CN110657985B - Gearbox fault diagnosis method and system based on singular value spectrum manifold analysis - Google Patents
Gearbox fault diagnosis method and system based on singular value spectrum manifold analysis Download PDFInfo
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Abstract
The invention relates to the technical fields of mechanical fault diagnosis, mode identification and the like, in particular to a method and a system for diagnosing faults of a gearbox based on singular value spectrum manifold analysis. The method comprises the steps of obtaining fault vibration signals of the gearbox and preprocessing the fault vibration signals to form a plurality of one-dimensional original vibration signal data, and performing phase space reconstruction processing to obtain a plurality of two-dimensional matrixes; singular value decomposition is carried out on the reconstructed two-dimensional matrix to obtain a singular value spectrum of the two-dimensional matrix; calculating the slope of the singular value spectrum to obtain singular value spectrum manifold characteristics; training a support vector machine by using the characteristic data to construct a fault diagnosis model; and inputting the vibration signal data of the gear box to be tested into the fault diagnosis model, and outputting the fault diagnosis classification result of the gear box to be tested. The method adopts singular value spectrum manifold analysis to realize the feature extraction of the gearbox fault data, can effectively extract the change trend of signal components, removes the influence of noise, enhances the characterization capability of the features on the fault, and can improve the precision of the gearbox fault diagnosis.
Description
Technical Field
The invention relates to the technical fields of mechanical fault diagnosis, mode identification and the like, in particular to a method and a system for diagnosing faults of a gearbox based on singular value spectrum manifold analysis.
Background
Along with the improvement of the integration and automation degree of mechanical equipment, the mechanical equipment is more and more prone to failure, the damage caused by equipment failure is more and more large, and the requirements on the reliability and the safety of the mechanical equipment are also improved. The early failure diagnosis of the mechanical equipment can avoid huge loss caused by accidents, so that enterprises can obtain good economic and social benefits. The gear box is used as a transmission mechanism which is most widely applied, the structure of the gear box is complex, the working environment is severe, and faults are very easy to occur, so that the gear box has great significance in fault diagnosis.
The fault of the gearbox is usually expressed in the form of a vibration signal, and characteristic information related to the fault can be extracted from the vibration signal to realize fault diagnosis of the gearbox. In most cases, the operating environment of the gearbox has strong interfering noise or the signature is weak, so it is necessary to select a suitable fault signature extraction method.
The spectrum analysis method based on Fourier transform is widely applied to feature extraction, but is only suitable for stationary signals, and fault signals are usually non-stationary signals, so that the application of spectrum analysis is greatly limited; wavelet transformation can also be used for feature extraction of fault signals, but the selection of wavelet bases has great influence on the result of feature extraction, and the selection of wavelet bases has no uniform standard which can be referred to; empirical mode decomposition is an effective method for processing non-stationary signals, but a 'boundary effect' occurs in the processing process, thereby causing data pollution. The Singular Value Decomposition (SVD) is used for processing the operating state signal of the mechanical device, and is essentially a matrix decomposition method, which decomposes the signal into a plurality of subspaces, can separate noise while extracting the signal variation trend, and the singular value spectrum obtained after SVD decomposition reflects the distribution of each signal component in the signal, so that the method is a feature extraction method with wide application.
However, the range of the singular value is usually very large, and a few singular values having a large value are actually used when the singular value is directly used for fault diagnosis, so that fault information contained in the singular value spectrum cannot be fully utilized. In addition, although the singular value can reflect the distribution of each signal component in the signal, the actual numerical value of the singular value is affected by various factors such as interference noise and the like, so that the singular value is not sensitive enough to the contained fault information.
Disclosure of Invention
Aiming at the problems, the invention provides a gearbox fault diagnosis method and system based on singular value spectrum manifold analysis, signal component distribution changes under different fault states can be directly reflected as the distribution changes of a singular value spectrum, and a manifold topological structure can directly represent the changes of the singular value spectrum, so that the running state change conditions of equipment are reflected. The method adopts the singular value spectrum slope to extract the manifold topological structure characteristic of the singular value spectrum, and simultaneously adopts a variable scale method to calculate the singular value spectrum slope in order to eliminate the influence of the singular value numerical value on the characteristic extraction, so that the characteristic extraction process has a self-weighting effect, and the characterization capability of the characteristic on the fault can be improved.
