CN111506994B - Motor rotor fault diagnosis method based on intelligent set - Google Patents

Motor rotor fault diagnosis method based on intelligent set Download PDF

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CN111506994B
CN111506994B CN202010289950.6A CN202010289950A CN111506994B CN 111506994 B CN111506994 B CN 111506994B CN 202010289950 A CN202010289950 A CN 202010289950A CN 111506994 B CN111506994 B CN 111506994B
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蒋雯
张瑜
邓鑫洋
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Northwestern Polytechnical University
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Abstract

The invention discloses a motor rotor fault diagnosis method based on a central intelligence set, which comprises the following steps: firstly, establishing a fault library Gaussian model according to a motor rotor fault sample data set; secondly, establishing a Gaussian test model of the test sample obtained by the detection of the sensor; step three, generating a middle intelligent set representation on each fault characteristic under each fault type according to matching of a fault library Gaussian model and a test sample Gaussian model; fusing the intelligent collection representation on each fault characteristic under each fault type by using an SNWA operator; and step five, judging the fault type of the test sample according to the fusion result of the step four. On the basis of a Gaussian model, the invention combines the advantages of processing uncertain and fuzzy information by the medium intelligence set theory, matches a Gaussian test model with a Gaussian model of a fault library to generate medium intelligence set representation on each fault characteristic under each fault type and fuses the medium intelligence set representation and the fuzzy information, thereby identifying the fault type of the test sample, being capable of flexibly and effectively processing uncertain information and fusing to improve the fault diagnosis accuracy.

Description

Motor rotor fault diagnosis method based on intelligent set
Technical Field
The invention belongs to the field of fault diagnosis, and particularly relates to a motor rotor fault diagnosis method based on a central intelligence set.
Background
The motor rotor is a rotating part in the motor, and the motor rotor is easy to break down due to difficult reversing, high rotating speed and the like, so that the service life of the motor is shortened. The safety of the motor rotor is a crucial problem, and the running state of the motor rotor can be effectively detected by timely performing fault diagnosis on the motor rotor, so that safety accidents are avoided.
The fault diagnosis mainly researches how to effectively identify and position the running state and the fault type of the motor. Existing fault diagnosis methods include model-based methods, signal processing-based methods, data-driven-based methods, knowledge-based methods, and the like. As motor devices tend to be complicated and large-sized, the randomness and ambiguity of fault information collected by sensors should be emphasized. In addition, due to factors such as environmental noise, system noise and measurement errors, fault information is generally uncertain and even conflicting, which may result in a large error in the fault diagnosis result. Therefore, how to effectively process the information of these characteristics is very important. The intelligent set theory has unique advantages in the aspects of processing uncertain information, fuzzy information and the like, and is widely applied to the fields of fault diagnosis, decision analysis, reliability assessment and the like.
In addition, the complexity of motor equipment also determines the intersection between a fault signal and a fault symptom, if the diagnosis result is deviated from the actual fault due to the fact that only a single fault information source is relied on, the information fusion technology can fuse multi-source fault type information, the goal can be more accurately and comprehensively known, and the method is widely applied to multiple fields.
Therefore, the motor rotor fault information detected by the sensor is used, the motor rotor fault type is identified by the intelligent set theory and the information fusion technology in the fusion, on one hand, the uncertainty of the sensor information can be better processed, and on the other hand, the fault identification accuracy can be improved.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: how to realize the fault diagnosis of the motor rotor. The method for realizing the motor rotor fault diagnosis has important significance to the field of equipment safety.
