CN108062572A - A kind of Fault Diagnosis Method of Hydro-generating Unit and system based on DdAE deep learning models - Google Patents
A kind of Fault Diagnosis Method of Hydro-generating Unit and system based on DdAE deep learning models Download PDFInfo
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Abstract
The present invention relates to Approach for Hydroelectric Generating Unit Fault Diagnosis technical fields, and in particular to a kind of Fault Diagnosis Method of Hydro-generating Unit and system based on DdAE deep learning models.The present invention is established on the basis of to the original vibrating data analysis of Hydropower Unit, employ the deep learning feature extracting method based on multilayer neural network model, artificial treatment and characteristic extraction procedure that need not be complicated, the structural parameters tuning of DdAE is carried out using the ASFA methods based on random search, achievees the purpose that policy optimization.The distributed expression of initial data by depth noise reduction autocoder model realization, and the reconstruct data after feature extraction are inputted to Softmax regression models to the working condition and fault type for judging Hydropower Unit.Network test interpretation of result shows that this method can be effectively applied to the fault diagnosis of Hydropower Unit.
Description
Technical field
The invention belongs to Approach for Hydroelectric Generating Unit Fault Diagnosis technical fields, and DdAE deep learnings are based on more particularly, to one kind
The Fault Diagnosis Method of Hydro-generating Unit and system of model.
Background technology
In a hydroelectric power system, turbine-generator units are the main equipments of most critical, and whether its operating status pacifies
It is complete be reliably directly related to power station can safely, the daily life that economy is national each economic department and the people provides reliably
Electric power, be also directly related to the safety of power station in itself.The condition monitoring and failure diagnosis system of optimization is continuously improved, not only
The economic benefit and social benefit in power station can be improved, is also beneficial to China in Large Hydropower Station fault diagnosis technology field side
The development in face.With the increasingly raising of present scientific and technological level, the especially reasons such as signal processing, knowledge engineering and computational intelligence
Development technologically, the fault diagnosises of turbine-generator units also just by Artificial Diagnosis to intelligent diagnostics, it is online by diagnosing offline
Diagnosis, by the gradual development of field diagnostic to remote diagnosis.
Conventional machines learn and signal processing technology explores the shallow-layer learning structure for containing only individual layer nonlinear transformation.Shallow-layer mould
One general character of type is to contain only the single simple structure that original input signal is transformed into particular problem space characteristics.It is typical shallow
Layer learning structure includes traditional hidden Markov model, and (hidden Markov model, are abbreviated as:HMM), condition random field
(conditional random field, are abbreviated as:CRFs), maximum entropy model (The Maximum Entropy model,
It is abbreviated as:MaxEnt), (Support Vector Machine, are abbreviated as support vector machines:SVM), kernel regression and only multilayer sense
Know that (Multi-Layer Perceptron, are abbreviated as device:MLP) etc..For example, SVM is with including one layer or zero Feature Conversion
The shallow-layer modal cutoff model of layer.Shallow structure is limited in that in the case of limited sample and computing unit to complicated function
Expression ability it is limited, for complicated classification problem, its generalization ability is centainly restricted.
The vibration of Hydropower Unit is different with the vibration of general dynamic power machine.Except need to consider unit rotation in itself or solid
Determine outside the vibration of part, it is still necessary to consider to act on the electromagnetic force of master section and act on hydroelectric machine overcurrent for Hydropower Unit vibration
Influence of the partial hydrodynamic pressure to system and its component vibration.In the case where unit operates, fluid, machinery, electromagnetism three
Part is interactional.Therefore, the vibration of Hydropower Unit is electrical, mechanical, fluid coupling vibration.It is accumulated according to power station
Success experience, can will cause and machinery, waterpower, electrical and noise etc. factor are divided into the reason for unit vibration.At present, exist
Be able to study in hydro-generating Unit vibrating failure diagnosis and apply it is main it is faulty tree method for diagnosing faults, fuzzy diagnosis side
The methods of method, wavelet analysis and neutral net.
Fault tree method for diagnosing faults traditional support vector machine (Classical-Support Vector Machine,
It is abbreviated as:C-SVM on the basis of), by integrated Fuzzy clustering techniques and algorithm of support vector machine, construction one kind is suitable for failure
The multistage binary tree grader of diagnosis.Shortcoming is cannot to diagnose unpredictable failure;Diagnostic result depends critically upon fault tree
The correctness and integrality of information.And Approach for Hydroelectric Generating Unit Fault Diagnosis is generally multi-fault Diagnosis, and support vector machines is a kind of allusion quotation
Two classification graders of type, for more classifying when, have the problem of computationally intensive.
Fuzzy diagnosis method is to solve failure using the membership function in set theory and the concept of fuzzy relation matrix
Uncertainty relationship between sign.Fuzzy Fault Diagnosis is disadvantageous in that complicated diagnostic system will be established just
True fuzzy rule and membership function is extremely difficult, and heavy workload.
Wavelet analysis can solve the problems, such as that many Fourier transforms are insoluble.It has good in time domain and frequency domain
Localization ability can focus on the arbitrary details of signal, have very strong recognition capability to the mutation of signal, can effectively denoising and
Extract useful signal.But it is difficult to choose that the wavelet basis in wavelet analysis method, which is, general is difficult to choose to meet the requirements very much
Wavelet basis, and the realization effect of wavelet transformation cannot be guaranteed in higher-dimension.
The diagnostic method of neutral net is generally shallow-layer network, such as extreme learning machine (Extreme Learning
Machine is abbreviated as:ELM), (Radical Basis Function network, are abbreviated as radial basis function network:RBF) etc..
This kind of shallow-layer network generally requires to combine other signal processing technologies and some are used for the intelligent algorithm of parameter optimization,
Last grader is served as in entire fault diagnosis flow scheme.The signal processing that its final diagnosis effect is done dependent on front is i.e.
Signal characteristic abstraction works.
The content of the invention
The object of the present invention is to provide a kind of Fault Diagnosis Method of Hydro-generating Unit based on DdAE deep learning models with being
System, to solve the problems, such as cumbersome manual working that magnanimity monitoring data are brought, and improve fault diagnosis system accuracy rate and
Stability.
The technical solution adopted by the present invention is:
In a first aspect, provide a kind of Fault Diagnosis Method of Hydro-generating Unit based on DdAE deep learning models, this method
Including following content:
Step (1):Initial data pre-processes:
This method uses normalized first using the initial data that Hydropower Unit is vibrated as input sample collection x, even if
Between data that treated are distributed in -1 to 1 by the distribution proportion of initial data, new input sample collection x' is obtained;Then will return
One changes that treated that sample set x' is divided into k group data blocks, it is contemplated that the periodicity of Hydropower Unit vibration fault, in order not to destroy
Fault message subject to a cycle according to being grouped.N groups are extracted from k group data blocks and are combined into training data as neutral net mould
The input of type can so obtainGroup training data increases the reusability of finite data, and every group
Training data, which is all equivalent to, has done noise reduction process, and the specific value of k is determined according to the actual conditions for collecting data.