A gearbox fault diagnosis method based on singular value spectral manifold analysis comprises the following steps:
and 5, inputting the vibration signal data of the gear box to be tested into a fault diagnosis model, and outputting a fault diagnosis classification result of the gear box to be tested.
Further, a gearbox fault diagnosis system based on singular value spectral manifold analysis is also provided, the system comprising:
the acceleration sensor is used for extracting a fault vibration signal of the gearbox;
the filter is used for filtering the extracted fault vibration signal of the gearbox;
the data reconstruction module is used for performing phase space reconstruction processing on the filtered one-dimensional original vibration signal and obtaining a two-dimensional Hankel matrix;
the data decomposition module is used for carrying out singular value decomposition on the data after reconstruction processing to obtain a singular value spectrum of the two-dimensional matrix;
the data characteristic module is used for processing singular value spectrum data to obtain singular value spectrum popular characteristics;
the model generation module is used for carrying out optimization training on the characteristic data output by the data characteristic module on a parameter optimization support vector machine adopting a competition mechanism firework algorithm to generate a fault diagnosis model;
and the fault diagnosis module is used for calling the fault diagnosis model generated by the model generation module, predicting the vibration signal data of the gear box to be tested and outputting the fault diagnosis classification result of the gear box to be tested.
Further, the present invention also provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of a method for gearbox fault diagnosis based on singular value spectral manifold analysis.
Furthermore, the invention also provides gearbox fault diagnosis equipment based on singular value spectral manifold analysis, which comprises a memory, a processor and a computer program which is run on the memory and the processor, wherein the processor realizes the steps of the gearbox fault diagnosis method based on singular value spectral manifold analysis when executing the program.
The invention has the beneficial effects that:
1. the method adopts singular value spectrum manifold analysis to realize the feature extraction of the gearbox fault data, and compared with other feature extraction methods, the method can effectively extract the change trend of signal components, remove the influence of noise, enhance the characterization capability of features on faults, and improve the precision of gearbox fault diagnosis.
2. The method adopts an improved self-adaptive Unscented Kalman Filter (UKF) to filter the extracted fault vibration signal of the gearbox, and compared with the traditional Unscented Kalman filter, a suboptimal unbiased MAP constant noise statistical estimator is deduced according to the Maximum A Posteriori (MAP) estimation principle; and then, on the basis, an exponential weighting method is adopted to provide a recursion formula of the time-varying noise statistical estimator, so that the self-adaptive UKF filter with the noise statistical estimator is obtained. The method has the main advantages that under the condition that the noise statistics is unknown and time-varying, the filtering still converges, the filtering precision and the filtering stability are obviously improved, and the method has the self-adaptive capacity of coping with the noise variation.
