CN114109859B - Centrifugal pump performance neural network prediction method without flow sensing - Google Patents
Centrifugal pump performance neural network prediction method without flow sensing Download PDFInfo
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Abstract
A centrifugal pump performance neural network prediction method without flow sensing comprises the following steps: step 1, obtaining centrifugal pump flow, lift, motor input power and working rotation speed frequency under different rotation speeds through a centrifugal pump hydraulic performance test; step 2, establishing a polynomial fitting equation of flow-lift and flow-power of the centrifugal pump at different rotating speeds, deriving the flow-power equation, and judging whether an extremum exists; step 3, if the power does not have an extreme value, establishing a centrifugal pump flow and lift dual-neural network prediction model by taking the working rotation speed frequency and the motor input power as input parameters; if the power has an extreme value, the working rotation speed frequency and the lift are used as input parameters, and a centrifugal pump flow single neural network prediction model is established; 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. The invention ensures the safety and reliability of the operation of the equipment.
Description
Technical Field
The invention belongs to the field of pump performance prediction methods, and particularly relates to a centrifugal pump performance neural network prediction method without flow sensors, 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 without flow sensors, realizing intelligent monitoring and diagnosis of equipment operation states, 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 operation of the system, the operation state of equipment needs to be mastered in real time, and the operation failure of the equipment needs to be prejudged. However, due to the limitation of physical space or the limitation of special working environment, the flow information of the actual equipment in operation cannot be obtained by installing a flow sensor in the existing system, and due to the lack of the flow information, on-site staff cannot effectively perform evaluation and diagnosis of the operation state of the equipment. In order to solve the problem, some researchers put forward a "method for predicting flow of centrifugal pump based on power and differential pressure" CN 201410538240.7, which predicts the flow of centrifugal pump by combining flow-torque (power) equation and flow-differential pressure equation, so as to realize the prediction of the flow of centrifugal pump, however, the method has certain defects in the prediction model selection and flow prediction precision. On the basis, researchers put forward a pump and fan performance prediction method based on uncertainty analysis, namely CN201910930803.X, the method aims at the problem of flow prediction of the pump and the fan under the condition of no flowmeter, and the accurate prediction of the pump and fan performance parameters and the real-time monitoring of the running state of equipment are realized 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 a traditional polynomial fitting equation as a mathematical prediction model in a prediction mode, and has poor prediction effect on the operation data of the centrifugal pump with larger volatility. Aiming at the problems, the invention provides a centrifugal pump performance neural network prediction method without flow sensing, which gets rid of the constraint of a traditional polynomial fitting equation, carries out large-scale training on random sample data by means of a neural network model, and obtains a centrifugal pump performance prediction model with higher precision and wider applicability so as to realize accurate prediction of the centrifugal pump performance without flow sensing.
The existing performance prediction method for the centrifugal pump without flow sensing has the following defects: 1) For a centrifugal pump model with an extremum in power, the traditional flow prediction method based on power and rotating speed has defects that two corresponding flow values exist under the same power, and effective prediction of flow cannot be realized; 2) Most of the traditional centrifugal pump flow prediction methods use a flow-lift polynomial fitting equation or a flow-power polynomial fitting equation, and the model aims at a centrifugal pump model with larger fluctuation, so that the fitting equation has larger error and a certain defect in flow prediction precision.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a neural network prediction method for the performance of a centrifugal pump without flow sensor, aiming at a specific centrifugal pump system, acquiring test data related to the centrifugal pump in a laboratory by means of a hydraulic performance test system of the pump, performing large-scale training on sample data by adopting a neural network model, accurately predicting flow and efficiency parameters of the pump under the condition without flow sensor by using the neural network prediction model, realizing intelligent monitoring and diagnosis of the running state of the equipment, and ensuring the running safety and reliability of the equipment.
The technical scheme adopted for solving the technical problems is as follows:
a centrifugal pump performance neural network prediction method without flow sensing comprises the following steps:
step 1, obtaining test sample data of centrifugal pump flow Q, lift H, motor input power P and working rotation speed frequency f under different rotation speeds through a centrifugal pump hydraulic performance test;
step 2, establishing a polynomial fitting equation of flow-lift and flow-power of the centrifugal pump at different rotating speeds, deriving the flow-power equation, and judging whether an extremum exists;
step 3, if the power does not have an extreme value, establishing a centrifugal pump flow and lift dual-neural network prediction model by taking the working rotation speed frequency and the motor input power as input parameters; if the power has an extreme value, a centrifugal pump flow single neural network prediction model is established by taking the working rotation 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 laboratory conditions, centrifugal pump flow Q, lift H, motor input power P, and working speed frequency f measured values at different 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 speeds are drawn.
