CN107016404A - Wind power generating set failure prediction method based on D S evidence fusions - Google Patents
Wind power generating set failure prediction method based on D S evidence fusions Download PDFInfo
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Abstract
Based on the wind power generating set failure prediction method of D S evidence fusions, this method constructs two SVMs Jing Guo parameter optimization for two kinds of signals, and final prediction failure mode is provided after D S fusions as two evidences；Advantages of the present invention is as follows with good effect：（1）Traditional method for oscillating is simply analyzed vibration signal, according to vibrational energy characteristic vector constructing neural network, the machine learning algorithm model such as decision tree.But simply observation vibration signal can be by some malfunction misclassifications, such as bearing damage and rotor eccentricity can all cause vibration signal abnormal, now just can preferably distinguish two kinds of malfunctions by current signal,（2）The forecast model that this method is set up can be stored, it is not necessary to historical data extract repeatedly and trained, under the real-time estimate environment of wind field, can be quickly provide predicts the outcome.
Description
Technical field
The present invention relates to the failure prediction method of wind power generating set, and in particular to a kind of D-S evidence fusions vibrational energy
The method of feature and current energy feature, belongs to Research on Fan Fault Forecasting technical field.
Background technology
Being widely popularized and using with clean energy resource, wind generating technology has become the important of clean reproducible energy
Research object.But wind field is in uninhabited, the exceedingly odious environment of natural environment, the real-time monitoring to fan condition mostly
Just become more and more difficult, especially the large-scale promotion of offshore wind turbine, also allow Fault Diagnosis of Fan to become rich in challenge.So
And blower fan involves great expense, if can be found fan condition is not good early stage, timely expatriate personnel's spot repair, with regard to that can keep away
Exempt from that some failures are continuous worsening to ultimately cause great economic damage.Wind field has had many years in the foundation and operation of China
Time, a large amount of valuable data are have accumulated, how fan operation have been found out by machine learning algorithm and computer technology
Relation between parameter and fan trouble just becomes more and more valuable.
Because D-S evidence theory has the ability of very strong processing uncertain information, in recent years the weight as information fusion
Means are wanted, but how to construct basic probability assignment function (BPA) is an important topic for first having to solve in merging.Due to
Machine learning algorithm can provide a kind of method for calculating Basic Probability As-signment for D-S evidence theory, therefore many scholars taste
The algorithm of examination application machine learning obtains BPA, herein also exactly in view of this thinking, by merging blower fan electrically and mechanically
The operational factor of two aspects, constructs machine learning algorithm respectively, is then merged two kinds of machine learning algorithms, draws pair
The prediction of fan condition finally.
The content of the invention
Goal of the invention
For generator of wind generating set in the failure of mechanically and electrically aspect, wavelet packet is based on this paper presents one kind
Decompose and D-S evidence theory multi-model fusion method, wherein WAVELET PACKET DECOMPOSITION is used for extracting vibration signal and current signal
Every feature vectors are constructed a SVMs, then obtained finally by D-S evidence fusions by the feature parameter vectors respectively
A forecast model.
Technical scheme
Because D-S evidence theory has the ability of very strong processing uncertain information, in recent years the weight as information fusion
Means are wanted, but how to construct basic probability assignment function (BPA) is an important topic for first having to solve in merging.Due to
Machine learning algorithm can provide a kind of method for calculating Basic Probability As-signment for D-S evidence theory, therefore many scholars taste
The algorithm of examination application machine learning obtains BPA, herein also exactly in view of this thinking, because the different conditions of generator are with shaking
Dynamic signal and current signal have close relation, herein for two kinds of signals construct two supports Jing Guo parameter optimization to
Amount machine, final prediction failure mode is provided as two evidences after D-S fusions.Due to shaking for generator different conditions
Dynamic signal and current signal have very big discrimination in frequency domain, extract vibration and electricity using three layers of WAVELET PACKET DECOMPOSITION herein
The feature parameter vectors of stream calculate model after being merged by D-S eventually through the mode of cross validation as SVM input
Error rate and loss, and by set forth herein Fusion Model and the grader of attribute superimposed structure without weight make respectively
Contrast, method of the data display herein based on multi-model evidence fusion has higher performance.