Step (2):Unsupervised training process based on deep learning:
DdAE network models are established, DdAE network models are trained using training sample.By adding in dropout's
Successively training method is trained DdAE network models to unsupervised greed, obtains the connection weight of DdAE network models.The mistake
Journey is a kind of unsupervised characteristic extraction procedure, the lossless guarantee in greed successively training offer characteristic extraction procedure, and with
Most fast speed convergence.The initialization connection weight of training guides the feature to different directions every time, provides the more of feature selecting
Sample ensures.
Step (3):Training process based on Softmax regression models and BP:
DdAE network models by (2) step do unsupervised feature extraction, can obtain one group of reconstruct feature vector,
Sorting technique of the Softmax regression models as Hydropower Unit failure is selected, handles more points of Hydropower Unit under various faults
Class problem.The feature vector reconstructed passes through one layer of combinational network connected entirely, obtains a kind of linear combination conduct of feature
The input of Softmax models calculates the probability for the appearance possibility for representing each failure.By minimize error function come
The connection weight of combinations of features network is corrected, the connection weight of whole network is carried out with gradient decline and back-propagation algorithm micro-
It adjusts.
Step (4):Structure parameter optimizing process based on AFSA:
I.e. adaptive structure adjustment process, process contain the above-mentioned unsupervised training process based on deep learning and are based on
The Training process of Softmax regression models and BP.Using the hyper parameter in the structural parameters and error function of DdAE as
The target component of AFSA, model exports object function of the error function of result as AFSA, by AFSA in entire model
Hyper parameter carry out Stochastic search optimization, each step iterative process of each Artificial Fish in AFSA optimizations is a DdAE
The parameter optimisation procedure of network model finally obtains the optimal Artificial Fish in position and obtains optimal models.
Second aspect, the present invention also provides a kind of Approach for Hydroelectric Generating Unit Fault Diagnosis systems based on DdAE deep learning models
System, the system comprises training data processing module, neural network model training module, reconstruct feature vector generation module and events
Hinder probability evaluation entity, above-mentioned each module is sequentially connected, specifically:
Training data processing module for obtaining data set, and extracts n group data blocks, as DdAE nets from data set
The training data of network model;
Neural network model training module, for establishing DdAE network models, and using the training data to DdAE nets
Network model is trained, and obtains the connection weight of DdAE network models;
Feature vector generation module is reconstructed, according to the DdAE network models being made of the connection weight, obtains one group of weight
Structure feature vector;
Probability of malfunction computing module for reconstructing feature vector by one layer of combinational network connected entirely, obtains feature
Linear combination, and as the input of Softmax models, calculate the probability for the appearance possibility for representing each failure.
The present invention compared with prior art the advantages of be:
(1) traditional time-frequency domain signal characteristic extracting methods are different from, signal processing technology and diagnosis are passed through in order to break away from
The dependence tested, it is proposed that a kind of depth autocoder feature learning method is automatically and efficiently learned from the vibration signal of measurement
Practise useful fault signature.
(2) influenced in preferred embodiment of the present invention in order to eliminate ambient noise, improve feature learning ability, obtaining data set
Preprocessing process in influence of noise reduced using the operation of data " broken, restructuring " and adopted in unsupervised training process
New depth self-encoding encoder loss function is designed with maximal correlation entropy.The influence of unknown noise is avoided well, is effectively improved
The anti-noise ability of deep learning model makes it have higher accuracy and stronger stability in fault diagnosis.
(3) in order to avoid over-fitting (overfitting) that deep learning model often occurs in preferred embodiment of the present invention
Problem during unsupervised learning, the hidden layer neuron of every layer of autocoder AE is operated using dropout, is solved
The over-fitting problem of parameter optimization;Parameter regular terms is added in the object function design of ASFA methods, it is excellent to solve structure
Over-fitting problem during change.
(4) select to use in preferred embodiment of the present invention and correct linear unit R eLU functions as excitation function, avoid
The problem of gradient disperse and gradient during BP are exploded.
(5) artificial fish-swarm algorithm ASFA is applied to the hyper parameter tuning of deep learning in preferred embodiment of the present invention,
Reduce the manual working selected to hyper parameter so that the intelligence learning ability enhancing of entire method, and then to multiple types
Like the extensive processing capacity of problem.
(6) propose that the training of deep learning Approach for Hydroelectric Generating Unit Fault Diagnosis model is divided into two mistakes in preferred embodiment of the present invention
Journey:Parameter optimisation procedure and policy optimization process, the two carry out faster more accurately converging to optimal jointly.Parameter optimization
Process includes unsupervised training process and Training process:Unsupervised process is using greedy hierarchical optimization, to supervise
The BP of journey provides good pre-training parameter, and two training process, which cooperate, improves Model Diagnosis precision.
Description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, it will make below to required in the embodiment of the present invention
Attached drawing is briefly described.It should be evident that drawings described below is only some embodiments of the present invention, for
For those of ordinary skill in the art, without creative efforts, other are can also be obtained according to these attached drawings
Attached drawing.
Fig. 1 is a kind of Fault Diagnosis Method of Hydro-generating Unit based on DdAE deep learning models provided in an embodiment of the present invention
Flow
Fig. 2 is a kind of autocoder structure chart provided in an embodiment of the present invention;
Fig. 3 is a kind of DdAE structure diagrams provided in an embodiment of the present invention;
Fig. 4 is a kind of successively greedy training pattern schematic diagram provided in an embodiment of the present invention;
Fig. 5 is a kind of AFSA flow diagrams provided in an embodiment of the present invention;
Fig. 6 is a kind of Fault Diagnosis Method of Hydro-generating Unit based on DdAE deep learning models provided in an embodiment of the present invention
Flow;
Fig. 7 is a kind of Approach for Hydroelectric Generating Unit Fault Diagnosis system based on DdAE deep learning models provided in an embodiment of the present invention
Organization Chart.
Specific embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, it is right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in the various embodiments of the present invention described below
Conflict is not formed each other to can be combined with each other.
Since two thousand six, the deep learning (deeplearning) proposed by Canadian professor Hinton has become machine
One emerging field of device learning areas.Hinton professors exist《Science》It publishes thesis and proposes a kind of deep learning model,
(Deepbeliefnetwork is abbreviated as deep layer belief network:DBN), the probability which includes the random hidden variable of multilayer is given birth to
Into model.Two layers of undirected symmetrical connection topmost, directed connection from top to bottom between low layer.
