extracts time domain charactreristic parameter and frequency domain character parameter;
Step 3.2, calculates 8 time domain indexes had the greatest impact;
Step 3.3, calculates 6 frequency-domain index;
Step 3.4; Time domain index after calculating and frequency-domain index are formed gear condition primitive character collection.
4. a kind of gearbox of wind turbine fault recognition method as claimed in claim 3, is characterized in that: the average of this time domain index involving vibrations signal, peak value, root-mean-square value, variance, kurtosis and the dimensionless group kurtosis factor, the pulse Summing Factor nargin factor.
5. a kind of gearbox of wind turbine fault recognition method as claimed in claim 3, is characterized in that: turn frequency amplitude, meshing frequency amplitude, meshing frequency double frequency amplitude, gravity frequency, all square frequency, the frequency variance of this frequency-domain index involving vibrations signal.
6. a kind of gearbox of wind turbine fault recognition method as claimed in claim 1, it is characterized in that, step 4 comprises further:
Step 4.1, chooses suitable kernel function;
Step 4.2, calculate nuclear matrix K;
Step 4.3, carries out eigendecomposition to this nuclear matrix K, and nuclear matrix K centralization is obtained K ';
Step 4.4, chooses normal sample notebook data, calculates its average and standard deviation, and to sample data standardization, builds training matrix X;
Step 4.5, calculates eigenwert and proper vector;
Step 4.6, calculates contribution rate of accumulative total of variance, determines pivot number, extracts maximum several eigenwert characteristic of correspondence vector V;
Step 4.7, calculates the projection t of proper vector V on feature space
k, i.e. the nonlinear principal component of matrix X.
7. a kind of gearbox of wind turbine fault recognition method as claimed in claim 6, is characterized in that, in step 4.6, in data set X, i-th component xi variance contribution ratio is:
The contribution rate of accumulative total of variance of a front k component is:
Wherein λ
ifor the eigenwert of covariance matrix Σ, and λ
1>=λ
2>=λ
m>=0.
8. a kind of gearbox of wind turbine fault recognition method as claimed in claim 7, it is characterized in that: if the contribution rate of accumulative total of variance of current k core pivot reaches more than 90%, then think that required core pivot can characterize primitive character information comprehensively, now definite kernel pivot number is k.
9. a kind of gearbox of wind turbine fault recognition method as claimed in claim 1, is characterized in that: in step one, obtains the gearbox of wind turbine input shaft within the scope of certain hour, historical data that output shaft amounts to 8 measuring points from data memory module.
10. a kind of gearbox of wind turbine fault recognition method as claimed in claim 1, is characterized in that: in step one, and the historical data of acquisition is input shaft, the axis of output shaft and the historical data of radial measuring point.
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