In order to solve the technical problems, the technical scheme adopted by the invention is a motor rotor fault diagnosis method based on a central intelligence set, and the method is characterized by comprising the following steps of:
firstly, establishing a fault library Gaussian model according to a motor rotor fault sample data set;
motor rotor fault sample data set D with n fault types and k fault characteristics inputijN fault types, i is 1,2, …, n, j is 1,2, …, k, and is marked as theta1,θ2,…,θi,…,θnAnd k fault features are noted as att1,att2,…,attj,…，attk(ii) a Motor rotor fault sample data set DijThe method is characterized in that the method is a measurement value of k fault characteristics, a Gaussian model is established for each fault characteristic of each fault type, and the establishing method of the Gaussian model comprises the following steps:
step 101: calculating motor rotor fault sample data set DijAll of which belong to the fault type thetaiIn the failure characteristic attjMean of
Figure GDA0003452047410000021
And standard deviation σij，
Figure GDA0003452047410000022
Figure GDA0003452047410000023
Wherein xijSample data set belonging to fault type theta for motor rotor faultiIn the failure characteristic attjA measured value of (a);
step 102: according to the mean value in step 101
Figure GDA0003452047410000024
And standard deviation σijCalculating the fault type thetaiIn the failure feature attjGaussian model of
Figure GDA0003452047410000025
Step two, establishing a Gaussian test model of the test sample t obtained by sensor detection;
step 201: test sample t is arranged at fault characteristic attjMeasured value t ofjTarget sample data set D as a mean of the Gaussian test modelijIn the failure feature attjStandard deviation σ of each target typeijMinimum value of (e)jAs standard deviation of the gaussian test model;
step 202: computer according to formula
Figure GDA0003452047410000031
Calculating failure characteristics attjGaussian test model ft j；
Step three, generating a middle intelligent set representation under each fault characteristic of each fault type according to matching of a fault library Gaussian model and a test sample Gaussian model;
step 301: att fault characteristics in fault library Gaussian modeljType of fault on thetaiGaussian model g ofi jAnd Gaussian test model ft jMatching, the computer is according to the formula
Figure GDA0003452047410000032
Generating test sample t at fault characteristic attjUpper is of fault type thetaiIn the middle wisdom set representation
Figure GDA0003452047410000033
Wherein
Figure GDA0003452047410000034
Representing a Gaussian test model ft jAnd Gaussian model
Figure GDA0003452047410000035
The area of the intersection portion is such that,
Figure GDA0003452047410000036
indicates a failure characteristic attjType of fault on thetaiGaussian model of
Figure GDA0003452047410000037
The area enclosed by the transverse shaft is the same as the area enclosed by the transverse shaft,
Figure GDA0003452047410000038
indicates a failure characteristic attjGaussian test model ft jThe area enclosed by the transverse shaft;
step 302: generating a centralized representation of the test sample t on all fault characteristics under all fault types according to the step 301;
fusing the intelligent set representation on each fault characteristic under each fault type by using an SNWA operator;
step 401: testing the sample obtained in the third step in the fault type thetaiThe k nook sets on the k fault features are fused by using an SNWA operator to obtain a fused nook set representation
Figure GDA0003452047410000039
The SNWA operator is as follows:
Figure GDA00034520474100000310
wherein any two noon-glean representations are according to a formula
Figure GDA0003452047410000041
Performing summation calculation, wherein j is 1,2, …, k, r is 1,2, …, k;
step 402: fusing the intelligent centralized representations of all fault types on each fault characteristic according to the step 401;
step five, judging the fault type of the test sample according to the fusion result of the step four;
step 501: according to the formula
Figure GDA0003452047410000042
Defuzzification is carried out on the intelligent set fused in the step four, and clear numbers of the test samples belonging to all fault types are obtained;
step 502: and D, sequencing the clear numbers obtained by calculation in the step five, wherein the largest clear number is the fault type of the test sample.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention has simple steps, reasonable design and convenient realization, use and operation.
2. According to the invention, the Gaussian model is used for representing the fault sample information and generating the intelligent set representation of the test sample, so that the uncertainty of the detection information of the sensor can be effectively processed;
3. according to the method, the intelligent set representation on a plurality of fault characteristics is fused through an information fusion method, so that the accuracy of motor rotor fault identification is improved.
In conclusion, the technical scheme of the invention is reasonable in design, the fault sample data set is generated into the fault library model, the test sample is matched with the fault library model to generate the intelligent set representation, and the intelligent set representation on each fault characteristic is fused based on the information fusion method, so that the uncertainty of fault information can be effectively processed, and the accuracy of fault identification is improved.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
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FIG. 1 is a flow chart of the method of the present invention
FIG. 2 shows the type of failure θ in the present inventioniLower fault feature attjUpper generation intelligent set representation diagram
Detailed Description
The method of the present invention is further described in detail below with reference to the accompanying drawings and embodiments of the invention.
