CN117520896A - Cable accessory fault diagnosis method, device, computer equipment and storage medium - Google Patents
Cable accessory fault diagnosis method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The present application relates to a method, apparatus, computer device, storage medium and computer program product for determining the health status of a cable accessory. The method comprises the following steps: obtaining simulation test data and process data of the cable accessory, carrying out feature fusion on a plurality of state feature indexes based on the simulation test data and the process data to obtain a state feature index vector of the cable accessory, obtaining a distance value by measuring the distance between the state feature index vector and a health state feature reference vector, and obtaining a health state evaluation result of the cable accessory according to the distance value. The method can be used for quickly and simply knowing the health condition of the cable accessory, is beneficial to preventive maintenance and fault diagnosis, improves the accuracy of fault diagnosis of the cable accessory, and provides guarantee for the reliability and safety of the cable accessory.
Description
Technical Field
The present application relates to the field of power equipment detection technology, and in particular, to a cable accessory fault diagnosis method, a device, a computer device, a storage medium, and a computer program product.
Background
Cable accessories refer to various components used to connect, protect, secure cables. Cable accessories, which are an important component in electrical power systems, may cause unstable operation of the system and present some safety hazards if the cable accessory fails.
At present, the traditional cable accessory fault diagnosis method mainly depends on self-accumulated experience of power equipment operation and maintenance personnel, and the health state of the cable accessory is evaluated to perform fault diagnosis.
Accordingly, it is desirable to provide a solution that can guarantee and improve the fault diagnosis accuracy of the cable accessories.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, a computer-readable storage medium, and a computer program product for determining a health state of a cable accessory that can improve the accuracy of fault diagnosis.
In a first aspect, the present application provides a method for determining a health status of a cable accessory. The method comprises the following steps:
Obtaining simulation test data and process data of a cable accessory;
extracting a plurality of state characteristic indexes based on simulation test data and process data;
performing feature fusion on the plurality of state feature indexes to obtain a state feature index vector of the cable accessory;
measuring the distance between the state characteristic index vector and the health state characteristic reference vector to obtain a distance value;
and according to the distance value, obtaining a health state evaluation result of the cable accessory.
In one embodiment, the status characteristic indicators include characteristic indicators of different status levels;
feature fusion is carried out on a plurality of state feature indexes, and the obtaining of the state feature index vector of the cable accessory comprises the following steps:
determining membership values of each state characteristic index for different state grades according to a preset membership function;
performing feature fusion on the membership values of the plurality of state feature indexes aiming at different state grades to obtain a state feature vector of the cable accessory;
wherein, membership functions are in one-to-one correspondence with the state grades.
In one embodiment, fusing membership values of a plurality of state feature indexes for different state levels to obtain a state feature vector of the cable accessory includes:
Determining the weight of membership values of each state characteristic index for different state grades;
and combining membership values of each state characteristic index aiming at different state grades according to the weights to obtain the state characteristic vector of the cable accessory.
In one embodiment, determining the weights of the membership values of the state feature indicators for the different state levels includes:
and determining the weights of the membership values of the state characteristic indexes aiming at different state grades by a fuzzy comprehensive evaluation method.
In one embodiment, measuring the distance between the state characteristic index vector and the health state characteristic reference vector, the obtaining the distance value includes:
and measuring the distance between the state characteristic index vector and the health state characteristic reference vector through a Euclidean distance algorithm to obtain the Euclidean distance value.
In one embodiment, obtaining the health status evaluation result of the cable accessory according to the distance value includes:
comparing the distance value with a preset distance reference range, and mapping the distance value to a corresponding distance reference interval;
and determining the health state evaluation result of the cable accessory according to the distance reference interval in which the distance value is located.