The step of a kind of method for carrying out Research on Fan Fault Forecasting based on D-S evidence fusions is included is as follows：
The first step, carries out data scrubbing to the historical data that wind field is gathered, removes some parameters unrelated with fan condition,
Because historical data is got off according to sequence of event, blower fan is all in good running status, institute in most cases
Retained with the data of several hours before for the data balancing before fault modeling, emphasis that blower fan breaks down
Come, and the failure mode of blower fan is indicated in behind data set, be convenient for the input of machine learning algorithm；
Second step, wind-driven generator is under different working condition, and the vibration signal and current signal of generator can all be showed
To be extremely strong non-stationary.Wavelet packet analysis can be simultaneously analyzed the time domain and frequency domain information of signal, can be effectively right
Non-stationary signal carries out feature extraction.Vibrating sensor gathers dynamo bearing x shaking to, y to, 45 degree, 135 degree four directions
Dynamic signal, current signal includes A phase, B phase, C phase three-phase electricity flow valuve, and sample frequency 50Hz is divided by three layers of WAVELET PACKET DECOMPOSITION
Not Chong Gou vibration energy signal and stator current frequency band energy distribution, finally respectively obtain one 32 dimension vibrational energy feature to
The current energy characteristic vector of amount and one 24 dimension；
3rd step, with vibrational energy characteristic signal and using current energy characteristic signal as input, constructs two warps respectively
Cross the SVMs of parameter optimization.Because the input parameter of D-S evidence fusions is the elementary probability on all classifying spaces
Assignment, so the hard output of traditional grader is converted into soft output, i.e., after the tag along sort output of grader is changed to
Test the prediction probability of probability output, i.e. grader in different classifications；
4th step, the output of two SVMs passes through D-S evidences as the Basic Probability As-signment of D-S evidence fusions
Fusion formula calculates predicted state of the final disaggregated model to blower fan.Analysis of experimental results meter by the way of five foldings intersection
The error rate and loss of context of methods are calculated, while being compared to be merged with D-S, tests and is not dividing proper subspace
In the case of, all properties participate in SVM training, and the weight of all properties is equal, and experimental result shows D-S evidence fusions
Classification accuracy and recall rate it is higher.
The step of wavelet packet extracts characteristic vector in second step is as follows：
(1) three layers of WAVELET PACKET DECOMPOSITION are carried out to vibration energy signal first, so as to obtain third layer 8 from low to high
The WAVELET PACKET DECOMPOSITION coefficient of sub-band
(2) coefficient of wavelet decomposition is reconstructed, extracts the signal of a sub- frequency band rangeThen resultant signal can be represented
For
(3) each sub-band signal energy is calculated.WithThe reconstruction signal of the 3rd layer of each node is represented,
Corresponding energy isBy SijThe energy of correspondence frequency band is designated as Eij, then have
X in formulajk(j=0 ... 7, k=1,2 ... n) represents reconstruction signalThe vibration amplitude of discrete point；
(4) energy of each sub-band is combined into a vector T, be expressed as
This characteristic vector is normalized
Normalization frequency band energy distribution after the WAVELET PACKET DECOMPOSITION of table 1
Normalization frequency band energy distribution after the WAVELET PACKET DECOMPOSITION of table 2
By vibration signal and current signal by three layers of WAVELET PACKET DECOMPOSITION and to being obtained after energy normalized in 3rd step
Characteristic vector as SVMs input, in order to ensure that the base classifier performance selected before fusion preferably, is melting
Tuning first is carried out to parameter before conjunction, D-S fusions are carried out from two minimum graders of test errors rate；SVM regulation ginseng
Number includes penalty factor, and gaussian kernel function parameter σ, initially set up standard SVM models, used in SVM parameters are selected
The mode of the folding cross validation of grid 5 determines optimized parameter, and wherein C hunting zone is set to [2-5,210], σ hunting zone is set
It is set to [2-10,25], both step-size in search changes are set to Cn+1=2*Cn,σn+1=2* σn+1, it is final to determine that two feature are empty
Between in SVM parameters it is as shown in table 3；
SVM parameters under 3 two proper subspaces of table
In 4th step, in D-S evidence theory, by mutual exclusive elementary sentence (it is assumed that) perfect set that constitutes is collectively referred to as
Identification framework Θ：
(1) basic probability assignment
BPA on identification framework Θ is one 2ΘThe function m of → [0,1], referred to as mass functions.And meetAndWherein so that m (A)>0 A is referred to as burnt first (Focal elements).Wherein Θ is also referred to as
Assuming that space, corresponds to [normal, bearing fault, turn-to-turn short circuit, rotor eccentricity] in this experiment, A represents the variable in Θ.
