Valve leakage diagnosis method and system based on improved Hereum algorithm optimized SVM (Support Vector Machine)

Abstract

The invention provides a valve leakage diagnosis method and system based on an improved Hereum algorithm optimized SVM (Support Vector Machine), and relates to the technical field of valve leakage diagnosis. Decomposing the collected original signal to obtain a limited number of IMF components; calculating a fuzzy entropy, and constructing a feature vector; taking the feature vectors as data samples, and dividing the data samples into a training set, a verification set and a test set; using the training set to train the SVM model, and according to the classification error rate of the verification set on the trained SVM model, performing iterative optimization on the parameters of the SVM model based on an improved He-Ma algorithm to obtain an optimized SVM model; inputting the test set into the optimized SVM model to obtain a valve leakage diagnosis model; and acquiring a new acoustic emission signal, and inputting the new acoustic emission signal into the valve leakage diagnosis model to obtain a diagnosis result. The method not only can improve the classification accuracy and generalization ability of the diagnosis model, but also can enhance the stability of the model under different working conditions and environmental noise.

Description

Technical Field

[0001] The present invention relates to the technical field of valve leakage diagnosis, and in particular to a valve leakage diagnosis method and system based on an improved Hippo algorithm to optimize SVM. Background Art

[0002] As an indispensable key component in life, valves play an important role in regulating the flow direction and flow of media, opening and closing pipelines, etc., and their sealing performance directly affects their use functions. During valve operation, once internal leakage occurs, its concealment is more significant than external leakage, so it is particularly important to achieve accurate diagnosis of valve internal leakage. Early research mainly focused on fault classification using traditional signal processing methods and unoptimized support vector machines (SVM). Although these methods have laid the foundation for valve internal leakage detection, they have limitations in processing complex nonlinear and non-stationary signals, and the classification accuracy needs to be improved.

[0003] As the research deepened, scholars began to introduce more advanced signal processing techniques, such as empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition (CEEMDAN), to better extract fault features. At the same time, in order to improve the classification performance of SVM, researchers began to explore various intelligent optimization algorithms to optimize the key parameters of SVM. These optimization algorithms include particle swarm optimization (PSO), grey wolf algorithm (GWO), etc., which have improved the parameter selection efficiency and classification accuracy of SVM to a certain extent.

[0004] However, despite the introduction of these optimization algorithms, valve internal leakage fault diagnosis technology still faces some challenges. First, the existing optimization algorithms still have room for improvement in global search capabilities and convergence speed, and are prone to falling into local optimal solutions. Second, these algorithms are sensitive to the quality of the initial population, which may affect the final optimization results. In addition, in the SVM parameter optimization process, existing methods often only focus on classification accuracy. These problems limit the overall performance and practical application effects of the fault diagnosis system, and more efficient and robust optimization methods are urgently needed to further improve the accuracy and reliability of valve internal leakage fault diagnosis. Summary of the invention

[0005] In view of this, the present invention proposes a valve leakage diagnosis method and system based on the improved Hippo algorithm to optimize SVM, improves the efficiency and accuracy of SVM parameter optimization by improving the Hippo optimization algorithm (IHO), and combines CEEMDAN signal decomposition and fuzzy entropy feature extraction technology to better capture the nonlinear and non-stationary characteristics of valve leakage signals. This method can not only improve the classification accuracy and generalization ability of the diagnostic model, but also enhance the stability of the model under different working conditions and environmental noise. Through these improvements, the scheme aims to provide a more reliable and efficient solution for valve leakage fault diagnosis in industrial practice.

[0006] The technical solution of the present invention is achieved in this way:

[0007] On the one hand, the present invention provides a valve leakage diagnosis method based on an improved Hippo algorithm to optimize SVM, comprising:

[0008] S1, collecting acoustic emission signals under different pressures and valve opening conditions as original signals;

[0009] S2, performing adaptive white noise complete empirical mode decomposition on the collected original signal to obtain a finite number of IMF components;

[0010] S3, calculating the correlation coefficient between each IMF component and the original signal, selecting the IMF component whose correlation coefficient is greater than a preset threshold, calculating the fuzzy entropy of the selected IMF component, and constructing a feature vector;

[0011] S4, taking the feature vector as a data sample, dividing it into a training set, a validation set and a test set;

[0012] S5. Train the SVM model using the training set, and iteratively optimize the parameters of the SVM model based on the improved Hippo algorithm according to the classification error rate of the validation set on the trained SVM model to obtain the optimal parameters and the optimized SVM model;

[0013] S6. Input the test set into the optimized SVM model, predict the valve state, calculate the classification accuracy, and evaluate the model performance. Fine-tune the model according to the evaluation results to obtain a valve leakage diagnosis model.

[0014] S7. Obtain a new acoustic emission signal, decompose and extract features of the signal, and input it into a valve leakage diagnosis model to obtain a diagnosis result.

[0015] Based on the above solution, preferably, step S5 includes:

[0016] S51, selecting the penalty parameter c and the kernel parameter g of the SVM model as the target optimization parameters, and logarithmically encoding the penalty parameter c and the kernel parameter g into individual positions in the Hippo algorithm;

[0017] S52, setting the population size N, the maximum number of iterations T and the upper and lower boundary parameters, and initializing the population using a combined chaotic map;

[0018] S53, for each parameter combination (c i ,g i ), use the training set to train the SVM model and calculate the fitness value on the validation set;

[0019] S54, iteratively updating the population according to the improved Hippo algorithm, in each iteration, performing a local search for the current optimal solution, and outputting the optimal solution, i.e., the optimal parameter combination, when the iteration stop condition is reached;

[0020] S55. Perform antilogarithmic transformation on the optimal parameter combination to obtain optimal parameters c and g.

