CN114109859A - A Neural Network Prediction Method of Centrifugal Pump Performance Without Flow Sensing - Google Patents
A Neural Network Prediction Method of Centrifugal Pump Performance Without Flow Sensing Download PDFInfo
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Abstract
一种无流量传感的离心泵性能神经网络预测方法，包括以下步骤：步骤1.通过离心泵水力性能试验，获取不同转速下的离心泵流量、扬程、电机输入功率和工作转速频率；步骤2.建立不同转速下的离心泵流量–扬程、流量–功率多项式拟合方程，对流量–功率方程进行求导，判定其是否存在极值；步骤3.若功率不存在极值，以工作转速频率、电机输入功率作为输入参数，建立离心泵流量、扬程双神经网络预测模型；若功率存在极值，以工作转速频率、扬程作为输入参数，建立离心泵流量单神经网络预测模型；步骤4.将训练好的神经网络预测模型，植入离心泵控制器，基于实时测量数据，实现离心泵性能的准确预测。本发明保证设备运行的安全性和可靠性。
A method for predicting the performance of a centrifugal pump without flow sensing by a neural network, comprising the following steps: Step 1. Obtain the flow rate, head, motor input power and working speed frequency of the centrifugal pump under different rotational speeds through a hydraulic performance test of the centrifugal pump; Step 2 .Establish the flow-head, flow-power polynomial fitting equations of the centrifugal pump at different speeds, and derive the flow-power equation to determine whether it has an extreme value; Step 3. If the power does not have an extreme value, use the working speed frequency 2. The input power of the motor is used as the input parameter to establish a dual neural network prediction model of centrifugal pump flow and head; if the power has an extreme value, the working speed frequency and head are used as input parameters to establish a single neural network prediction model for centrifugal pump flow; Step 4. The trained neural network prediction model is implanted into the centrifugal pump controller, and based on real-time measurement data, it can accurately predict the performance of the centrifugal pump. The present invention ensures the safety and reliability of equipment operation.
Description
Technical Field
The invention belongs to the field of pump performance prediction methods, and particularly relates to a flow-sensor-free centrifugal pump performance neural network prediction method which is mainly used for rapidly and accurately predicting working performance parameters of a centrifugal pump, accurately predicting flow and efficiency parameters of the pump under the condition of no flow sensor, realizing intelligent monitoring and diagnosis of an equipment operation state, and ensuring the safety and reliability of equipment operation.
Background
For a centrifugal pump system, in order to ensure the safety and reliability of the system operation, the operation state of equipment needs to be mastered in real time, and the operation fault of the equipment needs to be pre-judged. However, due to the limitation of physical space or the limitation of a special working environment, the flow sensor cannot be installed in the existing system to acquire the flow information during the operation of the actual device, and due to the lack of the flow information, the field worker cannot effectively perform the evaluation and diagnosis of the operation state of the device. In order to solve the problem, some researchers have proposed "a method for predicting centrifugal pump flow based on power and pressure difference" CN 201410538240.7, which predicts the flow of a centrifugal pump by combining a flow-torque (power) equation and a flow-pressure difference equation, so as to realize the prediction of the centrifugal pump flow, but the method has certain defects in the selection of a prediction model and the accuracy of flow prediction. On the basis, researchers provide a pump and fan performance prediction method based on uncertainty analysis (CN201910930803. X), aiming at the problem of flow prediction of a pump and a fan under the condition of no flow meter, the method is used for realizing accurate prediction of pump and fan performance parameters and real-time monitoring of the running state of equipment by establishing a flow-pressure difference and flow-power polynomial fitting equation, carrying out uncertainty analysis on the fitness of different models and selecting a prediction model with lower uncertainty. However, the method still adopts the traditional polynomial fitting equation as a mathematical prediction model in a prediction mode, and the prediction effect on the operation data of the centrifugal pump with high volatility is poor. Aiming at the problems, the invention provides a centrifugal pump performance neural network prediction method without flow sensor, which gets rid of the constraint of the traditional polynomial fitting equation, and carries out large-scale training on random sample data by means of a neural network model to obtain a centrifugal pump performance prediction model with higher precision and wider applicability so as to realize accurate prediction of the centrifugal pump performance without the flow sensor.