(2) Dempster composition rules
Dempster composition rules are also referred to as combining evidences formula[8], it is defined as follows：ForTwo on Θ
Mass functions m1,m2Dempster composition rules be：
Wherein, A represents to assume a kind of fault mode under space, m1(B) represent evidence 1 to failure A prediction probability, m2
(C) evidence 2 is represented to failure A prediction probability, and K is normaliztion constant
(3) mass functions are calculated, by taking bearing fault as an example
Wherein m1 represents the posterior probability that SVM1 is predicted bearing fault, and m2 represents the posteriority that SVM2 is predicted bearing fault
Probability, after being merged by above-mentioned formula, draws prediction probability of the Fusion Model to bearing fault, can similarly calculate fusion
Model is to the prediction probability of other three kinds of states, the final predicted state of last probability highest state representation Fusion Model.
The error rate and loss of context of methods are calculated by the way of five foldings intersection, while in order to merge work with D-S
Contrast, is tested in the case where not dividing proper subspace, and all properties participate in SVM training, and the weight of all properties is equal
It is equal.
Advantage and effect
Advantages of the present invention is as follows with good effect：
(1) traditional method for oscillating is simply analyzed vibration signal, and nerve is constructed according to vibrational energy characteristic vector
Network, the machine learning algorithm model such as decision tree.But simply observation vibration signal can be by some malfunction misclassifications, such as
Bearing damage and rotor eccentricity can all cause vibration signal abnormal, now just can preferably distinguish two kinds of failures by current signal
State
(2) forecast model that this method is set up can be stored, it is not necessary to historical data extract repeatedly and trained,
Under the real-time estimate environment of wind field, can be quickly provide predicts the outcome.
Brief description of the drawings
Fig. 1 is the flow chart of whole prediction process, i.e. the Research on Fan Fault Forecasting flow chart based on D-S evidence fusions.
Fig. 2 is the schematic diagram of SVMs.
Embodiment：
The present invention is described in further detail below in conjunction with the accompanying drawings.
