Slurry pump fault detection and service life prediction system and method based on big data

Abstract

The invention relates to the technical field of data analysis, and discloses a slurry pump fault detection and service life prediction system and method based on big data. According to the method, firstly, a slurry pump vibration signal is obtained, an abnormal vibration signal is detected and deleted, and then noise reduction processing is conducted on the slurry pump vibration signal; secondly, after the denoised slurry pump vibration signal is subjected to segmented aggregation approximation and Grubby angle field processing, a slurry pump vibration signal image is generated, the characteristics of the slurry pump vibration signal image are extracted, a convolutional neural network is trained, convolutional neural network parameters are optimized based on an electric eel foraging optimization algorithm, a convolutional neural network recognition model is obtained, and a recognition result of the convolutional neural network is obtained; fault detection of the slurry pump is realized; and decomposing the IMF component of the slurry pump vibration signal, training the LSTM neural network to obtain an LSTM neural network prediction model, and outputting a slurry pump life prediction value. The purposes of fault detection and service life prediction are achieved by processing the vibration signal of the slurry pump, and the method is accurate and objective.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a slurry pump fault detection and life prediction system and method based on big data. Background Art

[0002] Traditional slurry pump fault detection judges the running sound and previous service life of the slurry pump based on experience, which has problems such as large errors and slow detection speed; at the same time, the traditional slurry pump life prediction method judges by the degree of wear of the slurry pump, and the hardness of the pump conveying medium and solid materials is often difficult to measure. Different slurry pumps are made of different materials, which inevitably leads to errors in the slurry pump life prediction results; and slurry pump fault detection and life prediction do not use high-tech technologies such as neural networks, which brings inconvenience to data analysis. Summary of the invention

[0003] In response to the problems in the related technology, the present invention provides a slurry pump fault detection and life prediction system and method based on big data to overcome the above-mentioned technical problems existing in the existing related technology.

[0004] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0005] The present invention is a method for fault detection and life prediction of a slurry pump based on big data, comprising the following steps:

[0006] S1. Collect the vibration signal of the slurry pump in the running state to obtain the vibration signal of the slurry pump, detect the abnormal vibration signal of the slurry pump vibration signal based on the outlier factor algorithm, and delete the abnormal vibration signal; then perform singular value decomposition and reconstruction signal processing to complete the slurry pump vibration signal denoising and obtain the slurry pump vibration signal after denoising;

[0007] S2, the slurry pump vibration signal after noise reduction is composed of a slurry pump vibration signal set after noise reduction, and then the segmented aggregation approximation and Gram angle field processing are performed to generate a slurry pump vibration signal image, and then feature extraction is performed based on the Gauss-Markov random field to obtain a 12-dimensional feature vector;

[0008] S3, collecting new slurry pump vibration signals to obtain new 12-dimensional feature vectors, using generators and discriminators to perform augmentation processing on the new 12-dimensional feature vectors, and then training the convolutional neural network, and optimizing the convolutional neural network parameters based on the electric eel foraging optimization algorithm to obtain a convolutional neural network recognition model to achieve slurry pump fault detection;

[0009] S4. Obtain the original vibration signal of the slurry pump, obtain the IMF component of the original vibration signal of the slurry pump based on the empirical mode decomposition method, and train the LSTM neural network to obtain the LSTM neural network prediction model. Combine the slurry pump vibration signal to obtain the predicted value of the service life of the slurry pump, and complete the slurry pump life prediction.

[0010] The invention firstly collects the vibration signal of the slurry pump under the running state to obtain the slurry pump vibration signal, performs abnormal vibration signal detection on the slurry pump vibration signal, deletes the abnormal vibration signal, and then uses singular value decomposition and reconstruction signal to retain the effective information in the slurry pump vibration signal and realize the noise reduction of the slurry pump vibration signal; secondly, the slurry pump vibration signal after noise reduction is processed by piecewise aggregation approximation and Gram angle field to generate a slurry pump vibration signal image, and by setting the time series, the calculation complexity can be reduced and the signal quality can be improved, and then feature extraction is performed, and the extracted 12-dimensional feature vector effectively reduces redundancy and improves the accuracy of subsequent data processing; and then the new slurry pump vibration is used. The convolutional neural network is trained based on the signal extraction features, and the generator and discriminator are introduced for augmentation processing to minimize the error between the generated samples and the real samples. The convolutional neural network parameters are optimized based on the electric eel foraging optimization algorithm to obtain the convolutional neural network recognition model, which can determine whether the slurry pump is faulty. The electric eel foraging optimization algorithm finds the optimal solution to the problem by simulating the hunting behavior of electric eels, and has strong optimization ability and fast convergence speed. The IMF component of the original vibration signal of the slurry pump is then extracted, and the LSTM neural network is trained to obtain the LSTM neural network prediction model, and finally the predicted value of the service life of the slurry pump is output. The LSTM neural network has good prediction accuracy and can realize the life prediction of the slurry pump.

[0011] Preferably, the S1 comprises the following steps:

[0012] S11, when the slurry pump is in operation, a vibration sensor is placed at the center of the bearing of the slurry pump to collect the bearing vibration signal of the slurry pump, the bearing vibration signal of the slurry pump is converted into a 485 signal, and the 485 signal is regarded as the vibration signal of the slurry pump; a slurry pump vibration signal collection time interval is set, and the slurry pump vibration signal is repeatedly collected at an α sampling frequency to obtain a slurry pump vibration signal set, wherein the slurry pump vibration signal set includes a normal slurry pump vibration signal, a single fault slurry pump vibration signal, and a compound fault slurry pump vibration signal;

[0013] S12, setting the sliding window size to 100, performing sliding sampling in the slurry pump vibration signal set with a step size of 100, generating a slurry pump vibration signal data segments with a length of 100 in the slurry pump vibration signal set, and forming a slurry pump vibration signal data set A = {b1, b2, b3, ..., b a}, where b arepresents the ath slurry pump vibration signal data; select the ath slurry pump vibration signal data set The vibration signal data of a slurry pump is Data points, calculation The distance size of the slurry pump vibration signal data in the slurry pump vibration signal data set is obtained by arranging the distance sizes in ascending order, and the slurry pump vibration signal data corresponding to the distance less than k distance is found in the distance size set, and the slurry pump vibration signal data corresponding to the distance less than k distance is recorded as k distance Data point neighborhood;

[0014] When the slurry pump vibration signal data set contains slurry pump vibration signal data within k distance When the data point is within the neighborhood, it is recorded as data reachable, otherwise the data is unreachable; when the data is reachable, The formula for calculating the reachable data density of a data point is as follows:

[0015]

[0016] in, express The reachable data density of the data point, average represents the average value, Represents k distance The number of data points in the neighborhood of the data point, express Distance to k In the neighborhood of a data point The distance between the data points,

[0017] According to the The reachable data density of a data point is calculated The outlier factor of a data point is calculated as follows:

[0018]

[0019] in, express The outlier factor of the data point, B(i1) represents the k distance In the neighborhood of a data point The reachable data density of a data point,

[0020] Calculate the outlier factors of the slurry pump vibration signal data segments in the slurry pump vibration signal data set in turn, set the outlier threshold as ω, delete the data whose outlier factors are less than ω in the slurry pump vibration signal data segment in the slurry pump vibration signal data set, complete the deletion of abnormal vibration signal data, and obtain the abnormally deleted slurry pump vibration signal set;

[0021] S13, set the abnormal deleted slurry pump vibration signal set to A1 = {b1′, b2′, b3′, ..., b a ″}, where b a ″ represents the a′th slurry pump vibration signal, and the dimension of the slurry pump vibration signal in the abnormal deleted slurry pump vibration signal set is set to c, and the slurry pump vibration signal matrix C is generated as follows:

[0022]

[0023] Among them, b a ″ c represents the vibration signal of the a′th slurry pump with dimension c;

[0024] Set the left singular matrix C1, the right singular matrix C2 and the diagonal matrix And meet Where C2 T represents the transpose of the right singular matrix, and the column vector of the left singular matrix is ​​set as the left singular vector of the slurry pump vibration signal matrix, and the column vector of the right singular matrix is ​​set as the right singular vector of the slurry pump vibration signal matrix. At this time, the diagonal elements of the slurry pump vibration signal matrix are recorded as the singular value set A2 = {b1″, b2″, b3″, ..., b a ″ ′}, where b a ″ ′ represents the a′th singular value, completing the singular value decomposition; sorting the singular values ​​in the singular value set in descending order, setting the singular value threshold as ξ, discarding the singular values ​​in the singular value set that are less than ξ, and forming a valid singular value set with the singular values ​​in the singular value set that are greater than or equal to ξ, denoted as A3={b″′1,b″′2,b″′3,...,b″′ a″}, where b″′ a″ represents the a″th valid singular value, and the signal components corresponding to the valid singular values ​​in the valid singular value set are superimposed to obtain the reconstructed slurry pump vibration signal matrix, and the slurry pump vibration signal denoising is completed. The calculation formula of the slurry pump vibration signal after denoising is as follows:

[0025]

[0026] Among them, B″ represents the vibration signal of the slurry pump after noise reduction, represents the i3th left singular vector of the reconstructed slurry pump vibration signal matrix, represents the transpose of the i3th right singular vector of the reconstructed slurry pump vibration signal matrix, Represents the i3th valid singular value on the diagonal of a diagonal matrix, i3=1,2,3,...,a″.

[0027] The invention obtains the vibration signal of the slurry pump by collecting the vibration signal of the slurry pump under the running state, performs abnormal vibration signal detection on the slurry pump vibration signal based on the LOF algorithm, deletes the abnormal vibration signal, and then uses singular value decomposition and reconstruction signal to retain the effective information in the slurry pump vibration signal by superposition of signal components, thereby realizing noise reduction of the slurry pump vibration signal.

[0028] Preferably, S2 comprises the following steps:

[0029] S21, forming a set of slurry pump vibration signals after noise reduction according to the slurry pump vibration signals after noise reduction, denoted as A4={d1, d2, d3, ..., d e}, where d e represents the e-th noise-reduced slurry pump vibration signal, collects the time corresponding to the noise-reduced slurry pump vibration signal in the noise-reduced slurry pump vibration signal set, generates a slurry pump vibration signal time series, and divides the slurry pump vibration signal time series into a group of e′ slurry pump vibration signal time series. a slurry pump vibration signal time subsequence, and processing the slurry pump vibration signal time subsequence by a segmented clustering similarity method to obtain a processed slurry pump vibration signal time series;

[0030] S22, compress the processed slurry pump vibration signal time series to the interval [-1, 1] to obtain the compressed slurry pump vibration signal time series A5, map the compression value in the compressed slurry pump vibration signal time series into an angle χ1, set the time mapping radius to e1, and the calculation formula is as follows:

[0031] χ1=arccos(A5),

[0032] Among them, t represents the time corresponding to the processed slurry pump vibration signal time series, and δ represents the polar coordinate span coefficient;

[0033] The compressed value in the compressed slurry pump vibration signal time series is mapped to an angle χ2 again, and the Gram matrix C1=cos(χ1+χ2); in the Gram matrix, each data element represents the pixel value of a pixel point of the slurry pump vibration signal image, and the slurry pump vibration signal image is generated;

[0034] S23, extracting texture features from the slurry pump vibration signal image using a Gauss-Markov random field, the specific steps are as follows:

[0035] S231, select pixel points on the slurry pump vibration signal image to obtain a pixel point set, set represents the i4th pixel in the pixel set, represents the weight of the pixel in the symmetric Gauss-Markov neighborhood, represents Gaussian distribution noise, then the Gauss-Markov model calculation formula is as follows,

[0036]

[0037] Where D represents the Gauss-Markov model, g represents the number of pixel points, i4=1,2,3,...,g;

[0038] S232, substituting the pixel points of the slurry pump vibration signal image into the Gauss-Markov model to obtain a differential equation, and the calculation formula is as follows:

[0039]

[0040] Where D′ represents the difference equation, represents the eigenvector, C2 represents the difference equation matrix;

[0041] The difference equation error is solved using a least square error method, a 12-dimensional vector satisfying the difference equation error is selected, and the 12-dimensional vector satisfying the difference equation error is recorded as a 12-dimensional eigenvector.

[0042] The invention generates a slurry pump vibration signal image by using segmented aggregation approximation and Gram angle field processing on the noise-reduced slurry pump vibration signal. By setting the time series, the calculation complexity can be reduced and the signal quality can be improved. Feature extraction is then performed based on the Gauss-Markov random field to obtain a 12-dimensional feature vector, which can effectively reduce redundancy and improve data processing accuracy.