The existing method for predicting the performance of the centrifugal pump without flow sensing has the following defects: 1) for a centrifugal pump model with extreme power, the traditional flow prediction method based on power and rotating speed has the defects that two corresponding flow values exist under the same power, and the effective prediction of the flow cannot be realized; 2) most of traditional centrifugal pump flow prediction methods rely on a flow-lift polynomial fitting equation or a flow-power polynomial fitting equation, the model aims at a centrifugal pump model with high volatility, the error of the fitting equation is large, and certain defects exist in flow prediction accuracy.
Disclosure of Invention
Aiming at a specific centrifugal pump system, related test data of the centrifugal pump is obtained by a pump hydraulic performance test system in a laboratory, a neural network model is adopted to carry out large-scale training on sample data, and the flow and efficiency parameters of the pump are accurately predicted under the condition without a flow sensor through the neural network prediction model, so that the intelligent monitoring and diagnosis of the running state of equipment are realized, and the safety and the reliability of the running of the equipment are ensured.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a centrifugal pump performance neural network prediction method without flow sensing comprises the following steps:
step 3, if the power has no extreme value, establishing a centrifugal pump flow and lift dual-neural network prediction model by taking the working rotating speed frequency and the motor input power as input parameters; if the power has an extreme value, establishing a centrifugal pump flow single neural network prediction model by taking the working rotating speed frequency and the lift as input parameters;
and 4, implanting the trained neural network prediction model into a centrifugal pump controller, and realizing accurate prediction of the performance of the centrifugal pump based on real-time measurement data.
Further, in the step 1, a centrifugal pump characteristic test is performed under a laboratory condition, measured values of the centrifugal pump flow Q, the lift H, the motor input power P and the working rotational speed frequency f at different rotational speeds are obtained by means of a centrifugal pump hydraulic performance test system, test data are collated, and a centrifugal pump flow-lift curve and a flow-power curve at different rotational speeds are drawn.
Furthermore, in the step 2, according to the measured values of the flow Q, the lift H, the power P and the working rotational speed frequency f at different rotational speeds, a polynomial fitting equation is adopted to establish an approximate equation of the centrifugal pump lift, the flow and the rotational speed frequency, as shown in equation (1); an approximate equation of power, flow and rotating speed frequency is shown in a formula (2);
H＝a00+a10f+a01Q+a20f2+a11fQ+a02Q2+a21f2Q+a12fQ2+a03Q3+a30f3 (1)
P＝b00+b10f+b01Q+b20f2+b11fQ+b02Q2+b21f2Q+b12fQ2+b03Q3+b30f3 (2)
wherein Q is the flow rate of the pump, P is the input power of the motor, f is the working speed frequency of the water pump, H is the lift of the pump, a00To a30For the coefficients of the head approximation equation, b00To b30Are coefficients of a power approximation equation;
the fixed rotating speed is not changed, namely the working rotating speed frequency f is a constant value, the derivation is carried out on the power-flow approximate equation, and the calculation formula is shown as a formula (3);
in the formula, c00To c02Is the coefficient of the power derivative equation;
under the condition of specified rotating speed frequency, the value in the flow interval range, namely Q epsilon [0, Qmax]And substituting the value into the formula (3) to judge whether zero exists or not, namely whether an extreme value exists or not.