In a second aspect, the present application also provides a device for determining a health status of a cable accessory. The device comprises:
The data acquisition module is used for acquiring simulation test data and process data of the cable accessory;
the index extraction module is used for extracting a plurality of state characteristic indexes based on the simulation test data and the process data;
the feature fusion module is used for carrying out feature fusion on the plurality of state feature indexes to obtain a state feature index vector of the cable accessory;
the distance measurement module is used for measuring the distance between the state characteristic index vector and the health state characteristic reference vector to obtain a distance value;
and the state determining module is used for obtaining the health state evaluation result of the cable accessory according to the distance value.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
obtaining simulation test data and process data of a cable accessory;
extracting a plurality of state characteristic indexes based on simulation test data and process data;
performing feature fusion on the plurality of state feature indexes to obtain a state feature index vector of the cable accessory;
Measuring the distance between the state characteristic index vector and the health state characteristic reference vector to obtain a distance value;
and according to the distance value, obtaining a health state evaluation result of the cable accessory.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
obtaining simulation test data and process data of a cable accessory;
extracting a plurality of state characteristic indexes based on simulation test data and process data;
performing feature fusion on the plurality of state feature indexes to obtain a state feature index vector of the cable accessory;
measuring the distance between the state characteristic index vector and the health state characteristic reference vector to obtain a distance value;
and according to the distance value, obtaining a health state evaluation result of the cable accessory.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
obtaining simulation test data and process data of a cable accessory;
extracting a plurality of state characteristic indexes based on simulation test data and process data;
Performing feature fusion on the plurality of state feature indexes to obtain a state feature index vector of the cable accessory;
measuring the distance between the state characteristic index vector and the health state characteristic reference vector to obtain a distance value;
and according to the distance value, obtaining a health state evaluation result of the cable accessory.
According to the method, the device, the computer equipment, the storage medium and the computer program product for determining the health state of the cable accessory, simulation test data and process data of the cable accessory are obtained, then, based on the simulation test data and the process data, a plurality of comprehensive and accurate state characteristic indexes can be extracted, and then, characteristic fusion is carried out on the plurality of state characteristic indexes to obtain a state characteristic index vector of the cable accessory, so that the state characteristic index vector can reflect the whole health state of the cable accessory, the accuracy of an evaluation result is improved, then, a distance value is obtained by measuring the distance between the state characteristic index vector and a health state characteristic reference vector, the degree of difference between the health state of the cable accessory and the reference state can be quantized, objective evaluation indexes are provided, finally, the health state of the cable accessory can be quickly and simply obtained according to the distance value, preventive maintenance and fault diagnosis are facilitated, accurate judgment basis is provided for fault diagnosis of the cable accessory, and the reliability and safety of the cable accessory are improved. By adopting the method, the fault diagnosis accuracy of the cable accessory can be improved.
Drawings
FIG. 1 is an application environment diagram of a method for determining a health status of a cable attachment in one embodiment;
FIG. 2 is a flow chart of a method for determining health status of a cable attachment in one embodiment;
FIG. 3 is an architecture diagram of a cable accessory health model in one embodiment;
FIG. 4 is a flow chart of a method for determining the health status of a cable attachment according to another embodiment;
FIG. 5 is a flow chart of a method for determining the health status of a cable attachment according to yet another embodiment;
FIG. 6 is a detailed flow diagram of a method for determining the health status of a cable attachment in one embodiment;
FIG. 7 is a block diagram of a cable accessory health status determination device in one embodiment;
fig. 8 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The method for determining the health state of the cable accessory, provided by the embodiment of the application, can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. Specifically, the user uploads simulation test data and process data of the cable accessory to be tested to the server 104 through the terminal 102, submits a cable state health state assessment instruction to the server 104 on an operation interface of the terminal 102, the server 104 responds to the instruction to obtain simulation test data and process data of the cable accessory, then, based on the simulation test data and the process data, a plurality of comprehensive and accurate state characteristic indexes can be extracted, then, characteristic fusion is carried out on the plurality of state characteristic indexes to obtain a state characteristic index vector of the cable accessory, the state characteristic index vector can reflect the whole health state of the cable accessory, the accuracy of an assessment result is improved, then, a distance value is obtained through the distance between the measurement state characteristic index vector and the health state characteristic reference vector, the degree of difference between the health state and the reference state of the cable accessory can be quantified, objective assessment indexes are provided, finally, the health state assessment result of the cable accessory can be obtained according to the distance value, and further, preventive maintenance and fault diagnosis of the cable accessory can be accurately judged, and reliability and safety of the cable accessory are improved.