[0043] Preferably, S3 comprises the following steps:

[0044] S31. The deep convolutional generative adversarial network augmentation includes a generator and a discriminator. The generator is composed of a reshape layer and a deconvolution layer. The activation function is set to the ReLU function. The data is input into the deep convolutional generative adversarial network through the reshape layer. The reshape layer reshapes the input data sample and then the deconvolution layer amplifies the feature to generate sample similarity features. The discriminator is composed of a convolution layer and a fully connected layer. The input data sample and the sample similarity feature are subjected to feature extraction and dimensionality reduction processing. The sigmoid function is used as the activation function and the result is output after passing through the fully connected layer. The generator and the discriminator are alternately trained on the data samples until the Nash equilibrium is reached to obtain augmented sample data.

[0045] A new slurry pump vibration signal is collected to obtain a new slurry pump vibration signal set, and after outlier deletion and noise reduction processing are performed on the new slurry pump vibration signal set, a new slurry pump vibration signal image is generated to obtain a new 12-dimensional feature vector, and the new 12-dimensional feature vector is divided into a feature training set and a feature test set, and the feature training set is input into a generator to generate vibration signal similar features, and then the vibration signal similar features are input into a discriminator to determine whether Nash equilibrium is reached; if Nash equilibrium is reached, the discriminator outputs an augmented feature training set, otherwise S31 is repeated until Nash equilibrium is reached;

[0046] S32, input the augmented feature training set into the convolutional neural network, and set the current number of iterations to The maximum number of iterations is G. When the current number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain a trained convolutional neural network. The feature test set is input into the trained convolutional neural network, and the convolution kernel size and step size are optimized using the electric eel foraging optimization algorithm to obtain a convolutional neural network recognition model. The specific steps are as follows:

[0047] S321, using the trained convolutional neural network recognition accuracy as the fitness function, the electric eel population is in the interaction stage, using stirring as the interaction sign in the search space, setting the current iteration number to h, the maximum iteration number to H, selecting a random electric eel j from the electric eel population, and setting the random electric eel h-th iteration position x j (h), the h-th iteration position of the ith electric eel in the electric eel population The average position of the electric eels in the electric eel population at the hth iteration is recorded as ε represents the interaction parameter, then the position of the i-th electric eel in the electric eel population at the h+1th iteration is The calculation formula is as follows,

[0048] When the fitness function value of a random electric eel in the electric eel population is less than the fitness function value of the i-th electric eel in the electric eel population,

[0049] When the fitness function value of a random electric eel in the electric eel population is greater than or equal to the fitness function value of the i-th electric eel in the electric eel population,

[0050] When the electric eel population is in the resting stage, the electric eel position vector in the electric eel population is projected onto the main diagonal of the search space to establish a resting area; a three-dimensional coordinate axis is established for normalization, and the search space and the eel position are specified to be within the range of 0-1 of the three-dimensional coordinate axis, and the projected position is regarded as the resting area, and the electric eel position corresponding to the best fitness function value of the hth iteration is set as The initial scale of the rest area is φ, and the scale of the rest area is The upper bound of the rest area is k′, the upper bound of the rest area is k″, the normalized number of the hth iteration is E(h), g1 represents a random number and g1∈(0,1), then the calculation formula for the h+1th iteration rest position x2(h+1) is as follows,

[0051]

[0052] After determining the resting position, the electric eel population moves to the resting position. Let g2 represent a random number between 0 and 1 that follows a normal distribution, g3 represent a random number and g3∈(0,1), and round represent rounding. Then the resting position of the i-th electric eel in the electric eel population at the h+1th iteration is The calculation formula is as follows,

[0053]

[0054] S322, in the hunting stage, the electric eels surround the prey and continuously accelerate the search for the local fitness function value. The electric eels first determine the pre-position of the hunting area and set the scale of the hunting area to γ1. The h-th iteration hunting position of the electric eels in the electric eel population is recorded as The prey position is x′, and the hunting range of the electric eel is The electric eel moves to the pre-position of the hunting area, and the pre-position of the hunting area in the hth iteration is set to x4(h). The calculation formula for the pre-position of the hunting area in the h+1th iteration is x5(h+1),

[0055]

[0056] After the pre-position of the hunting area is determined, the electric eel population moves to the pre-position of the hunting area. The curl factor is set to η, g4 represents a random number and g4∈(0,1), then the pre-position of the hunting area for the h+1th iteration of the i-th electric eel in the electric eel population is The calculation formula is as follows,

[0057]

[0058] The electric eel population arrives at the pre-position of the hunting area, and the electric eel population begins the migration stage. At this time, it continuously searches for the optimal fitness function value, and the position of the h+1th iteration hunting area of ​​the i-th electric eel in the electric eel population is recorded as When the fitness function value of the i-th electric eel in the electric eel population at the h+1th iteration is less than the fitness function value of the i-th electric eel in the electric eel population at the hth iteration, the position of the i-th electric eel in the electric eel population at the h+1th iteration is Otherwise, the position of the i-th electric eel in the electric eel population at the h+1th iteration is The electric eel continuously iterates its own position. When the current number of iterations is equal to the maximum number of iterations, the iteration is stopped to obtain the final position of the i-th electric eel in the electric eel population. The coordinate values ​​of the two-dimensional coordinates of the final position of the i-th electric eel in the electric eel population correspond to the convolution kernel size and step size respectively.

[0059] S33. Input the 12-dimensional feature vector into the generator and the discriminator to obtain an augmented feature vector, and input the augmented feature vector into the convolutional neural network recognition model. The convolutional neural network recognition model outputs 0 to indicate that the vibration signal of the slurry pump is abnormal and the slurry pump is faulty. The convolutional neural network recognition model outputs 1 to indicate that the vibration signal of the slurry pump is normal and the slurry pump is not faulty, thereby completing the slurry pump fault detection.

[0060] This invention trains the convolutional neural network by using new slurry pump vibration signal extraction features, introduces a generator and a discriminator for augmentation processing, minimizes the error between generated samples and real samples, and optimizes the convolutional neural network parameters based on the electric eel foraging optimization algorithm. By simulating the hunting behavior of electric eels, the optimal solution is found, and the convolutional neural network recognition model is obtained to determine whether the slurry pump is faulty.