Further, the process of step 3 is as follows:
firstly, based on the judgment result of the step 2, if the power has no extreme value, establishing a centrifugal pump flow and delivery head dual-neural network prediction model by taking the working rotating speed frequency and the motor input power as input parameters, wherein the neural network prediction model adopts a three-layer back propagation BP neural network method and is an input layer, a hidden layer and an output layer respectively; the flow neural network prediction model of the centrifugal pump is defined as QNN1, an input layer of the model comprises 2 neurons which are respectively input power and working rotating speed frequency of a motor, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron which is output flow; the prediction model of the lift neural network is defined as HNN1, an input layer of the prediction model of the lift neural network comprises 2 neurons which are respectively flow and working rotating speed frequency, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron which is output for the lift; the structure of the concrete double neural network prediction model is shown in FIG. 6; if the power has an extreme value, establishing a centrifugal pump flow single neural network prediction model by taking the working rotating speed frequency and the lift as input parameters, wherein the neural network prediction model also adopts a three-layer back propagation BP neural network method and is respectively an input layer, a hidden layer and an output layer; the neural network prediction model of the centrifugal pump flow is defined as QNN2, an input layer of the neural network prediction model comprises 2 neurons which are respectively a head and a working rotating speed frequency, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron which is flow output; the structure of a specific single neural network prediction model is shown in fig. 7;
secondly, taking the test measurement sample values (flow, lift, motor input power and working rotating speed frequency) obtained in the step 1 as initial samples of neural network training, and respectively training a flow neural network prediction model QNN1 and a lift neural network prediction model HNN1 aiming at the condition that the power has no extreme value; when QNN1 is trained, working rotation speed frequency and motor input power are used as training input samples, flow is used as a training output target result, the training samples are distributed into a training sample data set, a verification sample data set and a test sample data set according to the proportion of 80%, 10% and 10% by adopting a sample random distribution method, a Bayesian Regularization algorithm is adopted in a neural network training algorithm, the number of training iterations is set to 5000, the learning rate is set to 0.1, and the target error is set to 0.00001; after the training is finished, respectively carrying out correlation analysis between a target value and an output value on a training result, a verification result and a test result, namely R value analysis, wherein the closer the R value is to 1, the more accurate the trained model is; when HNN1 is trained, working speed frequency and flow are used as training input samples, lift is used as a training output target result, the training samples are distributed into a training sample data set, a verification sample data set and a test sample data set according to the proportion of 80%, 10% and 10% by adopting a sample random distribution method, a Bayesian Regularization algorithm is adopted for a neural network training algorithm, the number of training iterations is set to 5000, the learning rate is set to 0.1, and the target error is set to 0.00001; after the training is finished, respectively carrying out correlation analysis between a target value and an output value on a training result, a verification result and a test result, namely R value analysis, wherein the closer the R value is to 1, the more accurate the trained model is;
aiming at the condition that the power has an extreme value, training a flow neural network prediction model QNN 2; the method comprises the steps of taking the working rotation speed frequency and the lift as training input samples, taking the flow as a training output target result, adopting a sample random distribution method to distribute the training samples into a training sample data set, a verification sample data set and a test sample data set according to the proportion of 80%, 10% and 10%, adopting a Bayesian Regularization algorithm for a neural network training algorithm, setting the number of training iterations to be 5000, setting the learning rate to be 0.1, and setting the target error to be 0.00001; after the training is finished, the correlation analysis between the target value and the output value is respectively carried out on the training result, the verification result and the test result, namely the R value analysis, and the closer the R value is to 1, the more accurate the trained model is.
In the step 4, finally determining a neural network prediction model selected by the centrifugal pump under different power characteristics through the step 3, implanting the trained neural network prediction model into a centrifugal pump controller, wherein the controller can be a standard programmable logic controller PLC or a customized singlechip controller, and respectively adopts a power sensor, a pressure sensor and a rotating speed sensor to measure the data of motor input power P, pressure difference delta P and rotating speed n when the equipment works in real time, and respectively converts the measured pressure difference value and rotating speed value into a lift value H and a working rotating speed frequency value f by using formulas (4) and (5); the flow value under the current state is predicted by utilizing a neural network prediction model, and meanwhile, the operation efficiency eta of the equipment under the current state is calculated through a formula (6), so that the accurate prediction of the equipment performance and the intelligent monitoring and diagnosis of the equipment operation state are realized;
in the formula, Δ p is a measured differential pressure value, ρ is the density of the medium, and g is the gravitational acceleration;
wherein n is the measured rotational speed value;
where η is the efficiency value of the centrifugal pump apparatus.
The invention has the following beneficial effects: 1) the method has the advantages that the centrifugal pump performance prediction model is quickly established by means of a neural network model through a large amount of sample data, the traditional centrifugal pump performance prediction mathematical model based on a polynomial fitting equation does not need to be established, meanwhile, the problem of large prediction error of the traditional mathematical model is solved, and the prediction accuracy and the applicability are obviously improved; 2) by means of the controller, the flow and efficiency parameters of the pump are accurately predicted under the condition of no flow sensor, the intelligent monitoring and diagnosis of the running state of the equipment are realized, and the safety and the reliability of the running of the equipment are ensured.
Drawings
FIG. 1 is a flow chart of a centrifugal pump performance prediction method based on a neural network.
Fig. 2 is a schematic diagram of a centrifugal pump flow-lift performance curve without extreme power at different rotational speed frequencies.
FIG. 3 is a schematic diagram of a centrifugal pump flow-power performance curve with no extreme power at different rotational speed frequencies.