In step 2, according to the measured values of the flow Q, the lift H, the power P and the working rotation speed frequency f at different rotation speeds, a polynomial fitting equation is adopted to establish an approximate equation of the lift, the flow and the rotation speed frequency of the centrifugal pump, as shown in a formula (1); the power, flow and rotating speed frequency approximate equation is shown in a formula (2);
H＝a 00 +a 10 f+a 01 Q+a 20 f 2 +a 11 fQ+a 02 Q 2 +a 21 f 2 Q+a 12 fQ 2 +a 03 Q 3 +a 30 f 3 (1)
P＝b 00 +b 10 f+b 01 Q+b 20 f 2 +b 11 fQ+b 02 Q 2 +b 21 f 2 Q+b 12 fQ 2 +b 03 Q 3 +b 30 f 3 (2)
wherein Q is the flow of the pump, P is the input power of the motor, f is the working rotation speed frequency of the water pump, H is the lift of the pump, a 00 To a 30 Coefficient of lift approximation equation, b 00 To b 30 Coefficients that are power approximation equations;
the fixed rotating speed is unchanged, namely the working rotating speed frequency f is a constant value, the power-flow approximation equation is derived, and the calculation formula is shown as a formula (3);
wherein, c 00 To c 02 Coefficients for the power derivative equation;
at a specified rotational speed frequency, values within the flow interval, i.e. Q.epsilon.0, Q max ]And (3) carrying out the formula (3) to judge whether zero values exist, namely whether extreme values exist.
Further, the process of the step 3 is as follows:
firstly, based on the judging result of the step 2, if the power does not have an extreme value, using the working rotation speed frequency and the motor input power as input parameters, establishing a centrifugal pump flow and lift dual-neural network prediction model, wherein the neural network prediction model 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 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 the input power and the working rotation speed frequency of a motor, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron and is used for outputting flow; the lift neural network prediction model is defined as HNN1, the input layer of the model comprises 2 neurons which are respectively flow and working rotation speed frequency, the hidden layer comprises 10 neurons, the output layer comprises 1 neuron, and the model is used for lift output; the structure of a specific dual neural network prediction model is shown in fig. 6; if the power has an extreme value, a centrifugal pump flow single neural network prediction model is established by taking the working rotation speed frequency and the lift as input parameters, and the neural network prediction model also adopts a three-layer back propagation BP neural network method which is respectively an input layer, a hidden layer and an output layer; the centrifugal pump flow neural network prediction model is defined as QNN2, an input layer of the centrifugal pump flow neural network prediction model comprises 2 neurons which are respectively the lift and the working rotation speed frequency, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron and is used for outputting flow; the structure of a specific single neural network prediction model is shown in fig. 7;
secondly, taking the test measurement sample value (flow, lift, motor input power and working rotation speed frequency) 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 according to the condition that the power is not extreme; when QNN1 is trained, the working rotation speed frequency and the motor input power are used as training input samples, the 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 proportions of 80%, 10% and 10% by adopting a sample random distribution method, a neural network training algorithm adopts a Bayesian regularization (Bayesian Regularization) algorithm, the training iteration number is set to 5000, the learning rate is set to 0.1, and the target error is set to 0.00001; after training, 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 rotation speed frequency and flow are used as training input samples, lift is used as a training output target result, a sample random distribution method is adopted to distribute 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%, a Bayesian regularization (Bayesian Regularization) algorithm is adopted by a neural network training 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 training, 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 working rotation speed frequency and the lift are used as training input samples, the flow is used as a training output target result, a sample random distribution method is adopted to distribute 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%, a Bayesian regularization (Bayesian Regularization) algorithm is adopted by a neural network training 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 training, carrying out correlation analysis between the target value and the output value on the training result, the verification result and the test result respectively, namely, R value analysis, wherein the closer the R value is to 1, the more accurate the trained model is.