Deep learning can allow those to possess the computation model of multiple process layers to learn with data abstract at many levels
Expression.These methods all bring significant improvement at many aspects, know comprising state-of-the-art speech recognition, visual object
Not, object detection and a lot of other fields, such as drug discovery and genomics etc..Deep learning is can be found that in big data
Labyrinth.It is that (BackPropagation is abbreviated as using backpropagation:BP) algorithm finishes this discovery procedure.
How BP algorithm can obtain error from preceding layer with guidance machine and change the inside Ginseng numbers of this layer, these internal Ginseng numbers can be used
It is represented in calculating.Depth convolutional network brings breakthrough in terms of processing image, video, voice and audio, and Recursive Networks exist
Glittering one side is shown in terms of processing sequence data, such as text and voice.
The development of depth learning technology generates wide influence to signal and information process field, and will continue to influence
To machine learning and other key areas of artificial intelligence, wherein failure modes and diagnosis are exactly an important development field.
Embodiment 1:
The embodiment of the present invention is based on depth noise reduction autocoder, and (Deep denoising Autoencoder, are abbreviated as
DdAE) the method for diagnosing faults flow of deep learning model is as shown in Figure 1.Including step performed below:
In step 201, data set is obtained, and n group data blocks, the instruction as DdAE network models are extracted from data set
Practice data.
By taking turbine-generator units as an example, in step 201, using the initial data that Hydropower Unit is vibrated as input sample collection
X, and be normalized before as training data, even if treated, data are distributed by the distribution proportion of initial data
Between -1 to 1, new input sample collection x' is obtained;Then the sample set x' after normalized is divided into k group data blocks,
In view of the periodicity of Hydropower Unit vibration fault, in order not to destructive malfunction information according to being grouped subject to a cycle.From k group numbers
Input of the training data as neural network model is combined into according to n groups are extracted in block, can so be obtained
Group training data increases the reusability of finite data, and every group of training data is all equivalent to and has done noise reduction process, wherein, k
Specific value determined according to the actual conditions for collecting data.
Wherein, normalization can be achieved be:It samples this concentration maximum and minimum value is denoted as XmaxAnd Xmin;According to formulaAll data in sample set are calculated, Hydropower Unit vibration data sample is obtained by result of calculation
Collect the sample set x' after x normalization.
In step 202, DdAE network models are established, and DdAE network models are instructed using the training data
Practice, obtain the connection weight of DdAE network models.
Wherein, the DdAE network models are made of at least two AE, wherein, the hidden layer of preceding layer AE is as later layer
The input layer of AE stacks to obtain;The value of each node of hidden layer of each AE is asked by the linear weighting connection of each nodal value of input layer
Be input in an excitation function and be calculated, i.e., the being input to output in hidden layer is connected by excitation function.In depth
It spends in neutral net, first layer is known as input layer, last layer is known as output layer, other interlayers are known as hidden layer, hidden layer
There is functional relation, this function is known as excitation function between the outputting and inputting of node.Wherein, the excitation function can be
Correcting linear unit, (Rectified Linear Unit, are abbreviated as:ReLU) function.
In step 203, join the hyper parameter in the structural parameters and error function of DdAE as the target of AFSA algorithms
Number, model export object function of the error function of result as AFSA, and the hyper parameter in entire model is carried out by AFSA
Stochastic search optimization, each step iterative process of each Artificial Fish in AFSA optimizations is the parameter of a DdAE network model
Optimization process, to obtain optimal DdAE network models;
In step 204, according to the DdAE network models being made of the connection weight, obtain one group of reconstruct feature to
Amount.
In step 205, the reconstruct feature vector obtains linear group of feature by one layer of combinational network connected entirely
Close, and be used as the input of Softmax models, calculate expression each failure appearance possibility probability.
An embodiment of the present invention provides a kind of Fault Diagnosis Method of Hydro-generating Unit based on DdAE deep learning models, with solution
The problem of cumbersome manual working that certainly magnanimity monitoring data are brought, and improve the accuracy rate and stabilization of fault diagnosis system
Property.
In embodiments of the present invention, in order to further optimize DdAE network models, also there is a kind of preferred realization content,
Specifically:Using the hyper parameter in the structural parameters and error function of DdAE as the target component of AFSA algorithms, model output knot
Object function of the error function of fruit as AFSA carries out Stochastic search optimization by AFSA to the hyper parameter in entire model,
Each step iterative process of each Artificial Fish in AFSA optimizations is the parameter optimisation procedure of a DdAE network model, so as to
Obtain optimal DdAE network models.And above-mentioned optimization DdAE processes can timely be performed in above-mentioned steps 202-205,
And the condition that its triggering performs is then the hyper parameter in the structural parameters and error function of DdAE.
It is described that DdAE network models are trained using the training data in step 202 of the embodiment of the present invention, have
Body includes being trained DdAE network models by adding in the unsupervised greedy successively training method of dropout, the no prison
Greed successively training method is superintended and directed, is specially:
In unsupervised training process, using the input layer of each layer of the DdAE network models AE independent as one,
The next layer of hidden layer as independent AE, construction one, with the output layer of dimension, so reconstruct multiple AE and carry out with input layer
Individually training;In unsupervised training process, dropout operations are carried out according to probability P to the hidden layer neuron of each independent AE.
Successively training method includes the unsupervised greed for adding in dropout:In the training process of DdAE networks, for nerve net
It is temporarily abandoned (corresponding content, in subsequently illustrating dropout expansion according to certain probability by network unit from network
It is related to).
Standard self-encoding encoder loss function is designed using MSE, does not have robustness to the feature learning of sophisticated signal, right
The susceptibility of noise is also very high.Cross entropy is a kind of non-linear and local similarity measurement, for complicated and non-stationary background
Noise, maximal correlation entropy are insensitive.Therefore, maximal correlation function has the potentiality of matching complex signal feature, can solve
The shortcomings that MSE.In embodiments of the present invention, it is as follows that new self-encoding encoder AE loss functions are designed using maximal correlation entropy:
The loss function of the self-encoding encoder AE is:
Wherein ω be AE weight parameters composition parameter vector, m be input layer dimension, ziFor the true mark of failure modes
Label,For by the diagnostic result of this method,For Mason's kernel function, kernel function is used to estimate actual value and prediction
The cross entropy of value.Wherein, Mason's kernel function generally uses gaussian kernel function.The gaussian kernel function expression formula is as follows:
WhereinFor gaussian kernel function, σ is the variance of Gaussian Profile, and for general value between 0 to 10, e is certainly
Right logarithm.