Fig. 4 is a diagram of a centrifugal pump flow-lift performance curve with extreme power at different rotational speed frequencies.
FIG. 5 is a schematic diagram of a centrifugal pump flow-power performance curve with extreme power at different rotational speed frequencies.
Fig. 6 is a schematic diagram of a centrifugal pump flow head dual neural network prediction model.
FIG. 7 is a schematic diagram of a single neural network prediction model of centrifugal pump flow.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 7, a neural network prediction method for centrifugal pump performance without flow sensing includes the following steps: (1) through a centrifugal pump hydraulic performance test, obtaining test sample data of centrifugal pump flow Q, lift H, motor input power P and working speed frequency f at different rotating speeds; (2) carrying out derivation on the flow-power equation, and judging whether an extreme value exists; (3) based on the power extreme value judgment, establishing a flow and lift double-neural network prediction model based on the motor input power P and the working rotating speed frequency f, or establishing a flow single-neural network prediction model based on the lift H and the working rotating speed frequency f; (4) and implanting the trained neural network prediction model into a centrifugal pump controller, and realizing accurate prediction of the flow and efficiency of the centrifugal pump based on the motor input power P and the working rotating speed frequency f or the head H and the working rotating speed frequency f which are measured in real time.
A method for predicting the performance of a centrifugal pump without flow sensing through a neural network is implemented as follows:
in this embodiment, a rated flow rate is Qn＝220m3H, rated lift is Hn54m power no-extreme centrifugal pump with rated speed n 1450r/min and rated flow Qn＝23m3H, rated lift is HnThe method comprises the steps that (6 m), a power extreme-value centrifugal pump with a rated rotating speed of n being 3000r/min serves as a test object, measured values of centrifugal pump flow Q, lift H, motor input power P and working rotating speed frequency f under different rotating speeds are obtained by means of a centrifugal pump hydraulic performance test system, and test data are arranged, wherein the performance data of the power non-extreme-value centrifugal pump are shown in figures 2 and 3; the performance data of the centrifugal pump with extreme power is shown in fig. 4 and 5;
according to the measured values of flow Q, lift H, power P and working speed frequency f at different rotating speeds, a polynomial fitting equation is adopted to establish an approximate equation of the lift of the centrifugal pump and the flow and the rotating speed frequency and an approximate equation of the power of the centrifugal pump and the flow and the rotating speed frequency, and for the centrifugal pump without extreme values, the fitting equations of the lift and the power of the centrifugal pump are shown as formulas (1) and (2); for the centrifugal pump with extreme value, the fitting equation of the head and the power is shown as the formula (3) and the formula (4);
in the formula, Q is the flow of the pump, P is the input power of the motor, f is the working rotating speed frequency of the water pump, and H is the pump head;
the fixed rotating speed is not changed, namely the working rotating speed frequency f of the electrodeless centrifugal pump is set to be 25Hz and the working rotating speed frequency f of the extremum centrifugal pump is set to be 50Hz respectively, and derivation is carried out on the power-flow approximate equation, so that the power derivation equation of the electrodeless centrifugal pump is obtained and is shown in a formula (5), and the power derivation equation of the extremum centrifugal pump is shown in a formula (6);
for no polarityThe centrifugal pump is designed to change the flow rate within a range of a predetermined rotational frequency of 25Hz, i.e., Q ∈ [0,304 ]]If the value is substituted into the formula (5), no zero value exists, that is, no extreme value exists; similarly, for a centrifugal pump with extreme values, the value within the range of the flow interval, i.e., Q ∈ [0,36.5 ], is set at a specified rotational frequency of 50Hz]If the flow rate Q is 23m, the flow rate is substituted into the formula (6)3At/h, there is an extreme value of power, i.e. one power corresponds to two flow values Q as shown in fig. 5aAnd QbThe case (1);
step 3, if the power has no extreme value, establishing a centrifugal pump flow and lift dual-neural network prediction model by taking the working rotating speed frequency and the motor input power as input parameters; if the power has an extreme value, establishing a centrifugal pump flow single neural network prediction model by taking the working rotating speed frequency and the lift as input parameters, wherein the process is as follows:
firstly, based on the judgment result of the step 2, if the power has no extreme value, establishing a centrifugal pump flow and delivery head dual-neural network prediction model by taking the working rotating speed frequency and the motor input power as input parameters, wherein the neural network prediction model adopts a three-layer back propagation BP neural network method and is an input layer, a hidden layer and an output layer respectively; the centrifugal pump flow neural network prediction model is defined as QNN1, an input layer of the centrifugal pump flow neural network prediction model comprises 2 neurons which are respectively input power and working rotating speed frequency of a motor, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron which is flow output; the prediction model of the lift neural network is defined as HNN1, an input layer of the prediction model of the lift neural network comprises 2 neurons which are respectively flow and working rotating speed frequency, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron which is output for the lift; the structure of the concrete double neural network prediction model is shown in FIG. 6; if the power has an extreme value, establishing a centrifugal pump flow single neural network prediction model by taking the working rotating speed frequency and the lift as input parameters, wherein the neural network prediction model also adopts a three-layer back propagation BP neural network method and is respectively an input layer, a hidden layer and an output layer; the centrifugal pump flow prediction neural network model is defined as QNN2, an input layer of the centrifugal pump flow prediction neural network model comprises 2 neurons which are respectively a lift and a working rotating speed frequency, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron which is flow output; the structure of a specific single neural network prediction model is shown in fig. 7;
secondly, taking the test measurement sample values (flow, lift, motor input power and working rotating speed frequency) obtained in the step 1 as initial samples of neural network training, and respectively training a flow neural network prediction model QNN1 and a lift neural network prediction model HNN1 aiming at the condition that the power has no extreme value; when QNN1 is trained, working rotation speed frequency and motor input power are used as training input samples, flow is used as a training output target result, the training samples are distributed into a training sample data set, a verification sample data set and a test sample data set according to the proportion of 80%, 10% and 10% by adopting a sample random distribution method, a Bayesian Regularization algorithm is adopted in a neural network training algorithm, the number of training iterations is set to 5000, the learning rate is set to 0.1, and the target error is set to 0.00001; after the training is finished, respectively carrying out correlation analysis between a target value and an output value on a training result, a verification result and a test result, namely R value analysis, wherein the closer the R value is to 1, the more accurate the trained model is;
the calculation process and the result of the flow neural network prediction model QNN1 are as follows:
(1) the number of training sample data of the QNN1 is 576, and a single sample data structure is a 2-input-1-output array; after training, the weight values between the hidden layer and the input layer are: wji＝[1.5185057177300039 -1.0816062382860367；-0.82170590557766132 -0.43368462242731592；-1.5375420691892427 2.4990878129156409；1.6345336154180024 1.9864682761404087；-1.6424582796932381 1.4694397221743163；-1.4049005912761443 0.64789294391630181；0.056123058048536491 0.45524318375251455；0.17106641465569442 4.4283954498788063；0.83960813389092581 3.2400543877778403；1.8590180437750123 -2.6087629357549194]；
(2) The weight value between the output layer and the hidden layer is: wjk＝[13.386565441561851 1.0673783842542564 -9.677625472550913 6.105565397594078 6.304115071751859 7.3514881020470959 6.6800873885367453 20.481181323311748 -15.330623470283937 -10.800015819777261]；
(3) The deviation value of the hidden layer is as follows: thetaj＝[-0.29791000637916154；0.68915843657038489；-1.0468508519532409；4.1695934284868894；0.49384489245817698；0.090653894638598576；0.77567907152490279；5.9608863081497017；4.8437485168780849；0.85857711613073995]；
(4) The deviation value of the output layer is: thetak＝-14.618225463470914；
The R value of the trained model is 0.99, which indicates that the model has higher precision;
when HNN1 is trained, working speed frequency and flow are used as training input samples, lift is used as a training output target result, the training samples are distributed into a training sample data set, a verification sample data set and a test sample data set according to the proportion of 80%, 10% and 10% by adopting a sample random distribution method, a Bayesian Regularization algorithm is adopted for a neural network training algorithm, the number of training iterations is set to 5000, the learning rate is set to 0.1, and the target error is set to 0.00001; after the training is finished, respectively carrying out correlation analysis between a target value and an output value on a training result, a verification result and a test result, namely R value analysis, wherein the closer the R value is to 1, the more accurate the trained model is;
