Life prediction method of centrifugal impeller pump based on LSTM model

Abstract

The present invention provides a centrifugal impeller pump life prediction method based on an LSTM model, comprising: collecting centrifugal impeller pump operating data and constructing a sample dataset; preprocessing the sample dataset; constructing a long short-term memory network model; training and testing the long short-term memory network model to obtain a trained long short-term memory network model; and predicting the life of the centrifugal impeller pump based on the trained long short-term memory network model. By constructing a long short-term memory network model for model training and centrifugal impeller pump life prediction, the present invention can automatically extract features through data-driven processing and process complex nonlinear time series data without relying on complex physical modeling processes. This method has higher accuracy, flexibility, and robustness, and is more practical.

Description

Technical Field

[0001] The present invention belongs to the technical field of centrifugal impeller pump life prediction, and in particular to a centrifugal impeller pump life prediction method based on an LSTM model. Background Art

[0002] Centrifugal impeller pumps are widely used in industries such as petrochemicals, water treatment, and pharmaceuticals. During operation, they convert mechanical energy into kinetic energy through the rotating impeller, used to transport liquids or gases. Their operating flow rate—the volume of liquid transported per unit time—is closely related to pump performance, and changes in flow rate directly affect the pump's operating status. Under different operating conditions, the pump's flow rate, pressure, power, and efficiency vary. Fluctuations or abnormalities in flow rate can be early signs of pump failure or damage. Flow rate variations can affect stress distribution and fatigue accumulation in the pump body and key components. Therefore, monitoring flow rate changes can provide a basis for predicting the accumulation of fatigue damage, which is crucial to improving the reliability and safety of centrifugal impeller pumps.

[0003] However, existing traditional methods for predicting the life of centrifugal impeller pumps, which typically rely on physical models or expert experience, suffer from numerous deficiencies, including limited ability to model complex nonlinear behavior, poor real-time performance, reliance on manual intervention, and difficulty coping with incomplete or noisy data. These deficiencies significantly limit the practical application of traditional methods, making it difficult to provide accurate and timely predictions in dynamic and complex environments. Therefore, to process complex nonlinear time series data, it is urgent and necessary to develop a centrifugal impeller pump life prediction method based on an LSTM model. This method, through data-driven automatic feature extraction, can predict the life of complex and dynamically changing centrifugal impeller pumps without the need for complex physical modeling or frequent updates. Summary of the Invention

[0004] In response to the shortcomings of the aforementioned prior art, the present invention proposes a centrifugal impeller pump life prediction method based on an LSTM model. The method comprises collecting centrifugal impeller pump operating data and constructing a sample dataset; preprocessing the sample dataset; constructing a long short-term memory network model; training and testing the long short-term memory network model to obtain a trained long short-term memory network model; and predicting the life of the centrifugal impeller pump based on the trained long short-term memory network model. By constructing a long short-term memory network model for model training and centrifugal impeller pump life prediction, the present invention is able to automatically extract features through data-driven processing and process complex nonlinear time series data without relying on complex physical modeling processes. This method offers higher accuracy, flexibility, and robustness, and is therefore more practical.

[0005] The present invention provides a centrifugal impeller pump life prediction method based on an LSTM model, which comprises the following steps:

[0006] S1. Collect the operation data of the centrifugal impeller pump and construct a sample data set;

[0007] S11, using a sensor to collect centrifugal impeller pump operating data, wherein the centrifugal impeller pump operating data includes centrifugal impeller pump operating time and flow rate;

[0008] S12. Construct a sample data set required for training the model based on the centrifugal impeller pump operation data;

[0009] S2. Preprocessing the sample data set: the preprocessing includes removing abnormal data, cleaning outlier data, and smoothing data;

[0010] S3. Divide the sample data set: divide the sample data set into a first data set and a second data set, where the first data set is used for model training and the second data set is used for model testing;

[0011] S4. Constructing a long short-term memory network model: the long short-term memory network model includes a forget gate, an input gate, and an output gate;

