CN113791429B - Satellite receiver fault analysis method based on SVM - Google Patents
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Abstract
The invention provides a satellite receiver fault analysis method based on SVM, which comprises the following steps: s10, collecting test data of a satellite receiver under various working conditions; s20, preprocessing test data under various working conditions; s30, obtaining an SVM multi-classification training model; s40, obtaining an SVM multi-classification training model after parameter optimization; s50, obtaining a verified SVM multi-classification training model; s60, obtaining the accuracy of the verified SVM multi-classification training model; s70, under the condition that the accuracy rate is larger than or equal to a preset value, obtaining the fault type of each test sample in the test sample set based on the accuracy rate of the verified SVM multi-classification training model; s80, under the condition that the accuracy rate is smaller than a preset value, acquiring a health state assessment function; s90, analyzing satellite receiver faults based on the health state evaluation function. The invention can solve the technical problems that the existing method can not quickly and accurately solve the fault detection and classification in the long-time operation process of the satellite receiver system.
Description
Technical Field
The invention relates to the technical field of satellite navigation fault analysis, in particular to a satellite receiver fault analysis method based on SVM.
Background
Satellite receivers are one of the key devices for satellite navigation. With the development of satellite navigation technology, the quality of satellite navigation data transmitted by a navigation receiver and the performance of the receiver are more and more concerned, and the research of satellite navigation test data fault analysis technology and receiver performance evaluation is more and more in depth. In recent decades, satellite navigation technology and application develop rapidly, and especially new navigation systems such as Beidou navigation and the like are added, so that fields of transmitted satellite navigation test data are more and more, data volume is more and more, generated fault types are more and more complex, and manual investigation is difficult to cover comprehensively.
Therefore, the existing method cannot rapidly and accurately solve fault detection and classification in the long-time operation process of the satellite receiver system.
Disclosure of Invention
The invention provides a satellite receiver fault analysis method based on SVM, which can solve the technical problems that the existing method can not quickly and accurately solve the fault detection and classification in the long-time operation process of a satellite receiver system.
According to an aspect of the present invention, there is provided a method for analyzing a fault of a satellite receiver based on an SVM, the method comprising:
s10, collecting test data of the satellite receiver under various working conditions, and marking the test data under each working condition with a label respectively for identification;
S20, preprocessing test data under various working conditions to obtain preprocessed sample data, and randomly dividing the preprocessed sample data into a training sample set and a test sample set;
s30, obtaining an SVM multi-classification training model based on the kernel function, the multi-classification problem types and the training sample set;
S40, carrying out parameter optimization on the SVM multi-classification training model by adopting a grid search method to obtain the SVM multi-classification training model after the parameter optimization;
S50, verifying the SVM multi-classification training model after parameter optimization by adopting a cross verification method to obtain a verified SVM multi-classification training model;
S60, importing the test sample set into the verified SVM multi-classification training model to obtain the accuracy of the verified SVM multi-classification training model;
S70, under the condition that the accuracy rate of the verified SVM multi-classification training model is greater than or equal to a preset value, obtaining the fault type of each test sample in the test sample set based on the accuracy rate of the verified SVM multi-classification training model so as to complete fault diagnosis of the satellite receiver;
S80, under the condition that the accuracy of the verified SVM multi-classification training model is smaller than a preset value, obtaining the fault type of each test sample in the test sample set based on the accuracy of the verified SVM multi-classification training model, and obtaining a health state evaluation function based on the fault type of each test sample and the decision function of the verified SVM multi-classification training model;
S90, analyzing the satellite receiver faults based on the health state evaluation function to complete fault diagnosis of the satellite receiver.
Preferably, in S60, the accuracy of the verified SVM multi-classification training model is obtained by the following formula:
In the formula, ACC is the accuracy of the SVM multi-classification training model after verification, TP is the number of positive cases of label data classified by the SVM multi-classification training model after verification, TN is the number of negative cases of label data classified by the SVM multi-classification training model after verification, FN is the number of negative cases of label data classified by the SVM multi-classification training model after verification, and FP is the number of negative cases of label data classified by the SVM multi-classification training model after verification.