[0061] Preferably, S4 comprises the following steps:

[0062] S41, reacquire the original vibration signal of the slurry pump, set the upper envelope of the original vibration signal of the slurry pump to l1, set the lower envelope of the original vibration signal of the slurry pump to l2, calculate the average of the upper envelope of the original vibration signal of the slurry pump and the lower envelope of the original vibration signal of the slurry pump, and obtain the average envelope of the vibration signal; use the original vibration signal of the slurry pump to subtract the average envelope of the vibration signal to obtain a new vibration signal component, calculate the average of the upper envelope of the new vibration signal component and the lower envelope of the new vibration signal component, and record it as A new vibration signal average envelope curve; when the new vibration signal average envelope curve is 0, and the absolute value of the difference between the number of zero points and the number of extreme points of the new vibration signal average envelope curve on the time domain diagram is less than or equal to 1, the new vibration signal component is used as the first IMF (inherent mode function) component; otherwise, S41 is repeated until the new vibration signal average envelope curve is 0, and the absolute value of the difference between the number of zero points and the number of extreme points of the new vibration signal average envelope curve on the time domain diagram is less than or equal to 1, and the first IMF component is obtained;

[0063] S42, removing the first IMF component from the original vibration signal of the slurry pump, decomposing to obtain a second original vibration signal of the slurry pump, performing S41 operation on the second original vibration signal of the slurry pump to obtain a second IMF component; continuously repeating S41 until the remaining original vibration signal of the slurry pump is a monotonic function, stopping the decomposition, recording the remaining original vibration signal of the slurry pump as an original residual vibration signal, and outputting the IMF component of the original vibration signal of the slurry pump and the original residual vibration signal;

[0064] In the LSTM (Long Short-Term Memory) neural network, the number of IMF components of the original vibration signal of the slurry pump is set to m, the sliding window size is n, the number of neurons is (m+2)×n, the current number of iterations is q, the maximum number of iterations is Q, the IMF components of the original vibration signal of the slurry pump, the original residual vibration signal and the original vibration signal of the slurry pump are used as prediction sample sets, the prediction sample sets are divided into prediction training sets and prediction test sets, the prediction training sets are input into the LSTM neural network, and when the current number of iterations reaches the maximum number of iterations, the trained LSTM neural network is obtained; the prediction test set is input into the trained LSTM neural network, the error threshold is set to ψ, and when the output error is less than ψ, the LSTM neural network prediction model is obtained, otherwise the weight is adjusted until the output error is less than ψ;

[0065] The IMF component of the slurry pump vibration signal is extracted to obtain the IMF component of the original vibration signal of the slurry pump and the residual vibration signal. The IMF component of the original vibration signal of the slurry pump, the residual vibration signal and the slurry pump vibration signal are used as a prediction verification set, and the prediction verification set is input into the LSTM neural network prediction model to output the predicted value of the service life of the slurry pump to complete the prediction of the service life of the slurry pump.

[0066] The invention obtains the original vibration signal of the slurry pump again, extracts the IMF component of the original vibration signal of the slurry pump, trains the LSTM neural network through the IMF component to obtain the LSTM neural network prediction model, and the neural network has good prediction accuracy. Finally, it outputs the predicted value of the service life of the slurry pump to realize the life prediction of the slurry pump.

[0067] This embodiment also discloses a system for a slurry pump fault detection and life prediction method based on big data, which specifically includes: a slurry pump vibration signal noise reduction module, a slurry pump vibration signal feature extraction module, a slurry pump fault detection module and a slurry pump life prediction module;

[0068] The slurry pump vibration signal noise reduction module is used to detect and delete abnormal vibration signals, and then realize noise reduction after singular value decomposition and reconstruction signal processing;

[0069] The slurry pump vibration signal feature extraction module is used to generate a slurry pump vibration signal image based on the vibration signal, and obtain a 12-dimensional feature vector after feature extraction;

[0070] The slurry pump fault detection module is used to train a convolutional neural network to obtain a convolutional neural network recognition model to detect slurry pump faults;

[0071] The slurry pump life prediction module is used to train the LSTM neural network to obtain an LSTM neural network prediction model and output a predicted value of the service life of the slurry pump.

[0072] The present invention has the following beneficial effects:

[0073] 1. The invention obtains the vibration signal of the slurry pump by collecting the vibration signal of the slurry pump under the running state, detects the abnormal vibration signal of the slurry pump vibration signal, deletes the abnormal vibration signal, and then uses singular value decomposition and reconstruction signal to realize the noise reduction of the slurry pump vibration signal; and uses segmented aggregation approximation and Gram angle field processing on the denoised slurry pump vibration signal to generate a slurry pump vibration signal image, and then performs feature extraction based on Gauss-Markov random field to extract 12-dimensional feature vectors, which can effectively reduce redundancy and improve data processing accuracy.

[0074] 2. The invention trains the convolutional neural network by using new slurry pump vibration signal extraction features, introduces a generator and a discriminator to perform augmentation processing on the 12-dimensional feature vector, which can reduce sample errors, and optimizes the convolutional neural network parameters based on the electric eel foraging optimization algorithm. By simulating the hunting behavior of electric eels, the optimal solution is found, and the convolutional neural network recognition model is obtained to realize slurry pump fault detection.

[0075] 3. The invention obtains the original vibration signal of the slurry pump again, extracts the IMF component of the original vibration signal of the slurry pump, trains the LSTM neural network to obtain the LSTM neural network prediction model, and the neural network has good prediction accuracy, outputs the predicted value of the service life of the slurry pump, and completes the prediction of the service life of the slurry pump.

[0076] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0078] Figure 1The present invention provides a flow chart of a slurry pump fault detection and life prediction system based on big data for slurry pump fault detection and life prediction. DETAILED DESCRIPTION

[0079] The following will be combined with the drawings in 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.

[0080] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0081] This implementation discloses a method for slurry pump fault detection and life prediction based on big data, which specifically includes the following contents:

[0082] S1. Collect the vibration signal of the slurry pump in the running state to obtain the vibration signal of the slurry pump, detect the abnormal vibration signal of the slurry pump vibration signal based on the outlier factor algorithm, and delete the abnormal vibration signal; then perform singular value decomposition and reconstruction signal processing to complete the slurry pump vibration signal denoising and obtain the slurry pump vibration signal after denoising;

[0083] The S1 comprises the following steps:

[0084] S11, when the slurry pump is in operation, a vibration sensor is placed at the center of the bearing of the slurry pump to collect the bearing vibration signal of the slurry pump, the bearing vibration signal of the slurry pump is converted into a 485 signal, and the 485 signal is regarded as the vibration signal of the slurry pump; a slurry pump vibration signal collection time interval is set, and the slurry pump vibration signal is repeatedly collected at an α sampling frequency to obtain a slurry pump vibration signal set, wherein the slurry pump vibration signal set includes a normal slurry pump vibration signal, a single fault slurry pump vibration signal, and a compound fault slurry pump vibration signal;