It trains since first layer AE, to minimize error function J (ω) as target, parameter set θ is optimized, hereafter
Each layer of AE input exports for preceding layer hidden layer, trains whole network successively.One can be obtained by unsupervised training process
The connection weight pre-training of the entire neutral net of group as a result, carry out the training for having supervision in next step, i.e., originally herein on the basis of result
The step 205 of inventive embodiments 1.
It, can also be by adding in the unsupervised greed successively training side of dropout in the step 202 of the embodiment of the present invention
Method is trained DdAE network models, obtains the connection weight of DdAE network models.The process is a kind of unsupervised feature
Extraction process, greed successively training provide the lossless guarantee in characteristic extraction procedure, and with most fast speed convergence.Instruction every time
Experienced initialization connection weight guides the feature to different directions, and the diversity for providing feature selecting ensures.
Dropout refers in the training process of deep learning network in embodiments of the present invention, for neutral net list
Member temporarily abandons it according to certain probability from network, i.e., according to determine the probability whether epicycle input calculating in neglect
The slightly network element makes it be not involved in epicycle calculating, and next round optimization is participated in without influencing it.In standard neural network, section
Correlation between point causes the influence of noise scope of a node to expand, and weakens the generalization ability of network, causes over-fitting
Problem, dropout destroy this correlation, avoid these problems.
During entire feature learning, it is openness that dropout so that the feature vector that study obtains has more, and contributes to
The sparse expression of DdAE network models and distributed expression.The drop probability P ranges of choice of dropout are preferred with 0.5 to 0.8
Value.
With reference to the embodiment of the present invention, there are a kind of preferred implementations, are realized available for matching step 203, specifically,
The connection weight of combinations of features network is corrected by minimizing error function, is declined with gradient and backpropagation BP algorithm is to whole
The connection weight of a network is finely adjusted.
Back-propagation process in the embodiment of the present invention is the optimization process based on gradient, by the result of Softmax classification
It is compared with training data label, finds the combinations of features value sequence number j corresponding to correct fault type, ladder is calculated by equation below
Degree:
Wherein ΔiFor i-th of element of gradient vector Δ, j is the sequence number of the corresponding true fault type of label, zjFor with
The corresponding combinations of features value of true fault type sequence number, ziFor with ΔiThe combinations of features value of the corresponding fault type of sequence number, zkFor
The combinations of features value of corresponding kth kind fault type, K are the number of faults that can classify in total.The depth proposed in embodiments of the present invention
Degree noise reduction autocoder DdAE models are obtained based on traditional single hidden layer autocoder design (as shown in Figure 2), single
Hidden layer autocoder AE is made of an input layer, a hidden layer and an output layer, theoretically requires input layer and defeated
Go out that layer result is equal, this feature for representing to hide representated by node layer can reconstruct input layer data, reach lossless feature extraction
Purpose.The DdAE network models discard output layer by multiple AE, using the hidden layer of preceding layer AE as the defeated of later layer AE
Enter layer to stack to obtain (as shown in Figure 3), wherein the value of each node of each hidden layer is connected by the linear weighting of each nodal value of input layer
Summation is connect, is input in an excitation function and is calculated.The output that is input to of wherein hidden layer is connected by an excitation function,
ReLU functions are selected in the embodiment of the present invention as excitation function.In entire DdAE network models, except first layer and finally
One layer, it is left all to be connected by excitation function between the neuron input in all interlayers and output.The superperformance of ReLU functions
It is possible to prevente effectively from the gradient attenuation during BP, ensure that trained optimization rate.
The embodiment of the present invention additionally provides a kind of method of the initialization of unsupervised training, specifically:
The initialization of unsupervised training includes the structure initialization of DdAE network models and DdAE network model connection weights
The initialization of parameter.The embodiment of the present invention uses empirical formula method, DdAE nets for the initialization of the structure of DdAE network models
The input layer of network model is 128 nodes, and neuron node quantity is successively half-and-half successively decreased, and final output layer is 8 nodes, with one
The combination layer of a 6 node connects to be input in Softmax models after linear combination entirely classifies, and DdAE network models part is total to
It is designed as 5 hidden layers.The initialization of DdAE network model interlayer connection weight parameters uses empirical equationWherein njFor the preceding layer neuron node number of weight matrix W connections, nj+1
For the later layer neuron node number of weight matrix W connections, weight matrix W is initialized according to such a be uniformly distributed.
On the other hand, the present invention additionally provides a kind of preferred Softmax models, specific Softmax models letter in real time
Counting expression formula is:
Wherein z is the feature vector of corresponding various failures, σ (z)jFor the fuzzy evaluation value of corresponding jth kind failure, zjTo be right
Answer the combinations of features value of jth kind fault type, zkFor the combinations of features value of corresponding kth kind fault type, K is the event that can classify in total
Hinder number.
By by unsupervised part learn as a result, reconstruct feature vector, carry out linear group of a fully-connected network
It closes, obtains to represent the assemblage characteristic vector z of various failures, be input in Softmax models and obtain the person in servitude to various failures
Category degree, it is final result to take degree of membership maximum fault type.
In embodiments of the present invention, the artificial fish-swarm algorithm process AFSA is a kind of optimization method of prevalence, with other
Optimization algorithm is compared, it has fast convergence rate, high to initial value tolerance, the advantage of strong robustness, and can find the overall situation
Optimal solution.Therefore, the embodiment of the present invention carries out double optimization using AFSA to the key parameter of depth autocoder.
In embodiments of the present invention, the specific implementation of the artificial fish-swarm algorithm AFSA proposed is as shown in figure 5, including step
It is as follows:
Step1:Prepare depth self-encoding encoder model, the initial parameter in the model is by step 202 of the embodiment of the present invention
Unsupervised learning train to obtain.
Step2:The basic parameter of artificial fish-swarm algorithm is set, including number of fish school L, adjusting range LB, UB of parameter, fish
The field range V of group, maximum step-length S, the trial number try_number of foraging behavior of shoal of fish movement, maximum procreation algebraically
Maxgen and crowding factor δ.The object function of AFSA is designed using the failure modes precision of depth autocoder.
Step3:In parameter variation range, the original of the initialization shoal of fish is generated according to the model initial parameter collection of Step1
State.Bulletin is established, records the optimum position of the shoal of fish and the minimum target functional value per a generation.
Step4:(Artificial Fish, are abbreviated as each Artificial Fish:AF appropriate row) is attempted according to optimization regulation
For.Update is announced to record optimum parameter value.