Preferably, in S80, when the accuracy of the verified SVM multi-classification training model is smaller than the preset value, obtaining the fault type of each test sample in the test sample set based on the accuracy of the verified SVM multi-classification training model, and obtaining the health state evaluation function based on the fault type of each test sample and the decision function of the verified SVM multi-classification training model includes:
S81, under the condition that the accuracy of the verified SVM multi-classification training model is smaller than a preset value, obtaining the fault type of each test sample in the test sample set based on the accuracy of the verified SVM multi-classification training model;
S82, under the condition that the fault type of the test sample is a normal working condition, assigning the maximum value of the decision function of the verified SVM multi-classification training model to a health state evaluation function;
s83, under the condition that the fault type of the test sample is the fault working condition, assigning the minimum value of the decision function of the verified SVM multi-classification training model to the health state evaluation function.
Preferably, in S90, analyzing the satellite receiver fault based on the health status evaluation function to complete fault diagnosis of the satellite receiver includes:
s91, under the condition that the health state evaluation function is greater than or equal to 1, the satellite receiver is in a health state;
s92, under the condition that the health state evaluation function is greater than or equal to 0 and less than 1, the satellite receiver is in a sub-health state;
s93, under the condition that the health state evaluation function is more than-1 and less than 0, the satellite receiver is in a critical maintenance state;
S94, under the condition that the health state evaluation function is smaller than or equal to-1, the satellite receiver is in a fault state.
Preferably, in S20, the test data under multiple working conditions is preprocessed, and the obtained preprocessed sample data includes: and carrying out unitization, normalization and dimension reduction treatment on the test data under various working conditions to obtain the preprocessed sample data.
Preferably, the unitizing, normalizing and dimension reducing treatment are performed on the test data under various working conditions, and the obtained preprocessed sample data comprises: and carrying out unitization, normalization and dimension reduction treatment on test data under various working conditions by adopting a principal component analysis method to obtain preprocessed sample data.
Preferably, in S30, obtaining the SVM multi-classification training model based on the kernel function, the multi-classification problem type, and the training sample set includes:
S31, selecting a kernel function and multiple classification problem types, and determining a penalty factor and the number k of working condition categories of a training sample set based on the selected kernel function, the multiple classification problem types and the training sample set;
S32, sorting the training samples in the training sample set in k categories to obtain k (k-1)/2 classification combinations, and obtaining decision functions of the classification combinations;
S33, obtaining an SVM multi-classification training model based on decision functions of all classification combinations.
Preferably, the plurality of working conditions comprise a normal working condition and a fault working condition, wherein the normal working condition comprises a normal running state, and the fault working condition comprises a test data frame loss state, a power word jump oversized state and a filter damaged state.
According to a further aspect of the present invention there is provided a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing any of the methods described above when executing the computer program.
By applying the technical scheme of the invention, the test data under various working conditions are preprocessed, the subsequent model calculation is convenient, and the classification accuracy of the support vector machine (Support Vector Machine, SVM) multi-classification training model is improved; parameter optimization is carried out on the SVM multi-classification training model, so that the classification accuracy of the SVM multi-classification training model is further improved; the performance of the SVM multi-classification training model is checked by adopting a cross verification method, so that the overfitting problem of the SVM multi-classification training model is reduced; the fault diagnosis of the satellite receiver is completed through the fault type or health state evaluation function of the test sample. The method of the invention can overcome the defects of the prior art, thereby more rapidly and accurately solving the fault detection and classification in the long-time operation process of the satellite receiver system.