The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In one embodiment, as shown in fig. 2, a method for determining health status of a cable attachment is provided, and the method is applied to the server 104 in fig. 1 for illustration, it is to be understood that the method may also be applied to a terminal, and may also be applied to a system including the terminal and the server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the steps of:
s202, simulation test data and process data of the cable accessory are obtained.
Cable accessories refer to various elements and devices for connecting, protecting and supporting cables. Common cable accessories include connectors, terminals, bends, insulation sleeves, cable holders, seals, and the like. The simulation test data of the cable accessory can be obtained by performing thermal simulation or thermal analysis on the cable accessory and performing simulation tests of dimensions such as electric field simulation tests. In particular, the simulation test data may include thermal simulation test data, electrical parameter data, voltage/current test data, transmission characteristic data, fault mode data, and the like. The process data of the cable accessory includes material parameter data, manufacturing process data, dimensional and geometric data, assembly process data, and the like. Specifically, the manufacturing process data includes key parameters in the manufacturing process of the cable accessory, such as temperature, pressure, time, etc., as well as physical characteristic parameters of the materials used for the cable accessory, such as the conductivity of the conductor material, the dielectric constant of the insulating material, etc. The assembly process data comprise key parameters in the assembly process of the cable accessory, such as tightness, connection mode, fastening force and the like.
S204, extracting a plurality of state characteristic indexes based on the simulation test data and the process data.
After the simulation test data and the process data of the cable accessory are obtained, a plurality of state characteristic indexes can be extracted according to the preset state evaluation indexes. In this embodiment, the state characteristic index may include a thermal field characteristic index, an electric field characteristic index, a seal characteristic index, and a process characteristic index. Specifically, the operation and maintenance personnel analyze the attribute of the cable accessory in advance to construct a cable accessory health state model, and the specific structure of the cable accessory health state model is shown in fig. 3. In the cable fitting health state model, four feature values (which can be regarded as first-level indexes) are shared, namely thermal field feature value x 1 Characteristic x of electric field 2 Sealing characteristic quantity x 3 Process characteristic quantity x 4 And each feature quantity corresponds to a plurality of feature indexes (which can be regarded as secondary indexes), wherein each feature quantity corresponds to a feature index x ni All satisfy x ni ∈[0,10]Wherein x is ni The closer to 10, the closer the representation state is to the case corresponding to the feature index amount. In the implementation, feature extraction can be performed on simulation test data and process data according to a pre-constructed cable accessory health state model, and a plurality of thermal field feature indexes, a plurality of electric field feature indexes, a plurality of sealing feature indexes, a plurality of process feature indexes and the like are extracted. It will be appreciated that in other embodiments, additional thermal, electrical, sealing, and process characteristics may be added in addition to those Other aspects of the characteristic index are added to evaluate the health state of the cable accessories, such as mechanical characteristic index, insulating characteristic index, conductive characteristic index, functional characteristic index and the like.
S206, carrying out feature fusion on the plurality of state feature indexes to obtain a state feature index vector of the cable accessory.
After the above-mentioned contents are received, after the multiple state characteristic indexes are extracted, the multiple state characteristic indexes can be subjected to characteristic fusion, that is, all the state characteristic indexes are integrated to form an integrated state characteristic index vector so as to describe the whole state of the cable accessory. Specifically, the extracted multiple state feature indexes may be normalized and converted into the same scale range, so as to eliminate the dimensional difference between different feature indexes. Then, according to the importance of the features, a corresponding weight is allocated to each feature index to reflect the contribution degree of the feature index in feature fusion. The weights may be automatically learned and determined based on knowledge, experience, or by machine learning algorithms of domain experts. And then carrying out weighted summation on the normalized characteristic indexes and the corresponding weights to obtain a final state characteristic index vector.