[0012] S41. Establish a forget gate: Use the Sigmoid activation function to convert the memory cell h at the previous moment t-1 t-1 The running time x of the centrifugal impeller pump input at the current time t t Converted to forget value f t ;

[0013] S42. Establish input gate: Use Sigmoid activation function and tanh function to obtain new memory cell state;

[0014] S421, use the Sigmoid activation function to activate the memory cell h at the previous moment t-1 t-1 The running time x of the centrifugal impeller pump input at the current time t t Converted into input value I t , expressed as:

[0015] I t =σ(W1[h t-1 , x t ]+b1) (4)

[0016] Where W1 and b1 represent the first weight value and the first compensation value of the input gate respectively; σ(·) represents the Sigmoid activation function operation;

[0017] S422, using the tanh function to compare the memory cell at the previous moment with the centrifugal impeller pump running time x input at the current moment t. t Transformation into a new candidate memory cell state Expressed as:

[0018]

[0019] Among them, W c , b c They are respectively represented as the second weight value and the second compensation value of the input gate;

[0020] S423, based on the forget gate acting on the memory cell state C at the previous time t-1 t-1 , will I t Acting on new candidate memory cell states Get the new memory cell state C at the current time t t :

[0021]

[0022] S43. Establish output gate: use Sigmoid activation function and tanh function to obtain new memory cells;

[0023] S431, use the Sigmoid activation function to combine the memory cell at the previous time t-1 with the centrifugal impeller pump running time x input at the current time t t Converted to output value O t , expressed as:

[0024] O t =σ(W0[h t-1 , x t ]+b0) (7)

[0025] Among them, W0 and b0 represent the weight value and compensation value of the output gate respectively;

[0026] S432, based on the output value O t , apply the tanh function to the new memory cell state C t , obtain the new memory cell h at the current time t t :

[0027] h t =O t tanhC t (8);

[0028] S5. Training and testing a long short-term memory network model to obtain a trained long short-term memory network model: training the long short-term memory network model based on the first data set, and testing the long short-term memory network model based on the second data set;

[0029] S6. Predict the life of the centrifugal impeller pump based on the trained long short-term memory network model: Based on the trained long short-term memory network model, continue to increase the running time of the centrifugal impeller pump and predict the flow of the centrifugal impeller pump. When the flow of the centrifugal impeller pump is lower than the set threshold, the centrifugal impeller pump fails, thereby obtaining the life of the centrifugal impeller pump.

[0030] Furthermore, step S2 specifically includes the following steps:

[0031] S21. Abnormal flow data elimination: According to the normal distribution law of random variables, the flow data falling outside the mean by 3 times the standard deviation is discarded;

[0032] S22. Cleaning traffic outlier data: Use the median absolute deviation (MAD) to identify traffic outlier data and use linear interpolation to replace traffic outlier data.

[0033] S221. Calculate the median absolute deviation MAD:

[0034] MAD=median(|y i -M|) (1)

[0035] Among them, y i represents the i-th flow value in the sample data set; M represents the median of all flow values ​​in the sample data set; median represents the median operation; || represents the absolute value operation;

[0036] S222, determine the outlier data in the sample data set: set the judgment formula, if the i-th flow value y in the sample data set i If the judgment formula is not satisfied, then y i Determined as traffic outlier data;

[0037] S223, based on the traffic data before and after the traffic outlier data, using linear interpolation to obtain traffic data interpolation and replace the traffic outlier data;

[0038] S23. Smoothing flow data: using Gaussian filtering to smooth the flow data in the sample data set.

[0039] Preferably, the forgetting value f in step S41 is t The calculation is:

[0040] f t =σ(W f [h t-1 , x t ]+b f ) (3)

[0041] Among them, W f , b fThey are respectively represented as the weight value and compensation value of the forget gate.