Step5:It checks whether the corresponding target function value of optimum parameter value reaches optimization purpose, is completed if reaching excellent
Change and export optimized parameter;Otherwise, check whether procreation algebraically reaches maximum procreation algebraically Maxgen, completed if reaching excellent
Change, and export optimized parameter;Otherwise return and perform Step4.
With reference to the embodiment of the present invention, a kind of policy optimization learning objective function is additionally provided, specially based on the core limit
(Kerner Extreme Learning Machine, are abbreviated as habit machine:KELM) theoretical error function.Feedforward neural network
Training error it is smaller, the norm of weight is smaller, and network is intended to obtain better Generalization Capability.ELM tends both to minimum
Change training error and maximize the openness standard of feature.
min||Hβ-T||2AND||β||
Wherein β is feature vector, and H is characterized combinatorial matrix, and T is true tag vector.H β can be by above-mentioned 3rd step
Softmax classification results vector represents.Objectives function is designed as minimizing such as minor function:
Wherein xiFor the diagnostic result of i-th group of input, ziFor the corresponding label vector of i-th group of input, m is training set size.
θ be parameter vector to be optimized, the hidden layer number of plies including DdAE, every layer of neuronal quantity nj, Gaussian kernel variances sigma and
The drop probability P of dropout.
Embodiment 2:
The embodiment of the present invention be by many extensions in embodiment 1 and the compound scheme that is integrated together of preferred scheme,
Wherein, with reference to the Fault Diagnosis Method of Hydro-generating Unit flow chart as shown in Figure 6 based on DdAE deep learning models.The present embodiment
Main two processes of training and test including DdAE network models.The training process of DdAE network models may be summarized as follows:
Step (1):Initial data pre-processes
The embodiment of the present invention using the initial data that Hydropower Unit is vibrated as input sample collection x, first using normalization at
Reason between even if treated data are distributed in -1 to 1 by the distribution proportion of initial data, obtains new input sample collection x';
Then the sample set x' after normalized is divided into k group data blocks, it is contemplated that the periodicity of Hydropower Unit vibration fault is
Destructive malfunction information is not grouped according to subject to a swing circle.N groups, which are randomly selected, from k group data blocks is combined into training
Input of the data as neural network model builds the distributed table of data characteristics by the deep learning of multilayer neural network
It reaches.It can so obtainGroup training data increases the reusability of finite data, and every group of training
Data, which are all equivalent to, has done noise reduction process, and the specific value of k is determined according to the actual conditions for collecting data.With entire sample set
Test set as model.
Above-mentioned normalized is specially:
Assuming that obtained Hydropower Unit vibration data sample set is X, samples this concentration maximum and minimum value is denoted as XmaxWith
Xmin, then all data in sample set are calculatedThe new sample set x ' so obtained is exactly
Data after the normalization of original sample collection.
Step (2):Initialize ASFA models
The basic parameter of artificial fish-swarm algorithm is set, (surpassing for L difference DdAE network model is represented including number of fish school L
Parameter set), adjusting range LB, UB (the adjustable range bound for representing DdAE network model hyper parameters) of parameter, the shoal of fish regards
Wild scope V (representing to be capable of the maximum range of interactional two Artificial Fishs), the maximum step-length S of shoal of fish movement (represents each
The maximum magnitude that DdAE network models hyper parameter adjusts in iteration), the maximum number of attempts try_number of foraging behavior (is represented
Maximum exploration number under DdAE network models hyper parameter guiding within the vision), maximum procreation algebraically Maxgen (represents institute
Have the greatest iteration optimization number of the DdAE network model hyper parameter collection of initialization) and crowding factor δ.Each AF in ASFA models
Represent the DdAE network models (being also called AF for short in the embodiment of the present invention) of a definite hyper parameter, the location determination of AF
The hyper parameter collection (being also called AF positions for short in the embodiment of the present invention) of its DdAE network model represented
The initial method of above-mentioned AFSA models is specially:
The AF location parameters of artificial fish-swarm model include the network number of plies Layer of DdAE network models, the god per layer network
Through first number of nodes Neti, the variances sigma of Gaussian Profile, dropout probability Ps.Wherein Layer is generally chosen between 3 to 8, optional
A random value is initialization value in the range of taking;NetiSelection range between desired taxonomic species counts to 1024, Net1General root
2 integer power, Net are initialized as according to the dimension of an input dataiInitialization value is by Net1Successively halve to obtain, and most
Later layer output is not less than the taxonomic species number of requirement;The selection range of σ is generally initialized as 1, the choosing of probability P between 0 to 10
Scope is taken between 0.5 to 0.8, is generally initialized as a random value in optional scope.Number of fish school L is initialized 10 to 100
Between, field range V is initialized between 0.01 to the 0.02 of entire parameter adjustment scope, and maximum step-length S is initialized as 2*V,
Maximum number of attempts try_number is initialized between 5 to 10, and maximum procreation algebraically Maxgen initialization is gathered around between 10 to 20
Factor delta initialization is squeezed between 0.2 to 0.5.
Step (3):Unsupervised training process based on deep learning
Hyper parameter collection representated by the position of each Artificial Fish in ASFA models, establishes DdAE network models, adopts
DdAE network models are trained with the training data that processing obtains in step (1).By adding in the unsupervised greedy of dropout
Successively training method is trained DdAE network models to the heart, obtains the connection weight of DdAE network models.The process is a kind of
Unsupervised characteristic extraction procedure, greed successively training provide the lossless guarantee in characteristic extraction procedure, and with most fast speed
Degree convergence.The initialization connection weight of training guides the feature to different directions every time, and the diversity for providing feature selecting ensures.
Above-mentioned depth noise reduction autocoder (DdAE) model is specially:
Depth noise reduction autocoder (DdAE) model that the embodiment of the present invention proposes is automatic based on traditional single hidden layer
Encoder is (as shown in Figure 2) to design what is obtained, and single hidden layer autocoder (AE) is by an input layer, a hidden layer and one
Output layer forms, and theoretically requires input layer and output layer result equal, this feature for representing to hide representated by node layer can
Reconstruct input layer data, achievees the purpose that lossless feature extraction.The DdAE network models discard output layer by multiple AE, in the past
The hidden layer of one layer of AE stacks to obtain (as shown in Figure 3) as the input layer of later layer AE, wherein each node of each hidden layer
Value is input in an excitation function and is calculated by the linear weighting connection summation of each nodal value of input layer.Wherein hidden layer
The output that is input to connected by excitation function, the embodiment of the present invention selects ReLU functions as excitation function.ReLU functions
Superperformance it is possible to prevente effectively from during BP gradient attenuation, ensure that trained optimization rate.