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The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 shows a flowchart of a method for analyzing a fault of an SVM-based satellite receiver according to an embodiment of the present invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the application, its application, or uses. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
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 exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description. Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but should be considered part of the specification where appropriate. In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
As shown in fig. 1, the present invention provides a method for analyzing faults of a satellite receiver based on an SVM, the method comprising:
s10, collecting test data of the satellite receiver under various working conditions, and marking the test data under each working condition with a label respectively for identification;
S20, preprocessing test data under various working conditions to obtain preprocessed sample data, and randomly dividing the preprocessed sample data into a training sample set and a test sample set;
s30, obtaining an SVM multi-classification training model based on the kernel function, the multi-classification problem types and the training sample set;
S40, carrying out parameter optimization on the SVM multi-classification training model by adopting a grid search method to obtain the SVM multi-classification training model after the parameter optimization;
S50, verifying the SVM multi-classification training model after parameter optimization by adopting a cross verification method to obtain a verified SVM multi-classification training model;
S60, importing the test sample set into the verified SVM multi-classification training model to obtain the accuracy of the verified SVM multi-classification training model;
S70, under the condition that the accuracy rate of the verified SVM multi-classification training model is greater than or equal to a preset value, obtaining the fault type of each test sample in the test sample set based on the accuracy rate of the verified SVM multi-classification training model so as to complete fault diagnosis of the satellite receiver;
S80, under the condition that the accuracy of the verified SVM multi-classification training model is smaller than a preset value, obtaining the fault type of each test sample in the test sample set based on the accuracy of the verified SVM multi-classification training model, and obtaining a health state evaluation function based on the fault type of each test sample and the decision function of the verified SVM multi-classification training model;
S90, analyzing the satellite receiver faults based on the health state evaluation function to complete fault diagnosis of the satellite receiver.
The invention preprocesses the test data under various working conditions, is convenient for the subsequent model calculation, and improves the classification accuracy of the support vector machine (Support Vector Machine, SVM) multi-classification training model; parameter optimization is carried out on the SVM multi-classification training model, so that the classification accuracy of the SVM multi-classification training model is further improved; the performance of the SVM multi-classification training model is checked by adopting a cross verification method, so that the overfitting problem of the SVM multi-classification training model is reduced; the fault diagnosis of the satellite receiver is completed through the fault type or health state evaluation function of the test sample. The method of the invention can overcome the defects of the prior art, thereby more rapidly and accurately solving the fault detection and classification in the long-time operation process of the satellite receiver system.
According to one embodiment of the present invention, the plurality of working conditions in S10 include a normal working condition and a fault working condition, wherein the normal working condition includes a normal operation state, and the fault working condition includes a test data frame loss state, a power word jump excessive state, and a filter damage state.
The test data frame loss state is that the outgoing data is discontinuous; the power word jumps too much, namely different positioning system powers are interfered to a certain extent; the filter damage state, i.e. the data display condition, is a power word of 0.
According to an embodiment of the present invention, in S20, the test data under multiple working conditions is preprocessed, and the obtained preprocessed sample data includes: and carrying out unitization, normalization and dimension reduction treatment on the test data under various working conditions to obtain the preprocessed sample data.
The test data under various working conditions are subjected to unitization treatment, so that the problem that part of fields are increased along with time is avoided; through carrying out normalization processing on test data under various working conditions, the attribute of a large value interval is prevented from overdominating the attribute of a small value interval, the complexity of the value in the calculation process is avoided, meanwhile, the data can be tidier, the convergence of a model is facilitated, and the contribution of each characteristic quantity in the model construction is uniform; the dimension reduction processing is carried out on the test data under various working conditions, so that the complexity of model calculation is reduced, and the classification accuracy is improved.
Specifically, since there are negative values in the test data, each test data can be linearly scaled to the interval [ -1,1] by:
Wherein y is normalized test data, x is original test data, ymin and ymax are respectively the minimum and maximum values of the normalized test data, and xmin and xmax are respectively the minimum and maximum values of the original test data.