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 customized singlechip controller, and the power sensor, the pressure sensor and the rotating speed sensor are respectively adopted to respectively measure the motor input power P, the pressure difference deltap and the rotating speed n data of the equipment in real time, and the measured pressure difference value and the rotating speed value are respectively converted into a lift value H and a working rotating speed frequency value f by utilizing formulas (4) and (5); the neural network prediction model is utilized to realize the prediction of the flow value in the current state, and meanwhile, the operation efficiency eta of the equipment in 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;
wherein Δp is the measured differential pressure value, ρ is the density of the medium, g is the gravitational acceleration;
wherein n is a measured rotation speed value;
where η is the efficiency value of the centrifugal pump apparatus.
The beneficial effects of the invention are mainly shown in the following steps: 1) By means of a neural network model, a centrifugal pump performance prediction model is quickly built through a large amount of sample data, a traditional centrifugal pump performance prediction mathematical model based on a polynomial fitting equation is not required to be built, meanwhile, the problem of large prediction error of the traditional mathematical model is solved, and the prediction accuracy and the applicability are remarkably 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, intelligent monitoring and diagnosis of the running state of the equipment are realized, and the running safety and reliability of the equipment are ensured.
Drawings
FIG. 1 is a flow chart of a method for predicting centrifugal pump performance based on a neural network.
Fig. 2 is a schematic diagram of flow-head performance curves of centrifugal pumps with no extreme power values at different rotational speed frequencies.
FIG. 3 is a graph showing the flow-power performance of a centrifugal pump with no power extremum at different rotational speeds and frequencies.
Fig. 4 is a schematic diagram of the flow-head performance curve of a centrifugal pump with extreme power values at different rotational frequencies.
FIG. 5 is a graph showing the flow-power performance of a centrifugal pump with extreme power values at different rotational 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 centrifugal pump flow single neural network prediction model.
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 performance of a centrifugal pump without flow sensing includes the steps of: (1) Obtaining test sample data of centrifugal pump flow Q, lift H, motor input power P and working rotation speed frequency f at different rotation speeds through a centrifugal pump hydraulic performance test; (2) Deriving a flow-power equation, and judging whether an extremum exists; (3) Based on the power extremum judgment, establishing a flow lift dual-neural network prediction model based on the input power P of the motor 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 real-time measured motor input power P and the working rotation speed frequency f or the lift H and the working rotation speed frequency f.
A centrifugal pump performance neural network prediction method without flow sensing is specifically implemented as follows:
step 1, obtaining test sample data of centrifugal pump flow Q, lift H, motor input power P and working rotation speed frequency f under different rotation speeds through a centrifugal pump hydraulic performance test;
in this embodiment, a rated flow is defined as Q n ＝220m 3 and/H, the rated lift is H n Power extremum-free centrifugal pump with nominal rotational speed n=1450 r/min =54 m and a nominal flow rate Q n ＝23m 3 and/H, the rated lift is H n A centrifugal pump with the power limit value and the rated rotating speed of n=3000 r/min is taken as a test object, the centrifugal pump flow Q, the lift H, the motor input power P and the working rotating speed frequency f measured values at different rotating speeds are obtained by means of a centrifugal pump hydraulic performance test system, test data are collated, and the centrifugal pump performance data without the power limit value are shown in fig. 2 and 3; the performance data of the centrifugal pump with the extreme value of the power is shown in fig. 4 and 5;
step 2, establishing a polynomial fitting equation of flow-lift and flow-power of the centrifugal pump at different rotating speeds, deriving the flow-power equation, and judging whether an extremum exists;
according to the measured values of flow Q, lift H, power P and working speed frequency f at different speeds, a polynomial fitting equation is adopted to establish a centrifugal pump lift and flow, speed frequency approximation equation, and a power and flow, speed frequency approximation equation, and for a centrifugal pump without extreme values, the fitting equation of lift and power is shown as formulas (1) and (2); for a centrifugal pump with an extreme value, fitting equations of the lift and the power of the centrifugal pump are shown in formulas (3) and (4);