Successively training process is specially above-mentioned unsupervised greed:
In unsupervised training process, to each layer of connection weight W of DdAE network modelsiIndividually training, from first layer
Connection weight W1Start, by W1Preceding layer neuron be considered as the input layer of an independent single hidden layer autocoder, by W1's
Later layer neuron is considered as hidden layer, then constructs one with input layer with the output layer of dimension for it, and hidden layer to output layer connects
Weight is connect as W1', W1' initial value byIt obtains, thus constructs the structure of a single hidden layer AE, such as Fig. 4.With instruction
Practice data for input, using the object function of design as guidance, this list hidden layer AE is trained, the W after the completion of training1As
The first layer connection weight of DdAE network models, then to train the hidden layer that the connection weight completed calculates this single hidden layer AE defeated
Go out data, the input data trained as next layer is successively trained successively.
In unsupervised training process, dropout operations are carried out according to probability P to the hidden layer neuron of each independent AE.
Standard self-encoding encoder loss function is using based on mean square error, (mean-square error, are abbreviated as:MSE)
Design does not have robustness to the feature learning of sophisticated signal, very high to the susceptibility of noise yet, is very easy to be made an uproar on a small quantity
Sound shadow is rung.Joint entropy is a kind of non-linear and local similarity measurement, for complicated and non-stationary ambient noise, maximal correlation
Entropy is insensitive.Therefore, maximal correlation entropy has the potentiality of matching sophisticated signal feature, can solve the disadvantage that MSE.At this
In inventive embodiments, it is as follows that new self-encoding encoder loss function is designed using maximal correlation entropy.
Wherein ω be AE weight parameters composition parameter vector, m be input layer dimension, ziPreferably to export as a result,For
By the output of hidden layer feature reconstruction as a result,For Mason's kernel function, generally using gaussian kernel function, Gaussian kernel
For function for estimating actual value and the cross entropy of predicted value, expression formula is as follows.
Wherein, σ is the variance of Gaussian Profile, and for general value between 0 to 10, e is natural logrithm.
The connection weight pre-training of one group of entire neutral net can be obtained by unsupervised training process as a result, tying herein
Next step Training is carried out on the basis of fruit.
Dropout in above-mentioned unsupervised training process, which is operated, is specially:
Dropout refers in the training process of deep learning network, for neutral net unit, according to certain probability
It is temporarily abandoned from network, even it is temporarily not involved in calculating in next step.Standard neural network, the correlation between node
So that the influence of noise scope of a node expands, weaken the generalization ability of network, cause over-fitting problem, dropout is broken
This correlation is broken, avoids these problems.
During entire feature learning, it is openness that dropout so that the feature vector arrived of study has more, and contributes to
The sparse expression of DdAE network models and distributed expression.
The initial method of above-mentioned unsupervised training is specially:
The embodiment of the present invention uses empirical formula method, and the initialization of DdAE network model interlayer connection weight parameters is using warp
Test formulaWherein, njFor the preceding layer neuron node of weight matrix W connections
Number, nj+1For the later layer neuron node number of weight matrix W connections, weight matrix W is uniformly distributed progress initially according to such a
Change.
Step (4):Training process based on Softmax regression models and BP
By the feature extraction that step (3) is unsupervised, one group of reconstruct feature vector can be obtained, Softmax is selected to return
Sorting technique of the model as Hydropower Unit failure, more classification problems of the processing Hydropower Unit under various faults.It reconstructs
Feature vector passes through one layer of combinational network connected entirely, obtains a kind of input of the linear combination of feature as Softmax models,
Calculate the probability of occurrence for representing each failure.The connection weight of combinations of features network is corrected by minimizing error function
Weight is declined with gradient and back-propagation algorithm is finely adjusted the connection weight of whole network.
Above-mentioned Softmax assorting processes are specially:
The function expression of Softmax models is
Wherein z is that the assemblage characteristic of corresponding various failures is vectorial, σ (z)jFor the fuzzy evaluation value of corresponding jth kind failure, zj
For the combinations of features value of corresponding jth kind fault type, zkFor the combinations of features value of corresponding kth kind fault type, K is that can divide in total
Class number of faults.
By by unsupervised part learn as a result, i.e. feature extraction is as a result, carry out linear group of a fully-connected network
It closes, obtains to represent the assemblage characteristic vector z of various failures, be input in Softmax models and obtain the person in servitude to various failures
Category degree, it is final result to take degree of membership maximum fault type.
Above-mentioned BP optimization process is specially:
The back-propagation process of the embodiment of the present invention is the optimization process based on gradient.By Softmax classification result with
Training data label compares, and finds the combinations of features value sequence number j corresponding to correct fault type, gradient is calculated by equation below:
Wherein ΔiFor i-th of element of gradient vector Δ, j is the sequence number of the corresponding true fault type of label, zjFor with
The corresponding combinations of features value of true fault type sequence number, ziFor with ΔiThe combinations of features value of the corresponding fault type of sequence number, zkFor
The combinations of features value of corresponding kth kind fault type, K are the number of faults that can classify in total.
Step (5):Calculate the object function of each AF in AFSA.
The Training of unsupervised training and step (4) by step (3) obtains what a parameter optimization was completed
The test data that processing obtains in step (1) is input in the DdAE network models by DdAE network models, according to failure modes
Overall accuracy designs the object function of AF.
The object function of above-mentioned policy optimization study, which designs, is specially:
The policy optimization learning objective function of design of the embodiment of the present invention is the mistake based on core extreme learning machine KELM theories
Difference function.The training error of feedforward neural network is smaller, and the norm of weight is smaller, and network is intended to obtain better generalization
Energy.ELM tends both to minimize training error and maximization feature is openness for standard.
min||Hβ-T||2AND||β||
Wherein β is feature vector, and H is characterized combinatorial matrix, and T is true tag vector.H β can be by above-mentioned 3rd step
Softmax classification results vector represents.Objectives function is designed as minimizing such as minor function:
Wherein xiFor the diagnostic result of i-th group of input, ziFor the corresponding label vector of i-th group of input, m is training set size.
θ be parameter vector to be optimized, the hidden layer number of plies including DdAE, every layer of neuronal quantity nj, Gaussian kernel variances sigma and
The drop probability P of dropout.
Step (6):The appropriate action of each AF is selected according to the principle of optimality.
The behavior of AF includes foraging behavior (Prey), clustering behavior (Swarm), behavior of knocking into the back (Follow) and random behavior
(Move).The optimization behavior conducted in current iteration process of each AF is selected according to the principle of optimality, is each DdAE
Optimization direction of the network model hyper parameter in current iteration.
