CN111523394A - Method and system for detecting foreign matter defects inside GIS equipment - Google Patents

Method and system for detecting foreign matter defects inside GIS equipment Download PDF

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CN111523394A
CN111523394A CN202010231397.0A CN202010231397A CN111523394A CN 111523394 A CN111523394 A CN 111523394A CN 202010231397 A CN202010231397 A CN 202010231397A CN 111523394 A CN111523394 A CN 111523394A
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吴旭涛
汲胜昌
马云龙
马波
叶逢春
何宁辉
沙伟燕
李秀广
周秀
倪辉
牛勃
张佩
杨朝旭
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Xi'an Zhonghui Electric Power Technology Co ltd
Xian Jiaotong University
Electric Power Research Institute of State Grid Ningxia Electric Power Co Ltd
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Xian Jiaotong University
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Abstract

The embodiment of the invention discloses a method and a system for detecting foreign matter defects in GIS equipment. The detection method comprises the following steps: training a foreign matter defect classification model by adopting training samples of sound signals inside GIS equipment, wherein each training sample corresponds to a standard type of a foreign matter defect; collecting sound signals inside the GIS equipment at preset time intervals; inputting the sound signal into a trained foreign matter defect classification model, and outputting the type of the foreign matter defect corresponding to the sound signal; the foreign matter defect classification model is an extended continuous adaptive fuzzy inference system, the extended continuous adaptive fuzzy inference system comprises an input layer, a fuzzy inference layer, a specification layer and an output layer which are sequentially arranged, and nodes of the fuzzy inference layer represent fuzzy rules. The embodiment of the invention can obtain the types of the foreign body defects and has higher accuracy and efficiency.

Description

Method and system for detecting foreign matter defects inside GIS equipment
Technical Field
The invention relates to the technical field of GIS equipment, in particular to a method and a system for detecting foreign matter defects in GIS equipment.
Background
Under the general war of the national smart grid, the transformer substation plays an irreplaceable role, and is composed of a plurality of closed space devices, particularly GIS (gas Insulated switchgear) devices. If the devices are in failure, the work of the transformer substation needs to be suspended for disassembly and maintenance, which is not in accordance with the goal of improving the power supply operation reliability of the power grid.
With the rapid development of mobile internet, sensor technology and big data technology in recent years, electronic stethoscope technology has also come into the spring, and various electronic stethoscopes have been widely used in many aspects such as fault diagnosis and intelligent medical treatment. With the rapid development of scientific technology, the existing electronic stethoscopes have certain progress compared with the traditional stethoscopes, and the mechanization of the electronic stethoscopes is gradually changed to the electronization and the digitalization, wherein the simple and clear electronic stethoscopes, the multifunctional electronic stethoscopes and the medical electronic stethoscopes have certain signal preprocessing functions, and the precision and the accuracy of signals are improved. A large number of researches show that the signals acquired by the electronic stethoscope technology have the characteristics of no distortion, long distance, portability and the like, so that the signals in the closed substation equipment can be completely collected, and the analysis and processing of the signals in the later period are realized.
Therefore, based on the advantages of the electronic auscultation technology, how to effectively combine the electronic auscultation technology with the identification of the type of the foreign object defect inside the GIS device so as to efficiently identify the type of the foreign object defect is a problem to be solved.
Disclosure of Invention
The embodiment of the invention provides a method and a system for detecting foreign matter defects inside GIS equipment, and aims to solve the problem that the prior art lacks a method for applying an electronic auscultation technology to identifying the types of the foreign matter defects inside the GIS equipment.
In a first aspect, a method for detecting a foreign object defect inside a GIS device is provided, including:
training a foreign matter defect classification model by adopting training samples of sound signals inside GIS equipment, wherein each training sample corresponds to a standard type of a foreign matter defect;
collecting sound signals inside the GIS equipment at preset time intervals;
inputting the sound signal into a trained foreign matter defect classification model, and outputting the type of the foreign matter defect corresponding to the sound signal;
the foreign matter defect classification model is an extended continuous adaptive fuzzy inference system, the extended continuous adaptive fuzzy inference system comprises an input layer, a fuzzy inference layer, a specification layer and an output layer which are sequentially arranged, and nodes of the fuzzy inference layer represent fuzzy rules.