[0021] Based on the above solution, preferably, in step S52, the combined chaotic map includes a Logistic map and a Tent map, and initializing the population using the combined chaotic map includes:

[0022] S521. Design a hybrid mapping function, and determine the mapping to be used by conditional judgment. The hybrid mapping function is as follows:

[0023]

[0024] Where r is a random number between [0,1], and μ is the control parameter of the Logistic mapping;

[0025] S522, determining the dimension m of the problem space, generating an initial value x0 for each dimension, and for each dimension, repeatedly applying the above hybrid mapping function multiple times to generate a chaotic sequence, and taking the final value as the initialization chaotic sequence value of the dimension;

[0026] S523, mapping the generated chaotic sequence value to the upper and lower bounds of the hippo population search space, the position x of each hippo individual i Expressed as: X i =(x i1 ,x i2 ,…,x im ), let z be the generated chaotic sequence value, then the initial position of the hippopotamus individual is calculated as follows:

[0027] X i :x ii =ll j +z·(ul j -ll j ), i=1,2,...,N; j=1,2,...,m

[0028] In the formula, xij is the position of the i-th hippopotamus in the j-th dimension, ll j and j are the lower and upper bounds of the j-th dimension respectively;

[0029] S524. Repeat steps S522-S523 for each hippopotamus individual and each dimension in the population until the entire population is initialized.

[0030] Based on the above scheme, preferably, the calculation formula of the fitness value is:

[0031]

[0032] Where E is the classification error rate on the validation set, n SV is the number of support vectors, M is the total number of data samples, and w is the weight coefficient, which balances the classification error rate and model complexity.

[0033] Based on the above solution, preferably, step S54 includes:

[0034] S541, based on the generated initialization population X={X1,X2,…,X N}, calculate each individual X in the population i The fitness value F i ;

[0035] S542, according to the size of the fitness value, record the current best individual X best and its fitness F best ;

[0036] S543, for the current optimal individual X best Perform local search, obtain local candidate solutions, calculate the fitness value of the local candidate solutions, and if it is greater than F best , then the local candidate solution and its fitness value are replaced by the current optimal individual X best and its fitness F best ;

[0037] S544. For each individual X i , updates the position according to a nonlinear adaptive weight strategy;

[0038] S545, using the preset probability to i Make a mutation;

[0039] S546. Calculate the fitness value of the new individual. If it is better than the current optimal individual, update X best and F best ;

[0040] S547. If the maximum number of iterations is reached or the accuracy requirement is met, the optimal solution X is output.best , otherwise, return to step S542 and iterate again.

[0041] On the basis of the above scheme, preferably, in step S544, the nonlinear adaptive weight strategy includes a nonlinear adaptive weight factor ω, and the position of the hippopotamus individual is updated according to the nonlinear adaptive weight factor ω. The hippopotamus individual position update formula and the weight factor ω are expressed as follows:

[0042]

[0043]

[0044] In the formula, T is the maximum number of iterations, t is the current number of iterations, and are two different individuals randomly selected from the population, β is the learning factor, is the individual's current location, Update the location for an individual, is the best individual in the current iteration.

[0045] Based on the above solution, preferably, in step S545, for individual X i The formula for mutation is as follows:

[0046]

[0047] Among them, rand is a random number between [0,1], X min and N max The value range of the variable.

[0048] Based on the above solution, preferably, in step S543, performing local search on the current optimal individual includes:

[0049]

[0050] Where δ is a small step size and N(0,1) is the standard normal distribution.

[0051] Based on the above solution, preferably, step S2 includes:

[0052] S21. Add standard normally distributed Gaussian white noise to the original signal x(t) to construct the sequence to be decomposed x i (t):

[0053] x i (t) = x(t) + εδ i (t)

[0054] Where ε is the Gaussian white noise weight coefficient, δ i(t) is the Gaussian white noise generated during the i-th processing;

[0055] S22, treat the decomposition sequence x i (t) performs EMD decomposition, and takes the average value of the first-order modal component IMF1 obtained by decomposition as the first-order IMF component IMF1(t) of the complete empirical mode decomposition of adaptive white noise:

[0056]

[0057] r1(t)=x(t)-IMF1(t)

[0058] Where IMF1(t) represents the first modal component obtained by the complete empirical mode decomposition of adaptive white noise; r1(t) represents the residual signal after the first decomposition;

[0059] S23, after adding a specific Gaussian white noise to the j-th order residual signal obtained after decomposition, continue to perform EMD decomposition to obtain a new IMF component and residual signal:

[0060]

[0061] r j (t) = r j-1 (t)-IMF j (t)

[0062] In the formula, IMF j (t) represents the j-th order modal component obtained by complete empirical mode decomposition of adaptive white noise; E j-1 () represents the j-1th order IMF component after EMD decomposition of the sequence; ε j-1 represents the weight coefficient of the noise added to the complete empirical mode of adaptive white noise at the j-1th iteration; r j (t) represents the j-th order residual signal;

[0063] S24. When the residual signal decomposed by the EMD becomes a monotonic signal or meets other preset conditions, the iteration stops, and the adaptive white noise complete empirical mode algorithm decomposition ends.

[0064] On the other hand, the present invention also provides a valve leakage diagnosis system based on the improved Hippo algorithm to optimize SVM, the system is used to perform any of the above methods, the system comprising:

[0065] Data acquisition module, used to collect acoustic emission signals under different pressure and valve opening conditions;

[0066] The data processing module is used to perform adaptive white noise complete empirical mode decomposition on the collected signal to obtain a finite number of IMF components;

[0067] The feature extraction module is used to calculate the correlation coefficient between the IMF component and the original signal, select the IMF component whose correlation coefficient is greater than a preset threshold, and calculate its fuzzy entropy to construct a feature vector;

[0068] The model optimization module is used to train the SVM model and optimize the parameters of the SVM model using the improved Hippo algorithm to obtain a valve leakage diagnosis model;

[0069] The fault diagnosis module is used to classify and predict the feature vector using the valve leakage diagnosis model to realize valve leakage fault diagnosis.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] (1) The valve leakage diagnosis method based on the improved Hippo algorithm optimized SVM proposed in the present invention effectively solves the problem of difficult feature extraction caused by the nonlinear and non-stationary characteristics of valve leakage signals, as well as the problem of poor classification performance caused by insufficient optimization of traditional SVM parameters, by combining CEEMDAN signal decomposition, fuzzy entropy feature extraction and improved IHO-SVM classifier. This method can accurately identify valve leakage faults under different pressures and valve opening conditions, significantly improving the diagnostic accuracy and stability. ;