[0042] Preferably, the judgment formula in step S222 is set as:

[0043] M-3×MAD <y i <M+3×MAD (2)。

[0044] Preferably, the loss function J during the training in step S5 is:

[0045]

[0046] Among them, Y and y represent the traffic prediction value and the actual traffic value respectively; || || 2 represents the 2-norm.

[0047] Preferably, in step S5, the device operation data flow observed at the next moment is used as the true value of the previous sampled data flow. When the deviation between it and the true value accumulates to the error threshold δ, that is, J≥δ, the weight matrix and bias vector in the model are updated by back propagation.

[0048] Preferably, the forgetting value f in step S41 is t The value range is [0,1], where 0 means discarding all information and 1 means retaining all information.

[0049] Preferably, the input and output of the sample data set in step S1 are the running time and flow rate of the centrifugal impeller pump respectively.

[0050] Compared with the prior art, the technical effects of the present invention are:

[0051] 1. The centrifugal impeller pump life prediction method based on the LSTM model designed in the present invention collects the operating status data of the centrifugal impeller pump and constructs a long short-term memory network model for model training and centrifugal impeller pump life prediction. Compared with the existing technology, the proposed method can predict the life of the centrifugal impeller pump more accurately and reliably, and is suitable for centrifugal impeller pump life prediction in complex and dynamically changing working environments.

[0052] 2. The centrifugal impeller pump life prediction method based on the LSTM model designed in the present invention can automatically extract features through data-driven processing, process complex nonlinear time series data, effectively deal with complex nonlinear time series data, and carry out prediction work in real time without relying on complex physical modeling processes or frequent updates. It has higher accuracy, flexibility and robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Other features, objects and advantages of the present application will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings.

[0054] Figure 1 This is a flow chart of the centrifugal impeller pump life prediction method based on the LSTM model of the present invention;

[0055] Figure 2a This is a data distribution diagram of the first group of centrifugal impeller pump test parts in a specific embodiment of the present invention;

[0056] Figure 2b This is a data distribution diagram of a second group of centrifugal impeller pump tests in a specific embodiment of the present invention;

[0057] Figure 2c This is a data distribution diagram of the third group of centrifugal impeller pump tests in a specific embodiment of the present invention;

[0058] Figure 3 is a schematic diagram of the training process of a model in a specific embodiment of the present invention;

[0059] Figure 4 This is a graph showing the comparison between the prediction results of the first data set and the actual data of the present invention;

[0060] Figure 5 This is a comparison diagram of the predicted results of the second data set of the present invention and the actual data. DETAILED DESCRIPTION

[0061] The present application will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to explain the relevant inventions and are not intended to limit the inventions. It should also be noted that, for ease of description, only portions relevant to the relevant inventions are shown in the accompanying drawings. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application may be combined with one another. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the examples.

[0062] Figure 1 The invention shows a centrifugal impeller pump life prediction method based on an LSTM model, which includes the following steps:

[0063] S1. Collect the operation data of the centrifugal impeller pump and construct a sample data set; the input and output of the sample data set are the operation time and flow rate of the centrifugal impeller pump respectively.

[0064] S11. Collect centrifugal impeller pump operation data using a sensor, where the centrifugal impeller pump operation data includes centrifugal impeller pump operation time and flow rate.

[0065] S12. Based on the centrifugal impeller pump operation data, construct a sample data set required for training the model.

[0066] S2. Preprocess the sample data set: Preprocessing includes removing abnormal data, cleaning outlier data, and smoothing data.

[0067] S21. Elimination of Abnormal Flow Data: Based on the normal distribution of random variables, over multiple tests, the probability of a flow measurement falling within the range of the mean plus or minus three standard deviations is 99.73%, while the probability of falling outside this range is only 0.27%. Therefore, in actual tests, if this occurs, the flow measurement data is considered unreliable and should be discarded. Therefore, flow data falling outside the range of three standard deviations above or below the mean is discarded.