In a second aspect, a system for detecting a foreign object defect inside a GIS device is provided, including:
the training module is used for training a foreign matter defect classification model by adopting training samples of sound signals inside the GIS equipment, wherein each training sample corresponds to a standard type of a foreign matter defect;
the acquisition module is used for acquiring sound signals inside the GIS equipment at preset time intervals;
the classification module is used for outputting the type of the foreign body defect corresponding to the sound signal after the sound signal is input into the trained foreign body defect classification model;
the foreign matter defect classification model is an extended continuous adaptive fuzzy inference system, the extended continuous adaptive fuzzy inference system comprises an input layer, a fuzzy inference layer, a specification layer and an output layer which are sequentially arranged, and nodes of the fuzzy inference layer represent fuzzy rules.
Therefore, the embodiment of the invention takes the trained extended continuous adaptive fuzzy inference system with good classification rate as the classification model of the foreign body defects, and inputs the sound signals obtained by the electronic auscultation technology into the classification model of the foreign body defects to obtain the types of the foreign body defects, thereby having higher accuracy and efficiency.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a flowchart of a method for detecting a foreign object defect inside a GIS device according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an extended continuous adaptive fuzzy inference system of an embodiment of the present invention;
fig. 3 is a block diagram of a system for detecting a foreign object defect inside a GIS device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a method for detecting foreign matter defects in GIS equipment. As shown in fig. 1, the method comprises the steps of:
step S101: and training a foreign matter defect classification model by adopting a training sample of the sound signal in the GIS equipment.
It should be understood that each training sample corresponds to a standard class of foreign object defects.
The foreign object defect classification model is an Extended continuous adaptive fuzzy inference system (ESAFIS). The ESAFIS is a sequence learning algorithm, and the ESAFIS establishes a rule base by means of a functional equivalence relation between a Radial Basis Function (RBF) neural network and a Fuzzy Inference System (FIS), and the structure changes adaptively along with the input of data. ESAFIS belongs to a typical feed-forward neural network.
As shown in fig. 2, the extended continuous adaptive fuzzy inference system includes an input layer, a fuzzy inference layer, a specification layer and an output layer, which are sequentially arranged. The input layer is used for inputting at least one-dimensional vector to be classified each time, specifically to an application scene for detecting the type of the foreign object defect, the vector to be classified is a sound signal, and the dimension of the sound signal is one-dimensional. The fuzzy layer is closely connected with the fuzzy inference layer, and each fuzzy rule has a membership function. The node number of the fuzzy layer changes along with the addition and deletion of the fuzzy rule. Each node of the fuzzy inference layer represents a fuzzy rule. The ESAFIS fuzzy rule evolution is based on a distance criterion and a correction influence standard, and nodes are adaptively added and deleted along with the input of data. The standard layer is used for normalizing the output of the fuzzy inference layer, and the number of the nodes is the same as that of the fuzzy inference layer. The output layer is used for outputting the classification result, and the number of the nodes is the same as the number of the classified categories.
The fuzzy rule with sequence number k of the extended continuous adaptive fuzzy inference system can be expressed as: if (x)1Is Aik)，……，(xiIs Aik)，……(
Figure BDA0002429391370000041
Is that
Figure BDA0002429391370000042
) Then (1)
Figure BDA0002429391370000043
Is ak1)，……，(
Figure BDA0002429391370000044
Is akj)，……，(
Figure BDA0002429391370000045
Is that
Figure BDA0002429391370000046
). Wherein the vector is input
Figure BDA0002429391370000047
AikRepresenting the membership value of the ith component of the input vector to the fuzzy rule with the sequence number k, and outputting the vector
Figure BDA0002429391370000048
akjThe jth back-piece parameter of the fuzzy rule with the sequence number k is shown. k is 1, 2, … …, Nh。i＝1，2，……，Nx。j＝1，2，……，Ny。NhIndicating the number of fuzzy rules. N is a radical ofxAnd NyBoth represent dimensions. For the application scenario of the classification of the foreign object defect inside the GIS device of the embodiment of the present invention, Nx＝1，Ny＝1。
The excitation function of the fuzzy rule with the sequence number of k of the extended continuous adaptive fuzzy inference system is a Gaussian function, and can be calculated by using a general formula of the excitation function:
Figure BDA0002429391370000049
wherein R isk(x) When a vector x is input, the excitation function of the fuzzy rule with the sequence number k is obtained through calculation. SigmakRadius of excitation function, mu, of fuzzy rule with index kkRepresenting the center of the excitation function of the fuzzy rule with index k. For the application scenario of the classification of the foreign object defect inside the GIS device in the embodiment of the present invention, x is the input sound signal.