[0072] (2) The present invention uses a variety of chaotic maps to initialize the IHO algorithm population, which effectively solves the problem that the traditional IHO algorithm has a single initialization and is prone to falling into local optimality. By introducing a diverse initial population, the global search capability of the algorithm is enhanced and the probability of finding the optimal solution is increased. This improvement enables the IHO algorithm to explore the parameter space more comprehensively when optimizing SVM parameters, avoiding performance degradation caused by improper initialization, thereby providing better quality SVM model parameters for subsequent valve leakage fault diagnosis;

[0073] (3) The nonlinear adaptive weight strategy and elite retention mechanism designed in the present invention effectively solve the problems of poor balance between global search and local development and slow convergence speed in the iterative process of the traditional IHO algorithm. The nonlinear adaptive weight factor expands the search range in the early stage of iteration and enhances the global exploration capability; it gradually decreases in the later stage, which is conducive to local refined search. The elite retention mechanism ensures that the optimal solution is not replaced by a new solution, thereby improving the stability of the algorithm. These improvements significantly improve the convergence speed and optimization accuracy of the IHO algorithm, making it possible to find the optimal parameter combination faster and more accurately when optimizing SVM parameters;

[0074] (4) The present invention introduces a mutation operator, combined with an improved fitness function, which effectively solves the problem that the IHO algorithm is prone to falling into local optimality and the SVM model is overfitting. The mutation operation increases the diversity of the population and prevents the algorithm from converging prematurely; the improved fitness function not only considers the classification accuracy, but also the model complexity, balancing the performance and generalization ability of the model. These improvements make the optimized SVM model have stronger generalization ability and robustness, and can better adapt to the valve leakage fault diagnosis task under different working conditions and environmental noise;

[0075] (5) The present invention uses the CEEMDAN algorithm to decompose the valve leakage signal, and combines it with the fuzzy entropy feature extraction method to effectively solve the problem of difficult feature extraction caused by the nonlinear and non-stationary characteristics of the valve leakage signal. The CEEMDAN algorithm overcomes the modal aliasing problem in the traditional EMD method and improves the accuracy and reliability of signal decomposition; the fuzzy entropy feature can effectively capture the complexity and irregularity of the signal. This combination not only makes full use of the time-frequency characteristics of the valve leakage signal, but also enhances the anti-noise ability of the feature, providing a more effective and robust feature input for the subsequent SVM classifier. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0077] Figure 1 is a flow chart of the method of the present invention;

[0078] Figure 2 It is a technical implementation diagram of the present invention;

[0079] Figure 3 It is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0080] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0081] like Figure 1 As shown, the present invention provides a valve leakage diagnosis method based on an improved Hippo algorithm to optimize SVM, comprising:

[0082] S1, collecting acoustic emission signals under different pressures and valve opening conditions as original signals;

[0083] S2, performing adaptive white noise complete empirical mode decomposition on the collected original signal to obtain a finite number of IMF components;

[0084] S3, calculating the correlation coefficient between each IMF component and the original signal, selecting the IMF component whose correlation coefficient is greater than the threshold τ, calculating the fuzzy entropy of the selected IMF component, and constructing a feature vector;

[0085] S4, taking the feature vector as a data sample, dividing it into a training set, a validation set and a test set;

[0086] S5. Train the SVM model using the training set, and iteratively optimize the parameters of the SVM model based on the improved Hippo algorithm according to the classification error rate of the validation set on the trained SVM model to obtain the optimal parameters and the optimized SVM model;

[0087] S6. Input the test set into the optimized SVM model, predict the valve state, calculate the classification accuracy, and evaluate the model performance. Fine-tune the model according to the evaluation results to obtain a valve leakage diagnosis model.

[0088] S7. Obtain a new acoustic emission signal, decompose and extract features of the signal, and input it into a valve leakage diagnosis model to obtain a diagnosis result.

[0089] See also Figure 2The technical route of the present invention includes: 1) collecting acoustic emission signals under different pressures and different valve opening conditions, that is, original signals; 2) using CEEMDAN algorithm to decompose the original signal to obtain a finite number of IMF components; 3) calculating the correlation coefficient between each IMF component and the original signal, that is, similarity, using Pearson correlation coefficient, setting a similarity threshold τ, taking the IMF components greater than the threshold as selected IMF components, calculating the fuzzy entropy of the selected IMF components, and using the fuzzy entropy of these IMF components as extracted features to construct a feature vector; 4) using the feature vector as a data sample, dividing the training set, the validation set, and the test set according to a certain ratio; 5) selecting the classification model as the SVM model, using the radial basis function as the kernel function, selecting the parameters to be optimized in the SVM model as the penalty parameter c and the kernel parameter g, that is, the dimension in the problem space of the optimization algorithm is set to 2, and using the improved Hippo algorithm to iteratively optimize the parameters to be optimized of the SVM model. During the optimization, each The SVM model corresponding to the group optimization parameters is trained on the training set, and then the fitness value is calculated on the validation set. The optimal individual is determined according to the fitness descending order, and the position of the hippopotamus individual is updated according to the designed nonlinear adaptive weight factor and position update strategy to update the hippopotamus population. The optimization parameters to be optimized are iteratively optimized until the iteration stop condition is reached. At this time, the optimal solution is output, and the SVM model corresponding to the optimal solution is the optimized model; 6) The test set is input into the optimized model for final tuning, the classification accuracy of the optimized model on the test set is calculated, the model performance is evaluated, and the model is fine-tuned if necessary, to obtain the final SVM model, which is used as the valve leakage diagnosis model; 7) Thereafter, the acoustic emission signal under the new pressure and valve opening conditions is obtained, the CEEMDAN algorithm is used to decompose the signal, and the fuzzy entropy of the selected IMF component is calculated to obtain the eigenvector of the signal, and the eigenvector is input into the valve leakage diagnosis model to obtain the diagnosis result.

[0090] Specifically, in one embodiment of the present invention, the collected acoustic emission signals are mainly fault signals, so as to facilitate the training and optimization of subsequent models.