[0085] S12, setting the sliding window size to 100, performing sliding sampling in the slurry pump vibration signal set with a step size of 100, generating a slurry pump vibration signal data segments with a length of 100 in the slurry pump vibration signal set, and forming a slurry pump vibration signal data set A = {b1, b2, b3, ..., b a}, where b arepresents the ath slurry pump vibration signal data; select the ath slurry pump vibration signal data set The vibration signal data of a slurry pump is Data points, calculation The distance size of the slurry pump vibration signal data in the slurry pump vibration signal data set is obtained by arranging the distance sizes in ascending order, and the slurry pump vibration signal data corresponding to the distance less than k distance is found in the distance size set, and the slurry pump vibration signal data corresponding to the distance less than k distance is recorded as k distance Data point neighborhood;

[0086] When the slurry pump vibration signal data set contains slurry pump vibration signal data within k distance When the data point is within the neighborhood, it is recorded as data reachable, otherwise the data is unreachable; when the data is reachable, The formula for calculating the reachable data density of a data point is as follows:

[0087]

[0088] in, express The reachable data density of the data point, average represents the average value, Represents k distance The number of data points in the neighborhood of the data point, express Distance to k In the neighborhood of a data point The distance between the data points,

[0089] According to the The reachable data density of a data point is calculated The outlier factor of a data point is calculated as follows:

[0090]

[0091] in, express The outlier factor of the data point, B(i1) represents the k distance In the neighborhood of a data point The reachable data density of a data point,

[0092] Calculate the outlier factors of the slurry pump vibration signal data segments in the slurry pump vibration signal data set in turn, set the outlier threshold as ω, delete the data whose outlier factors are less than ω in the slurry pump vibration signal data segment in the slurry pump vibration signal data set, complete the deletion of abnormal vibration signal data, and obtain the abnormally deleted slurry pump vibration signal set;

[0093] S13, set the abnormal deleted slurry pump vibration signal set to A1 = {b1′, b2′, b3′, ..., b a ″}, where b a ″ represents the a′th slurry pump vibration signal, and the dimension of the slurry pump vibration signal in the abnormal deleted slurry pump vibration signal set is set to c, and the slurry pump vibration signal matrix C is generated as follows:

[0094]

[0095] Among them, b a ″ c represents the vibration signal of the a′th slurry pump with dimension c;

[0096] Set the left singular matrix C1, the right singular matrix C2 and the diagonal matrix And meet Where C2 T represents the transpose of the right singular matrix, and the column vector of the left singular matrix is ​​set as the left singular vector of the slurry pump vibration signal matrix, and the column vector of the right singular matrix is ​​set as the right singular vector of the slurry pump vibration signal matrix. At this time, the diagonal elements of the slurry pump vibration signal matrix are recorded as the singular value set A2 = {b1″, b2″, b3″, ..., b a ″ ′}, where b a ″ ′ represents the a′th singular value, completing the singular value decomposition; sorting the singular values ​​in the singular value set in descending order, setting the singular value threshold as ξ, discarding the singular values ​​in the singular value set that are less than ξ, and forming a valid singular value set with the singular values ​​in the singular value set that are greater than or equal to ξ, denoted as A3 = {b1″,′b2″′,b3″′,...,b a ″″ ′}, where b a ″″ ′ represents the a″th valid singular value, and the signal components corresponding to the valid singular values ​​in the valid singular value set are superimposed to obtain the reconstructed slurry pump vibration signal matrix, and the slurry pump vibration signal denoising is completed. The calculation formula of the slurry pump vibration signal after denoising is as follows:

[0097]

[0098] Among them, B″ represents the vibration signal of the slurry pump after noise reduction, represents the i3th left singular vector of the reconstructed slurry pump vibration signal matrix, represents the transpose of the i3th right singular vector of the reconstructed slurry pump vibration signal matrix, Represents the i3th valid singular value on the diagonal of the diagonal matrix, i3=1,2,3,...,a″;

[0099] S2, the slurry pump vibration signal after noise reduction is composed of a slurry pump vibration signal set after noise reduction, and then the segmented aggregation approximation and Gram angle field processing are performed to generate a slurry pump vibration signal image, and then feature extraction is performed based on the Gauss-Markov random field to obtain a 12-dimensional feature vector;

[0100] The S2 comprises the following steps:

[0101] S21, forming a set of slurry pump vibration signals after noise reduction according to the slurry pump vibration signals after noise reduction, denoted as A4={d1, d2, d3, ..., d e}, where d e represents the e-th noise-reduced slurry pump vibration signal, collects the time corresponding to the noise-reduced slurry pump vibration signal in the noise-reduced slurry pump vibration signal set, generates a slurry pump vibration signal time series, and divides the slurry pump vibration signal time series into a group of e′ slurry pump vibration signal time series. a slurry pump vibration signal time subsequence, and processing the slurry pump vibration signal time subsequence by a segmented clustering similarity method to obtain a processed slurry pump vibration signal time series;

[0102] S22, compress the processed slurry pump vibration signal time series to the interval [-1, 1] to obtain the compressed slurry pump vibration signal time series A5, map the compression value in the compressed slurry pump vibration signal time series into an angle χ1, set the time mapping radius to e1, and the calculation formula is as follows:

[0103] χ1=arccos(A5),

[0104] Among them, t represents the time corresponding to the processed slurry pump vibration signal time series, and δ represents the polar coordinate span coefficient;

[0105] The compressed value in the compressed slurry pump vibration signal time series is mapped to an angle χ2 again, and the Gram matrix C1=cos(χ1+χ2); in the Gram matrix, each data element represents the pixel value of a pixel point of the slurry pump vibration signal image, and the slurry pump vibration signal image is generated;

[0106] S23, extracting texture features from the slurry pump vibration signal image using a Gauss-Markov random field, the specific steps are as follows:

[0107] S231, select pixel points on the slurry pump vibration signal image to obtain a pixel point set, set represents the i4th pixel in the pixel set, represents the weight of the pixel in the symmetric Gauss-Markov neighborhood, represents Gaussian distribution noise, then the Gauss-Markov model calculation formula is as follows,

[0108]

[0109] Where D represents the Gauss-Markov model, g represents the number of pixel points, i4=1,2,3,...,g;

[0110] S232, substituting the pixel points of the slurry pump vibration signal image into the Gauss-Markov model to obtain a differential equation, and the calculation formula is as follows:

[0111]

[0112] Where D′ represents the difference equation, represents the eigenvector, C2 represents the difference equation matrix;

[0113] Solving the difference equation error using the least square error method, selecting a 12-dimensional vector that satisfies the difference equation error, and recording the 12-dimensional vector that satisfies the difference equation error as a 12-dimensional eigenvector;