Further, unitizing, normalizing and dimension reducing are carried out on test data under various working conditions, and the obtained preprocessed sample data comprises the following steps: and carrying out unitization, normalization and dimension reduction treatment on test data under various working conditions by adopting a principal component analysis method to obtain preprocessed sample data.
Specifically, when the main component analysis method is adopted to perform the dimension reduction processing on the test data, the n-dimensional feature is mapped to the k-dimension (k < n), the k-dimension is a brand new orthogonal feature, the k-dimensional feature is called as the main component, and the k-dimensional feature is the reconstructed k-dimensional feature.
According to one embodiment of the present invention, in S30, obtaining an SVM multi-classification training model based on the kernel function, the multi-classification problem type, and the training sample set includes:
S31, selecting a kernel function and multiple classification problem types, and determining a penalty factor and the number k of working condition categories of a training sample set based on the selected kernel function, the multiple classification problem types and the training sample set;
S32, sorting the training samples in the training sample set in k categories to obtain k (k-1)/2 classification combinations, and obtaining decision functions of the classification combinations;
S33, obtaining an SVM multi-classification training model based on decision functions of all classification combinations.
The SVM is developed based on the idea of a linear classifier, and in a two-dimensional space, there is necessarily a unitary function g (x) =wx+b to classify samples, wherein the samples are classified into one class when the value of the substituted function is smaller than 0 and the class when the value of the substituted function is larger than 0; similarly, in three dimensions or higher, a function g (x) =wx+b can be found, where x is a vector, w is a coefficient matrix, and b is a constant. In three-dimensional space, the physical meaning of g (x) is a plane that divides the sample into two classes, and in higher-dimensional space is called a hyperplane.
According to one embodiment of the invention, in S40, parameter optimization is performed by adopting a grid search method, and in S50, verification is performed by adopting a k-fold cross verification method, so that the overfitting resistance of the model is improved, and the adaptability and the classification accuracy of the model to satellite receiver data are improved.
In S60, according to one embodiment of the present invention, the accuracy of the verified SVM multi-classification training model is obtained by the following equation:
In the formula, ACC is the accuracy of the SVM multi-classification training model after verification, TP is the number of positive cases of label data classified by the SVM multi-classification training model after verification, TN is the number of negative cases of label data classified by the SVM multi-classification training model after verification, FN is the number of negative cases of label data classified by the SVM multi-classification training model after verification, and FP is the number of negative cases of label data classified by the SVM multi-classification training model after verification.
The accuracy of the verified SVM multi-classification training model is obtained through an confusion matrix, and the formula of the confusion matrix is as follows:
In the formula, CM is an confusion matrix, A is a positive example, and B is a negative example.
According to an embodiment of the present invention, in S80, obtaining the fault type of each test sample in the test sample set based on the accuracy of the verified SVM multi-classification training model and obtaining the health state evaluation function based on the fault type of each test sample and the decision function of the verified SVM multi-classification training model, where the accuracy of the verified SVM multi-classification training model is smaller than a preset value, includes:
S81, under the condition that the accuracy of the verified SVM multi-classification training model is smaller than a preset value, obtaining the fault type of each test sample in the test sample set based on the accuracy of the verified SVM multi-classification training model;
S82, under the condition that the fault type of the test sample is a normal working condition, assigning the maximum value of the decision function of the verified SVM multi-classification training model to a health state evaluation function;
s83, under the condition that the fault type of the test sample is the fault working condition, assigning the minimum value of the decision function of the verified SVM multi-classification training model to the health state evaluation function.
In the present invention, the health status evaluation function is obtained by the following formula:
wherein M is a health state evaluation function, N is the number of test samples, a iyixi is a Lagrangian function representation of a feature vector omega, x is any test sample, and b is a feature vector.