The above-mentioned principle of optimality is specially:
1. preferentially attempting clustering behavior, calculation formula is:
WhereinFor the position of the AF in estimated next step iteration;For the position of the AF in current iteration;XcTo work as
The place-centric of preceding AF all AF within sweep of the eye;S is maximum step-length;Rand () is a random number generation function, generates 0
Random number between to 1.
Calculate XcThe object function result Y of corresponding AF positionsc,The object function result Y of corresponding AF positionsi.IfWherein nfFor AF quantity within sweep of the eye, then otherwise AF selection clustering behaviors in current iteration optimization are attempted
It knocks into the back behavior.
The behavior 2. trial is knocked into the back, calculation formula are:
Wherein XjFor optimal AF positions within sweep of the eye.
Calculate XjThe object function result Y of corresponding AF positionsj,The object function result Y of corresponding AF positionsi.IfThen the AF selects behavior of knocking into the back in current iteration optimization, otherwise attempts foraging behavior.
3. attempting foraging behavior, calculation formula is:
Wherein, XjPosition for the AF randomly selected within sweep of the eye.
It calculatesObject function under position, if object function result is better thanAs a result, then current iteration is excellent under position
The AF selects foraging behavior in change;Otherwise, if selection XjNumber is less than maximum number of attempts try_number, then reselects Xj;It is no
It then abandons foraging behavior and carries out random behavior.
4. attempting random behavior, calculation formula is:
Wherein, V is field range.
Random behavior is the default behavior of above-mentioned three kinds of optimization behavior, and in above-mentioned three behaviors, optional time does not perform at random
Behavior.
Step (7):Iteration carries out structure optimization process
The process of the hyper parameter of AFSA model optimization DdAE network models is structure optimization process.By step (6)
After fish school behavior performs, the corresponding AF location informations of optimal objective function are recorded on billboard, and checks whether and meets iteration
Suspension condition exports the AF positions recorded on last billboard if suspension condition is reached and surpasses as final DdAE network models
Parameter determines a DdAE network architecture as the model structure for fault diagnosis;Otherwise return and perform step (3).
Embodiment 3:
The embodiment of the present invention additionally provides a kind of Approach for Hydroelectric Generating Unit Fault Diagnosis system based on DdAE deep learning models, such as
Shown in Fig. 7, the system comprises training data processing module, neural network model training module, DdAE network models optimization moulds
Block, reconstruct feature vector generation module and probability of malfunction computing module, above-mentioned each module are sequentially connected, specifically:
Training data processing module for obtaining data set, and extracts n group data blocks, as DdAE nets from data set
The training data of network model;
Neural network model training module, for establishing DdAE network models, and using the training data to DdAE nets
Network model is trained, and obtains the connection weight of DdAE network models;
DdAE network model optimization modules, for the hyper parameter in the structural parameters and error function using DdAE as AFSA
The target component of algorithm, model exports object function of the error function of result as AFSA, by AFSA in entire model
Hyper parameter carry out Stochastic search optimization, each step iterative process of AFSA optimizations for one group of DdAE network model parameter optimization
Process, to obtain optimal DdAE network models;
Feature vector generation module is reconstructed, according to the DdAE network models being made of the connection weight, obtains one group of weight
Structure feature vector;
Probability of malfunction computing module for reconstructing feature vector by one layer of combinational network connected entirely, obtains feature
Linear combination, and as the input of Softmax models, calculate the probability for the appearance possibility for representing each failure.
What deserves to be explained is the contents such as information exchange, implementation procedure between module, unit in above system, due to
Same design is based on the processing method embodiment of the present invention, particular content can be found in the narration in the method for the present invention embodiment 1,
Details are not described herein again.
Embodiment 4:
The embodiment of the present invention additionally provides a kind of electronic equipment, be used to implement embodiment 1 or described in embodiment 2 based on
The Fault Diagnosis Method of Hydro-generating Unit of DdAE deep learning models, described device include:
At least one processor;And the memory being connected at least one processor communication;Wherein, it is described to deposit
Reservoir is stored with the instruction that can be performed by least one processor, and described instruction is had by the memory storage can be described
The instruction repertorie that at least one processor performs, described instruction are arranged to carry out realizing 2 institute of embodiment 1 or embodiment by program
The Fault Diagnosis Method of Hydro-generating Unit based on DdAE deep learning models stated.
One of ordinary skill in the art will appreciate that all or part of step in the various methods of embodiment is can to lead to
Program is crossed relevant hardware to be instructed to complete, which can be stored in a computer readable storage medium, storage medium
It can include:Read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access
Memory), disk or CD etc..
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, not to
The limitation present invention, all any modification, equivalent and improvement made within the spirit and principles of the invention etc., should all include
Within protection scope of the present invention.
Claims (10)
1. a kind of Fault Diagnosis Method of Hydro-generating Unit based on DdAE deep learning models, which is characterized in that method includes:
Data set is obtained, and n group data blocks, the training data as DdAE network models are extracted from data set;
DdAE network models are established, and DdAE network models are trained using the training data, obtain DdAE network moulds
The connection weight of type;The input value of each neuron node in each layer network in DdAE network models, by previous
The output valve of all neuron nodes of layer asks weighted average to obtain, and all weights between every two-tier network combine shape
Into a connection weight matrix, abbreviation connection weight;
Using the hyper parameter in the structural parameters and error function of DdAE as the target component of AFSA algorithms, model exports result
Object function of the error function as AFSA carries out Stochastic search optimization, AFSA by AFSA to the hyper parameter in entire model
Each step iterative process of optimization is the parameter optimisation procedure of one group of DdAE network model, to obtain optimal DdAE networks mould
Type;
According to the DdAE network models being made of the connection weight, one group of reconstruct feature vector is obtained;
The reconstruct feature vector obtains the linear combination of feature, and is used as Softmax by one layer of combinational network connected entirely
The input of model calculates the probability for the appearance possibility for representing each failure.
2. the Fault Diagnosis Method of Hydro-generating Unit according to claim 1 based on DdAE deep learning models, feature exist
In the DdAE network models of the foundation are specially:
The DdAE network models are made of at least two AE, wherein, the input of the hidden layer of preceding layer AE as later layer AE
Layer is stacked and obtained;
The value of each node of hidden layer of each AE is input to an excitation by the linear weighting connection summation of each nodal value of input layer
It is calculated in function.