[0114] S3, collecting new slurry pump vibration signals to obtain new 12-dimensional feature vectors, using generators and discriminators to perform augmentation processing on the new 12-dimensional feature vectors, and then training the convolutional neural network, and optimizing the convolutional neural network parameters based on the electric eel foraging optimization algorithm to obtain a convolutional neural network recognition model to achieve slurry pump fault detection;

[0115] The S3 comprises the following steps:

[0116] S31. The deep convolutional generative adversarial network augmentation includes a generator and a discriminator. The generator is composed of a reshape layer and a deconvolution layer. The activation function is set to the ReLU function. The data is input into the deep convolutional generative adversarial network through the reshape layer. The reshape layer reshapes the input data sample and then the deconvolution layer amplifies the feature to generate sample similarity features. The discriminator is composed of a convolution layer and a fully connected layer. The input data sample and the sample similarity feature are subjected to feature extraction and dimensionality reduction processing. The sigmoid function is used as the activation function and the result is output after passing through the fully connected layer. The generator and the discriminator are alternately trained on the data samples until the Nash equilibrium is reached to obtain augmented sample data.

[0117] A new slurry pump vibration signal is collected to obtain a new slurry pump vibration signal set, and after outlier deletion and noise reduction processing are performed on the new slurry pump vibration signal set, a new slurry pump vibration signal image is generated to obtain a new 12-dimensional feature vector, and the new 12-dimensional feature vector is divided into a feature training set and a feature test set, and the feature training set is input into a generator to generate vibration signal similar features, and then the vibration signal similar features are input into a discriminator to determine whether Nash equilibrium is reached; if Nash equilibrium is reached, the discriminator outputs an augmented feature training set, otherwise S31 is repeated until Nash equilibrium is reached;

[0118] S32, input the augmented feature training set into the convolutional neural network, and set the current number of iterations to The maximum number of iterations is G. When the current number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain a trained convolutional neural network. The feature test set is input into the trained convolutional neural network, and the convolution kernel size and step size are optimized using the electric eel foraging optimization algorithm to obtain a convolutional neural network recognition model. The specific steps are as follows:

[0119] S321, using the trained convolutional neural network recognition accuracy as the fitness function, the electric eel population is in the interaction stage, using stirring as the interaction sign in the search space, setting the current iteration number to h, the maximum iteration number to H, selecting a random electric eel j from the electric eel population, and setting the random electric eel h-th iteration position x j (h), the h-th iteration position of the ith electric eel in the electric eel population The average position of the electric eels in the electric eel population at the hth iteration is recorded as ε represents the interaction parameter, then the position of the i-th electric eel in the electric eel population at the h+1th iteration is The calculation formula is as follows,

[0120] When the fitness function value of a random electric eel in the electric eel population is less than the fitness function value of the i-th electric eel in the electric eel population,

[0121] When the fitness function value of a random electric eel in the electric eel population is greater than or equal to the fitness function value of the i-th electric eel in the electric eel population,

[0122] When the electric eel population is in the resting stage, the electric eel position vector in the electric eel population is projected onto the main diagonal of the search space to establish a resting area; a three-dimensional coordinate axis is established for normalization, and the search space and the eel position are specified to be within the range of 0-1 of the three-dimensional coordinate axis, and the projected position is regarded as the resting area, and the electric eel position corresponding to the best fitness function value of the hth iteration is set as The initial scale of the rest area is φ, and the scale of the rest area is The upper bound of the rest area is k′, the upper bound of the rest area is k″, the normalized number of the hth iteration is E(h), g1 represents a random number and g1∈(0,1), then the calculation formula for the h+1th iteration rest position x2(h+1) is as follows,

[0123]

[0124] After determining the resting position, the electric eel population moves to the resting position. Let g2 represent a random number between 0 and 1 that follows a normal distribution, g3 represent a random number and g3∈(0,1), and round represent rounding. Then the resting position of the i-th electric eel in the electric eel population at the h+1th iteration is The calculation formula is as follows,

[0125]

[0126] S322, in the hunting stage, the electric eels surround the prey and continuously accelerate the search for the local fitness function value. The electric eels first determine the pre-position of the hunting area and set the scale of the hunting area to γ1. The h-th iteration hunting position of the electric eels in the electric eel population is recorded as The prey position is x′, and the hunting range of the electric eel is The electric eel moves to the pre-position of the hunting area, and the pre-position of the hunting area in the hth iteration is set to x4(h). The calculation formula for the pre-position of the hunting area in the h+1th iteration is x5(h+1),

[0127]

[0128] After the pre-position of the hunting area is determined, the electric eel population moves to the pre-position of the hunting area. The curl factor is set to η, g4 represents a random number and g4∈(0,1), then the pre-position of the hunting area for the h+1th iteration of the i-th electric eel in the electric eel population is The calculation formula is as follows,

[0129]

[0130] The electric eel population arrives at the pre-position of the hunting area, and the electric eel population begins the migration stage. At this time, it continuously searches for the optimal fitness function value, and the position of the h+1th iteration hunting area of ​​the i-th electric eel in the electric eel population is recorded as When the fitness function value of the i-th electric eel in the electric eel population at the h+1th iteration is less than the fitness function value of the i-th electric eel in the electric eel population at the hth iteration, the position of the i-th electric eel in the electric eel population at the h+1th iteration is Otherwise, the position of the i-th electric eel in the electric eel population at the h+1th iteration is The electric eel continuously iterates its own position. When the current number of iterations is equal to the maximum number of iterations, the iteration is stopped to obtain the final position of the i-th electric eel in the electric eel population. The coordinate values ​​of the two-dimensional coordinates of the final position of the i-th electric eel in the electric eel population correspond to the convolution kernel size and step size respectively.