[0091] Specifically, in one embodiment of the present invention, step S2 includes:

[0092] S21. Add standard normally distributed Gaussian white noise to the original signal x(t) to construct the sequence to be decomposed x i (t):

[0093] x i (t) = x(t) + εδ i (t)

[0094] Where ε is the Gaussian white noise weight coefficient, δ i (t) is the Gaussian white noise generated during the i-th processing;

[0095] S22, treat the decomposition sequence x i (t) performs EMD decomposition, and takes the average value of the first-order modal component IMF1 obtained by decomposition as the first-order IMF component IMF1(t) of the complete empirical mode decomposition of adaptive white noise:

[0096]

[0097] r1(t)=x(t)-IMF1(t)

[0098] Where IMF1(t) represents the first modal component obtained by the complete empirical mode decomposition of adaptive white noise; r1(t) represents the residual signal after the first decomposition;

[0099] S23, after adding a specific Gaussian white noise to the j-th order residual signal obtained after decomposition, continue to perform EMD decomposition to obtain a new IMF component and residual signal:

[0100]

[0101] r j (t) = r j-1 (t)-IMF j (t)

[0102] In the formula, IMF j (t) represents the j-th order modal component obtained by complete empirical mode decomposition of adaptive white noise; E j-1 () represents the j-1th order IMF component after EMD decomposition of the sequence; ε j-1 represents the weight coefficient of the noise added to the complete empirical mode of adaptive white noise at the j-1th iteration; r j (t) represents the j-th order residual signal;

[0103] S24. When the residual signal decomposed by the EMD becomes a monotonic signal or meets other preset conditions, the iteration stops, and the adaptive white noise complete empirical mode algorithm decomposition ends.

[0104] The CEEMDAN algorithm is an improvement on the traditional EMD (Empirical Mode Decomposition) method. It effectively solves the modal aliasing problem in the traditional EMD method by introducing adaptive white noise and multiple ensemble averaging strategies. CEEMDAN not only retains the time-frequency local characteristics of the signal, but also improves the accuracy and reliability of signal decomposition. The CEEMDAN algorithm reduces modal aliasing by adding white noise of different intensities. Multiple decompositions are averaged to reduce the impact of noise and improve the stability of decomposition. The IMF components are gradually extracted to ensure the physical meaning of each component.

[0105] Through CEEMDAN decomposition, meaningful IMF components can be extracted from complex valve leakage signals, laying the foundation for subsequent feature extraction and fault diagnosis. This method is particularly suitable for processing nonlinear and non-stationary fault signals such as valve leakage, and can effectively capture subtle changes in the signal and improve the accuracy and reliability of fault diagnosis.

[0106] Specifically, in one embodiment of the present invention, step S3 includes:

[0107] For each IMF component, calculate the Pearson correlation coefficient between it and the original signal. The calculation formula of the Pearson correlation coefficient is as follows:

[0108]

[0109] Among them, ρ X,Y is the Pearson correlation coefficient between X and Y, cov(X,Y) is the covariance of X and Y, σ X , σ Y is the standard deviation of X and Y, E represents the expected value, and v X , μ Y are the means of X and Y respectively.

[0110] A threshold τ is set and the IMF components with correlation coefficients greater than τ are selected. This step can filter out the IMF components most relevant to the original signal and remove the components that are irrelevant or have large noise components.

[0111] For the selected IMF component, calculate its fuzzy entropy. Fuzzy entropy is an indicator to measure the complexity of time series, which is particularly effective for nonlinear and non-stationary signals. The calculation of fuzzy entropy involves the following steps: a) converting time series data into fuzzy sets; b) for each data point in the time series, calculate its fuzzy similarity with other data points; c) based on the similarity of fuzzy vectors, define fuzzy entropy.

[0112] Fuzzy entropy is defined as:

[0113]

[0114] In the formula, b is the embedding dimension, a is the data length, R is the similarity discrimination threshold, Φ b (R) is the proportion of template matching. The larger the value of fuzzy entropy, the higher the probability of generating new patterns in the sequence, reflecting the greater the complexity of the sequence.

[0115] The calculated fuzzy entropy values ​​are combined into a feature vector. The fuzzy entropy value of each IMF component is used as a dimension of the feature vector.

[0116] In this embodiment, fuzzy entropy is used to extract the features of the IMF components obtained after CEEMDAN decomposition. By calculating the fuzzy entropy of each IMF component, a vector that can effectively characterize the valve leakage fault features can be constructed.

[0117] After obtaining the feature vector, the feature vector is used as a data sample and divided into a training set, a validation set, and a test set. The training set and the validation set are used to train and optimize the SVM model.

[0118] As a powerful binary classification model, the core of support vector machine (SVM) is to build a linear classifier in feature space. The uniqueness of this classifier lies in its principle of pursuing the maximum interval, which is significantly different from the traditional perceptron method. In order to achieve wider applicability, SVM introduces the kernel technique. This innovation enables SVM to transcend linear boundaries and become a nonlinear classifier in essence, effectively handling complex data distribution.

[0119] SVM uses kernel function to perform nonlinear mapping of original data to solve multi-classification problems in high-dimensional space. The present invention uses radial basis function as kernel function.

[0120] The performance of support vector machine (SVM) depends on the choice of penalty parameter c and kernel parameter g. In order to improve the classification performance of SVM, it is necessary to find the optimal parameter combination (c, g).

[0121] Specifically, the present invention uses an improved Hippo algorithm to optimize the parameters of the SVM model. During the optimization process, each set of parameters is trained on the training set, and then the fitness value is calculated on the validation set, so as to iteratively optimize the SVM model. In this embodiment, step S5 includes:

[0122] S51, select the penalty parameter c and kernel parameter g of the SVM model as the target optimization parameters, and logarithmically encode the penalty parameter c and kernel parameter g into individual positions in the Hippo algorithm:

[0123] X i =(log2c i ,log2g i )

[0124] Using a logarithmic scale helps to better explore the parameter space, since SVM parameters usually have better performance at different orders of magnitude.

[0125] S52, setting the population size N, the maximum number of iterations T and the upper and lower boundary parameters, and initializing the population using the combined chaotic map.

[0126] This embodiment improves the mapping and combines Logistic mapping and Tent mapping to initialize the population, so as to increase the diversity and uniformity of the initial population.