[0068] S22. Cleaning flow outlier data: Due to the influence of working conditions, the collected flow data is usually missing in time. The present invention uses the median absolute deviation judgment and linear interpolation method to supplement the missing flow data.

[0069] The median absolute deviation (MAD) was discovered and popularized by Hampel in 1974. Like the mean, the median (M) is a measure of central tendency, but it has the advantage of being highly insensitive to outliers. When the breakdown point is 0.5, the median is the outlier estimation model with the highest breakdown point. MAD, as an outlier detection model, exploits this property of the median. Furthermore, MAD is completely unaffected by sample size.

[0070] The median absolute deviation (MAD) is used to identify outlier data and linear interpolation is used to replace outlier data.

[0071] S221. Calculate the median absolute deviation MAD:

[0072] MAD=median(|y i -M|) (1)

[0073] Among them, y i represents the i-th flow value in the sample data set; M represents the median of all flow values ​​in the sample data set; median represents the median operation; | | represents the absolute value operation.

[0074] S222, determine the outlier data in the sample data set: set the judgment formula, if the i-th flow value y in the sample data set i If the judgment formula is not satisfied, then y i Identified as outlier data.

[0075] Generally speaking, fluctuations within 3 times the MAD value above and below the median are considered normal data, and fluctuations exceeding 3 times the MAD value are considered outliers. The judgment formula is set as:

[0076] M i -3×MAD <y i <M i +3×MAD (2).

[0077] S223. Based on the flow data before and after the outlier flow data, a flow interpolation method is used to obtain a flow interpolation value and replace the outlier flow data. Linear interpolation refers to an interpolation method in which the interpolation function is a first-order polynomial, and the interpolation error at the interpolation node is zero.

[0078] S23. Smoothing traffic data: Gaussian filtering is used to smooth the traffic data in the sample dataset. The basic principle of Gaussian filtering is to use a Gaussian function to perform a convolution operation on the image to achieve image smoothing and noise reduction. The Gaussian function is a bell-shaped curve with central symmetry and a single peak. Its mathematical expression is:

[0079]

[0080] Where (x, y) represents the spatial coordinates, and ε represents the standard deviation, which controls the width of the Gaussian function and determines the smoothness of the filter. A smaller ε results in less blurring, while a larger ε produces a stronger blurring effect.

[0081] Gaussian filtering uses a convolution kernel (also called a template or mask) to scan every pixel in the image, replacing the value of the central pixel in the template with the weighted average grayscale value of the pixels in the neighborhood determined by the template. The convolution kernel value is no longer 1, but is set according to the value of the Gaussian function, giving the center point a larger weight and points farther away a smaller weight, thereby achieving image smoothing and noise reduction.

[0082] S3. Divide the sample data set: Divide the sample data set into a first data set and a second data set. The first data set is used for model training, and the second data set is used for model testing.

[0083] The first length is λ s The sequence is used as the model input, and the second length is λ t The sequence is used as the label when training the model. The formula for calculating the sequence length is:

[0084]

[0085] Among them, α represents the partition factor; λ n Represents all sequences.

[0086] S4. Construct a long short-term memory network model: The long short-term memory network model includes a forget gate, an input gate, and an output gate.

[0087] The Long Short-Term Memory (LSTM) network model is a variant of the RNN (Recurrent Neural Network) and is particularly well-suited for processing sequential data. It can handle variable-length sequence inputs and leverage the hidden state from the previous moment to influence the output at the current moment. However, traditional RNNs encounter vanishing or exploding gradients when handling tasks with long-term dependencies, making it difficult to learn long-term memories. The LSTM network is a specialized implementation of the RNN that primarily improves the hidden layer of the RNN by adding three gate control units to the hidden layer to control self-loops, addressing the RNN's inability to capture long-term dependencies.