The output vector of the extended continuous adaptive fuzzy inference system can be calculated by the following output vector general formula:
Figure BDA0002429391370000051
wherein, akAnd expressing a weight matrix from the fuzzy rule with the sequence number k to the specification layer, namely a back-part parameter matrix. For the first order Takagi-Sugeno (TS) fuzzy model,
Figure BDA0002429391370000052
xeextended by x and 1, i.e. xe＝[1,x]T。qkIs a parameter matrix of the fuzzy rule with the sequence number k. The general formula of the parameter matrix is as follows:
Figure BDA0002429391370000053
as can be seen from the above description of the extended continuous adaptive fuzzy inference system, the output vector is affected by the input vector, the fuzzy rule and the parameters of the back-part. The parameters of the back-part are further subjected to a parameter matrix qkThe fuzzy rule itself is affected by muk、σkEtc. of the parameters. Therefore, the fuzzy rules of the extended continuous adaptive fuzzy inference system are not fixed, but dynamically adjusted in the learning process, so as to add the fuzzy rules, delete the fuzzy rules or update the corresponding parameters.
Specifically, step S101 includes the following steps:
1. and inputting each training sample in turn according to the sequence number of the training sample.
Every time a training sample is input, a fuzzy rule is added to the assumed foreign object defect classification model.
It should be appreciated that the training samples described in the embodiments of the present invention are novel. Specifically, if the training samples satisfy psi<EsThen the training sample has novelty.
Where ψ represents the spherical potential energy of the training sample in the high-dimensional feature space. The high-dimensional feature space appears spherical. The embodiment of the invention describes the high-dimensional feature space by using the front piece parameters of the Gaussian rule, including the central point mu and the width sigma.
Figure BDA0002429391370000061
T denotes the dimension of the high-dimensional feature space, phi (x, mu)t) And representing the mapping function of the high-dimensional feature space corresponding to the training sample. Mu.stRepresenting the center of the t-th mapping function. It should be understood that the mapping function of embodiments of the present invention is a gaussian function. EsThe novelty threshold is expressed and can be preset empirically. Only if the training sample meets the above-mentioned novelty condition, the training sample is inputted into the extended continuous adaptive fuzzy inference system.
2. If the sequence number of the currently input training sample is 1, the fuzzy rule added correspondingly to the currently input training sample is reserved.
It should be appreciated that the extended continuous adaptive fuzzy inference system has an initial number of fuzzy rules of 0 before inputting the training samples. Therefore, when the first training sample is input, no judgment is made, and the fuzzy rule added corresponding to the input training sample is reserved.
3. And if the serial number of the currently input training sample is greater than 1, judging whether to keep the fuzzy rule which is correspondingly added to the currently input training sample.
After the first training sample is input, since the extended continuous adaptive fuzzy inference system already has a fuzzy rule, the input of other training samples and the assumption of the added fuzzy rule need to be judged so as to determine whether the fuzzy rule can be added.
Specifically, the steps include the following processes:
(1) and judging whether the added fuzzy rules corresponding to the currently input training sample meet the distance criterion and the influence criterion.
Specifically, the distance criterion is as follows:
||xn-μnr||>en。
xnwhich represents the currently entered training sample with sequence number n. n is>1。μnrRepresenting distance training samples xnThe center of the nearest fuzzy rule. Nearest refers to training sample xnAnd the center of the fuzzy rule is the smallest. e.g. of the typenRepresenting a distance threshold. In particular, en＝max{emax×γn,emin}. Wherein e ismaxThe maximum value representing the distance threshold may be preset empirically. e.g. of the typeminThe minimum value representing the distance threshold may be preset empirically. Gamma is a decaying exponential, is a positive number less than 1, and can be preset empirically, so enInitially at a maximum and then decays exponentially until the decay is at a minimum.