3. the Fault Diagnosis Method of Hydro-generating Unit according to claim 1 based on DdAE deep learning models, feature exist
In, it is described that DdAE network models are trained using the training data, it specifically includes by adding in the unsupervised of dropout
Successively training method is trained DdAE network models to greed, and the unsupervised greedy successively training method is specially:
In unsupervised training process, using each layer of the DdAE network models single hidden layer autocoder independent as one,
Multiple independent AE are reconstructed individually to be trained;
Successively training method includes the unsupervised greed for adding in dropout:In the training process of DdAE networks, for god
Through network element, it is temporarily abandoned from network according to certain probability;Wherein, temporarily abandoned from network and be specially:It presses
Whether in the calculating of epicycle input ignore the network element according to determine the probability, it is made to be not involved in epicycle calculating, and in next round
Decide whether to participate in calculating by probability again in calculating.
4. the Fault Diagnosis Method of Hydro-generating Unit according to claim 3 based on DdAE deep learning models, feature exist
In the initialization of the DdAE network models includes structure initialization and parameter initialization:
The structure initialization of DdAE network models is AF position initializations process in ASFA models, including DdAE network model layers
Number, DdAE network model input layer quantity, the variances sigma of Gaussian Profile and dropout probability Ps;AF in ASFA models
The initialization put is evenly distributed according to initialized parameter adjustment range L B, UB in parameter adjustment space;
The parameter initialization of DdAE network models is the initialization of unsupervised training, is joined for DdAE network models connection weight
Several initialization uses empirical formula method, using empirical equationWherein njFor
The preceding layer neuron node number of weight matrix W connections, nj+1For the later layer neuron node number of weight matrix W connections, weight
Each element is initialized according to such a be uniformly distributed in matrix W.
5. according to any Fault Diagnosis Method of Hydro-generating Unit based on DdAE deep learning models of claim 1-4,
It is characterized in that, the function expression of the Softmax models is:
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<mi>z</mi>
<mi>k</mi>
</msub>
</msup>
</mrow>
</mfrac>
</mrow>
Wherein z is the feature vector of corresponding various failures, σ (z)jFor the fuzzy evaluation value of corresponding jth kind failure, zjFor corresponding jth
The combinations of features value of kind fault type, zkFor the combinations of features value of corresponding kth kind fault type, K is the number of faults that can classify in total.
6. the Fault Diagnosis Method of Hydro-generating Unit according to claim 1 based on DdAE deep learning models, feature exist
In after the connection weight is calculated, the method is further included corrects the company of combinational network by minimizing error function
Weight is connect, is declined with gradient and back-propagation algorithm is finely adjusted the connection weight of whole network.
7. the Fault Diagnosis Method of Hydro-generating Unit according to claim 6 based on DdAE deep learning models, feature exist
In the gradient declines and back-propagation algorithm is specially:
Back-propagation process is the optimization process based on gradient, and the result of Softmax classification with training data label is compared, is looked for
To the combinations of features value sequence number j corresponding to correct fault type, gradient is calculated by equation below:
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Wherein ΔiFor i-th of element of gradient vector Δ, j is the sequence number of the corresponding true fault type of label, zjFor with it is true
The corresponding combinations of features value of fault type sequence number, ziFor with ΔiThe combinations of features value of the corresponding fault type of sequence number, zkFor correspondence
The combinations of features value of kth kind fault type.
8. according to any Fault Diagnosis Method of Hydro-generating Unit based on DdAE deep learning models of claim 1-7,
It is characterized in that, the artificial fish-swarm algorithm AFSA is specially:
Step1:Prepare depth self-encoding encoder model;
Step2:The basic parameter of artificial fish-swarm algorithm is set;Wherein, the basic parameter includes number of fish school L, the tune of parameter
Whole range L B, UB, the field range V of the shoal of fish, maximum step-length S, the trial number try_number of foraging behavior of shoal of fish movement, most
One or more in big procreation algebraically Maxgen and crowding factor δ, uses the failure modes precision of depth autocoder
To design the object function of AFSA;
Step3:In parameter variation range, the reset condition of the initialization shoal of fish is generated according to the model initial parameter collection of Step1,
Record the optimum position of the shoal of fish and the minimum target functional value per a generation;
Step4:Each Artificial Fish AF attempts appropriate behavior according to optimization regulation, records optimum parameter value;
Step5:It checks whether the corresponding target function value of optimum parameter value reaches optimization purpose, optimization is completed if reaching simultaneously
Export optimized parameter;Otherwise, check whether procreation algebraically reaches maximum procreation algebraically Maxgen, if procreation algebraically is more than or waits
It then completes to optimize in Maxgen, and exports optimized parameter;Otherwise return and perform Step4.
9. a kind of Approach for Hydroelectric Generating Unit Fault Diagnosis system based on DdAE deep learning models, which is characterized in that the system comprises
Training data processing module, neural network model training module, DdAE network models optimization module, reconstruct feature vector generation mould
Block and probability of malfunction computing module, above-mentioned each module are sequentially connected, specifically:
Training data processing module for obtaining data set, and extracts n group data blocks, as DdAE network moulds from data set
The training data of type;
Neural network model training module, for establishing DdAE network models, and using the training data to DdAE network moulds
Type is trained, and obtains the connection weight of DdAE network models;
DdAE network model optimization modules, for the hyper parameter in the structural parameters and error function using DdAE as AFSA algorithms
Target component, object function of the error function as AFSA of model output result, by AFSA to surpassing in entire model
Parameter carries out Stochastic search optimization, and each step iterative process of AFSA optimizations is the parameter optimization mistake of one group of DdAE network model
Journey, to obtain optimal DdAE network models;
Feature vector generation module is reconstructed, according to the DdAE network models being made of the connection weight, it is special to obtain one group of reconstruct
Sign vector;
Probability of malfunction computing module for reconstructing feature vector by one layer of combinational network connected entirely, obtains the linear of feature
Combination, and be used as the input of Softmax models, calculate expression each failure appearance possibility probability.
10. the Approach for Hydroelectric Generating Unit Fault Diagnosis system according to claim 9 based on DdAE deep learning models, feature exist
In the function expression of the Softmax models is:
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<mi>&sigma;</mi>
<msub>
<mrow>
<mo>(</mo>
<mi>z</mi>
<mo>)</mo>
</mrow>
<mi>j</mi>
</msub>
<mo>=</mo>
<mfrac>
<msup>
<mi>e</mi>
<msub>
<mi>z</mi>
<mi>j</mi>
</msub>
</msup>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>K</mi>
</munderover>
<msup>
<mi>e</mi>
<msub>
<mi>z</mi>
<mi>k</mi>
</msub>
</msup>
</mrow>
</mfrac>
</mrow>
Wherein z is the feature vector of corresponding various failures, σ (z)jFor the fuzzy evaluation value of corresponding jth kind failure, zjFor corresponding jth
The combinations of features value of kind fault type, zkFor the combinations of features value of corresponding kth kind fault type, K is the number of faults that can classify in total.
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