the calculation process and the result of the lift neural network prediction model HNN1 are as follows:
(1) training sample data of HNN1 are 576, and a single sample data structure is a 2-input-1-output array; after training, the weight values between the hidden layer and the input layer are: wlm＝[0.37643479210148711 -1.9494116853222863；-0.31533835611991956 0.87882442207770328；-0.11942789817950646 1.517331460816995；0.31589940227211472 -0.22393931304543141；0.060840780309218176 0.39691357070649635；0.3445628086186337 -1.9517855886889086；0.45319772290758298 -0.12236582432135261；-0.31557100052407211 -0.13373480926869158；0.44211798841786093 0.0041035139725915017；0.29280674754855807 -0.17482482891633419]；
(2) The weight value between the output layer and the hidden layer is: wmn＝[0.49552913902096524 -1.1751452109190943 1.748875394887244 1.697893450307995 1.2479514147553616 -0.48972767968701764 -0.94083944143662701 -0.23305069460661784 1.5654072572678313 1.1579090774449388]；
(3) The deviation value of the hidden layer is as follows: thetam＝[-0.98268762197267001；-0.81350176851257261；3.0508293804100064；-0.96004780385719368；0.069822568508764393；-0.96022154501324142；1.1681716713186154；0.29167584386288459；-1.2886605150512442；0.011950150180516012]；
(4) The deviation value of the output layer is: thetan＝0.65938471925523356；
The R value of the trained model is 0.99, which indicates that the model has higher precision;
aiming at the condition that the power has an extreme value, training a flow prediction neural network model QNN 2; the method comprises the steps of taking the working rotation speed frequency and the lift as training input samples, taking the flow as a training output target result, adopting a sample random distribution method to distribute the training samples into a training sample data set, a verification sample data set and a test sample data set according to the proportion of 80%, 10% and 10%, adopting a Bayesian Regularization algorithm for a neural network training algorithm, setting the number of training iterations to be 5000, setting the learning rate to be 0.1, and setting the target error to be 0.00001; after the training is finished, respectively carrying out correlation analysis between a target value and an output value on a training result, a verification result and a test result, namely R value analysis, wherein the closer the R value is to 1, the more accurate the trained model is;
the calculation process and the result of the flow neural network prediction model QNN2 are as follows:
(1) 176 training sample data of the QNN2, wherein a single sample data structure is a 2-input-1-output array; after training, the weight values between the hidden layer and the input layer are: wji＝[-2.5821511516348687 5.4375862612935899；-1.0527209497446122 0.78095192090266619；-0.2851390366636492 4.9731750299431345；4.9625499562710287 -5.5313896919574148；-0.040428344124143978 0.59493820813650489；-0.047370611484339263 0.040261134106334411；0.69574360993195339 0.097211842069689219；-3.727297437881592 -0.36261347109359521；-3.2962671666415182 7.6660579417285444；5.3593475560431978 -4.243655923809893]；
(2) The weight value between the output layer and the hidden layer is: wjk＝[2.9862607919819064 -1.0783877647645854 -3.0552578008767455 6.8282219192223774 0.47194204951342272 -0.74701648722196379 -0.51146361990571809 -2.9260886520334828 4.4419681307142298 -6.2290541666178347]；
(3) The deviation value of the hidden layer is as follows: thetaj＝[-1.3438240014154892；0.85606637375224992；-3.4509799809951427；0.35272911696791281；0.00016848921099855323；-0.63262208474434412；0.5262009328915247；-4.5416630034137837；-3.6583055068226664；1.658309475708992]；
(4) The deviation value of the output layer is: thetak＝1.3026550645425219；
The R value of the trained model is 0.98, which indicates that the model has higher precision;
step 4, implanting the trained neural network prediction model into a centrifugal pump controller, and realizing accurate prediction of the performance of the centrifugal pump based on real-time measurement data, wherein the process is as follows:
finally determining a neural network prediction model selected by the centrifugal pump under different power characteristics through step 3, implanting the trained neural network prediction model into a centrifugal pump controller, wherein the controller can be a standard Programmable Logic Controller (PLC) or a customized controller based on a single chip microcomputer system, and respectively measuring data of motor input power P, differential pressure delta P and rotating speed n when the equipment works by adopting a power sensor, a pressure sensor and a rotating speed sensor in real time; taking a centrifugal pump without extreme power as an example, the measured power and rotating speed data are shown in table 1, and the working rotating speed frequencies corresponding to different measured rotating speeds are calculated by adopting a formula (7); the comparison between the output predicted flow and lift and the measured flow and lift of the model is shown in table 2, wherein the calculation of the prediction efficiency is calculated by formula (8).