It should be noted that, in the case of no conflict, the embodiments and the fault types in the embodiments in the present application may be combined with each other. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Spatially relative terms, such as "above … …," "above … …," "above … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial relationship to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". The device may be otherwise variously oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
As shown in fig. 1, the present invention comprises the steps of:
firstly, establishing a fault library Gaussian model according to a motor rotor fault sample data set;
in practical use, the sensors are adopted to collect n fault types theta1,θ2,…,θi,…,θnAtt in k fault characteristics1,att2,…,attj,…,attkGenerating a motor rotor fault sample data set D from the measured values of (D)ij1,2, …, n, j 1,2, …, k; the target condition can be more fully reflected by the fault characteristic data, so that the accuracy of target identification is improved; secondly, the Gaussian distribution is relatively difficult to be interfered, and the stability is good. Therefore, a gaussian model is established for each fault type of each target type, and the establishing method of the gaussian model comprises the following steps:
step 101: calculating motor rotor fault sample data set DijAll of which belong to the fault type thetaiIn the failure characteristic attjMean of
Figure GDA0003452047410000061
And standard deviation σij，
Figure GDA0003452047410000062
Figure GDA0003452047410000063
Wherein xijSample data set belonging to fault type theta for motor rotor faultiIn the failure characteristic attjA measured value of (a);
step 102: according to the mean value in step 101
Figure GDA0003452047410000064
And standard deviation σijCalculating the fault type thetaiIn the failure feature attjGaussian model of
Figure GDA0003452047410000065
Step two, establishing a Gaussian test model of the test sample t obtained by sensor detection;
in consideration of certain error between the measured value and the true value of the sensor, the method adopts the system error to expand the measured value of the sensor into Gaussian distribution, thereby effectively processing the uncertainty of the measured value of the sensor. To avoid adding too much uncertainty to the test model, the failure feature att is usedjThe minimum standard deviation of (c) is taken as the system error and is recorded as epsilonj＝min(σij) The method comprises the following specific steps:
step 201: test sample t is arranged at fault characteristic attjMeasured value t ofjTarget sample data set D as a mean of the Gaussian test modelijIn the failure feature attjStandard deviation σ of each target typeijMinimum value of (e)jAs standard deviation of the gaussian test model;
step 202: computer according to formula
Figure GDA0003452047410000071
Calculating failure characteristics attjGaussian test model ft j；
Step three, generating a middle intelligent set representation under each fault characteristic of each fault type according to matching of a fault library Gaussian model and a test sample Gaussian model;
the method is based on the central intelligence set theory, and the test sample is matched with the Gaussian model of the fault library to generate the central intelligence set representation of the test sample t on each fault characteristic under each fault type. As shown in fig. 2, the fault type θ in the gaussian model of the fault library is usediIn the failure feature attjThe Gaussian model of (2) is matched with the Gaussian test model generated by the test sample in the step two, wherein the fault type theta in the Gaussian model of the fault library isiIn the failure feature attjThe Gaussian model of (1) is a curve represented by a solid line, then
Figure GDA0003452047410000072
The area enclosed by the cross shaft and the cross shaft; the curve indicated by the dotted line is a Gaussian test model generated for the test sample, then
Figure GDA0003452047410000073
The area enclosed by the cross shaft and the cross shaft; the shaded part is the intersection of the two parts and is used for area
Figure GDA0003452047410000074
And (4) showing. The generated nook-set representation in this case contains the test sample t at the failure feature attjUpper is of fault type thetaiDegree of membership of
Figure GDA0003452047410000075
Degree of uncertainty
Figure GDA0003452047410000076
Degree of non-membership
Figure GDA0003452047410000077
The method has the advantages of processing uncertain and fuzzy information, and the specific generation method comprises the following steps:
step 301: att fault characteristics in fault library Gaussian modeljType of fault on thetaiGaussian model of
Figure GDA0003452047410000081
And Gaussian test model ft jMatching, the computer is according to the formula
Figure GDA0003452047410000082
Generating test sample t at fault characteristic attjUpper is of fault type thetaiIn the middle wisdom set representation
Figure GDA0003452047410000083
Wherein
Figure GDA0003452047410000084
Representing a Gaussian test model ft jAnd Gaussian model
Figure GDA0003452047410000085
The area of the intersection portion is such that,
Figure GDA0003452047410000086
indicates a failure characteristic attjType of fault on thetaiGaussian model of
Figure GDA0003452047410000087
The area enclosed by the transverse shaft is the same as the area enclosed by the transverse shaft,
Figure GDA0003452047410000088
indicates a failure characteristic attjGaussian test model ft jThe area enclosed by the transverse shaft;
step 302: generating a centralized representation of the test sample t on all fault characteristics under all fault types according to the step 301;
fusing the intelligent sets on the fault characteristics under each fault type by using an SNWA operator;
after the intelligent set representation on each fault characteristic under each fault type is generated, a plurality of fault information sources are formed. If the fault type is judged by only depending on the expression of the central intelligence set on the single fault characteristic, a large error may exist, so that the central intelligence set expressions on all the fault characteristics need to be fused, and the diagnosis accuracy is improved, and the specific method comprises the following steps:
step 401: testing the sample obtained in the third step in the fault type thetaiThe k nook sets on the k fault features are fused by using an SNWA operator to obtain a fused nook set representation
Figure GDA0003452047410000089
The SNWA operator is as follows:
Figure GDA00034520474100000810
wherein any two noon-glean representations are according to a formula
Figure GDA00034520474100000811
Performing summation calculation, wherein j is 1,2, …, k, r is 1,2, …, k;
step 402: fusing the intelligent centralized representations of all fault types on each fault characteristic according to the step 401;
step five, judging the fault type of the test sample according to the fusion result of the step four;
after fusion, the test sample t belongs to a certain fault type and is still represented by a mesopic set, and the fault type of the test sample t needs to be determined by defuzzifying the test sample t to obtain a definite numerical value, and the specific method comprises the following steps:
step 501: according to the formula
Figure GDA0003452047410000091
Defuzzification is carried out on the intelligent set fused in the step four, and clear numbers of the test samples belonging to all fault types are obtained;
step 502: and D, sequencing the clear numbers obtained by calculation in the step five, wherein the largest clear number is the fault type of the test sample.