Specifically, the impact criteria are as follows:
Emin f(Nh+1)>eg。
wherein E ismin f(Nh+1) represents the impact value of the currently input training sample for the added fuzzy rule. N is a radical ofhIndicating the number of fuzzy rules that exist. e.g. of the typegIndicating that the threshold is increased, may be preset empirically.
Example E of the inventionmin f(Nh+1) is calculated using the formula:
Figure BDA0002429391370000071
wherein x islRepresentation for calculating Emin f(Nh+1) training samples with sequence number l.
Figure BDA0002429391370000072
Representing the use of training samples xlThe calculated fuzzy rule (with the sequence number N) which is correspondingly added to the currently input training sampleh+1) excitation function. Rk(xl) Representing the use of training samples xlAnd calculating an excitation function of the fuzzy rule with the sequence number k. It should be understood that the sequence numbers of the fuzzy rules are arranged in order of retention. M represents the number of most recently input training samples including the currently input training sample with sequence number n.
With reference to the general formula of the excitation function, Rk(xl) Calculated using the formula:
Figure BDA0002429391370000073
wherein σkRadius of excitation function, mu, of fuzzy rule with index kkRepresenting the center of the excitation function of the fuzzy rule with index k.
In the same way as above, the first and second,
Figure BDA0002429391370000081
wherein the content of the first and second substances,
Figure BDA0002429391370000082
fuzzy rule (with sequence number N) representing corresponding increase of currently input training sampleh+1) radius of the excitation function of the fuzzy rule,
Figure BDA0002429391370000083
fuzzy rule (with sequence number N) representing corresponding increase of currently input training sampleh+1) center of the excitation function.
(2) If the distance criterion and the influence criterion are met, the fuzzy rules which are correspondingly added to the currently input training samples are reserved.
If both the distance criterion and the influence criterion are met, the added fuzzy rule may be retained, otherwise, the added fuzzy rule is not retained,
4. and if the added fuzzy rule corresponding to the currently input training sample is reserved, setting the parameter of the added fuzzy rule corresponding to the currently input training sample according to the first-order Takagi-Sugeno fuzzy model.
Specifically, the parameters of the fuzzy rule mainly include: parameter matrix, center and radius of the excitation function.
Therefore, this step specifically sets the corresponding parameters by the following procedure:
(1) and setting a parameter matrix of the fuzzy rule which is added corresponding to the currently input training sample by adopting a first equation group.
Referring to the general formula of the parameter matrix above, the first set of equations is as follows:
Figure BDA0002429391370000084
wherein j is 1, 2, … …, Ny。i＝1，2，……，Nx。
Because of the application scenario of the classification of the foreign object defect inside the GIS device of the embodiment of the present invention, Nx＝1，NyWhen the current input training sample corresponds to the added fuzzy rule, the parameter matrix is 1
Figure BDA0002429391370000091
Thus, the first set of equations is as follows:
Figure BDA0002429391370000092
(2) and setting the center of the excitation function of the added fuzzy rule corresponding to the currently input training sample by adopting a second equation.
Wherein the second equation is as follows:
Figure BDA0002429391370000093
(3) and setting the radius of the excitation function of the added fuzzy rule corresponding to the currently input training sample by adopting a third program.
Wherein the third process is as follows:
Figure BDA0002429391370000094
k represents an overlap factor for controlling the overlap of the added fuzzy rule in the input space, which can be preset empirically.
Therefore, the relevant parameters of the currently input training sample corresponding to the added fuzzy rule can be set through the three equations (sets) described above.
5. And if the fuzzy rule which is added correspondingly to the currently input training sample is not reserved, judging whether to delete the existing fuzzy rule or not after updating the back part parameters of the existing fuzzy rule of the foreign body defect classification model by adopting a least square recursive error method (RLSE).
Specifically, the equation set of the least squares recursive error method is as follows:
Figure BDA0002429391370000101
wherein, anA latter parameter matrix representing a fuzzy rule with a sequence number n. y isn-1And the standard type of the foreign body defect corresponding to the training sample with the sequence number of n-1 is shown.