[0127] Specifically, using the combined chaotic map to initialize the population includes:

[0128] S521. Design a hybrid mapping function, and determine the mapping to be used by conditional judgment. The hybrid mapping function is as follows:

[0129]

[0130] Where r is a random number between [0,1], and μ is the control parameter of the Logistic mapping;

[0131] S522, determining the dimension m of the problem space, generating an initial value x0 for each dimension, and for each dimension, repeatedly applying the above hybrid mapping function multiple times to generate a chaotic sequence, and taking the final value as the initialization chaotic sequence value of the dimension;

[0132] S523, mapping the generated chaotic sequence value to the upper and lower bounds of the hippo population search space, the position x of each hippo individual i Expressed as: X i =(x i1 ,x i2 ,…,x im ), let z be the generated chaotic sequence value, then the initial position of the hippopotamus individual is calculated as follows:

[0133] X i :x ij =ll j +z·(ul j -ll j ), i=1,2,...,N; j=1,2,...,m

[0134] In the formula, x ij is the position of the i-th hippopotamus in the j-th dimension, ll j and j are the lower and upper bounds of the j-th dimension respectively;

[0135] S524. Repeat steps S522-S523 for each hippopotamus individual and each dimension in the population until the entire population is initialized.

[0136] Specifically, m represents the dimension, and each dimension represents an independent variable in the problem space. For IHO, the position X of each hippopotamus individual i Expressed as: X i =(x i1 ,x i2 ,…,x im). During the initialization process, an initial value needs to be generated for each dimension. This means that for each hippopotamus individual, m initial values ​​need to be generated, each value corresponding to a dimension. In the hybrid mapping initialization method provided by the present invention, a hybrid mapping function is independently applied to each dimension to generate an initial value. This ensures that a good initial distribution can be obtained in each dimension.

[0137] S53, for each parameter combination (c i ,g i ), use the training set to train the SVM model, and calculate the fitness value on the validation set.

[0138] Specifically, to avoid overfitting, the fitness function not only considers the classification accuracy, but also the complexity of the model (the number of support vectors). The fitness function is defined as:

[0139]

[0140] Where E is the classification error rate on the validation set, n SV is the number of support vectors, M is the total number of data samples, and w is the weight coefficient, which balances the classification error rate and model complexity.

[0141] S54. Iteratively update the population according to the improved Hippo algorithm. In each iteration, a local search is performed on the current optimal solution. When the iteration stop condition is reached, the optimal solution, that is, the optimal parameter combination, is output.

[0142] The Hippopotamus algorithm HO is a swarm intelligence optimization algorithm inspired by the social behavior of hippos. The algorithm simulates the position update of hippos in rivers or ponds, and their defense and escape strategies against predators to find the optimal solution to the optimization problem. The complete optimization process of the Hippopotamus algorithm is as follows:

[0143] 1) Population initialization

[0144] The hippo population is mathematically represented by a matrix. Each hippo's position corresponds to a candidate solution, and its position update represents the value of the decision variable.

[0145] 2) Update of hippopotamus’ position in the river or pond (exploration phase)

[0146] The structure of hippopotamus groups is complex. They are composed of females and young, multiple males and a dominant male. The dominant male guards the territory, and the females are protected by the male. Adult males may be expelled after growing up and need to establish their own family, attracting females or competing for the top position. The following formula describes the position of male hippopotamus members in the group.

[0147]

[0148] in, represents the position of the male hippo, Dhippo represents the position of the dominant hippo, y1 is a random number in the range 0-1, and I1 is an integer between 1 and 2.

[0149]

[0150] Where h1 and h2 are numbers or vectors randomly selected from the formula of h, I1 and I2 are integers between 1 and 2, Q1 and Q2 are random integers 0 or 1, is a random vector between 0 and 1, r5, r6, r7 are random numbers between 0 and 1, MG i is the average of some randomly selected hippos.

[0151] 3) Hippo defends against predators (Exploration)

[0152] 3) Hippo defends against predators (exploration)

[0153] When faced with a threat, a hippopotamus's defence mechanism includes swerving to approach the predator and emitting a deafening roar as a warning signal intended to drive the predator away.

[0154]

[0155] in represents a random vector from 0 to 1. Predator represents the position of the predator, represents the distance from the i-th hippopotamus to the predator. During this period, the hippopotamus adopted a factors to protect themselves from predators. Less than F i , when the predator is very close to the hippopotamus, the hippopotamus will quickly turn towards the predator and move towards it, causing it to retreat. Greater than F i , it means that the predator is far away from the hippopotamus.

[0156]

[0157] in, is a random vector with Levy distribution, Levy(θ) is its calculation formula; w and v are random numbers between 0 and 1, f is a uniform random number between 2 and 4, and c is a random number between 1 and 1.5. is a random number between 2 and 3. g represents a random number between -1 and 1. is a random vector with dimension 1*m.

[0158] 4) When hippos encounter a group of predators or are unable to successfully drive away predators through defensive means, they will adopt an escape strategy and choose to leave the current area. At this time, the position of the hippos is updated.

[0159] The present invention takes into account that the balance between global search and local development of the Hippo algorithm may be poor, and it is easy to fall into the local optimum, resulting in insufficient search ability. The convergence speed may be slow during the iteration process, especially in high-dimensional complex optimization problems, affecting the efficiency of the algorithm. The initial population quality of the algorithm has a great influence on the final optimization result. If the initialization is improper, it may cause the algorithm performance to decline.

[0160] Therefore, the above-mentioned combined chaotic mapping is used to solve the problem of initial population quality. In addition, an innovative nonlinear adaptive weight factor ω is designed to dynamically adjust the exploration and utilization balance of the algorithm to improve the comprehensiveness and depth of the search. The current optimal individual is retained in each iteration to ensure that the optimal solution is not replaced by a new solution, thereby improving the stability of the algorithm and the search efficiency.