wherein Q is the flow of the pump, P is the input power of the motor, f is the working rotation speed frequency of the water pump, and H is the lift of the pump;
the fixed rotation speed is unchanged, namely the working rotation speed frequency f of the electrodeless centrifugal pump is respectively set to be 25Hz and the working rotation speed frequency f of the centrifugal pump with the extremum is set to be 50Hz, and the power-flow approximation equation is derived, so that the power derivation equation of the electrodeless centrifugal pump is obtained as shown in a formula (5), and the power derivation equation of the centrifugal pump with the extremum is shown as a formula (6);
for a centrifugal pump without extreme value, the value in the range of the flow interval, namely Q epsilon [0,304 ], is calculated at the prescribed rotation speed frequency of 25Hz]Bringing into equation (5) that there is no zero value, i.e., no extremum; similarly, for a centrifugal pump with an extremum, at a given rotational frequency of 50Hz, the value in the flow interval range, i.e., Qε [0,36.5]Brought into equation (6), when the flow Q is 23m 3 At/h, there is an extreme value of power, i.e., one power corresponds to two flow values Q as shown in FIG. 5 a And Q b Is the case in (2);
step 3, if the power does not have an extreme value, establishing a centrifugal pump flow and lift dual-neural network prediction model by taking the working rotation speed frequency and the motor input power as input parameters; if the power has an extreme value, the working rotation speed frequency and the lift are used as input parameters, a centrifugal pump flow single neural network prediction model is built, and the process is as follows:
firstly, based on the judging result of the step 2, if the power does not have an extreme value, using the working rotation speed frequency and the motor input power as input parameters, establishing a centrifugal pump flow and lift dual-neural network prediction model, wherein the neural network prediction model 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 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 the input power and the working rotation speed frequency of a motor, a hidden layer comprises 10 neurons, an output layer comprises 1 neuron and is used for outputting flow; the lift neural network prediction model is defined as HNN1, the input layer of the model comprises 2 neurons which are respectively flow and working rotation speed frequency, the hidden layer comprises 10 neurons, the output layer comprises 1 neuron, and the model is used for lift output; the structure of a specific dual neural network prediction model is shown in fig. 6; if the power has an extreme value, a centrifugal pump flow single neural network prediction model is established by taking the working rotation speed frequency and the lift as input parameters, and the neural network prediction model also adopts a three-layer back propagation BP neural network method which 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 neural network model comprises 2 neurons which are respectively the lift and the working rotation speed frequency, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron and is used for outputting flow; the structure of a specific single neural network prediction model is shown in fig. 7;
secondly, taking the test measurement sample value (flow, lift, motor input power and working rotation speed frequency) 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 according to the condition that the power is not extreme; when QNN1 is trained, the working rotation speed frequency and the motor input power are used as training input samples, the 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 proportions of 80%, 10% and 10% by adopting a sample random distribution method, a neural network training algorithm adopts a Bayesian regularization (Bayesian Regularization) algorithm, the training iteration number is set to 5000, the learning rate is set to 0.1, and the target error is set to 0.00001; after training, 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 result of the flow neural network prediction model QNN1 are as follows:
(1) The training sample data of QNN1 is 576, and the single sample data structure is a 2-input 1-output array; after training, the weight value between the hidden layer and the input layer is as follows: w (W) ji ＝[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 as follows: w (W) jk ＝[13.386565441561851 1.0673783842542564 -9.677625472550913 6.105565397594078 6.304115071751859 7.3514881020470959 6.6800873885367453 20.481181323311748 -15.330623470283937 -10.800015819777261]；
(3) The bias value of the hidden layer is: θ j ＝[-0.29791000637916154；0.68915843657038489；-1.0468508519532409；4.1695934284868894；0.49384489245817698；0.090653894638598576；0.77567907152490279；5.9608863081497017；4.8437485168780849；0.85857711613073995]；