[0131] S33, inputting the 12-dimensional feature vector into the generator and the discriminator to obtain an augmented feature vector, and inputting the augmented feature vector into the convolutional neural network recognition model, the convolutional neural network recognition model outputs 0 to indicate that the vibration signal of the slurry pump is abnormal and the slurry pump is faulty, and the convolutional neural network recognition model outputs 1 to indicate that the vibration signal of the slurry pump is normal and the slurry pump is not faulty, thereby completing the slurry pump fault detection;

[0132] S4, obtaining the original vibration signal of the slurry pump, obtaining the IMF component of the original vibration signal of the slurry pump based on the empirical mode decomposition method, and training the LSTM neural network to obtain the LSTM neural network prediction model, combining the slurry pump vibration signal to obtain the predicted value of the service life of the slurry pump, and completing the life prediction of the slurry pump;

[0133] The S4 comprises the following steps:

[0134] S41, reacquire the original vibration signal of the slurry pump, set the upper envelope of the original vibration signal of the slurry pump to l1, set the lower envelope of the original vibration signal of the slurry pump to l2, calculate the average value of the upper envelope of the original vibration signal of the slurry pump and the lower envelope of the original vibration signal of the slurry pump, and obtain the average envelope of the vibration signal; use the original vibration signal of the slurry pump to subtract the average envelope of the vibration signal to obtain a new vibration signal component, calculate the average value of the upper envelope of the new vibration signal component and the lower envelope of the new vibration signal component value, recorded as the new vibration signal average envelope; when the new vibration signal average envelope is 0, and the absolute value of the difference between the number of zero points and the number of extreme points of the new vibration signal average envelope on the time domain diagram is less than or equal to 1, the new vibration signal component is taken as the first IMF component; otherwise, S41 is repeated until the new vibration signal average envelope is 0, and the absolute value of the difference between the number of zero points and the number of extreme points of the new vibration signal average envelope on the time domain diagram is less than or equal to 1, and the first IMF component is obtained;

[0135] S42, removing the first IMF component from the original vibration signal of the slurry pump, decomposing to obtain a second original vibration signal of the slurry pump, performing S41 operation on the second original vibration signal of the slurry pump to obtain a second IMF component; continuously repeating S41 until the remaining original vibration signal of the slurry pump is a monotonic function, stopping the decomposition, recording the remaining original vibration signal of the slurry pump as an original residual vibration signal, and outputting the IMF component of the original vibration signal of the slurry pump and the original residual vibration signal;

[0136] In the LSTM neural network, the number of IMF components of the original vibration signal of the slurry pump is set to m, the sliding window size is n, the number of neurons is (m+2)×n, the current number of iterations is q, the maximum number of iterations is Q, the IMF components of the original vibration signal of the slurry pump, the original residual vibration signal and the original vibration signal of the slurry pump are used as prediction sample sets, the prediction sample sets are divided into prediction training sets and prediction test sets, the prediction training sets are input into the LSTM neural network, and when the current number of iterations reaches the maximum number of iterations, the trained LSTM neural network is obtained; the prediction test set is input into the trained LSTM neural network, the error threshold is set to ψ, and when the output error is less than ψ, the LSTM neural network prediction model is obtained, otherwise the weight is adjusted until the output error is less than ψ;

[0137] The IMF component of the slurry pump vibration signal is extracted to obtain the IMF component of the original vibration signal of the slurry pump and the residual vibration signal. The IMF component of the original vibration signal of the slurry pump, the residual vibration signal and the slurry pump vibration signal are used as a prediction verification set, and the prediction verification set is input into the LSTM neural network prediction model to output the predicted value of the service life of the slurry pump to complete the prediction of the service life of the slurry pump.

[0138] This embodiment also discloses a system for a slurry pump fault detection and life prediction method based on big data, which specifically includes: a slurry pump vibration signal noise reduction module, a slurry pump vibration signal feature extraction module, a slurry pump fault detection module and a slurry pump life prediction module;

[0139] The slurry pump vibration signal noise reduction module is used to detect and delete abnormal vibration signals, and then realize noise reduction after singular value decomposition and reconstruction signal processing;

[0140] The slurry pump vibration signal feature extraction module is used to generate a slurry pump vibration signal image based on the vibration signal, and obtain a 12-dimensional feature vector after feature extraction;

[0141] The slurry pump fault detection module is used to train a convolutional neural network to obtain a convolutional neural network recognition model to detect slurry pump faults;

[0142] The slurry pump life prediction module is used to train the LSTM neural network to obtain an LSTM neural network prediction model and output a predicted value of the service life of the slurry pump.

[0143] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0144] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A method for fault detection and life prediction of slurry pumps based on big data, characterized in that: The steps include: S1. Collect the vibration signal of the slurry pump in the running state to obtain the vibration signal of the slurry pump, detect the abnormal vibration signal of the slurry pump vibration signal based on the outlier factor algorithm, and delete the abnormal vibration signal; then perform singular value decomposition and reconstruction signal processing to complete the slurry pump vibration signal denoising and obtain the slurry pump vibration signal after denoising; S2, the slurry pump vibration signal after noise reduction is composed of a slurry pump vibration signal set after noise reduction, and then the segmented aggregation approximation and Gram angle field processing are performed to generate a slurry pump vibration signal image, and then feature extraction is performed based on the Gauss-Markov random field to obtain a 12-dimensional feature vector; S3, collecting new slurry pump vibration signals to obtain new 12-dimensional feature vectors, using generators and discriminators to perform augmentation processing on the new 12-dimensional feature vectors, then training the convolutional neural network, and optimizing the convolutional neural network parameters to obtain a convolutional neural network recognition model to achieve slurry pump fault detection; S4. Obtain the original vibration signal of the slurry pump, obtain the IMF component of the original vibration signal of the slurry pump based on the empirical mode decomposition method, and train the LSTM neural network to obtain the LSTM neural network prediction model. Combine the slurry pump vibration signal to obtain the predicted value of the service life of the slurry pump, and complete the slurry pump life prediction.

2. A method for fault detection and life prediction of a slurry pump based on big data according to claim 1, characterized in that: The S1 comprises the following steps: S11, when the slurry pump is in operation, collecting a vibration signal of the slurry pump to obtain a vibration signal of the slurry pump; S12, using an outlier factor algorithm to calculate the outlier factor of the slurry pump vibration signal, deleting abnormal vibration signals according to the outlier factor, and obtaining a set of abnormally deleted slurry pump vibration signals; S13, performing singular value decomposition and signal reconstruction processing on the abnormally deleted slurry pump vibration signal set to obtain a denoised slurry pump vibration signal.

3. A method for slurry pump fault detection and life prediction based on big data according to claim 2, characterized in that: The S2 comprises the following steps: S21, forming a noise-reduced slurry pump vibration signal set according to the noise-reduced slurry pump vibration signal, and obtaining a processed slurry pump vibration signal time series after processing by a segmented clustering similarity method; S22, generating a Gram matrix from the processed slurry pump vibration signal time series to obtain a slurry pump vibration signal image; S23, extracting texture features from the slurry pump vibration signal image using a Gauss-Markov random field to obtain a 12-dimensional feature vector.

4. A method for slurry pump fault detection and life prediction based on big data according to claim 3, characterized in that: The S23 comprises the following steps: S231, establishing a Gauss-Markov model on the slurry pump vibration signal image; S232. Using the differential equation of the Gauss-Markov model, select a 12-dimensional vector that satisfies the error of the differential equation and record it as a 12-dimensional feature vector.