Figure BDA0002429391370000102
The type of the foreign body defect output by the foreign body defect classification model can be calculated by the output vector general formula when the training sample with the sequence number of n-1 is input into the foreign body defect classification model. I denotes an identity matrix. I is a1×z1An identity matrix of dimensions. z is a radical of1And representing the dimensionality of the back-piece parameter of the added fuzzy rule corresponding to the currently input training sample. It should be understood that when the training samples of the current input correspond to the added fuzzy rule, PnAlso increase in the dimension of (i.e. the
Figure BDA0002429391370000103
H0The initial value representing the preset is a constant, which can be set empirically. HnRepresenting an input xnThe output vector of the time normalization layer is specifically calculated by adopting the following formula:
Figure BDA0002429391370000104
see xeGeneral formula (II) of (II), can know that xne＝[1,xn]T。Rk(xn) The general calculation of the excitation function is referred to above and will not be described in detail here.
6. If the existing fuzzy rule is deleted, the parameter of the existing fuzzy rule is deleted, and the number of the existing fuzzy rules is updated.
For example, deleting 1 fuzzy rule, then subtracting 1 from the number of existing fuzzy rules.
And finishing the training process after all the training samples are input into the foreign matter defect classification model for training. Through the training process, the fuzzy rule and the back piece parameters of the foreign matter defect classification model can be determined, so that the foreign matter defect classification model can classify the foreign matter defects more accurately.
Step S102: and collecting sound signals inside the GIS equipment at preset time intervals.
The preset time interval may be set empirically. The sound signal collection can be realized by an electronic auscultation technology. The electronic auscultation technology can improve the efficiency and accuracy of data acquisition inside the GIS equipment. In particular, physical signals inside the GIS device, such as ultrasonic signals, vibration signals, etc., may be acquired. The physical signal is sequentially converted into an electric signal, a digital signal and an analog signal through corresponding electric elements, and finally the analog signal is converted into a sound signal. For example, the corresponding sensor may be connected to the GIS device to be tested, and a physical signal generated by a foreign object defect inside the GIS device may be converted into a corresponding electrical signal. During the conversion, conventional amplification processing, filtering processing, and the like may be performed by the respective electric elements.
Step S103: and inputting the sound signal into the trained foreign matter defect classification model, and outputting the type of the foreign matter defect corresponding to the sound signal.
Specifically, the feature values of the sound signals, which may be frequencies, amplitudes, etc., may be extracted and input to the foreign object defect classification model. The feature value of the sound signal is input to the foreign matter defect classification model from the input layer, and the output layer outputs the type of the foreign matter defect corresponding to the sound signal.
In a preferred embodiment of the present invention, the types of the foreign object defect include: 0.5mm spherical particles, 1mm spherical particles, 2mm spherical particles, 1mm linear particles, 2mm linear particles. The accuracy of classification by the foreign matter defect classification model was 98%.
In summary, the method for detecting the foreign object defect inside the GIS device according to the embodiment of the present invention uses the extended continuous adaptive fuzzy inference system with good classification rate obtained by training as the classification model of the foreign object defect, and inputs the sound signal obtained by the electronic auscultation technology into the classification model of the foreign object defect to obtain the type of the foreign object defect, thereby having higher accuracy and efficiency.
The embodiment of the invention also discloses a system for detecting the foreign matter defect in the GIS equipment. As shown in fig. 3, the detection system includes the following modules:
the training module 301 is configured to train a foreign object defect classification model by using a training sample of a sound signal inside the GIS device.
Wherein, each training sample corresponds to a standard type of the foreign body defect.
And the acquisition module 302 is configured to acquire a sound signal inside the GIS device at preset time intervals.
The classification module 303 is configured to output a type of the foreign object defect corresponding to the sound signal after the sound signal is input into the trained foreign object defect classification model.
The foreign matter defect classification model is an extended continuous adaptive fuzzy inference system, the extended continuous adaptive fuzzy inference system comprises an input layer, a fuzzy inference layer, a specification layer and an output layer which are sequentially arranged, and nodes of the fuzzy inference layer represent fuzzy rules.
Preferably, the training module 301 comprises:
and the input submodule is used for sequentially inputting each training sample according to the sequence number of the training sample.
Wherein, when inputting a training sample, the foreign body defect classification model is supposed to be added with a fuzzy rule; the training samples have a novelty.