And S208, measuring the distance between the state characteristic index vector and the health state characteristic reference vector to obtain a distance value.
The health state characteristic reference vector refers to a reference characteristic vector for describing the health state of the cable accessory, and is a characteristic vector obtained by fusing characteristic data of the cable accessory under the known normal working state and the known health working state.
In the implementation, after the state characteristic index vector of the cable accessory is obtained, a preset distance measurement algorithm may be called to measure the distance between the state characteristic index vector and the health state characteristic reference vector, so as to quantify the similarity between the state characteristic index vector and the health state characteristic reference vector and obtain the distance value between the state characteristic index vector and the health state characteristic reference vector. In this embodiment, the distance metric algorithm may include, but is not limited to, euclidean distance, manhattan distance, minkowski distance, and the like.
And S210, obtaining a health state evaluation result of the cable accessory according to the distance value.
The health state evaluation result of the cable accessory comprises data such as health state grade, judging result of whether a fault exists or not, possible fault reasons and the like.
After the distance value between the two vectors is obtained, the health state evaluation result of the cable accessory can be evaluated according to the distance value. Specifically, the smaller the distance value is, the higher the similarity between the state characteristic index vector and the health state characteristic reference vector is, the better the health state of the cable accessory can be judged, the larger the distance value is, the lower the similarity between the state characteristic index vector and the health state characteristic reference vector is, the worse the health state of the cable accessory can be judged, and the possibility of abnormality or failure exists.
According to the method for determining the health state of the cable accessory, simulation test data and process data of the cable accessory are obtained, then, based on the simulation test data and the process data, a plurality of comprehensive and accurate state characteristic indexes can be extracted, then, characteristic fusion is carried out on the plurality of state characteristic indexes to obtain a state characteristic index vector of the cable accessory, the state characteristic index vector can reflect the whole health state of the cable accessory, accuracy of an evaluation result is improved, then, a distance value is obtained through measuring the distance between the state characteristic index vector and a health state characteristic reference vector, the degree of difference between the health state and the reference state of the cable accessory can be quantized, objective evaluation indexes are provided, finally, the health state evaluation result of the cable accessory is obtained according to the distance value, the health state of the cable accessory can be quickly and simply known, preventive maintenance and fault diagnosis are facilitated, accurate judgment basis is provided for fault diagnosis of the cable accessory, and reliability and safety of the cable accessory are improved. By adopting the method, the fault diagnosis accuracy of the cable accessory can be improved.
In one embodiment, the status characteristic indicators comprise characteristic indicators of different status levels.
As shown in fig. 4, S206 includes: s226, determining membership values of each state characteristic index aiming at different state grades according to a preset membership function, and carrying out characteristic fusion on the membership values of a plurality of state characteristic indexes aiming at different state grades to obtain a state characteristic vector of the cable accessory, wherein the membership function corresponds to the state grade one by one.
As shown in fig. 3, in the cable accessory health status model, each feature quantity includes a plurality of feature indicators of different status levels. The ambiguity of the boundaries between the state levels results from the ambiguity of the boundaries between the state levels of each index. In order to eliminate ambiguity between state levels, in this embodiment, an ambiguity mathematical theory is introduced, and membership of feature index amounts to different state levels is used to eliminate ambiguity of boundaries between state levels.
In practical application, a corresponding membership function may be constructed for each state level in advance to describe membership between the feature index amount and each state level. Common membership functions include triangular membership functions, trapezoidal membership functions, gaussian membership functions, and the like.
In the specific implementation, each characteristic index quantity can be normalized in a standardized way and converted into a range between 0 and 1 so as to eliminate the dimension difference between different indexes. Then, for each state level, a membership function value of the feature index quantity on that state level is calculated from the selected membership function. The membership function value represents the degree of match between the feature index and the state level. In this embodiment, the membership function may be:
wherein f (x) ni ) Representing characteristic index x ni The corresponding membership function value.