(4) The bias value of the output layer is: θ k ＝-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 rotation speed frequency and flow are used as training input samples, lift is used as a training output target result, a sample random distribution method is adopted to distribute 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%, a Bayesian regularization (Bayesian Regularization) algorithm is adopted by a neural network training 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 training, 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 result of the lift neural network prediction model HNN1 are as follows:
(1) The training sample data of HNN1 is 576, and the single sample data structure is a 2-input 1-output array; after training, the weight value between the hidden layer and the input layer is as follows: w (W) lm ＝[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 as follows: w (W) mn ＝[0.49552913902096524 -1.1751452109190943 1.748875394887244 1.697893450307995 1.2479514147553616 -0.48972767968701764 -0.94083944143662701 -0.23305069460661784 1.5654072572678313 1.1579090774449388]；
(3) The bias value of the hidden layer is: θ m ＝[-0.98268762197267001；-0.81350176851257261；3.0508293804100064；-0.96004780385719368；0.069822568508764393；-0.96022154501324142；1.1681716713186154；0.29167584386288459；-1.2886605150512442；0.011950150180516012]；
(4) The bias value of the output layer is: θ n ＝0.65938471925523356；
The R value of the trained model is 0.99, which indicates that the model has higher precision;
training a flow prediction neural network model QNN2 aiming at the condition that the power has an extreme value; the working rotation speed frequency and the lift are used as training input samples, the flow is used as a training output target result, a sample random distribution method is adopted to distribute 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%, a Bayesian regularization (Bayesian Regularization) algorithm is adopted by a neural network training 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 training, 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 result of the flow neural network prediction model QNN2 are as follows:
(1) The training sample data of QNN2 is 176, and the single sample data structure is a 2-input 1-output array; after training, the weight value between the hidden layer and the input layer is as follows: w (W) ji ＝[-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 as follows: w (W) jk ＝[2.9862607919819064 -1.0783877647645854 -3.0552578008767455 6.8282219192223774 0.47194204951342272 -0.74701648722196379 -0.51146361990571809 -2.9260886520334828 4.4419681307142298 -6.2290541666178347]；
(3) The bias value of the hidden layer is: θ j ＝[-1.3438240014154892；0.85606637375224992；-3.4509799809951427；0.35272911696791281；0.00016848921099855323；-0.63262208474434412；0.5262009328915247；-4.5416630034137837；-3.6583055068226664；1.658309475708992]；
(4) The bias value of the output layer is: θ k ＝1.3026550645425219；
The R value of the trained model is 0.98, which indicates that the model has higher precision;
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, wherein the process is as follows:
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, and the controller can be a standard Programmable Logic Controller (PLC) or a customized controller based on a singlechip system and respectively adopts a power sensor, a pressure sensor and a rotating speed sensor to measure the motor input power P, the pressure difference deltap and the rotating speed n data when the equipment works in real time; taking a centrifugal pump with no power extremum as an example, the measured power and the rotating speed data are shown in a table 1, and the working rotating speed frequencies corresponding to different measured rotating speeds are calculated by adopting a formula (7); the comparison of the predicted flow and lift values and the measured flow and lift values output by the two-neural network prediction model based on P and f is shown in table 2, wherein the calculation of the prediction efficiency is calculated by adopting a formula (8).
Wherein n is a measured rotation speed value;
where η is the efficiency value of the centrifugal pump apparatus.
Table 1 shows real-time measurement data of a centrifugal pump with no extreme power.
| Working rotation speed n (r/min) | Frequency f of working rotation speed (Hz) | Motor input power P (kW) |
| 1500 | 25 | 16.42 |
| 1350 | 22.5 | 27.34 |
| 1200 | 20 | 19.2 |
| 900 | 15 | 8.89 |
| 750 | 12.5 | 4.53 |
TABLE 1
Table 2 shows the comparative analysis of predicted performance and measured performance of a centrifugal pump with power electrodeless value.
TABLE 2
Finally, the real-time flow, lift and efficiency operation parameter values are obtained through a neural network prediction model, and under the condition of no flow sensor, the performance prediction of the centrifugal pump and the real-time monitoring of the equipment operation state are realized, and the safety and reliability of the equipment operation are ensured.
The embodiments described in the present specification are merely examples of implementation forms of the inventive concept, and the scope of protection of the present invention should not be construed as being limited to the specific forms set forth in the embodiments, but the scope of protection of the present invention encompasses equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.