[0161] By applying this nonlinear adaptive weight factor to the Hippopotamus position update formula, the search process can be guided more flexibly, so that the algorithm can achieve a better balance between exploring unknown areas and refining the search for the optimal solution. This improvement not only broadens the search range, but also significantly enhances the algorithm's optimization ability, providing a more effective and powerful tool for solving practical optimization problems. The Hippopotamus position update formula and the weight factor ω are expressed as follows:

[0162]

[0163]

[0164] In the formula, T is the maximum number of iterations, t is the current number of iterations, and are two different individuals randomly selected from the population, β is the learning factor, is the individual's current location, Update the location for an individual, is the best individual in the current iteration. In the early stage of the algorithm iteration, the designed weight factor ω has a large value and a large slope. This strategy aims to quickly expand the search range of the male hippo population and enhance the global exploration ability of the algorithm, thereby covering the solution space more widely and increasing the chance of discovering potential high-quality solutions. As the iteration process deepens and enters the later stage, the slope of the weight factor ω is small, and the change of ω gradually slows down. This adjustment helps the algorithm to conduct refined searches in the explored better areas, reduce blind exploration, and increase the possibility of jumping out of the local optimal trap. This nonlinear weight factor strategy not only improves the accuracy of the algorithm, but also helps to find a more suitable and optimized solution location in the end.

[0165] In addition, some individuals are randomly mutated to increase population diversity and prevent premature convergence.

[0166] The mutation operation is as follows:

[0167] With a certain probability, i To mutate:

[0168]

[0169] Among them, rand is a random number between [0,1], X min and X max The value range of the variable.

[0170] When using IHO to optimize SVM, the problem of insufficient parameter optimization accuracy must also be considered, that is, the optimal parameter combination cannot be fully searched, which affects the classification performance. Therefore, in each iteration, a local search is performed on the current optimal solution to improve accuracy:

[0171]

[0172] Where δ is a small step size and N(0,1) is the standard normal distribution.

[0173] In summary, in this embodiment, the specific process of step S54 is as follows:

[0174] S541, based on the generated initialization population X={X1,X2,…,X N}, calculate each individual X in the population i The fitness value F i ;

[0175] S542, according to the size of the fitness value, record the current best individual X best and its fitness F best ;

[0176] S543, for the current optimal individual X best Perform local search, obtain local candidate solutions, calculate the fitness value of the local candidate solutions, and if it is greater than F best , then the local candidate solution and its fitness value are replaced by the current optimal individual X best and its fitness F best ;

[0177] S544. For each individual X i , updates the position according to a nonlinear adaptive weight strategy;

[0178] S545, using the preset probability to i Make a mutation;

[0179] S546. Calculate the fitness value of the new individual. If it is better than the current optimal individual, update X best and F best ;

[0180] S547. If the maximum number of iterations is reached or the accuracy requirement is met, the optimal solution X is output. best , otherwise, return to step S542 and iterate again.

[0181] S55. Perform antilogarithmic transformation on the optimal parameter combination to obtain optimal parameters c and g.

[0182] Specifically, in one embodiment of the present invention, after iteratively training and optimizing the SVM model based on the improved Hippo algorithm using the training set and the validation set, the optimal parameters are obtained, and the model corresponding to the optimal parameters is the optimized SVM model. Afterwards, the pre-reserved test set data is input into the optimized SVM model. The specific steps are as follows:

[0183] The feature vector of the test set is input into the optimized SVM model.

[0184] The model is used to make predictions for each test sample to obtain the predicted valve state.

[0185] Compare the predictions with the true labels of the test set.

[0186] After obtaining the prediction results, multiple performance indicators need to be calculated to comprehensively evaluate the performance of the model. The main performance indicators include:

[0187] Classification accuracy: the number of correctly classified samples divided by the total number of samples. Confusion matrix: a detailed display of the classification between categories, which helps to identify which categories the model performs better or worse on. Precision, recall, and F1 score: these indicators can more carefully reflect the performance of the model in each category. ROC curve and AUC value: used to evaluate the classification ability and robustness of the model.

[0188] Based on the calculated performance indicators, the overall performance of the model needs to be comprehensively evaluated. The evaluation results include the performance of the model under different types of valve leakage conditions and whether the model is overfitting or underfitting. If the evaluation results are lacking in one aspect, further fine-tuning can be performed, and the final SVM model, that is, the trained model, can be trained again using all data samples to obtain a valve leakage diagnosis model. The model is packaged into a deployable form, and new acoustic emission signals are subsequently obtained. After the signals are decomposed and feature extracted, they are input into the valve leakage diagnosis model to obtain the diagnosis results.

[0189] In the present invention, the classification decision function of the SVM model is:

[0190]

[0191] In the formula, α i is the Lagrange multiplier, y i is the category label of the data sample, K(χ i , χ) is the kernel function, and the present invention adopts the radial basis kernel function (RBF):

[0192]

[0193] b is the bias term.

[0194] Specifically, the present invention experimentally verifies the proposed improved Hippo algorithm optimized SVM model (IHO-SVM):

[0195] By simulating different degrees of internal leakage of gas tank valves, acoustic emission signals are collected under multiple air pressure and valve opening conditions for subsequent classification experiments. The experimental platform is divided into two parts: one is the gas tank valve internal leakage experimental platform, which integrates a compressor, gas tank, adjustable valve and pressure gauge to ensure that various internal leakage scenarios can be simulated stably and accurately; the other is the acoustic emission acquisition system, including acoustic emission sensors, preamplifiers, high-speed data acquisition cards and acoustic emission signal acquisition software, which performs real-time and high-speed acquisition of acoustic emission signals.

[0196] In the experiment, the air tank was first pressurized by the compressor. After the pressure gauge indicated a preset value of 0.5 bar, the valve opening was gradually adjusted, and the levels were set to 1 / 20 (slight internal leakage), 3 / 40 (a small amount of internal leakage), 1 / 10 (medium internal leakage) and 1 / 8 (serious leakage) to simulate different degrees of internal leakage. At the same time, the acoustic emission acquisition system remained in operation to capture and record the acoustic emission signals under each working condition in real time. Subsequently, the air pressure in the air tank was increased to another preset value of 1 bar, and the above valve opening adjustment and signal acquisition steps were repeated to fully cover the internal leakage under different air pressure conditions. At the same time, it was ensured that the acoustic emission signals under each working condition (i.e., different air pressure and valve opening combinations) were accurately recorded. The classification information of valve internal leakage faults is shown in Table 1. The 8 faults in Table 1 each contain 100 sets of data, and each set of data has 5000 sampling data points.