In accordance with one embodiment of the present invention, in S90, analyzing the satellite receiver fault based on the health status assessment function to complete the fault diagnosis of the satellite receiver includes:
s91, under the condition that the health state evaluation function is greater than or equal to 1, the satellite receiver is in a health state;
s92, under the condition that the health state evaluation function is greater than or equal to 0 and less than 1, the satellite receiver is in a sub-health state;
s93, under the condition that the health state evaluation function is more than-1 and less than 0, the satellite receiver is in a critical maintenance state;
S94, under the condition that the health state evaluation function is smaller than or equal to-1, the satellite receiver is in a fault state.
That is, the values of the different health assessment functions M are derived from the different positions at which the current input test sample sits between the normal classification boundary, the hyperplane, and the fault classification boundary.
The method of the present invention will be specifically described below with reference to the satellite receiver having four working conditions, each of which has 450 sets of data samples.
In this embodiment, the satellite receiver has four working conditions of normal running state, frame loss of test data, excessive power word jump and filter damage, and marks a tag 1, a tag 2, a tag 3 and a tag 4 respectively. The data sample dimension is 50 dimensions. Wherein 70% can be selected as the training sample set and 30% can be selected as the test sample set.
In this embodiment, the dimension reduction processing is performed by using a principal component analysis method. The data of different dimensions is related to the accuracy of the model. The accuracy can reach 88% when the temperature is reduced to 5D, 94% when the temperature is reduced to 6D and 98% when the temperature is reduced to 9D.
In this embodiment, the verification is performed using a 4-fold cross-verification method. That is, the samples are randomized sequentially, the nth (n=1, 2,3, 4) samples are taken as test samples, the rest 3/4 samples are taken as training samples, namely, training and testing of the submodel are carried out for 4 times, and the cross verification result of the training sample set is obtained. After verification, the final cross verification accuracy is up to 99%, and the adaptability of the SVM model is ensured.
The accuracy of the SVM multi-classification training model obtained through the data is 94.87% and is larger than a preset value, so that the fault type of each test sample in the test sample set is obtained based on the accuracy of the SVM multi-classification training model obtained through the data, and the fault diagnosis of the satellite receiver is completed.
In the invention, because satellite navigation data is large in quantity and large in noise, is easily interfered by external environment, and is not easy to identify the running state when the data is slightly changed, the fault of a satellite receiver is analyzed by adopting an SVM (support vector machine) -based algorithm, the satellite navigation data is classified by the algorithm, the decision boundary of the algorithm is a classification hyperplane for solving the maximum margin of a learning sample, and the satellite navigation data is classified by utilizing the hyperplane, so that the fault analysis is performed.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing any of the methods described above when executing the computer program.
Spatially relative terms, such as "above … …," "above … …," "upper surface on … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial location relative 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 in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "above" or "over" other devices or structures would then be oriented "below" or "beneath" the other devices or structures. Thus, the exemplary term "above … …" may include both orientations "above … …" and "below … …". The device may also be positioned in other different ways (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In addition, the terms "first", "second", etc. are used to define the components, and are only for convenience of distinguishing the corresponding components, and the terms have no special meaning unless otherwise stated, and therefore should not be construed as limiting the scope of the present invention.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A method for analyzing faults of a satellite receiver based on an SVM, the method comprising:
s10, collecting test data of the satellite receiver under various working conditions, and marking the test data under each working condition with a label respectively for identification;
S20, preprocessing test data under various working conditions to obtain preprocessed sample data, and randomly dividing the preprocessed sample data into a training sample set and a test sample set;
s30, obtaining an SVM multi-classification training model based on the kernel function, the multi-classification problem types and the training sample set;
S40, carrying out parameter optimization on the SVM multi-classification training model by adopting a grid search method to obtain the SVM multi-classification training model after the parameter optimization;
S50, verifying the SVM multi-classification training model after parameter optimization by adopting a cross verification method to obtain a verified SVM multi-classification training model;
S60, importing the test sample set into the verified SVM multi-classification training model to obtain the accuracy of the verified SVM multi-classification training model;