Wherein n is the measured rotational speed value;
where η is the efficiency value of the centrifugal pump apparatus.
Table 1 shows real-time measurement data of the power stepless centrifugal pump.
| Working speed n (r/min) | Operating speed frequency f (Hz) | Input power P (kW) of motor |
| 1500 | 25 | 16.42 |
| 1350 | 22.5 | 27.34 |
| 1200 | 20 | 19.2 |
| 900 | 15 | 8.89 |
| 750 | 12.5 | 4.53 |
TABLE 1
And table 2 shows comparative analysis of the predicted performance and the measured performance of the power stepless centrifugal pump.
TABLE 2
Finally, real-time flow, lift and efficiency operation parameter values are obtained through a neural network prediction model, performance prediction of the centrifugal pump and real-time monitoring of the operation state of the equipment are achieved under the condition of no flow sensor, and safety and reliability of equipment operation are guaranteed.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but encompasses equivalent technical means as would be appreciated by those skilled in the art based on the inventive concept.
Claims (5)
1. A neural network prediction method for centrifugal pump performance without flow sensing is characterized by comprising the following steps:
step 1, acquiring test sample data of centrifugal pump flow Q, lift H, motor input power P and working rotating speed frequency f at different rotating speeds through a centrifugal pump hydraulic performance test;
step 2, establishing a polynomial fitting equation of the flow-lift and the flow-power of the centrifugal pump at different rotating speeds, and carrying out derivation on the flow-power equation to judge whether an extreme value exists;
step 3, if the power has no extreme value, establishing a centrifugal pump flow and lift dual-neural network prediction model by taking the working rotating speed frequency and the motor input power as input parameters; if the power has an extreme value, establishing a centrifugal pump flow single neural network prediction model by taking the working rotating speed frequency and the lift as input parameters;
and 4, implanting the trained neural network prediction model into a centrifugal pump controller, and realizing accurate prediction of the performance of the centrifugal pump based on real-time measurement data.
2. The neural network prediction method for centrifugal pump performance without flow sensing in claim 1, wherein in step 1, a centrifugal pump characteristic test is performed under laboratory conditions, measured values of centrifugal pump flow Q, pump lift H, motor input power P and operating speed frequency f at different rotating speeds are obtained by means of a centrifugal pump hydraulic performance test system, test data are collated, and a centrifugal pump flow-pump lift curve and a flow-power curve at different rotating speeds are drawn.
3. The neural network prediction method for centrifugal pump performance without flow sensing in claim 1 or 2, characterized in that in the step 2, a polynomial fitting equation is used to establish an approximate equation of the centrifugal pump head, the flow and the rotating speed frequency according to the measured values of the flow Q, the head H, the power P and the working rotating speed frequency f at different rotating speeds, as shown in the equation (1); an approximate equation of power, flow and rotating speed frequency is shown in a formula (2);
H＝a00+a10f+a01Q+a20f2+a11fQ+a02Q2+a21f2Q+a12fQ2+a03Q3+a30f3 (1)
P＝b00+b10f+b01Q+b20f2+b11fQ+b02Q2+b21f2Q+b12fQ2+b03Q3+b30f3 (2)
wherein Q is the flow rate of the pump, P is the input power of the motor, f is the working speed frequency of the water pump, H is the lift of the pump, a00To a30Is a square approximation of the liftCoefficient of the equation, b00To b30Are coefficients of a power approximation equation;
the fixed rotating speed is not changed, namely the working rotating speed frequency f is a constant value, the derivation is carried out on the power-flow approximate equation, and the calculation formula is shown as a formula (3);
in the formula, c00To c02Is the coefficient of the power derivative equation;
under the condition of specified rotating speed frequency, the value in the flow interval range, namely Q epsilon [0, Qmax]And substituting the value into the formula (3) to judge whether zero exists or not, namely whether an extreme value exists or not.