[0197] Table 1 Fault classification and labels

[0198] Fault type Label 0.5bar slight internal leakage 0 0.5bar small amount of internal leakage 1 0.5 bar medium internal leakage 2 0.5bar severe leakage 3 1bar slight internal leakage 4 1bar small amount of internal leakage 5 1 bar medium internal leakage 6 1bar serious leakage 7

[0199] The CEEMDAN algorithm is used to decompose the original signal under 8 states to obtain the IMF under each state. Since the number of IMFs decomposed under different states is different, the Pearson correlation coefficient between the original signal and the IMFs of each order obtained by decomposition is calculated, and the IMFs with the top 5 correlation coefficients are taken.

[0200] The fuzzy entropy of the 5th-order IMF of each fault type constitutes a 100×5 fault feature matrix and is input into the IHO-SVM model. 60% of the samples of each fault type are taken as training samples, 20% as validation sets, and 20% as test samples. The hippo population size is set to 20 and the maximum number of iterations is 50. At the same time, in order to verify the superiority of IHO-SVM in valve fault diagnosis and classification, it is compared with HO-SVM, PSO-SVM and GWO-SVM models. In the PSO-SVM model, c1=2 and c2=2.

[0201] The accuracy of each model is as follows:

[0202] IHO-SVM accuracy: 98.74%; HO-SVM accuracy: 96.85%; PSO-SVM accuracy: 96.22%; GWO-SVM accuracy: 96.22%; traditional SVM accuracy: 91.19%.

[0203] By comparing the results of 10 independent runs, IHO-SVM showed higher accuracy and stability.

[0204] In summary, the valve leakage diagnosis method based on improved IHO-SVM proposed in the present invention shows superiority in accuracy and stability. Through CEEMDAN decomposition and fuzzy entropy feature extraction, combined with IHO optimized SVM classifier, this method can effectively identify valve leakage under different pressures and openings, providing a reliable solution for fault diagnosis in industrial practice.

[0205] In addition, if Figure 3 As shown, the present invention also provides a valve leakage diagnosis system based on an improved Hippo algorithm to optimize SVM, the system is used to execute any of the above methods, the system comprising:

[0206] The data acquisition module is used to collect acoustic emission signals under different pressures and valve opening conditions; and to perform preliminary digital processing and storage on the collected signals.

[0207] The data processing module is used to perform adaptive white noise complete empirical mode decomposition on the collected signal to obtain a finite number of IMF components to obtain a more effective signal representation. The specific steps include: adding Gaussian white noise of standard normal distribution to the original signal to construct a sequence to be decomposed; performing EMD decomposition on the sequence to be decomposed to obtain the first-order modal component; iteratively performing EMD decomposition, adding specific Gaussian white noise to the residual signal each time; when the residual signal becomes a monotonic signal or meets other preset conditions, the decomposition process ends; and finally a series of IMF (intrinsic mode function) components are obtained, which can better represent the time-frequency characteristics of the signal.

[0208] The feature extraction module is used to calculate the correlation coefficient between the IMF component and the original signal, select the IMF component with a correlation coefficient greater than a preset threshold, and calculate its fuzzy entropy to construct a feature vector; the calculation of the fuzzy entropy takes into account the complexity and uncertainty of the signal, and can better capture the nonlinear and non-stationary characteristics of the valve leakage signal.

[0209] The model optimization module is used to train the SVM model and optimize the parameters of the SVM model using the improved Hippo algorithm to obtain a valve leakage diagnosis model;

[0210] The feature vector is divided into training set, validation set and test set. The training set is used to perform preliminary training on the SVM model. The penalty parameter c and kernel parameter g of the SVM model are optimized using the improved Hippo algorithm (IHO). The optimization process includes: parameter encoding: logarithmically encode c and g; population initialization: use a mixed mapping function to initialize the population; fitness calculation: consider the classification error rate and model complexity; population update: use a nonlinear adaptive weight strategy to update individual positions; local search: perform local search on the current optimal solution to improve accuracy; mutation operation: increase population diversity to prevent premature convergence; iterative optimization until the maximum number of iterations is reached or the accuracy requirements are met. Output the optimal parameter combination to obtain the optimized SVM model. Finally, use the test set to fine-tune the model to obtain the valve leakage diagnosis model.

[0211] The fault diagnosis module is used to classify and predict the feature vector using the valve leakage diagnosis model to realize valve leakage fault diagnosis.

[0212] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A valve leakage diagnosis method based on improved Hippo algorithm to optimize SVM, characterized in that: include: S1, collecting acoustic emission signals under different pressures and valve opening conditions as original signals; S2, performing adaptive white noise complete empirical mode decomposition on the collected original signal to obtain a finite number of IMF components; S3, calculating the correlation coefficient between each IMF component and the original signal, selecting the IMF component whose correlation coefficient is greater than a preset threshold, calculating the fuzzy entropy of the selected IMF component, and constructing a feature vector; S4, taking the feature vector as a data sample, dividing it into a training set, a validation set and a test set; S5. Train the SVM model using the training set, and iteratively optimize the parameters of the SVM model based on the improved Hippo algorithm according to the classification error rate of the validation set on the trained SVM model to obtain the optimal parameters and the optimized SVM model; S6. Input the test set into the optimized SVM model, predict the valve state, calculate the classification accuracy, and evaluate the model performance. Fine-tune the model according to the evaluation results to obtain a valve leakage diagnosis model. S7. Obtain a new acoustic emission signal, decompose and extract features of the signal, and input it into a valve leakage diagnosis model to obtain a diagnosis result.

2. The valve leakage diagnosis method based on the improved Hippo algorithm optimized SVM as claimed in claim 1, characterized in that: Step S5 includes: S51, selecting the penalty parameter c and the kernel parameter g of the SVM model as the target optimization parameters, and logarithmically encoding the penalty parameter c and the kernel parameter g into individual positions in the Hippo algorithm; S52, setting the population size N, the maximum number of iterations T and the upper and lower boundary parameters, and initializing the population using a combined chaotic map; S53, for each parameter combination (c i ,g i ), use the training set to train the SVM model and calculate the fitness value on the validation set; S54, iteratively updating the population according to the improved Hippo algorithm, in each iteration, performing a local search for the current optimal solution, and outputting the optimal solution, i.e., the optimal parameter combination, when the iteration stop condition is reached; S55. Perform antilogarithmic transformation on the optimal parameter combination to obtain optimal parameters c and g.

