CN110705186B - Real-time online instrument checksum diagnosis method through RBF particle swarm optimization algorithm - Google Patents
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Abstract
An instant on-line instrument checksum diagnosis method through RBF particle swarm optimization algorithm comprises the following steps: s1, constructing a flow net model; s2, iterating on-site actual measurement data, and calculating and determining parameters in the model through an RBF particle swarm optimization algorithm to enable the model to be usable; s3, restarting the steps regularly, and optimizing parameters; s4, checking the sampled variables one by using the model in a stable flow field state; s5, after the suspected failure points are eliminated, performing inverse iterative operation by using the rest data, and reversely deducing theoretical calculation values of the suspected failure points; s6, eliminating process condition changes, comparing and analyzing actual instrument signals by using the theoretical calculation value, realizing verification and fault diagnosis, and determining signal health level; s7, recording sampling signals and calculation signals according to the measurement time, and alarming and positioning faults according to deterministic fault diagnosis conditions. The invention can realize early detection and early report of instrument faults, intelligently correct results and improve working efficiency.
Description
Technical Field
The invention relates to an instant on-line meter checksum diagnostic method.
Background
In recent years, the intellectualization and automation of industrial production are increasingly emphasized. In the intelligent manufacturing process, the intelligence of the meter is an important component. Currently mainstream meters adopt manual periodicity to detect one by one to judge, and the staff can't in time accurately judge whether the instrument measured value is accurate to the opportunity of handling has been musied, and then whole production activity is influenced. When the instrument works, intelligent diagnosis of the traditional instrument or the electronic equipment is only aimed at the instrument, and open loop self-verification can only be carried out, so that the accuracy of data and whether a streaming network system operates normally cannot be verified.
Disclosure of Invention
The invention aims to provide an instant online instrument checksum diagnosis method through an RBF particle swarm optimization algorithm
The invention can realize the aim by designing an instant online instrument check sum diagnosis method through an RBF particle swarm optimization algorithm, which comprises the following steps:
s1, constructing a flow network model comprising a flow channel model and an equipment assembly model through a hydrodynamic continuity equation, a momentum equation and an energy equation;
s2, iterating on-site actual measurement data, and calculating and determining parameters in the model through an RBF particle swarm optimization algorithm to enable the model to be usable;
s3, restarting the steps regularly, and optimizing model parameters so as to adapt to new working conditions again, so that the model is automatically learned and maintained;
s4, utilizing the model obtained in the step, and checking the sampled variables one by one in a stable flow field state;
s5, after the suspected failure points are eliminated, performing inverse iterative operation by using the rest data, and reversely deducing theoretical calculation values of the suspected failure points;
s6, eliminating process condition changes, comparing and analyzing actual instrument signals by using the theoretical calculation value, obtaining deviation parameters of the actual signals by adopting a predefined fault mode and deviation evaluation, and realizing verification and fault diagnosis by threshold judgment, fuzzy logic and fault hypothesis verification to determine the signal health level;
s7, recording sampling signals and calculation signals according to the measurement time, and realizing alarming and fault positioning according to diagnosis conditions of a flow network knowledge base and an instrument fault feature base.
Further, first the flow equation is reduced to f= (1-K) 0 )*a 1 *(P 1 -P 2 -KZ)+K 0 *F 1p
wherein ,
wherein ,
is the pressure from the last iteration, kz=ρg (Z 2 -Z 1 ) Wherein ρ is fluid density, g is gravitational acceleration, Z 1 For elevation at point 1, Z 2 Is the elevation at point 2; f (F) 1p Value F obtained by the last iteration; k (K) 0 For a user selectable constant, K can be adjusted by 0 Obtaining stability of numerical solution;
in the above, F, P 1 and P2 For the unknown quantity, the height difference KZ is a system constant, and the rest is a value obtained by the last iteration and can be considered as the known quantity;
and a mass balance equation is also set, wherein the inflow node is (+) and the outflow node is (-).
Further, a matrix equation formed according to step S1Group, pair F (F 3 ) Factors influencing the calculation of the values are taken as model inputs and F values are taken as outputs.
Further, determining membership of the fuzzy equation;
let the fuzzy equation system have c * The centers of the fuzzy groups k and j are v respectively k 、v j Then the ith training sample X i Membership μ for fuzzy group k ik The method comprises the following steps:
wherein n is a blocking matrix index required in the fuzzy classification process, and is usually taken as 2; II is a norm expression;
using the above membership value or its variants to obtain a new input matrix;
for the fuzzy group k, its input matrix is deformed as:
φ ik (X i ,μ ik )＝[1 func(μ ik )X i ]
wherein func (μ) ik ) For membership value mu ik Is generally taken as
φ ik (X i ,μ ik ) Representing the ith input variable X i And membership mu of its fuzzy group k ik The corresponding new input matrix.