And calculating the membership function to obtain membership values corresponding to the characteristic indexes, and then carrying out characteristic fusion on the membership values of the state characteristic indexes aiming at different state grades to obtain the state characteristic vector of the cable accessory. In particular, a higher membership function value indicates that the feature index quantity is more consistent with the features of the state class, while a lower membership function value indicates that the feature index quantity does not match the features of the state class.
In this embodiment, by introducing a mathematical fuzzy theory, ambiguity of boundaries between state levels can be eliminated by using feature index amounts to membership degrees of different state levels, and by calculating membership degree function values, matching degrees of the feature index amounts on different state levels can be accurately described and evaluated, and evaluation results can be used in applications such as fault diagnosis, state monitoring and decision making to help judge states and health conditions of cable accessories.
In one embodiment, S226 includes: and determining the weights of the membership values of the state characteristic indexes aiming at different state grades, and combining the membership values of the state characteristic indexes aiming at different state grades according to the weights to obtain the state characteristic vector of the cable accessory.
In the above embodiment, after determining a suitable membership function to describe membership between the feature index and each state level, calculating a membership value of the feature index on the state level may be performed by determining weights of membership values of each state feature index for different state levels according to sample data of a known state, and then combining membership values of each state feature index for different state levels according to the weights, to obtain a state feature vector of the cable accessory. Specifically, the state characteristic vector of the cable accessory can be obtained by combining membership values of each state characteristic index for different state grades in a weighted average or weighted summation mode. In the present embodiment, the expression of the vector X of the power characteristic amount obtained by the combination is:
X＝[ω 1 f(x 11 ),ω 2 f(x 11 ),ω 3 f(x 21 ),ω 4 f(x 22 ),ω 5 f(x 31 ),ω 6 f(x 32 ),ω 7 f(x 41 ),ω 8 f(x 42 ),ω 9 f(x 43 )]
wherein omega is 1 ,…,ω 9 Respectively corresponding state characteristic indexes x ni The weight of the corresponding membership function value.
It will be appreciated that in other embodiments, a neural network model may be used to input a plurality of state feature indicators into the network, and a comprehensive state feature indicator vector may be obtained through learning and training of the network. The neural network may automatically learn the relationships and weights between features and generate appropriate state feature index vectors. Or, a feature vector merging mode is adopted to directly merge each state feature index into a feature vector. Each feature index can be used as one dimension of the feature vector, and all feature indexes are sequentially arranged to form a complete state feature index vector.
In this embodiment, weights of membership values of each state feature index for different state levels are determined, and then a weighted summation mode is adopted to combine to obtain a comprehensive state feature vector of the cable accessory, so that the weights of each feature index are considered, the contributions of a plurality of indexes to the state can be represented more uniformly, and a more accurate state feature index vector is obtained.
In one embodiment, determining the weights of the membership values of the state feature indicators for the different state levels includes: and determining the weights of the membership values of the state characteristic indexes aiming at different state grades by a fuzzy comprehensive evaluation method.
Fuzzy comprehensive evaluation methods include, but are not limited to, analytic hierarchy process and fuzzy comprehensive evaluation method. In this embodiment, it may be: and determining the weights of the membership values of the state characteristic indexes aiming at different state grades by adopting an analytic hierarchy process. Specifically, the decision problem can be decomposed into factors and criteria of different levels based on the cable accessory health state model to form a hierarchical structure. Then, for each state characteristic index, a judgment matrix is constructed for comparing the importance among the indexes. The judgment matrix is a square matrix in which each element represents a relative importance comparison between two indices. In this embodiment, the structure of the judgment matrix corresponding to each state characteristic index is as follows:
B＝(b ij ) 9×9
wherein B is a judgment matrix. b ij Is an element of a matrix, b ij The following constraints are satisfied: b ij >0；b jj ＝1。
After constructing the judgment matrix B, comparing the importance of each element in pairs, B 12 Denoted as f 1 And f 2 The importance of the comparison (provided b 12 =5, according to the constraintThen->)。
After the judgment matrix B is obtained, the root number of the element product of each row of the judgment matrix B is calculated to be 9 th power, and the following is obtained:
further, the weights of the membership values of the state characteristic indexes for different state grades are as follows:
It can be appreciated that in other embodiments, a fuzzy comprehensive evaluation method may also be used to determine the weights of the membership values of the state feature indicators for different state levels.