5. A method for slurry pump fault detection and life prediction based on big data according to claim 4, characterized in that: The S3 comprises the following steps: S31, collecting new slurry pump vibration signals to obtain new slurry pump vibration signal sets, obtaining new 12-dimensional feature vectors, dividing the new 12-dimensional feature vectors into feature training sets and feature test sets, inputting the feature training sets into the generator to generate vibration signal similar features, and then inputting the vibration signal similar features into the discriminator to determine whether Nash equilibrium is reached; if Nash equilibrium is reached, the discriminator outputs an augmented feature training set, otherwise S31 is repeated until Nash equilibrium is reached; S32, input the augmented feature training set into the convolutional neural network, set the current number of iterations to g~, the maximum number of iterations to G, and when the current number of iterations reaches the maximum number of iterations, stop iterating to obtain a trained convolutional neural network; input the feature test set into the trained convolutional neural network, use the electric eel foraging optimization algorithm to optimize the convolution kernel size and step size, and obtain a convolutional neural network recognition model.

6. A method for slurry pump fault detection and life prediction based on big data according to claim 5, characterized in that: The 12-dimensional feature vector is input into the generator and the discriminator to obtain an augmented feature vector, and the augmented feature vector is input into the convolutional neural network recognition model. The convolutional neural network recognition model outputs 0, indicating that the vibration signal of the slurry pump is abnormal and the slurry pump is faulty. The convolutional neural network recognition model outputs 1, indicating that the vibration signal of the slurry pump is normal and the slurry pump is not faulty, thereby completing the slurry pump fault detection.

7. The method for fault detection and life prediction of a slurry pump based on big data according to claim 5, characterized in that: The S32 comprises the following steps: S321, using the trained convolutional neural network recognition accuracy as the fitness function, the electric eel population is in the interaction stage, using stirring as the interaction sign in the search space, setting the current iteration number to h, the maximum iteration number to H, selecting a random electric eel j from the electric eel population, and setting the random electric eel h-th iteration position x j (h), the h-th iteration position of the ith electric eel in the electric eel population The average position of the electric eels in the electric eel population at the hth iteration is recorded as ε represents the interaction parameter, then the position of the i-th electric eel in the electric eel population at the h+1th iteration is The calculation formula is as follows, When the fitness function value of a random electric eel in the electric eel population is less than the fitness function value of the i-th electric eel in the electric eel population, When the fitness function value of a random electric eel in the electric eel population is greater than or equal to the fitness function value of the i-th electric eel in the electric eel population, When the electric eel population is in the resting stage, the electric eel position vector in the electric eel population is projected onto the main diagonal of the search space to establish a resting area, and the resting position of the h+1th iteration is recorded as x2(h+1); After the resting position is determined, the electric eel population moves to the resting position, and the h+1th iteration resting position of the ith electric eel in the electric eel population is recorded as S322, in the hunting stage, the electric eels surround the prey and continuously speed up the search for the local fitness function value. The pre-position of the hunting area in the h+1th iteration is recorded as x5(h+1); After the pre-position of the hunting area is determined, the electric eel population moves to the pre-position of the hunting area, and the pre-position of the hunting area of ​​the i-th electric eel in the electric eel population at the h+1th iteration is recorded as The electric eel population arrives at the pre-position of the hunting area, and the electric eel population begins the migration phase. At this time, it continuously searches for the optimal fitness function value, and the position of the h+1th iteration hunting area of ​​the i-th electric eel in the electric eel population is recorded as When the fitness function value of the i-th electric eel in the electric eel population at the h+1th iteration is less than the fitness function value of the i-th electric eel in the electric eel population at the hth iteration, the position of the i-th electric eel in the electric eel population at the h+1th iteration is Otherwise, the position of the i-th electric eel in the electric eel population at the h+1th iteration is The electric eel continuously iterates its own position. When the current number of iterations is equal to the maximum number of iterations, the iteration is stopped to obtain the final position of the i-th electric eel in the electric eel population. The coordinate values ​​of the two-dimensional coordinates of the final position of the i-th electric eel in the electric eel population correspond to the convolution kernel size and step size respectively.

8. The method for fault detection and life prediction of a slurry pump based on big data according to claim 7, characterized in that: The S4 comprises the following steps: S41, reacquiring the original vibration signal of the slurry pump, extracting the IMF component of the original vibration signal of the slurry pump using the empirical mode decomposition method, and outputting the IMF component of the original vibration signal of the slurry pump and the original residual vibration signal; S42. In the LSTM neural network, the current number of iterations is set to q, the maximum number of iterations is set to Q, the IMF component of the original vibration signal of the slurry pump, the original residual vibration signal and the original vibration signal of the slurry pump are used as prediction sample sets, the prediction sample sets are divided into prediction training sets and prediction test sets, the prediction training sets are input into the LSTM neural network, and when the current number of iterations reaches the maximum number of iterations, a trained LSTM neural network is obtained; the prediction test set is input into the trained LSTM neural network, and the error threshold is set to ψ. When the output error is less than ψ, the LSTM neural network prediction model is obtained, otherwise the weights are adjusted until the output error is less than ψ.

9. A method for slurry pump fault detection and life prediction based on big data according to claim 8, characterized in that: The IMF component of the slurry pump vibration signal is extracted to obtain the IMF component of the original vibration signal of the slurry pump and the residual vibration signal. The IMF component of the original vibration signal of the slurry pump, the residual vibration signal and the slurry pump vibration signal are used as a prediction verification set, and the prediction verification set is input into the LSTM neural network prediction model to output the predicted value of the service life of the slurry pump to complete the prediction of the service life of the slurry pump.

10. A system for implementing the method for slurry pump fault detection and life prediction based on big data according to any one of claims 1 to 9, characterized in that: Specifically include: Slurry pump vibration signal noise reduction module, slurry pump vibration signal feature extraction module, slurry pump fault detection module and slurry pump life prediction module; The slurry pump vibration signal noise reduction module is used to detect and delete abnormal vibration signals, and then realize noise reduction after singular value decomposition and reconstruction signal processing; The slurry pump vibration signal feature extraction module is used to generate a slurry pump vibration signal image based on the vibration signal, and obtain a 12-dimensional feature vector after feature extraction; The slurry pump fault detection module is used to train a convolutional neural network to obtain a convolutional neural network recognition model to detect slurry pump faults; The slurry pump life prediction module is used to train the LSTM neural network to obtain an LSTM neural network prediction model and output a predicted value of the service life of the slurry pump.

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