Claims (4)
1. A method for predicting performance of a centrifugal pump without flow sensing by using a neural network, which is characterized by comprising the following steps:
step 1, obtaining test sample data of centrifugal pump flow Q, lift H, motor input power P and working rotation speed frequency f under different rotation speeds through a centrifugal pump hydraulic performance test;
step 2, establishing a polynomial fitting equation of flow-lift and flow-power of the centrifugal pump at different rotating speeds, deriving the flow-power equation, and judging whether an extremum exists;
step 3, if the power does not have an extreme value, establishing a centrifugal pump flow and lift dual-neural network prediction model by taking the working rotation speed frequency and the motor input power as input parameters; if the power has an extreme value, a centrifugal pump flow single neural network prediction model is established by taking the working rotation speed frequency and the lift as input parameters;
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;
the process of the step 3 is as follows:
firstly, based on the judging result of the step 2, if the power does not have an extreme value, using the working rotation speed frequency and the motor input power as input parameters, establishing a centrifugal pump flow and lift dual-neural network prediction model, wherein the neural network prediction model 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 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 the input power and the working rotation speed frequency of a motor, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron and is used for outputting flow; the lift neural network prediction model is defined as HNN1, the input layer of the model comprises 2 neurons which are respectively flow and working rotation speed frequency, the hidden layer comprises 10 neurons, the output layer comprises 1 neuron, and the model is used for lift output; if the power has an extreme value, a centrifugal pump flow single neural network prediction model is established by taking the working rotation speed frequency and the lift as input parameters, and the neural network prediction model also adopts a three-layer back propagation BP neural network method which is respectively an input layer, a hidden layer and an output layer; the centrifugal pump flow neural network prediction model is defined as QNN2, an input layer of the centrifugal pump flow neural network prediction model comprises 2 neurons which are respectively the lift and the working rotation speed frequency, a hidden layer comprises 10 neurons, and an output layer comprises 1 neuron and is used for outputting flow;
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 power is not extreme; when QNN1 is trained, the working rotation speed frequency and the motor input power are used as training input samples, the flow is used as a training output target result, a sample random distribution method is adopted to distribute training samples into a training sample data set, a verification sample data set and a test sample data set according to the proportions of 80%, 10% and 10%, a Bayesian regularization algorithm is adopted by a neural network training 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 training, 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 rotation speed frequency and flow are used as training input samples, lift is used as a training output target result, a sample random distribution method is adopted to distribute 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%, a Bayesian regularization algorithm is adopted by 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 training, 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 working rotation speed frequency and the lift are used as training input samples, the flow is used as a training output target result, a sample random distribution method is adopted to distribute 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%, a Bayesian regularization algorithm is adopted by a neural network training 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 training, carrying out correlation analysis between the target value and the output value on the training result, the verification result and the test result respectively, namely, R value analysis, wherein the closer the R value is to 1, the more accurate the trained model is.
2. The neural network prediction method for centrifugal pump performance without flow sensing according to claim 1, wherein in step 1, under laboratory conditions, a centrifugal pump characteristic test is performed, centrifugal pump flow Q, lift H, motor input power P, and operating speed frequency f measured values at different 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 speeds are drawn.
3. The neural network prediction method for performance of a centrifugal pump without flow sensing according to claim 1 or 2, wherein in the step 2, polynomial fitting equations are adopted to establish approximate equations of the centrifugal pump lift, flow and rotational frequency according to the measured values of the flow Q, the lift H, the power P and the working rotational frequency f at different rotational speeds, as shown in a formula (1); the power, flow and rotating speed frequency approximate equation is shown in a formula (2);
H＝a 00 +a 10 f+a 01 Q+a 20 f 2 +a 11 fQ+a 02 Q 2 +a 21 f 2 Q+a 12 fQ 2 +a 03 Q 3 +a 30 f 3 (1)
P＝b 00 +b 10 f+b 01 Q+b 20 f 2 +b 11 fQ+b 02 Q 2 +b 21 f 2 Q+b 12 fQ 2 +b 03 Q 3 +b 30 f 3 (2)
wherein Q is the flow of the pump, P is the input power of the motor, f is the working rotation speed frequency of the water pump, H is the lift of the pump, a 00 To a 30 Coefficient of lift approximation equation, b 00 To b 30 Coefficients that are power approximation equations;
the fixed rotating speed is unchanged, namely the working rotating speed frequency f is a constant value, the power-flow approximation equation is derived, and the calculation formula is shown as a formula (3);
wherein, c 00 To c 02 Coefficients for the power derivative equation;
at a specified rotational speed frequency, values within the flow interval, i.e. Q.epsilon.0, Q max ]And (3) carrying out the formula (3) to judge whether zero values exist, namely whether extreme values exist.
4. The neural network prediction method for centrifugal pump performance 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 is a standard Programmable Logic Controller (PLC) or a customized single chip microcomputer controller, and the power sensor, the pressure sensor and the motor input power P, the differential pressure Δp and the rotational speed n data of the rotational speed sensor when the real-time measurement device works are respectively adopted, and the measured differential pressure value and the rotational speed value are respectively converted into a lift value H and a working rotational speed frequency value f by using formulas (4) and (5); the neural network prediction model is utilized to realize the prediction of the flow value in the current state, and meanwhile, the operation efficiency eta of the equipment in 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;
wherein Δp is the measured differential pressure value, ρ is the density of the medium, g is the gravitational acceleration;
wherein n is a measured rotation speed value;
where η is the efficiency value of the centrifugal pump apparatus.
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