Drawings
FIG. 1 is a schematic flow chart diagram of a fault diagnosis method based on singular value spectral manifold analysis according to a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of a vibration signal vector of an outer ring fault in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a vibration signal vector of an inner ring fault in an embodiment of the present invention;
FIG. 4 is a vibration signal vector diagram of a combinational fault in an embodiment of the present invention;
FIG. 5 is a vibration signal vector diagram illustrating a root erosion fault in an embodiment of the present invention;
FIG. 6 is a vibration signal vector diagram of a root break fault in an embodiment of the present invention;
FIG. 7 is a diagram of a gearbox test stand in an embodiment of the present invention;
FIG. 8 is a singular value spectral manifold feature diagram in an embodiment of the invention;
FIG. 9 is a singular value feature map in an embodiment of the invention;
fig. 10 is a block diagram of a fault diagnosis system based on singular value spectral manifold analysis according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly and completely apparent, the technical solutions in the embodiments of the present invention are described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
A preferred embodiment of the fault diagnosis method based on singular value spectrum manifold analysis according to the present invention, as shown in fig. 1, includes the following steps:
s11, acquiring various fault vibration signals of the gearbox, and preprocessing the fault vibration signals, including filtering, smoothing and the like;
s21, correspondingly forming a plurality of one-dimensional original vibration signal data by the preprocessed fault vibration signals, and respectively carrying out phase space reconstruction processing to obtain a two-dimensional matrix of the plurality of original vibration signal data;
s31, performing singular value decomposition on the reconstructed two-dimensional matrix to obtain a singular value spectrum of the two-dimensional matrix;
s41, extracting singular value spectrum manifold topological structure characteristics by calculating the slope of a singular value spectrum, thereby obtaining singular value spectrum manifold characteristics;
s51, training by using singular value spectrum manifold characteristic data, and completing construction of a fault diagnosis model by using a parameter optimization support vector machine of a competition mechanism firework algorithm;
and S61, inputting the vibration signal data of the gear box to be tested into the fault diagnosis model, and outputting the fault diagnosis classification result of the gear box to be tested.
In this embodiment, mainly 5 kinds of fault vibration signals in certain gearbox fault data are analyzed, including: outer ring failure, inner ring failure, combination failure, tooth root corrosion failure, tooth root fracture failure.
Firstly, receiving one-dimensional original vibration signal data of a gearbox, wherein a vibration signal tested each time comprises N sampling points; the time domain waveform of the vibration signal of the inner ring fault is shown in figure 2, the time domain waveform of the vibration signal of the outer ring fault is shown in figure 3, the time domain waveform of the vibration signal of the combined fault is shown in figure 4, the time domain waveform of the vibration signal of the tooth root corrosion fault is shown in figure 5, and the time domain waveform of the vibration signal of the tooth root fracture fault is shown in figure 6; carrying out phase space reconstruction processing on the vibration signal of the gearbox to obtain a two-dimensional matrix A; the phase space reconstruction means reconstructing the vibration signal vector of the gearbox into a high-dimensional matrix Hankel matrix, wherein the Hankel matrix is defined as a given set of vibrationMotion signal
Wherein p is more than 1 and less than N, q is more than 1 and less than N, and N is p + q; and N is the number of sampling points of the vibration signal.
In this embodiment, N is 4096, and the vibration signal vector phase space is reconstructed into a Hanke matrix of 25 × 4072, that is, p is 25 and q is 4072. Of course, the specific data size can be selected according to actual conditions.
Carrying out SVD on the two-dimensional matrix A to obtain a singular value spectrum of the two-dimensional matrix A; the SVD decomposition refers to the decomposition of a two-dimensional Hanke matrix A belonging to R25×4072The following transformations are performed:
A＝UΣVT
wherein U is [ U ]1,u2,…,u25]∈R25×25And V ═ V1,v2,…,v4072]∈R4072×4072Is two orthogonal matrices, Σ ═ diag (λ)1,λ2,…λn),O]∈R25×4072Is a singular value matrix, where n ═ min (p, q), i.e. n ═ min (25,4072) ═ 25, λ1＞λ2＞…＞λ25For the obtained singular values, SVs ═ λ1,λ2,…,λ25]The obtained singular value spectrum.
Calculating the slope of the obtained singular value spectrum to obtain the manifold topological structure characteristic of the singular value spectrum; the characteristic information contained in the singular value spectrum needs to be further extracted, and through calculation of the slope of the characteristic information, the method can be expressed as follows:
wherein i belongs to {1, 2.., n-1 }; the singular value spectrum SVs can be calculated to obtain 24 singular value spectrum slopes SLs ═ β1,β2,…,βn-1]SLs are singular value spectrum manifold features, represent manifold topological structure of singular value spectrum, and include fault information of vibration signal vectors.