Further, using RBF neural network as a local equation of a fuzzy equation system, and performing optimization fitting on each fuzzy group; let the output of the kth RBF neural network fuzzy equation be,
wherein ,Clk and ωlk Is the center and the output weight value of the kth node of the kth RBF neural network fuzzy equation, phi lk (‖X i -C lk II) is the radial basis function of the kth node of the fuzzy equation of the kth RBF neural network, and is determined by the following formula:
wherein ,σlk Is the width of the kth gaussian membership function of the ith fuzzy rule.
Further, adopting a particle swarm algorithm to perform C on a RBF neural network local equation in the fuzzy equation lk 、σ lk 、ω lk Optimizing, wherein the optimizing steps are as follows:
s201, determining the optimized parameter of the particle number as C of RBF neural network local equation lk 、σ lk 、ω lk Particle swarm individual number pop ize, maximum cycle optimization number iter max Initial position r of the p-th particle p Initial velocity v p Local optimum value Lbest p And a global optimum Gbest for the entire population of particles;
s202, setting an optimization objective function, converting the optimization objective function into fitness, and evaluating each local fuzzy equation; calculating a fitness function through the corresponding error function, and considering that the particle fitness with large error is small, and the fitness function of the particle p is expressed as:
f p ＝1/(E p +1)
in the formula ,Ep Is an error function of the fuzzy equation,
in the formula ,
is the predictive output of the fuzzy equation system, F i Target output for the fuzzy equation system;
s203, circularly updating the speed and the position of each particle according to the following formula,
v p (iter+1)＝ω×v p (iter)+m 1 a 1 (Lbest p -r p (iter))+m 2 a 2 (Gbest-r p (iter))；
r p (iter+1)＝r p (iter)+v p (iter+1)；
in the formula ,vp Representing the velocity of the update particles p, r p Lbest represents the individual optimum value of the updated particle p, gbest represents the global optimum value of the whole particle swarm, iter represents the number of cycles, ω is the inertia weight in the particle swarm algorithm, m 1 、m 2 Corresponding acceleration coefficient, a 1 、a 2 Is [0,1 ]]Random numbers in between;
s204, for the particle p, if the new fitness is larger than the original individual optimal value, updating the individual optimal value of the particle: lbest p ＝f p ；
S205, if the individual optimum value Lbest of particle p p If the particle swarm is larger than the original global optimal value Gbest, gbest=Lbest p ；
S206, judging whether the performance requirement is met, if yes, ending the optimizing to obtain a set of local equation parameters of the optimized fuzzy equation; otherwise, returning to the step S203, continuing the iterative optimization until the maximum iterative number item is reached max 。
Further, the periodicity in step S3 is defined as monthly or quarterly or annually.
Further, the variable in step S4 is a meter signal; recording measurement time, and comparing the calculated value with a measured value corresponding to the measurement time to obtain the percentage or variance or mean square error of the deviation range; after the complete verification is performed a plurality of times, the instrument is considered to be possibly invalid according to the deterministic fault diagnosis condition.
Further, theoretical calculation value P of suspected failure point i The formula of (c) is given by,
wherein ,Pi 、P j Indicating the pressure measured by the ith and jth sensors, Z i 、Z j Representing the elevation at the i and j th positions, F ij The mass flow rate between i and j is represented, ρ is represented by the fluid density, g is represented by the gravitational acceleration, and a is the flow coefficient.
Further, the predefined failure modes include drift, leakage, blockage, and failure modes; the flow network knowledge base comprises energy transfer characteristics of flow network nodes and branches; the instrument fault feature library comprises numerical drift, abnormal change rate, open circuit and short circuit fault features.
The invention combines the algorithm and the computer intelligent analysis, replaces the traditional manual inspection by month or quarter, can realize early detection, early report and intelligent correction of the faults of the instrument, greatly saves manpower and material resources and improves the working efficiency. Meanwhile, when partial meters are maintained offline due to faults, the invention can calculate the numerical value of an offline monitoring point by using the built flow network model and the readings of a sensor which normally works, and the normal operation of the system is not influenced.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the present invention;
FIG. 2 is a schematic illustration of a fluid network in accordance with a preferred embodiment of the present invention.
Detailed Description
The invention is further described below with reference to examples.
As shown in fig. 1, an instant on-line instrument checksum diagnosis method through an RBF particle swarm optimization algorithm comprises the following steps:
s1, constructing a flow network model comprising a flow channel model and an equipment assembly model through a hydrodynamic continuity equation, a momentum equation and an energy equation.
And constructing a flow network model by using a node method through a hydrodynamic continuity equation, a momentum equation (a Navier-Stokes equation) and an energy equation. For large-scale flow networks, simplifying the large-scale flow network or system into a plurality of small flow networks or systems can be adopted, so that the modeling flow is simplified.