As shown in Figure 1, the D-S evidence fusions algorithm that the present invention is designed is used for the failure predication of wind power generating set, it
The step of it is as follows：
The first step, carries out data scrubbing to the historical data that wind field is gathered, removes some parameters unrelated with fan condition,
Because historical data is got off according to sequence of event, blower fan is all in good running status, institute in most cases
Retained with the data of several hours before for the data balancing before fault modeling, emphasis that blower fan breaks down
Come, and the failure mode of blower fan is indicated in behind data set, be convenient for the input of machine learning algorithm；
Second step, wind-driven generator is under different working condition, and the vibration signal and current signal of generator can all be showed
To be extremely strong non-stationary.Wavelet packet analysis can be simultaneously analyzed the time domain and frequency domain information of signal, can be effectively right
Non-stationary signal carries out feature extraction.Vibrating sensor gathers dynamo bearing x shaking to, y to, 45 degree, 135 degree four directions
Dynamic signal, current signal includes A phase, B phase, C phase three-phase electricity flow valuve, and sample frequency 50Hz is divided by three layers of WAVELET PACKET DECOMPOSITION
Not Chong Gou vibration energy signal and stator current frequency band energy distribution, finally respectively obtain one 32 dimension vibrational energy feature to
The current energy characteristic vector of amount and one 24 dimension.Experiment is very stable in the range of 30m/s ± 5 from wind speed interval in data set
Data set, now blower fan be in normal power generating state.Bear vibration acceleration signal is decomposed with wavelet packet, carried
Each frequency band Wavelet Packet Energy Spectrum is taken, and energy normalized processing is carried out to it, characteristic vector is used as.Wavelet packet extracts characteristic vector
The step of it is as follows：
(1) three layers of WAVELET PACKET DECOMPOSITION are carried out to vibration energy signal first, so as to obtain third layer 8 from low to high
The WAVELET PACKET DECOMPOSITION coefficient of sub-band
(2) coefficient of wavelet decomposition is reconstructed, extracts the signal of a sub- frequency band rangeThen resultant signal can be represented
For
(3) each sub-band signal energy is calculated.WithThe reconstruction signal of the 3rd layer of each node is represented,
Corresponding energy isBy SijThe energy of correspondence frequency band is designated as Eij, then have
X in formulajk(j=0 ... 7, k=1,2 ... n) represents reconstruction signalThe vibration amplitude of discrete point；
(4) energy of each sub-band is combined into a vector T, be expressed as
This characteristic vector is normalized
Normalization frequency band energy distribution after the WAVELET PACKET DECOMPOSITION of table 1
Normalization frequency band energy distribution after the WAVELET PACKET DECOMPOSITION of table 2
3rd step, with vibrational energy characteristic signal and using current energy characteristic signal as input, constructs two warps respectively
Cross the SVMs of parameter optimization.Because the input parameter of D-S evidence fusions is the elementary probability on all classifying spaces
Assignment, so the hard output of traditional grader is converted into soft output, i.e., after the tag along sort output of grader is changed to
Test the prediction probability of probability output, i.e. grader in different classifications.By vibration signal and current signal by three layers of wavelet packet
Decompose and to the input of the characteristic vector that is obtained after energy normalized as SVMs, in order to ensure to be selected before fusion
Base classifier performance preferably, first tuning is carried out before fusion to parameter, from two classification that test errors rate is minimum
Device carries out D-S fusions.SVM regulation parameter include penalty factor, and gaussian kernel function parameter σ, initially set up standard
SVM models[14], optimized parameter, wherein C search model are determined by the way of the folding cross validation of grid 5 in the selection of SVM parameters
Enclose and be set to [2-5,210], σ hunting zone is set to [2-10,25], both step-size in search changes are set to Cn+1=2*Cn,
σn+1=2* σn+1, finally determine that SVM parameters are as shown in table 3 in two proper subspaces.
SVM parameters under 3 two proper subspaces of table
4th step, the output of two SVMs passes through D-S evidences as the Basic Probability As-signment of D-S evidence fusions
Fusion formula calculates predicted state of the final disaggregated model to blower fan.In D-S evidence theory, by mutual exclusive basic
Proposition (it is assumed that) composition perfect set be collectively referred to as identification framework Θ, several basic conceptions are first explained below：
(1) basic probability assignment
BPA on identification framework Θ is one 2ΘThe function m of → [0,1], referred to as mass functions.And meetAndWherein so that m (A)>0 A is referred to as burnt first (Focal elements).Wherein Θ is also referred to as
Assuming that space, corresponds to [normal, bearing fault, turn-to-turn short circuit, rotor eccentricity] in this experiment, A represents the variable in Θ.
(2) Dempster composition rules
Dempster composition rules are also referred to as combining evidences formula[8], it is defined as follows：ForTwo on Θ
Mass functions m1,m2Dempster composition rules be：
Wherein, A represents to assume a kind of fault mode under space, m1(B) represent evidence 1 to failure A prediction probability, m2
(C) evidence 1 is represented to failure A prediction probability, and K is normaliztion constant
(3) mass functions are calculated, by taking bearing fault as an example
Wherein m1 represents the posterior probability that SVM1 is predicted bearing fault, and m2 represents the posteriority that SVM2 is predicted bearing fault
Probability, after being merged by above-mentioned formula, draws prediction probability of the Fusion Model to bearing fault, can similarly calculate fusion
Model is to the prediction probability of other three kinds of states, the final predicted state of last probability highest state representation Fusion Model.