The method comprises the following steps of training a support vector machine by using singular value spectrum manifold features (SLs) to complete the construction of a fault diagnosis model, inputting vibration signal data of a gear box to be tested into the fault diagnosis model to obtain a final classification result, and training the support vector machine by using singular value spectrum manifold feature data to optimize parameters of a competition mechanism firework algorithm, wherein the support vector machine comprises:
the method for training the support vector machine by using the singular value spectral manifold feature data comprises the following steps of training the support vector machine by using the singular value spectral manifold feature data, and optimizing parameters of the support vector machine by using a competition mechanism firework algorithm, and specifically comprises the following steps:
s401, randomly selecting two groups of firework parameters by using a firework algorithm, wherein the two groups of firework parameters respectively correspond to a group of kernel function parameters and a group of punishment function parameters of a support vector machine;
s402, constructing a support vector machine from each kernel function parameter and each penalty function parameter; inputting singular value spectrum manifold feature data into each support vector machine, and outputting the identification rate of fault diagnosis;
s403, sorting according to the recognition rate, and respectively selecting a kernel function parameter and a penalty function parameter corresponding to the support vector machine with a larger recognition rate;
s404, optimizing the kernel function parameters and the penalty function parameters selected in the step S403 by adopting a competition mechanism firework algorithm, taking the optimized result as a new group of kernel function parameters and a new group of penalty function parameters, and returning to the step S402 until the kernel function parameters and the penalty function parameters corresponding to the support vector machine with the highest recognition rate, namely the optimal kernel function parameters and penalty function parameters, are selected.
In a still further aspect of the present invention,
1) competition mechanism firework algorithm:
randomly initializing two groups of mu fireworks in a search space; evaluating the fitness of each firework; the following process is repeated until the termination condition is satisfied: respectively calculating lambda of the two groups of fireworks from 1 to mujAnd AjEvaluating the fitness of all fireworks in each group respectively, and selecting better fireworks individuals (including the generation, fireworks, explosion fireworks and guide fireworks) as the next generation of smoke of the j generation in the groupInputting the fireworks into a support vector machine for training, selecting part of fireworks corresponding to a higher recognition rate as the next generation of fireworks, and executing a competition mechanism; finally, returning the position and the fitness of the optimal firework individual; the optimal firework individuals in the two groups respectively correspond to a kernel function parameter and a penalty function parameter of the support vector machine;
wherein λ isrRepresenting the number of explosion sparks of the r-th firework;
represents a constant control parameter; r represents the fitness value ranking of the fireworks, mu is the total number of the fireworks, and alpha is the shape distribution controlled by one parameter;
the explosion amplitude of the jth fireworks in g generation
Presetting as a constant;
g generation j firework; ca represents an amplification factor; represents a reduction coefficient;
representing the fitness value of the jth firework in the g generation;
wherein the contention mechanism includes defining a maximum generation algebra gmaxThe number of fireworks is from 1 to mu, and finding out the fireworks with better quality in g generations is reasonable
Time, calculate
If it is
G is substituted for j fireworks
The firework is used as a better firework of the generation;
wherein the content of the first and second substances,
representing the improvement degree of the jth firework in the g generation;
representing the minimum value in the fitness value of g-generation fireworks;
2) a support vector machine:
wherein | · | purple sweet2Expressing two norms, w is a normal vector of the hyperplane, C is a punishment parameter calculated by a competition mechanism firework algorithm, epsilonlAs a relaxation variable, ylA label for classifying the l hyperplane; b is the displacement, i.e. the distance to the hyperplane;
is the corresponding first vibration signal x after the kernel function processinglAnd M denotes the dimension of the support vector machine.
In one embodiment, the kernel function parameters and the penalty function parameters are respectively trained, that is, each kernel function parameter corresponds to μ penalty function parameters, that is, μ × μ support vector machines are generated based on μ kernel function parameters and μ penalty function parameters, the support vector machines are respectively trained, singular value spectral manifold feature data is input, the recognition rate of fault diagnosis is output, and the kernel function parameters and the penalty function parameters corresponding to the support vector machine with a higher recognition rate are selected, for example, before selection
And continuously optimizing the selected kernel function parameters and penalty function parameters by adopting a competition mechanism firework algorithm, and repeatedly selecting and optimizing until the optimal kernel function parameters and penalty function parameters are selected.