[0088] S41. Establish a forget gate: Use the Sigmoid activation function to convert the memory cell h at the previous moment t-1 t-1 The running time x of the centrifugal impeller pump input at the current time t t Converted to forget value f t ; Forgetting value f t The calculation is:

[0089] f t =σ(W f [h t-1 , x t ]+b f ) (3)

[0090] Among them, W f , b f They represent the weight value and compensation value of the forget gate respectively; σ(·) represents the Sigmoid activation function operation.

[0091] Forgetting value f t The value range is [0,1], where 0 means discarding all information and 1 means retaining all information.

[0092] S42. Establish input gate: Use Sigmoid activation function and tanh function to obtain the new memory cell state.

[0093] S421, use the Sigmoid activation function to activate the memory cell h at the previous moment t-1 t-1 The running time x of the centrifugal impeller pump input at the current time t t Converted into input value I t , expressed as:

[0094] I t =σ(W1[h t-1 , x t ]+b1) (4)

[0095] Wherein, W1 and b1 represent the first weight value and the first compensation value of the input gate respectively.

[0096] S422, using the tanh function to compare the memory cell at the previous moment with the centrifugal impeller pump running time x input at the current moment t. t Transformation into a new candidate memory cell state Expressed as:

[0097]

[0098] Among them, W c , b c They are represented as the second weight value and the second compensation value of the input gate respectively.

[0099] S423, based on the forget gate acting on the memory cell state C at the previous time t-1 t-1 , will I t Acting on new candidate memory cell states Get the new memory cell state C at the current time t t :

[0100]

[0101] S43. Establish output gate: Use Sigmoid activation function and tanh function to obtain new memory cells.

[0102] S431, use the Sigmoid activation function to combine the memory cell at the previous time t-1 with the centrifugal impeller pump running time x input at the current time t t Converted to output value O t , expressed as:

[0103] O t =σ(W0[h t-1 , x t ]+b0) (7)

[0104] Among them, W0 and b0 represent the weight value and compensation value of the output gate respectively.

[0105] S432, based on the output value O t , apply the tanh function to the new memory cell state C t , obtain the new memory cell h at the current time t t :

[0106] h t =O t tanhC t (8).

[0107] S5. Train and test the long short-term memory network model to obtain a trained long short-term memory network model: train the long short-term memory network model based on the first data set, and test the long short-term memory network model based on the second data set.

[0108] The loss function J during training is:

[0109]

[0110] Among them, Y and y represent the traffic prediction value and the actual traffic value respectively; || || 2 represents the 2-norm.

[0111] The device's operating data flow observed at the next moment is used as the true value of the previously sampled data flow. When the cumulative deviation from the true value reaches the error threshold δ, that is, when J ≥ δ, the weight matrix and bias vector in the model are updated through backpropagation. As the model runs longer, the model accuracy will continue to improve. δ is customized according to the actual situation.

[0112] S6. Predict the life of the centrifugal impeller pump based on the trained long short-term memory network model: Based on the trained long short-term memory network model, continue to increase the running time of the centrifugal impeller pump and predict the flow of the centrifugal impeller pump. When the flow of the centrifugal impeller pump is lower than the set threshold, the centrifugal impeller pump fails, that is, the life of the centrifugal impeller pump is obtained.

[0113] In a specific embodiment, taking a company's centrifugal impeller pump life survey test as an example, the life survey prediction behavior of the centrifugal impeller pump is simulated based on the LSTM model.

[0114] Continue testing using the test piece that has already undergone durability testing. Power the test bench motor and start the centrifugal impeller pump according to the equipment manual. After the centrifugal impeller pump starts, adjust the pressure gauge and flow valve to control the centrifugal impeller pump's output flow rate. Use the pressure gauge and flowmeter to record the pump's head and flow rate in real time under 100% operating conditions.

[0115] The operating status data of the centrifugal impeller pump, including pressure and flow, are collected and preprocessed. Values ​​that do not change over time are deleted, and the data are normalized so that the values ​​of each group are close to 1. Random variable-length sample data are obtained by randomly selecting values ​​using the operating status data to enhance the diversity of the sample data.