S70, under the condition that the accuracy rate of the verified SVM multi-classification training model is greater than or equal to a preset value, obtaining the fault type of each test sample in the test sample set based on the accuracy rate of the verified SVM multi-classification training model so as to complete fault diagnosis of the satellite receiver;
S80, under the condition that the accuracy of the verified SVM multi-classification training model is smaller than a preset value, obtaining the fault type of each test sample in the test sample set based on the accuracy of the verified SVM multi-classification training model, and obtaining a health state evaluation function based on the fault type of each test sample and the decision function of the verified SVM multi-classification training model;
S90, analyzing the satellite receiver faults based on the health state evaluation function to complete fault diagnosis of the satellite receiver;
In S80, when the accuracy of the verified SVM multi-classification training model is smaller than the preset value, obtaining the fault type of each test sample in the test sample set based on the accuracy of the verified SVM multi-classification training model, and obtaining the health state evaluation function based on the fault type of each test sample and the decision function of the verified SVM multi-classification training model includes:
S81, under the condition that the accuracy of the verified SVM multi-classification training model is smaller than a preset value, obtaining the fault type of each test sample in the test sample set based on the accuracy of the verified SVM multi-classification training model;
S82, under the condition that the fault type of the test sample is a normal working condition, assigning the maximum value of the decision function of the verified SVM multi-classification training model to a health state evaluation function;
s83, under the condition that the fault type of the test sample is the fault working condition, assigning the minimum value of the decision function of the verified SVM multi-classification training model to the health state evaluation function;
In S90, analyzing the satellite receiver fault based on the health status evaluation function to complete the fault diagnosis of the satellite receiver includes:
s91, under the condition that the health state evaluation function is greater than or equal to 1, the satellite receiver is in a health state;
s92, under the condition that the health state evaluation function is greater than or equal to 0 and less than 1, the satellite receiver is in a sub-health state;
s93, under the condition that the health state evaluation function is more than-1 and less than 0, the satellite receiver is in a critical maintenance state;
S94, under the condition that the health state evaluation function is smaller than or equal to-1, the satellite receiver is in a fault state.
2. The method of claim 1, wherein in S60, the accuracy of the verified SVM multi-classification training model is obtained by:
In the formula, ACC is the accuracy of the SVM multi-classification training model after verification, TP is the number of positive cases of label data classified by the SVM multi-classification training model after verification, TN is the number of negative cases of label data classified by the SVM multi-classification training model after verification, FN is the number of negative cases of label data classified by the SVM multi-classification training model after verification, and FP is the number of negative cases of label data classified by the SVM multi-classification training model after verification.
3. The method of claim 1, wherein in S20, preprocessing test data under a plurality of working conditions to obtain preprocessed sample data includes: and carrying out unitization, normalization and dimension reduction treatment on the test data under various working conditions to obtain the preprocessed sample data.
4. A method according to claim 3, wherein the unitizing, normalizing and dimension reducing of the test data under the plurality of conditions to obtain the preprocessed sample data comprises: and carrying out unitization, normalization and dimension reduction treatment on test data under various working conditions by adopting a principal component analysis method to obtain preprocessed sample data.
5. The method of claim 1, wherein in S30, deriving an SVM multi-classification training model based on the kernel function, the multi-classification problem type, and the training sample set comprises:
S31, selecting a kernel function and multiple classification problem types, and determining a penalty factor and the number k of working condition categories of a training sample set based on the selected kernel function, the multiple classification problem types and the training sample set;
S32, sorting the training samples in the training sample set in k categories to obtain k (k-1)/2 classification combinations, and obtaining decision functions of the classification combinations;
S33, obtaining an SVM multi-classification training model based on decision functions of all classification combinations.
6. The method of claim 1, wherein the plurality of operating conditions includes a normal operating condition and a fault operating condition, wherein the normal operating condition includes a normal operating condition, and wherein the fault operating condition includes a test data frame loss condition, a power word transition excessive condition, and a filter damage condition.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 6 when executing the computer program.
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