The purpose of fault diagnosis of mechanical equipment is to classify fault types, which is essentially a pattern recognition problem. The main steps of fault diagnosis of mechanical equipment comprise signal preprocessing, feature extraction, fault identification and the like, and the feature extraction plays an important role in fault diagnosis precision.
In order to verify the application effect of the invention in mechanical fault diagnosis, the effectiveness of the method is illustrated by certain gearbox fault data, and a gearbox test bed is shown in FIG. 7. The present invention may be used for mechanical fault diagnosis including, but not limited to, gearboxes.
The experiment selects and analyzes 5 kinds of fault vibration signals of the variable-speed gearbox of the intermediate shaft with the rotating speed of 20Hz-40Hz, the load of 0HP and the sampling frequency of 25600Hz, and the fault vibration signals comprise: outer ring failure, inner ring failure, combination failure, root erosion failure, and root fracture failure. 320 groups of samples are respectively measured under various fault states, and the sampling point number of each group of samples is 4096 points. 160 of the data sets were used as training samples, and the remaining 160 were used as test samples. The feature of the invention and the other five feature extraction methods are respectively extracted from the vibration signal of each sample: singular value characteristics, an AR model (AR model for short), time-frequency characteristics, wavelet characteristics and entropy characteristics, and fault diagnosis is performed by adopting a support vector machine for parameter optimization of a competition mechanism firework algorithm.
TABLE 1 Pattern recognition accuracy of the present invention corresponding to singular values, AR models, time-frequency, wavelets, entropy
Table 1 shows the final pattern recognition accuracy for 6 different feature extraction methods. The characteristic diagram of the method provided by the invention is shown in figure 8, and the singular value characteristic diagram is shown in figure 9. As can be seen from fig. 9, the singular value features in various fault states are mixed together, and the range of the numerical variation of the singular value is very large, so that the singular value features cannot identify the fault type, and the identification rate is 20%. As can be seen from the table, the pattern recognition accuracy of other feature extraction methods does not reach the classification accuracy of the invention. Therefore, the invention is a good application background in mechanical fault diagnosis.
In addition, a gearbox fault diagnosis system based on singular value spectral manifold analysis according to the present invention is shown in fig. 10, and the system includes:
the acceleration sensor is used for extracting a fault vibration signal of the gearbox;
the filter is used for filtering the extracted fault vibration signal of the gearbox;
the data reconstruction module is used for performing phase space reconstruction processing on the filtered one-dimensional original vibration signal and obtaining a two-dimensional Hankel matrix;
the data decomposition module is used for carrying out singular value decomposition on the data after reconstruction processing to obtain a singular value spectrum of the two-dimensional matrix;
the data characteristic module is used for processing singular value spectrum data to obtain singular value spectrum popular characteristics;
the model generation module is used for carrying out optimization training on the characteristic data output by the data characteristic module on a parameter optimization support vector machine adopting a competition mechanism firework algorithm to generate a fault diagnosis model;
and the fault diagnosis module is used for calling the fault diagnosis model generated by the model generation module, predicting the vibration signal data of the gear box to be tested and outputting the fault diagnosis classification result of the gear box to be tested.
As a preferred embodiment, the filter is a modified adaptive Unscented kalman filter. For example, an adaptive Unscented Kalman filter algorithm based on Maximum a posteriori estimation and exponential weighting proposed by zhao lin et al in "automated science and newspapers" 2010, which is designed to solve the problem of nonlinear filtering precision reduction and even divergence of a conventional Unscented Kalman filter (Unscented Kalman filter ) under the condition of unknown time-varying noise prior statistics, is a suboptimal unbiased MAP constant noise statistical estimator according to the principle of MAP estimation based on Maximum A Posteriori (MAP); and then, on the basis, an exponential weighting method is adopted to provide a recursion formula of the time-varying noise statistical estimator, so that the self-adaptive UKF filter with the noise statistical estimator is obtained. The method has the main advantages that under the condition that the noise statistics is unknown and time-varying, the filtering still converges, the filtering precision and the filtering stability are obviously improved, and the method has the self-adaptive capacity of coping with the noise variation. In the gearbox fault diagnosis model in the embodiment, various fault vibration signals can be acquired through the acceleration sensor additionally arranged on the gearbox.