4. The neural network prediction method for centrifugal pump performance without flow sensing as claimed in claim 1 or 2, wherein the process of step 3 is as follows:
firstly, based on the judgment result of the step 2, if the power has no extreme value, establishing a centrifugal pump flow and delivery head dual-neural network prediction model by taking the working rotating speed frequency and the motor input power as input parameters, wherein the neural network prediction model adopts a three-layer back propagation BP neural network method and is an input layer, a hidden layer and an output layer respectively; the flow neural network prediction model of the centrifugal pump is defined as QNN1, an input layer of the model comprises 2 neurons which are respectively input power and working rotating speed frequency of a motor, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron which is output flow; the prediction model of the lift neural network is defined as HNN1, an input layer of the prediction model of the lift neural network comprises 2 neurons which are respectively flow and working rotating speed frequency, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron which is output for the lift; the structure of the concrete double neural network prediction model is shown in FIG. 6; if the power has an extreme value, establishing a centrifugal pump flow single neural network prediction model by taking the working rotating speed frequency and the lift as input parameters, wherein the neural network prediction model also adopts a three-layer back propagation BP neural network method and is respectively an input layer, a hidden layer and an output layer; the neural network prediction model of the centrifugal pump flow is defined as QNN2, an input layer of the neural network prediction model comprises 2 neurons which are respectively a head and a working rotating speed frequency, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron which is flow output; the structure of a specific single neural network prediction model is shown in fig. 7;
secondly, taking the test measurement sample value obtained in the step 1 as an initial sample of neural network training, and respectively training a flow neural network prediction model QNN1 and a lift neural network prediction model HNN1 aiming at the condition that the power has no extreme value; when QNN1 is trained, working speed frequency and motor input power are used as training input samples, flow is used as a training output target result, the training samples are distributed into a training sample data set, a verification sample data set and a test sample data set according to the proportion of 80%, 10% and 10% by adopting a sample random distribution method, a Bayesian regularization algorithm is adopted in a neural network training algorithm, the number of training iterations is set to 5000, the learning rate is set to 0.1, and the target error is set to 0.00001; after the training is finished, respectively carrying out correlation analysis between a target value and an output value on a training result, a verification result and a test result, namely R value analysis, wherein the closer the R value is to 1, the more accurate the trained model is; when HNN1 is trained, the working speed frequency and the flow are used as training input samples, the lift is used as a training output target result, the training samples are distributed into a training sample data set, a verification sample data set and a test sample data set according to the proportion of 80%, 10% and 10% by adopting a sample random distribution method, the neural network training algorithm adopts a Bayesian regularization algorithm, the iteration number of training is set to 5000, the learning rate is set to 0.1, and the target error is set to 0.00001; after the training is finished, respectively carrying out correlation analysis between a target value and an output value on a training result, a verification result and a test result, namely R value analysis, wherein the closer the R value is to 1, the more accurate the trained model is;
aiming at the condition that the power has an extreme value, training a flow neural network prediction model QNN 2; the method comprises the steps of taking the working rotating speed frequency and the lift as training input samples, taking the flow as a training output target result, adopting a sample random distribution method to distribute the training samples into a training sample data set, a verification sample data set and a test sample data set according to the proportion of 80%, 10% and 10%, adopting a Bayesian regularization algorithm for a neural network training algorithm, setting the number of training iterations to be 5000, setting the learning rate to be 0.1, and setting the target error to be 0.00001; after the training is finished, the correlation analysis between the target value and the output value is respectively carried out on the training result, the verification result and the test result, namely the R value analysis, and the closer the R value is to 1, the more accurate the trained model is.
5. The method for predicting the performance of the centrifugal pump without flow sensing according to claim 1 or 2, wherein in the step 4, the neural network prediction model selected by the centrifugal pump under different power characteristics is finally determined through the step 3, the trained neural network prediction model is implanted into a centrifugal pump controller, the controller can be a standard programmable logic controller PLC or a custom-made single chip microcomputer controller, the motor input power P, the differential pressure Δ P and the rotating speed n data of the device during operation are measured in real time by respectively adopting a power sensor, a pressure sensor and a rotating speed sensor, and the measured differential pressure value and the rotating speed value are converted into a lift value H and a working rotating speed frequency value f by respectively adopting formulas (4) and (5); the flow value under the current state is predicted by utilizing a neural network prediction model, and meanwhile, the operation efficiency eta of the equipment under the current state is calculated through a formula (6), so that the accurate prediction of the equipment performance and the intelligent monitoring and diagnosis of the equipment operation state are realized;
in the formula, Δ p is a measured differential pressure value, ρ is the density of the medium, and g is the gravitational acceleration;
wherein n is the measured rotational speed value;
where η is the efficiency value of the centrifugal pump apparatus.
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