3. The valve leakage diagnosis method based on the improved Hippo algorithm optimized SVM as claimed in claim 2, characterized in that: In step S52, the combined chaotic map includes a Logistic map and a Tent map, and initializing the population using the combined chaotic map includes: S521. Design a hybrid mapping function, and determine the mapping to be used by conditional judgment. The hybrid mapping function is as follows: Where r is a random number between [0,1], and μ is the control parameter of the Logistic mapping; S522, determining the dimension m of the problem space, generating an initial value x0 for each dimension, and for each dimension, repeatedly applying the above hybrid mapping function multiple times to generate a chaotic sequence, and taking the final value as the initialization chaotic sequence value of the dimension; S523, mapping the generated chaotic sequence value to the upper and lower bounds of the hippo population search space, the position x of each hippo individual i Expressed as: X i =(x i1 ,x i2 ,…,x im ), let z be the generated chaotic sequence value, then the initial position of the hippopotamus individual is calculated as follows: X i :x ij =ll j +z·(ul j -ll j ),i=1,2,…,N;j=1,2,…,m In the formula, x ij is the position of the i-th hippopotamus in the j-th dimension, ll j and j are the lower and upper bounds of the j-th dimension respectively; S524. Repeat steps S522-S523 for each hippopotamus individual and each dimension in the population until the entire population is initialized.

4. The valve leakage diagnosis method based on the improved Hippo algorithm optimized SVM as claimed in claim 2, characterized in that: The calculation formula of fitness value is: Where E is the classification error rate on the validation set, n SV is the number of support vectors, M is the total number of data samples, and w is the weight coefficient, which balances the classification error rate and model complexity.

5. The valve leakage diagnosis method based on the improved Hippo algorithm optimized SVM as claimed in claim 4, characterized in that: Step S54 includes: S541, based on the generated initialization population X={X1,X2,…,X N }, calculate each individual X in the population i The fitness value F i ; S542, according to the size of the fitness value, record the current best individual X best and its fitness F best ; S543, for the current optimal individual X best Perform local search, obtain local candidate solutions, calculate the fitness value of the local candidate solutions, and if it is greater than F best , then the local candidate solution and its fitness value are replaced by the current optimal individual X best and its fitness F best ; S544. For each individual X i , updates the position according to a nonlinear adaptive weight strategy; S545, using the preset probability to i Make mutations; S546. Calculate the fitness value of the new individual. If it is better than the current optimal individual, update X best and F best ; S547. If the maximum number of iterations is reached or the accuracy requirement is met, the optimal solution X is output. best , otherwise, return to step S542 and iterate again.

6. The valve leakage diagnosis method based on the improved Hippo algorithm optimized SVM as claimed in claim 5, characterized in that: In step S544, the nonlinear adaptive weight strategy includes a nonlinear adaptive weight factor ω, and the position of the hippopotamus individual is updated according to the nonlinear adaptive weight factor ω. The hippopotamus individual position update formula and the weight factor ω are expressed as follows: In the formula, T is the maximum number of iterations, t is the current number of iterations, and are two different individuals randomly selected from the population, β is the learning factor, is the individual's current location, Update the location for an individual, is the best individual in the current iteration.

7. The valve leakage diagnosis method based on the improved Hippo algorithm optimized SVM as claimed in claim 5, characterized in that: In step S545, for individual X i The formula for mutation is as follows: Among them, rand is a random number between [0,1], X min and X max The value range of the variable.

8. The valve leakage diagnosis method based on the improved Hippo algorithm optimized SVM as claimed in claim 5, characterized in that: In step S543, performing local search on the current optimal individual includes: Where δ is a small step size and N(0,1) is the standard normal distribution.

9. The valve leakage diagnosis method based on the improved Hippo algorithm optimized SVM as claimed in claim 1, characterized in that: Step S2 includes: S21. Add standard normally distributed Gaussian white noise to the original signal x(t) to construct the sequence to be decomposed x i (t): x i (t)=x(t)+εδ i (t) Where ε is the Gaussian white noise weight coefficient, δ i (t) is the Gaussian white noise generated during the i-th processing; S22, treat the decomposition sequence x i (t) performs EMD decomposition, and takes the average value of the first-order modal component IMF1 obtained by decomposition as the first-order IMF component IMF1(t) of the complete empirical mode decomposition of adaptive white noise: r1(t)=x(t)-IMF1(t) Where IMF1(t) represents the first modal component obtained by the complete empirical mode decomposition of adaptive white noise; r1(t) represents the residual signal after the first decomposition; S23, after adding a specific Gaussian white noise to the j-th order residual signal obtained after decomposition, continue to perform EMD decomposition to obtain a new IMF component and residual signal: r j (t)=r j-1 (t)-IMF j (t) In the formula, IMF j (t) represents the j-th order modal component obtained by complete empirical mode decomposition of adaptive white noise; E j-1 () represents the j-1th order IMF component after EMD decomposition of the sequence; ε j-1 represents the weight coefficient of the noise added to the complete empirical mode of adaptive white noise at the j-1th iteration; r j (t) represents the j-th order residual signal; S24. When the residual signal decomposed by the EMD becomes a monotonic signal or meets other preset conditions, the iteration stops, and the adaptive white noise complete empirical mode algorithm decomposition ends.

10. The valve leakage diagnosis system based on the improved Hippo algorithm to optimize SVM is characterized by: The system is used to perform the method according to any one of claims 1 to 9, and the system comprises: Data acquisition module, used to collect acoustic emission signals under different pressure and valve opening conditions; The data processing module is used to perform adaptive white noise complete empirical mode decomposition on the collected signal to obtain a finite number of IMF components; The feature extraction module is used to calculate the correlation coefficient between the IMF component and the original signal, select the IMF component whose correlation coefficient is greater than a preset threshold, and calculate its fuzzy entropy to construct a feature vector; The model optimization module is used to train the SVM model and optimize the parameters of the SVM model using the improved Hippo algorithm to obtain a valve leakage diagnosis model; The fault diagnosis module is used to classify and predict the feature vector using the valve leakage diagnosis model to realize valve leakage fault diagnosis.

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