Further, the present invention also provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of a method for gearbox fault diagnosis based on singular value spectral manifold analysis.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: ROM, RAM, magnetic or optical disks, and the like.
Furthermore, the invention also provides gearbox fault diagnosis equipment based on singular value spectral manifold analysis, which comprises a memory, a processor and a computer program which is run on the memory and the processor, wherein the processor realizes the steps of the gearbox fault diagnosis method based on singular value spectral manifold analysis when executing the program.
The fault diagnosis device comprises one or more processors and a memory, wherein one processor is taken as an example;
the device for executing list item operation may further comprise a collecting device and an output device.
The processor, memory, acquisition device and output device may be connected by a CAN bus or other means.
The processor may be a central processing unit CPU, but may also be other general purpose processors, digital signal processors, etc.
It is to be understood that some features of the fault diagnosis method, system, computer readable storage medium and fault diagnosis apparatus of the present invention may be mutually cited, and the present invention is not to be construed as illustrative for the sake of brevity.
The above-mentioned embodiments, which further illustrate the objects, technical solutions and advantages of the present invention, should be understood that the above-mentioned embodiments are only preferred embodiments of the present invention, and should not be construed as limiting the present invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A gearbox fault diagnosis method based on singular value spectral manifold analysis is characterized by comprising the following steps:
s1, acquiring various fault vibration signals of the gearbox, preprocessing the fault vibration signals, correspondingly forming a plurality of one-dimensional original vibration signal data, and respectively performing phase space reconstruction processing to obtain a two-dimensional matrix of the original vibration signal data;
s2, performing singular value decomposition on the reconstructed two-dimensional matrix to obtain a singular value spectrum of the two-dimensional matrix;
step S3, extracting singular value spectrum manifold topological structure characteristics by calculating the slope of a singular value spectrum, thereby obtaining singular value spectrum manifold characteristics; i.e. calculating each phaseTwo adjacent singular values λiAnd λi+1The slope of (1), a vector SLs formed by the obtained n-1 singular value spectrum slopes is [ beta ]1,β2,…,βn-1]As a singular value spectral manifold feature; wherein the content of the first and second substances,
n is min (p, q), and the two-dimensional matrix is a p × q two-dimensional matrix; 1<p<N，1<q<N, wherein N is p + q, and N is the number of sampling points of the vibration signal of the gearbox;
s4, training a support vector machine by using singular value spectrum manifold characteristic data to complete the construction of a fault diagnosis model;
and step S5, inputting the vibration signal data of the gear box to be tested into the fault diagnosis model, and outputting the fault diagnosis classification result of the gear box to be tested.
2. The method of claim 1, wherein the fault vibration signal data comprises an outer ring fault, an inner ring fault, a combination fault, a root corrosion fault, and a root fracture fault.
3. The method for diagnosing the fault of the gearbox based on the singular value spectral manifold analysis as claimed in claim 1, wherein the phase space reconstruction process in the step S1 includes obtaining a one-dimensional vibration signal vector X ═ X of the gearbox1,x2,…,xN]Reconstructing the matrix into a two-dimensional Hankel matrix
Wherein x ispRepresenting the vibration signal corresponding to the p sampling point; x is the number ofqRepresenting the vibration signal corresponding to the q sampling point; 1<p<N，1<q<N; and N ═ p + q; and N is the number of sampling points of the vibration signal of the gearbox.