In the embodiment, uncertainty and ambiguity can be effectively processed by a fuzzy comprehensive evaluation method, the influence of a plurality of indexes is comprehensively considered, and the method has the advantages of flexibility, adjustability, intuitiveness, interpretability and the like.
As shown in fig. 5, in one embodiment, S208 includes: and S228, measuring the distance between the state characteristic index vector and the health state characteristic reference vector by using a Euclidean distance algorithm to obtain the Euclidean distance value.
In this embodiment, the state characteristic index vector X and the n specific health state characteristic reference vectors X can be calculated i ＝[a i(1) ,a i(2) ,…,a i(9) ]Euclidean distance d of (2) i Thereby judging the health state of the cable to be tested. Euclidean distance is a common distance measurement method used to measure the spatial distance between two vectors. Specifically, euclidean distance d i The formula is as follows:
wherein if itSatisfy d j ＝min{d i I=1, 2, …, n, then the cable accessory is considered to have a j-th cable specific health status (preset amount).
By calculating the Euclidean distance between the state characteristic index vector and the health state characteristic reference vector, a set of distance values can be obtained, which represent the degree of difference between the cable accessory to be tested and the reference cable accessory. Based on these distance values, a determination of the health state can be made. In general, a smaller distance indicates that the more similar the cable accessory to be tested is to the reference cable accessory, the better the health status is; the greater the distance is indicative of the less similar the test cable accessory is to the reference cable accessory, and the health status may be problematic.
In this embodiment, the distance between the state feature index vector and the health state feature reference vector is measured by the euclidean distance algorithm, so that the degree of difference between the vectors can be intuitively reflected, and the method plays an important role in evaluating the health degree and similarity of the state features.
As shown in fig. 5, in one embodiment, S210 includes: s230, comparing the distance value with a preset distance reference range, mapping the distance value to a corresponding distance reference interval, and determining a health state evaluation result of the cable accessory according to the distance reference interval where the distance value is located.
With the above embodiment, after obtaining the euclidean distance between the state feature index vector and the health state feature reference vector, the health state of the cable accessory can be estimated according to the distance value. Specifically, the smaller the distance value is, the closer the state characteristic index vector is to the health state characteristic reference vector, and the better the health state of the cable accessory is; the larger the distance value is, the larger the difference between the state characteristic index vector and the health state characteristic reference vector is, and the worse the health state of the cable accessory is.
In specific implementation, the method can be as follows: according to specific requirements and actual conditions, a certain evaluation standard or a distance reference range is set, wherein the distance reference range comprises a plurality of distance reference sections, and each distance reference section corresponds to a health state grade. Such as excellent, good, medium, bad, etc. And then mapping the distance value to a corresponding distance reference section, determining the health state grade of the cable accessory according to the distance reference section where the distance value is located, and further, judging whether the cable accessory has a fault according to the health state grade and a preset fault judgment basis. For example, if the distance value maps in an interval in which the health status level is "excellent", it may be determined that the health status of the cable accessory is excellent; if the distance value is mapped in the interval with the health status grade of 'bad', the health status of the cable accessory can be judged to be bad, potential faults can exist, further, whether the cable accessory has faults can be judged according to the preset fault judgment basis, and the fault mode and the fault occurrence cause are analyzed according to the duration experimental data and the opinion of field experts. It will be appreciated that in other embodiments, the distance value may be compared with a plurality of preset distance thresholds, where each distance threshold corresponds to a health status level, and the smaller the distance threshold, the higher the corresponding health status level, and vice versa. If the distance value is smaller than or equal to the distance threshold, the health state grade of the cable accessory is judged to be the health state grade corresponding to the distance threshold, and if the distance value is larger than the distance threshold, the health state grade is judged to be the health state grade next to the health state grade corresponding to the distance threshold.