The error rate and loss of context of methods are calculated by the way of five foldings intersection, while in order to merge work with D-S
Contrast, is tested in the case where not dividing proper subspace, and all properties participate in SVM training, and the weight of all properties is equal
Equal, experimental result shows that the classification accuracy and recall rate of D-S evidence fusions are higher, and experimental result is as shown in table 4.
The all properties of table 4 are compared (mean value ± variance) (%) without Weighted Fusion with context of methods
Multiple experimental result shows, D-S Fusion Models the predicting the outcome in error rate to failure based on multi-categorizer
With the forecast model for being below not passing through evidence fusion in loss.
Claims (6)
1. the wind power generating set failure prediction method based on D-S evidence fusions, it is characterised in that：This method is for two kinds of signals
Two SVMs Jing Guo parameter optimization are constructed, final prediction is provided after D-S fusions as two evidences
Failure mode；
This method constructs machine learning algorithm, so respectively by merging operational factor of the blower fan at electrically and mechanically two aspects
Two kinds of machine learning algorithms are merged afterwards, the prediction final to fan condition is drawn；Extracted using three layers of WAVELET PACKET DECOMPOSITION
The feature parameter vectors of vibration and electric current are calculated eventually through the mode of cross validation and merged by D-S as SVM input
The error rate and loss of model afterwards, and by set forth herein Fusion Model and the classification of the attribute superimposed structure without weight
Device is compared respectively.
2. the wind power generating set failure prediction method according to claim 1 based on D-S evidence fusions, its feature exists
In：
The step of a kind of method for carrying out Research on Fan Fault Forecasting based on D-S evidence fusions is included is as follows：
The first step, carries out data scrubbing to the historical data that wind field is gathered, removes some parameters unrelated with fan condition, because
Historical data is got off according to sequence of event, and blower fan is all in good running status in most cases, so being
Data balancing before fault modeling, emphasis blower fan is broken down before the data of several hours remain,
And the failure mode of blower fan is indicated in behind data set, it is convenient for the input of machine learning algorithm；
Second step, wind-driven generator is under different working condition, and the vibration signal and current signal of generator can all show as pole
Strong is non-stationary；Wavelet packet analysis is analyzed the time domain and frequency domain information of signal simultaneously, and effectively non-stationary can be believed
Number carry out feature extraction；Vibrating sensor gathers vibration signals of the dynamo bearing x to, y to, 45 degree, 135 degree four directions,
Current signal includes A phase, B phase, C phase three-phase electricity flow valuve, and sample frequency 50Hz is shaken by three layers of WAVELET PACKET DECOMPOSITION to reconstruct respectively
Energy signal and the distribution of stator current frequency band energy, finally respectively obtain the one 32 vibrational energy characteristic vector tieed up and one
The current energy characteristic vector of 24 dimensions；
3rd step, with vibrational energy characteristic signal and using current energy characteristic signal as input, constructs two by ginseng respectively
The SVMs of number optimization；Because the input parameter of D-S evidence fusions is the Basic Probability As-signment on all classifying spaces,
So the hard output of traditional grader is converted into soft output, i.e., the tag along sort output of grader is changed to posterior probability
Output, i.e. prediction probability of the grader in different classifications；
4th step, the output of two SVMs passes through D-S evidence fusions as the Basic Probability As-signment of D-S evidence fusions
Formula calculates predicted state of the final disaggregated model to blower fan；Analysis of experimental results is calculated by the way of five foldings intersection
The error rate and loss of context of methods, while being compared to be merged with D-S, test and are not dividing the feelings of proper subspace
Under condition, all properties participate in SVM training, and the weight of all properties is equal, and experimental result shows point of D-S evidence fusions
Class accuracy rate and recall rate are higher.