4. Singular value based spectral manifold according to claim 3Method for diagnosing faults of an analytical gearbox, characterized in that said step S2 comprises dividing a two-dimensional matrix A e R of size p qp×qThe following transformations are performed: a ═ U ∑ VT(ii) a Wherein U and V are two orthogonal matrices, and T represents transposition; u ═ U1,u2,...,up]∈Rp×p；V＝[v1,v2,...,vq]∈Rq×q；∑＝[diag(λ1,λ2,...,λn),O]∈Rp×qThe matrix is a singular value matrix, is a diagonal matrix, and the other elements except the diagonal elements are 0; n ═ min (p, q), λ1>λ2>...>λnFor the obtained singular values, SVs ═ λ1,λ2,...,λn]The obtained singular value spectrum.
5. The method of claim 3, wherein the training of the SVM using the singular value spectral manifold feature data comprises: the method adopts singular value spectrum manifold characteristic data to train a support vector machine, and adopts a competition mechanism firework algorithm to optimize parameters of the support vector machine, and specifically comprises the following steps:
s401, randomly selecting two groups of firework parameters by using a firework algorithm, wherein the two groups of firework parameters respectively correspond to a group of kernel function parameters and a group of punishment function parameters of a support vector machine;
s402, constructing a support vector machine from each kernel function parameter and each penalty function parameter; inputting singular value spectrum manifold feature data into each support vector machine, and outputting the identification rate of fault diagnosis;
s403, sorting according to the recognition rate, and respectively selecting a kernel function parameter and a penalty function parameter corresponding to the support vector machine with a larger recognition rate;
s404, optimizing the kernel function parameters and the penalty function parameters selected in the step S403 by adopting a competition mechanism firework algorithm, taking the optimized result as a new group of kernel function parameters and a new group of penalty function parameters, and returning to the step S402 until the kernel function parameters and the penalty function parameters corresponding to the support vector machine with the highest recognition rate, namely the optimal kernel function parameters and penalty function parameters, are selected.
6. A system for implementing a method for gearbox fault diagnosis based on singular value spectral manifold analysis, the system comprising:
the acceleration sensor is used for extracting a fault vibration signal of the gearbox;
the filter is used for filtering the extracted fault vibration signal of the gearbox;
the data reconstruction module is used for performing phase space reconstruction processing on the filtered one-dimensional original vibration signal and obtaining a two-dimensional Hankel matrix;
the data decomposition module is used for carrying out singular value decomposition on the data after reconstruction processing to obtain a singular value spectrum of the two-dimensional matrix;
the data characteristic module is used for extracting singular value spectrum manifold topological structure characteristics by calculating the slope of a singular value spectrum to obtain singular value spectrum manifold characteristics; i.e. calculating every two adjacent singular values lambdaiAnd λi+1The slope of (1), a vector SLs formed by the obtained n-1 singular value spectrum slopes is [ beta ]1,β2,…,βn-1]As a singular value spectral manifold feature; wherein the content of the first and second substances,
n is min (p, q), and the two-dimensional matrix is a p × q two-dimensional matrix; 1<p<N，1<q<N, wherein N is p + q, and N is the number of sampling points of the vibration signal of the gearbox;
the model generation module is used for carrying out optimization training on the support vector machine by using the characteristic data output by the data characteristic module to generate a fault diagnosis model;
and the fault diagnosis module is used for calling the fault diagnosis model generated by the model generation module, predicting the vibration signal data of the gear box to be tested and outputting the fault diagnosis classification result of the gear box to be tested.
7. The system for implementing the gearbox fault diagnosis method based on the singular value spectral manifold analysis according to claim 6, wherein the filter is a modified adaptive Unscented Kalman filter.
8. A computer readable storage medium having stored thereon computer instructions, wherein the instructions when executed by a processor implement the steps of a method for gearbox fault diagnosis based on singular value spectral manifold analysis according to any of claims 1 to 5.
9. A gearbox fault diagnosis device based on singular value spectral manifold analysis, comprising a memory, a processor and a computer program running on the memory and on the processor, characterized in that the processor implements the steps of a gearbox fault diagnosis method based on singular value spectral manifold analysis according to any one of claims 1 to 5 when executing the program.
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