The above embodiments are only examples of the present invention, and are not intended to limit the present invention, and all simple modifications, changes and equivalent structural changes made to the above embodiments according to the technical spirit of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims (1)

1. A motor rotor fault diagnosis method based on an intelligent set is characterized by comprising the following steps:
firstly, establishing a fault library Gaussian model according to a motor rotor fault sample data set;
motor rotor fault sample data set D with n fault types and k fault characteristics inputijN fault types, i is 1,2, …, n, j is 1,2, …, k, and is marked as theta1,θ2,…,θi,…,θnAnd k fault features are noted as att1,att2,…,attj,…,attk(ii) a Motor rotor fault sample data set DijThe method is characterized in that the method is a measurement value of k fault characteristics, a Gaussian model is established for each fault characteristic of each fault type, and the establishing method of the Gaussian model comprises the following steps:
step 101: calculating motor rotor fault sample data set DijAll of which belong to the fault type thetaiIn the failure characteristic attjMean of
Figure FDA0003452047400000011
And standard deviation σij，
Figure FDA0003452047400000012
Figure FDA0003452047400000013
Wherein xijSample data set belonging to fault type theta for motor rotor faultiIn the failure characteristic attjA measured value of (a);
step 102: according to the mean value in step 101
Figure FDA0003452047400000014
And standard deviation σijCalculating the fault type thetaiIn the failure feature attjGaussian model of
Figure FDA0003452047400000015
Figure FDA0003452047400000016
Step two, establishing a Gaussian test model of the test sample t obtained by sensor detection;
step 201: test sample t is arranged at fault characteristic attjMeasured value t ofjTarget sample data set D as a mean of the Gaussian test modelijIn the failure feature attjStandard deviation σ of each target typeijMinimum value of (e)jAs standard deviation of the gaussian test model;
step 202: computer according to formula
Figure FDA0003452047400000017
Calculating failure characteristics attjGaussian test model ft j；
Step three, generating a middle intelligent set representation under each fault characteristic of each fault type according to matching of a fault library Gaussian model and a test sample Gaussian model;
step 301: att fault characteristics in fault library Gaussian modeljType of fault on thetaiGaussian model of
Figure FDA0003452047400000021
And Gaussian test model ft jMatching, the computer is according to the formula
Figure FDA0003452047400000022
Generating test sample t at fault characteristic attjUpper is of fault type thetaiIn the middle wisdom set representation
Figure FDA0003452047400000023
Wherein
Figure FDA0003452047400000024
Representing a Gaussian test model ft jAnd Gaussian model
Figure FDA0003452047400000025
The area of the intersection portion is such that,
Figure FDA0003452047400000026
indicates a failure characteristic attjType of fault on thetaiGaussian model of
Figure FDA0003452047400000027
The area enclosed by the transverse shaft is the same as the area enclosed by the transverse shaft,
Figure FDA0003452047400000028
indicates a failure characteristic attjGaussian test model ft jThe area enclosed by the transverse shaft;
step 302: generating a centralized representation of the test sample t on all fault characteristics under all fault types according to the step 301;
fusing the intelligent set representation on each fault characteristic under each fault type by using an SNWA operator;
step 401: testing the sample obtained in the third step in the fault type thetaiThe k nook sets on the k fault features are fused by using an SNWA operator to obtain a fused nook set representation
Figure FDA0003452047400000029
The SNWA operator is as follows:
Figure FDA00034520474000000210
wherein any two noon-glean representations are according to a formula
Figure FDA00034520474000000211
Performing summation calculation, wherein j is 1,2, …, k, r is 1,2, …, k;
step 402: fusing the intelligent centralized representations of all fault types on each fault characteristic according to the step 401;
step five, judging the fault type of the test sample according to the fusion result of the step four;
step 501: according to the formula
Figure FDA0003452047400000031
Defuzzification is carried out on the intelligent set fused in the step four, and clear numbers of the test samples belonging to all fault types are obtained;
step 502: and D, sequencing the clear numbers obtained by calculation in the step five, wherein the largest clear number is the fault type of the test sample.
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