In this embodiment, by setting the distance reference interval and determining the health status of the cable accessory, an accurate and reliable health status evaluation result of the cable accessory can be obtained simply and quickly.
In order to make a clearer description of the method for determining the health status of a cable accessory provided in the present application, a specific embodiment is described below with reference to fig. 6, where the specific embodiment includes the following steps:
s202, simulation test data and process data of the cable accessory are obtained.
S204, extracting a plurality of state characteristic indexes based on the simulation test data and the process data.
S266, determining membership values of each state characteristic index for different state grades according to a preset membership function, determining weights of the membership values of each state characteristic index for different state grades, and combining the membership values of each state characteristic index for different state grades according to the weights to obtain a state characteristic vector of the cable accessory.
And S228, measuring the distance between the state characteristic index vector and the health state characteristic reference vector by using a Euclidean distance algorithm to obtain the Euclidean distance value.
S230, comparing the distance value with a preset distance reference range, mapping the distance value to a corresponding distance reference interval, and determining a health state evaluation result of the cable accessory according to the distance reference interval where the distance value is located.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a cable accessory health state determining device for realizing the cable accessory health state determining method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the device for determining the health status of the cable accessory provided below may be referred to the limitation of the method for determining the health status of the cable accessory hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 7, there is provided a cable accessory health status determining apparatus 700 comprising: a data acquisition module 710, an index extraction module 720, a feature fusion module 730, a distance measurement module 740, and a state determination module 750, wherein:
the data acquisition module 710 is configured to acquire simulation test data and process data of the cable accessory.
The index extraction module 720 is configured to extract a plurality of state feature indexes based on the simulation test data and the process data.
And a feature fusion module 730, configured to perform feature fusion on the plurality of state feature indexes to obtain a state feature index vector of the cable accessory.
The distance measurement module 740 is configured to measure a distance between the state feature index vector and the health state feature reference vector, so as to obtain a distance value.
And the state determining module 750 is configured to obtain a health state evaluation result of the cable accessory according to the distance value.
According to the cable accessory health state determining device, simulation test data and process data of the cable accessory are obtained, then, based on the simulation test data and the process data, a plurality of comprehensive and accurate state characteristic indexes can be extracted, then, characteristic fusion is carried out on the plurality of state characteristic indexes to obtain a state characteristic index vector of the cable accessory, the state characteristic index vector can reflect the whole health state of the cable accessory, accuracy of an evaluation result is improved, then, a distance value is obtained through measuring the distance between the state characteristic index vector and a health state characteristic reference vector, the degree of difference between the health state and the reference state of the cable accessory can be quantized, objective evaluation indexes are provided, finally, the health state evaluation result of the cable accessory is obtained according to the distance value, the health state of the cable accessory can be quickly and simply known, preventive maintenance and fault diagnosis are facilitated, accurate judgment basis is provided for fault diagnosis of the cable accessory, and reliability and safety of the cable accessory are improved. By adopting the device, the fault diagnosis accuracy of the cable accessory can be improved.
In one embodiment, the status characteristic indicators include characteristic indicators of different status levels;
the feature fusion module 730 is further configured to determine membership values of each state feature indicator for different state levels according to a preset membership function, and perform feature fusion on the membership values of the plurality of state feature indicators for different state levels to obtain a state feature vector of the cable accessory, where the membership function corresponds to the state level one by one.
In one embodiment, the feature fusion module 730 is further configured to determine weights of membership values of each state feature indicator for different state levels, and combine the membership values of each state feature indicator for different state levels according to the weights to obtain a state feature vector of the cable accessory.