[0116] The distribution of the test data of the centrifugal impeller pump is as follows: Figures 2a to 2c As shown, the horizontal axis is the data serial number, and the vertical axis is the pump flow m 3 / h, Figure 2aThe flow rate increases due to the large opening of the medium valve (i.e., the head change). The outlier data are determined based on the median absolute deviation (MAD) and replaced by linear interpolation.

[0117] The operating status data is divided into a first data set and a second data set, 60% of the operating status data is used as the first data set for model training, and the remaining 40% of the operating status data is used as the second data set for life prediction.

[0118] A long short-term memory network model was established, and the operating status data was used for model training. The overall degradation characteristics of the centrifugal pump life cycle were obtained through weighted smoothing.

[0119] After obtaining the trained model, the second data set is input into the model for iterative calculation to obtain the flow prediction result of the centrifugal impeller pump. When the flow of the centrifugal impeller pump is lower than the set threshold, the centrifugal impeller pump fails, and the life prediction of the centrifugal impeller pump is obtained.

[0120] In this example, the LSTM model is trained with a learning rate of 0.0001 and 200 iterations are completed. The loss function is determined, the prediction results are compared with the actual data, the prediction accuracy is determined, and the training process is plotted and output, as shown in the following figure: Figure 3 The comparison between the prediction results of the first dataset and the real data is shown in Figure 4 As shown, the comparison results between the prediction results of the second data set and the real data are as follows Figure 5 shown.

[0121] The present invention designs a centrifugal impeller pump life prediction method based on the LSTM model. By collecting the operating status data of the centrifugal impeller pump, a long short-term memory network model is constructed to perform model training and centrifugal impeller pump life prediction. Compared with the existing technology, the proposed method can more accurately and reliably predict the life of the centrifugal impeller pump, and is suitable for centrifugal impeller pump life prediction in complex and dynamically changing working environments; it can automatically extract features through data-driven processing, process complex nonlinear time series data, effectively cope with complex nonlinear time series data, and can carry out prediction work in real time without relying on complex physical modeling processes or frequent updates, and has higher accuracy, flexibility and robustness.

[0122] Finally, it should be noted that the above embodiments are only intended to illustrate rather than limit the technical solutions of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the present invention can still be modified or replaced by equivalents. Any modification or partial replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A centrifugal impeller pump life prediction method based on LSTM model, characterized in that: It includes the following steps: S1. Collect the operation data of the centrifugal impeller pump and construct a sample data set; S11, using a sensor to collect centrifugal impeller pump operating data, wherein the centrifugal impeller pump operating data includes centrifugal impeller pump operating time and flow rate; S12. Construct a sample data set required for training the model based on the centrifugal impeller pump operation data; S2. Preprocessing the sample data set: the preprocessing includes removing abnormal data, cleaning outlier data, and smoothing data; S3. Divide the sample data set: divide the sample data set into a first data set and a second data set, where the first data set is used for model training and the second data set is used for model testing; S4. Constructing a long short-term memory network model: the long short-term memory network model includes a forget gate, an input gate, and an output gate; S41. Establish a forget gate: Use the Sigmoid activation function to convert the memory cell h at the previous moment t-1 t-1 The running time x of the centrifugal impeller pump input at the current time t t Converted to forget value f t ; S42. Establish input gate: Use Sigmoid activation function and tanh function to obtain new memory cell state; S421, use the Sigmoid activation function to activate the memory cell h at the previous moment t-1 t-1 The running time x of the centrifugal impeller pump input at the current time t t Converted into input value I t , expressed as: AND t =σ(W1[h t-1 ,x t ]+b1) (4) Where W1 and b1 represent the first weight value and the first compensation value of the input gate respectively; σ(·) represents the Sigmoid activation function operation; S422, using the tanh function to compare the memory cell at the previous moment with the centrifugal impeller pump running time x input at the current moment t. t Transformation into a new candidate memory cell state Expressed as: Among them, W c , b c They are respectively represented as the second weight value and the second compensation value of the input gate; S423, based on the forget gate acting on the memory cell state C at the previous time t-1 t-1 , will I t Acting on new candidate memory cell states Get the new memory cell state C at the current time t t : S43. Establish output gate: use Sigmoid activation function and tanh function to obtain new memory cells; S431, use the Sigmoid activation function to combine the memory cell at the previous time t-1 with the centrifugal impeller pump running time x input at the current time t t Converted to output value O t , expressed as: ABOUT t =σ(W0[h t-1 ,x t ]+b0) (7) Among them, W0 and b0 represent the weight value and compensation value of the output gate respectively; S432, based on the output value O t , apply the tanh function to the new memory cell state C t , obtain the new memory cell h at the current time t t : h t =Oh t underC t (8); S5. Training and testing a long short-term memory network model to obtain a trained long short-term memory network model: training the long short-term memory network model based on the first data set, and testing the long short-term memory network model based on the second data set; S6. Predict the life of the centrifugal impeller pump based on the trained long short-term memory network model: Based on the trained long short-term memory network model, continue to increase the running time of the centrifugal impeller pump and predict the flow of the centrifugal impeller pump. When the flow of the centrifugal impeller pump is lower than the set threshold, the centrifugal impeller pump fails, thereby obtaining the life of the centrifugal impeller pump.