If the training samples satisfy psi<EsThen the training sample has novelty. Wherein psi represents the spherical potential energy of the training sample in the high-dimensional feature space,
Figure BDA0002429391370000121
t denotes the dimension of the high-dimensional feature space, phi (x, mu)t) A mapping function, mu, representing a high-dimensional feature space corresponding to the training samplestRepresenting the center of the t-th mapping function, which is a Gaussian function, EsRepresenting a novelty threshold.
And the retention submodule is used for retaining the fuzzy rule which is correspondingly added to the currently input training sample if the serial number of the currently input training sample is 1.
And the first judgment submodule is used for judging whether to keep the fuzzy rule which is correspondingly added to the currently input training sample if the serial number of the currently input training sample is greater than 1.
And the setting submodule is used for setting parameters of the fuzzy rule which is added to the currently input training sample according to the first-order Takagi-Sugeno fuzzy model if the fuzzy rule which is added to the currently input training sample is reserved.
And the second judgment submodule is used for judging whether to delete the existing fuzzy rule or not after updating the back part parameters of the existing fuzzy rule of the foreign matter defect classification model by adopting a least square recursive error method if the fuzzy rule which is correspondingly added to the currently input training sample is not reserved.
And the deleting submodule is used for deleting the parameters of the existing fuzzy rules and updating the quantity of the existing fuzzy rules if the existing fuzzy rules are deleted.
Preferably, the first judgment sub-module includes:
the first judging unit is used for judging whether the added fuzzy rule corresponding to the currently input training sample meets the distance criterion and the influence criterion.
And the retaining unit is used for retaining the fuzzy rule which is correspondingly added to the currently input training sample if the distance criterion and the influence criterion are met.
Wherein, the distance criterion is | | xn-μnr||>en，xnTraining samples with sequence number n representing the current input, n>1，μnrRepresenting distance training samples xnCenter of the nearest fuzzy rule, enRepresenting a distance threshold. e.g. of the typen＝max{emax×γn,eminIn which emaxRepresents the maximum value of the distance threshold, eminRepresents the minimum value of the distance threshold and gamma represents the decay exponent.
Wherein the influence criterion is Emin f(Nh+1)>eg，NhIndicating the number of fuzzy rules present, Emin f(Nh+1) represents the influence value of the currently input training sample on the added fuzzy rule, egIndicating that the threshold is increased.
Figure BDA0002429391370000131
Wherein，xlRepresentation for calculating Emin f(Nh+1) training samples with sequence number l,
Figure BDA0002429391370000132
representing the use of training samples xlCalculating an excitation function, R, of the fuzzy rule added to the currently input training samplek(xl) Representing the use of training samples xlAnd calculating an excitation function of the fuzzy rule with the sequence number of k, wherein the sequence numbers of the fuzzy rule are sequentially arranged according to a reserved sequence, and M represents the number of the most recently input training samples including the currently input training sample with the sequence number of n.
Figure BDA0002429391370000133
Wherein σkRadius of excitation function, mu, of fuzzy rule with index kkRepresenting the center of the excitation function of the fuzzy rule with index k.
Figure BDA0002429391370000141
Wherein the content of the first and second substances,
Figure BDA0002429391370000142
the radius of the excitation function representing the training sample of the current input corresponding to the added fuzzy rule,
Figure BDA0002429391370000143
the training samples representing the current input correspond to the center of the excitation function of the added fuzzy rule.
Preferably, the setting submodule includes:
and the first setting unit is used for setting a parameter matrix of the fuzzy rule which is correspondingly added to the currently input training sample by adopting a first program group.
Wherein the parameter matrix of the fuzzy rule added corresponding to the currently input training sample is
Figure BDA0002429391370000144
The first process group is
Figure BDA0002429391370000145
And the second setting unit is used for setting the center of the excitation function of the added fuzzy rule corresponding to the currently input training sample by adopting a second equation.
Wherein the second equation is
Figure BDA0002429391370000146
And the third setting unit is used for setting the radius of the excitation function of the fuzzy rule which is added corresponding to the currently input training sample by adopting a third program.
Wherein the third equation is
Figure BDA0002429391370000147
κ denotes an overlap factor.