In one embodiment, the feature fusion module 730 is further configured to determine weights of membership values of each state feature indicator for different state levels by using a fuzzy comprehensive evaluation method.
In one embodiment, the distance measurement module 740 is further configured to measure the distance between the state feature index vector and the health state feature reference vector by using a euclidean distance algorithm to obtain a euclidean distance value.
In one embodiment, the distance measurement module 740 is further configured to compare the distance value with a preset distance reference range, map the distance value to a corresponding distance reference interval, and determine a health status evaluation result of the cable accessory according to the distance reference interval in which the distance value is located.
The respective modules in the above-described cable attachment health status determination device may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 8. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing simulation test data and process data of the cable accessories to be tested, health state evaluation results and other data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of determining a health status of a cable accessory.
It will be appreciated by those skilled in the art that the structure shown in fig. 8 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor, when executing the computer program, performing the steps of the above-described embodiments of the method for determining the health status of a cable attachment.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the above-described embodiments of a method for determining the health status of a cable accessory.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, implements the steps of the above-described embodiments of the method for determining the health status of a cable accessory.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use, and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of determining the health status of a cable accessory, the method comprising:
obtaining simulation test data and process data of a cable accessory;
extracting a plurality of state characteristic indexes based on the simulation test data and the process data;
performing feature fusion on a plurality of state feature indexes to obtain a state feature index vector of the cable accessory;
Measuring the distance between the state characteristic index vector and the health state characteristic reference vector to obtain a distance value;
and obtaining a health state evaluation result of the cable accessory according to the distance value.
2. The method of claim 1, wherein the status characteristic indicators comprise characteristic indicators of different status levels;
the step of carrying out feature fusion on the plurality of state feature indexes to obtain a state feature index vector of the cable accessory comprises the following steps:
determining membership values of each state characteristic index for different state grades according to a preset membership function;
performing feature fusion on the membership values of the plurality of state feature indexes aiming at different state grades to obtain a state feature vector of the cable accessory;
wherein, the membership function corresponds to the state grade one by one.
3. The method of claim 2, wherein the fusing the membership values of the plurality of state feature indicators for different state levels to obtain the state feature vector of the cable accessory comprises:
determining the weight of membership values of each state characteristic index for different state grades;
and combining membership values of each state characteristic index for different state grades according to the weights to obtain the state characteristic vector of the cable accessory.
4. A method according to claim 3, wherein determining the weights of the membership values of the respective state characteristic indicators for the different state classes comprises:
and determining the weights of the membership values of the state characteristic indexes aiming at different state grades by a fuzzy comprehensive evaluation method.
5. The method of any one of claims 1 to 4, wherein measuring the distance between the state characteristic index vector and the health state characteristic reference vector, obtaining a distance value comprises:
and measuring the distance between the state characteristic index vector and the health state characteristic reference vector through a Euclidean distance algorithm to obtain the Euclidean distance value.
6. The method according to any one of claims 1 to 4, wherein obtaining the health status evaluation result of the cable accessory according to the distance value comprises:
comparing the distance value with a preset distance reference range, and mapping the distance value to a corresponding distance reference interval;
and determining the health state evaluation result of the cable accessory according to the distance reference interval in which the distance value is located.
7. A cable accessory health status determining device, the device comprising:
The data acquisition module is used for acquiring simulation test data and process data of the cable accessory;
the index extraction module is used for extracting a plurality of state characteristic indexes based on the simulation test data and the process data;
the feature fusion module is used for carrying out feature fusion on the plurality of state feature indexes to obtain a state feature index vector of the cable accessory;
the distance measurement module is used for measuring the distance between the state characteristic index vector and the health state characteristic reference vector to obtain a distance value;
and the state determining module is used for obtaining the health state evaluation result of the cable accessory according to the distance value.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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| CN202311533940.2A CN117520896A (en) | 2023-11-16 | 2023-11-16 | Cable accessory fault diagnosis method, device, computer equipment and storage medium |
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