2. The centrifugal impeller pump life prediction method based on the LSTM model according to claim 1 is characterized in that: The step S2 specifically includes the following steps: S21. Abnormal flow data elimination: According to the normal distribution law of random variables, the flow data falling outside the mean by 3 times the standard deviation is discarded; S22. Cleaning traffic outlier data: Use the median absolute deviation (MAD) to identify traffic outlier data and use linear interpolation to replace traffic outlier data. S221. Calculate the median absolute deviation MAD: MAD=median(|y i -M|) (1) Among them, y i represents the i-th flow value in the sample data set; M represents the median of all flow values ​​in the sample data set; median represents the median operation; || represents the absolute value operation; S222, determine the outlier data in the sample data set: set the judgment formula, if the i-th flow value y in the sample data set i If the judgment formula is not satisfied, then y i Determined as traffic outlier data; S223, based on the traffic data before and after the traffic outlier data, using linear interpolation to obtain traffic data interpolation and replace the traffic outlier data; S23. Smoothing flow data: using Gaussian filtering to smooth the flow data in the sample data set.

3. The centrifugal impeller pump life prediction method based on the LSTM model according to claim 1 is characterized in that: The forgotten value f in step S41 t The calculation is: f t =σ(W f [h t-1 ,x t ]+b f ) (3) Among them, W f , b f They are respectively represented as the weight value and compensation value of the forget gate.

4. The centrifugal impeller pump life prediction method based on the LSTM model according to claim 2, characterized in that: The judgment formula in step S222 is set as: M-3×MAD<y i <M+3×MAD (2)。 5. The centrifugal impeller pump life prediction method based on the LSTM model according to claim 2, characterized in that: The loss function J during the training in step S5 is: Among them, Y, y represent the traffic prediction value and the actual traffic value respectively; |||| 2 represents the 2-norm.

6. The centrifugal impeller pump life prediction method based on the LSTM model according to claim 1, characterized in that: In step S5, the device operation data flow observed at the next moment is used as the true value of the previous sampled data flow. When the deviation between it and the true value accumulates to the error threshold δ, that is, J ≥ δ, the weight matrix and bias vector in the model are updated through back propagation.

7. The centrifugal impeller pump life prediction method based on the LSTM model according to claim 1 is characterized in that: The forgotten value f in step S41 t The value range is [0,1], where 0 means discarding all information and 1 means retaining all information.

8. The centrifugal impeller pump life prediction method based on the LSTM model according to claim 1, characterized in that: The input and output of the sample data set in step S1 are the running time and flow rate of the centrifugal impeller pump respectively.

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