Preferably, the system of equations of the least squares recursive error method is:
Figure BDA0002429391370000148
wherein, anA back-piece parameter matrix, y, representing a fuzzy rule with a sequence number nn-1Indicates the standard type of the foreign body defect corresponding to the training sample with the sequence number of n-1,
Figure BDA0002429391370000149
indicates the type of the foreign body defect output by the foreign body defect classification model when the training sample with the sequence number of n-1 is input into the foreign body defect classification model, H0Denotes a preset initial value, HnRepresenting an input xnThe output vector of the time-normalized layer,
Figure BDA0002429391370000151
xne＝[1,xn]Tand I represents an identity matrix.
Preferably, the second judgment sub-module includes:
and the second judging unit is used for judging whether the existing fuzzy rule meets the deletion criterion.
And the deleting unit is used for deleting the existing fuzzy rule if the deleting criterion is met.
Wherein the deletion criterion is Emin f(k)<ep，Emin f(k) Representing the influence value of the fuzzy rule with the sequence number k, epIndicating a deletion threshold.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
In summary, the system for detecting the foreign object defect inside the GIS device according to the embodiment of the present invention uses the extended continuous adaptive fuzzy inference system with good classification rate obtained by training as the classification model of the foreign object defect, and inputs the sound signal obtained by the electronic auscultation technique into the classification model of the foreign object defect to obtain the type of the foreign object defect, thereby having higher accuracy and efficiency.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A method for detecting foreign matter defects inside GIS equipment is characterized by comprising the following steps:
training a foreign matter defect classification model by adopting training samples of sound signals inside GIS equipment, wherein each training sample corresponds to a standard type of a foreign matter defect;
collecting sound signals inside the GIS equipment at preset time intervals;
inputting the sound signal into a trained foreign matter defect classification model, and outputting the type of the foreign matter defect corresponding to the sound signal;
the foreign matter defect classification model is an extended continuous adaptive fuzzy inference system, the extended continuous adaptive fuzzy inference system comprises an input layer, a fuzzy inference layer, a specification layer and an output layer which are sequentially arranged, and nodes of the fuzzy inference layer represent fuzzy rules.
2. The detection method according to claim 1, wherein the step of training the foreign object defect classification model by using the training sample of the sound signal inside the GIS device comprises:
sequentially inputting each training sample according to the sequence number of the training sample, wherein each training sample is input, a fuzzy rule is added to the foreign matter defect classification model, and the training sample has novelty;
if the serial number of the currently input training sample is 1, keeping a fuzzy rule which is correspondingly added to the currently input training sample;
if the serial number of the currently input training sample is larger than 1, judging whether a fuzzy rule which is correspondingly added to the currently input training sample is reserved;
if the added fuzzy rule corresponding to the currently input training sample is reserved, setting the parameter of the added fuzzy rule corresponding to the currently input training sample according to the first-order Takagi-Sugeno fuzzy model;
if the fuzzy rule which is added correspondingly to the currently input training sample is not reserved, judging whether the existing fuzzy rule is deleted or not after the back part parameters of the existing fuzzy rule of the foreign matter defect classification model are updated by adopting a least square recursive error method;
if the existing fuzzy rule is deleted, the parameter of the existing fuzzy rule is deleted, and the number of the existing fuzzy rules is updated.
3. The detection method according to claim 2, wherein the step of determining whether to retain the fuzzy rule added to the currently input training sample comprises:
judging whether the added fuzzy rules corresponding to the currently input training sample meet a distance criterion and an influence criterion;
if the distance criterion and the influence criterion are met, keeping the fuzzy rule which is added correspondingly to the currently input training sample;
wherein, the distance criterion is | | xn-μnr||>en，xnTraining samples with sequence number n representing the current input, n>1，μnrRepresenting distance training samples xnCenter of the nearest fuzzy rule, enRepresents a distance threshold;
wherein the influence criterion is Emin f(Nh+1)>eg，NhIndicating the number of fuzzy rules present, Emin f(Nh+1) represents the influence value of the currently input training sample on the added fuzzy rule, egIndicating that the threshold is increased.
4. The detection method according to claim 3, characterized in that:
en＝max{emax×γn,eminin which emaxRepresents the maximum value of the distance threshold, eminRepresents the minimum value of the distance threshold and gamma represents the decay exponent.