3. the wind power generating set failure prediction method according to claim 2 based on D-S evidence fusions, its feature exists
In：The step of wavelet packet extracts characteristic vector in second step is as follows：
(1) three layers of WAVELET PACKET DECOMPOSITION are carried out to vibration energy signal first, so as to obtain third layer 8 son frequencies from low to high
The WAVELET PACKET DECOMPOSITION coefficient of band
(2) coefficient of wavelet decomposition is reconstructed, extracts the signal of a sub- frequency band rangeThen resultant signal is expressed as
(3) each sub-band signal energy is calculated；WithThe reconstruction signal of the 3rd layer of each node is represented, correspondence
Energy beBy SijThe energy of correspondence frequency band is designated as Eij, then have
X in formulajk(j=0 ... 7, k=1,2 ... n) represents reconstruction signalThe vibration amplitude of discrete point；
(4) energy of each sub-band is combined into a vector T, be expressed as
This characteristic vector is normalized
4. the wind power generating set failure prediction method according to claim 3 based on D-S evidence fusions, its feature exists
In：
Normalization frequency band energy distribution after the WAVELET PACKET DECOMPOSITION of table 1
Normalization frequency band energy distribution after the WAVELET PACKET DECOMPOSITION of table 2
5. the wind power generating set failure prediction method according to claim 2 based on D-S evidence fusions, its feature exists
In：Feature in 3rd step by vibration signal and current signal by three layers of WAVELET PACKET DECOMPOSITION and to being obtained after energy normalized
The vectorial input as SVMs, in order to ensure the base classifier performance selected before fusion preferably, before fusion
Tuning first is carried out to parameter, D-S fusions are carried out from two minimum graders of test errors rate；SVM regulation parameter includes
Penalty factor, and gaussian kernel function parameter σ, initially set up standard SVM models, SVM parameters selection in use grid 5
The mode of folding cross validation determines optimized parameter, and wherein C hunting zone is set to [2-5,210], σ hunting zone is set to
[2-10,25], both step-size in search changes are set to Cn+1=2*Cn,σn+1=2* σn+1, finally determine in two proper subspaces
SVM parameters are as shown in table 3；
SVM parameters under 3 two proper subspaces of table
6. the wind power generating set failure prediction method according to claim 2 based on D-S evidence fusions, its feature exists
In：In 4th step, in D-S evidence theory, by mutual exclusive elementary sentence (it is assumed that) perfect set that constitutes is collectively referred to as identification
Framework Θ：
(1) basic probability assignment
BPA on identification framework Θ is one 2ΘThe function m of → [0,1], referred to as mass functions；And meet
AndWherein so that m (A)>0 A is referred to as burnt first (Focal elements)；Wherein Θ is also referred to as assuming empty
Between, [normal, bearing fault, turn-to-turn short circuit, rotor eccentricity] in this experiment is corresponded to, A represents the variable in Θ；
(2) Dempster composition rules
Dempster composition rules are also referred to as combining evidences formula[8], it is defined as follows：ForTwo mass letters on Θ
Number m1,m2Dempster composition rules be：
Wherein, A represents to assume a kind of fault mode under space, m1(B) represent evidence 1 to failure A prediction probability, m2(C) table
Show prediction probability of the evidence 2 to failure A, K is normaliztion constant
(3) mass functions are calculated, by taking bearing fault as an example
Wherein m1Represent the posterior probability that SVM1 is predicted bearing fault, m2The posterior probability that SVM2 is predicted bearing fault is represented,
After being merged by above-mentioned formula, prediction probability of the Fusion Model to bearing fault is drawn, Fusion Model is similarly calculated to it
The prediction probability of his three kinds of states, the final predicted state of last probability highest state representation Fusion Model；
The error rate and loss of context of methods are calculated by the way of five foldings intersection, while compared to be merged with D-S,
Test in the case where not dividing proper subspace, all properties participate in SVM training, the weight of all properties is equal.
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