5. The detection method according to claim 3, characterized in that:
Figure FDA0002429391360000021
wherein x islRepresentation for calculating Emin f(Nh+1) training samples with sequence number l,
Figure FDA0002429391360000022
representing the use of training samples xlCalculating an excitation function, R, of the fuzzy rule added to the currently input training samplek(xl) Representing the use of training samples xlCalculating an excitation function of the fuzzy rule with the sequence number of k, wherein the sequence numbers of the fuzzy rule are sequentially arranged according to a reserved sequence, and M represents the number of the most recently input training samples including the currently input training sample with the sequence number of n;
Figure FDA0002429391360000023
wherein σkRadius of excitation function, mu, of fuzzy rule with index kkRepresenting the center of the excitation function of the fuzzy rule with the sequence number k;
Figure FDA0002429391360000031
wherein the content of the first and second substances,
Figure FDA0002429391360000032
the radius of the excitation function representing the training sample of the current input corresponding to the added fuzzy rule,
Figure FDA0002429391360000033
the training samples representing the current input correspond to the center of the excitation function of the added fuzzy rule.
6. The detection method according to claim 3, wherein the step of setting parameters of the fuzzy rule added corresponding to the currently input training sample according to a first-order Takagi-Sugeno fuzzy model comprises:
setting a parameter matrix of the added fuzzy rule corresponding to the currently input training sample by adopting a first equation group, wherein the parameter matrix of the added fuzzy rule corresponding to the currently input training sample is
Figure FDA0002429391360000034
The first process group is
Figure FDA0002429391360000035
Setting the center of the excitation function of the fuzzy rule added corresponding to the currently input training sample by adopting a second equation, wherein the second equation is
Figure FDA0002429391360000036
Setting the radius of the excitation function of the fuzzy rule which is added corresponding to the currently input training sample by adopting a third equation
Figure FDA0002429391360000037
κ denotes an overlap factor.
7. The detection method according to claim 3, wherein the system of equations of the least squares recursive error method is:
Figure FDA0002429391360000038
wherein, anA back-piece parameter matrix, y, representing a fuzzy rule with a sequence number nn-1Indicates the standard type of the foreign body defect corresponding to the training sample with the sequence number of n-1,
Figure FDA0002429391360000039
indicates the type of the foreign body defect output by the foreign body defect classification model when the training sample with the sequence number of n-1 is input into the foreign body defect classification model, H0Denotes a preset initial value, HnRepresenting an input xnThe output vector of the time-normalized layer,
Figure FDA0002429391360000041
xne＝[1,xn]Tand I represents an identity matrix.
8. The detection method according to claim 3, wherein the step of determining whether to delete an existing fuzzy rule comprises:
judging whether the existing fuzzy rule meets the deletion criterion;
if the existing fuzzy rule meets the deletion criterion, deleting the existing fuzzy rule;
wherein the deletion criterion is Eminf(k)<ep，Eminf(k) Representing the influence value of the fuzzy rule with the sequence number k, epIndicating a deletion threshold.
9. The detection method according to claim 2, characterized in that: if the training sample satisfies psi<EsThen the training sample has novelty;
wherein psi represents the spherical potential energy of the training sample in the high-dimensional feature space,
Figure FDA0002429391360000042
t denotes the dimension of the high-dimensional feature space, phi (x, mu)t) A mapping function, mu, representing a high-dimensional feature space corresponding to the training samplestRepresenting the center of the t-th mapping function, said mapping function being a Gaussian function, EsRepresenting a novelty threshold.
10. A detection system for foreign matter defects inside GIS equipment is characterized by comprising:
the training module is used for training a foreign matter defect classification model by adopting training samples of sound signals inside the GIS equipment, wherein each training sample corresponds to a standard type of a foreign matter defect;
the acquisition module is used for acquiring sound signals inside the GIS equipment at preset time intervals;
the classification module is used for outputting the type of the foreign body defect corresponding to the sound signal after the sound signal is input into the trained foreign body defect classification model;
the foreign matter defect classification model is an extended continuous adaptive fuzzy inference system, the extended continuous adaptive fuzzy inference system comprises an input layer, a fuzzy inference layer, a specification layer and an output layer which are sequentially arranged, and nodes of the fuzzy inference layer represent fuzzy rules.
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