In order to obtain an easy-to-calculate fluid network model, it is assumed that the fluid flows uniformly only along the catheter direction and that the response to changes in boundary conditions is very rapid. For compressible fluids, the node mass will increase or decrease depending on the actual operating conditions, assuming that the mass of the incoming conduit is not equal to the mass of the outgoing conduit. Compressibility and mass balance terms are introduced into the equation.
Wherein: f=mass flow rate=ρva, ρ=fluid density, v=flow rate, a=conduit cross-sectional area, x=conduit flow length, p=node pressure, t=node absolute temperature, α=compression coefficient.
The conservation of momentum equation can be written over the pipe length L:
wherein ：P1 ,P 2 Pressure at points 1,2, Z 1 ,Z 2 The elevation at points 1,2, ρ=fluid density, g=gravitational acceleration, H L Head loss of the length L of the pipe, v=flow rate,
the head loss term HL, i.e., the sum of all the main head losses due to friction effects and the small head losses due to inlets, fittings, area variations, etc., can be expressed generally as proportional to the square of the fluid:
ρgHL＝F 2 /a 2 (3)
wherein: a is calculated from the fluid flow rate, pressure drop and height difference.
Substituting (3) into (2)
Using a quasi-stable simplification, the last term is omitted and the equation is simplified to
The flow equation can be expressed as
F＝a[P 1 -P 2 -KZ] 1 / 2 (6)
Wherein: kz=ρg (Z 2 -Z 1 ) (7)
Equation (6) defines the relationship between the conduit flow rate and pressure.
A fluid network such as that shown in fig. 2 may be assumed to be a collection of closed pipes. Writing an equation as in equation (6) for each flow term results in a series of second order equations. To obtain pressure and flow in the network, these equations as well as the node mass balance equations must be solved simultaneously. For this purpose, the second order equation must first be linearized.
Formula (6) can be linearized into
F＝a 1 *[P 1 -P 2 -KZ] (8)
wherein 
wherein 
Is the pressure from the last iteration
Attempting to numerically solve a set of simultaneous equations such as equation (8) sometimes results in non-convergence of the iterative results. In order to guide the stability of the numerical solution, it is necessary to be in range
The relaxation factor Ko is introduced and equation (8) is modified as follows:
F＝a 1 *(P 1 -P 2 -KZ)-K 0 [a 1 *(P 1 -P 2 -KZ)-F 1p ] (9)
wherein ：
F 1p =last timeIterating to obtain a value F
Simplifying the above process to obtain
F＝(1-K 0 )*a 1 *(P 1 -P 2 -KZ)+K 0 *F 1p (10)
In practical application, K 0 Becomes a user selectable constant by adjusting K 0 And obtaining stability of numerical solution. K reduction 0 Physically can be considered as introducing inertia in the system.
In formula (10), F, P 1 and P2 Is an unknown quantity. The height difference KZ is a system constant, and the remainder is a value obtained by the previous iteration and can be regarded as a known quantity. KZ is typically ignored for simplicity of calculation.
As with the flow net in fig. 2, equation (10) can be expressed as follows:
in addition to momentum balance, mass balance equations are also required. Also, for the example problem in fig. 2, it is possible to give:
F 1 +F 2 -F 3 ＝0 (16)
F 3 -F 4 -F 5 ＝0 (17)
in the above formula, the inflow node is (+) and the outflow node is (-).
Equations (11) through (17) provide a complete set of seven equations for seven unknown independent variables, F 1 ，F 2 ，F 3 ，F 4 ，F 5 ，P 1 and P2 . In this problem, it is assumed that the boundary pressure P is given B Is known. The system of equations in matrix form is shown below.
All F lps The last iteration delivers a value that is considered to be known in the current time step.
S2, iterating the field actual measurement data, and calculating and determining parameters in the model through an RBF particle swarm optimization algorithm to enable the model to be usable. The calculation process is as follows:
according to the matrix equation set, pair F (F 3 ) Various factors (P 1 、P 2 、P B 、P C 、P D 、P E Six modeling variables) as model inputs and F values as outputs.
Establishing this blur model includes the following 3 parts:
(1) And (3) determining membership of a fuzzy equation: let the fuzzy equation system have c * The centers of the fuzzy groups k and j are v respectively k 、v j Then the ith training sample X i Membership μ for fuzzy group k ik The method comprises the following steps:
where n is the index of the blocking matrix required in the fuzzy classification process, it is generally taken as 2 and, II is a norm expression.
Using the above membership value or its variants to obtain a new input matrix, for the fuzzy group k, its input matrix is deformed as:
φ ik (X i ,μ ik )＝[1 func(μ ik )X i ] (18)
wherein func (mu) ik ) For membership value mu ik Is generally taken as
Wait for phi ik (X i ,μ ik ) Representing the ith input variable X i And membership mu of its fuzzy group k ik The corresponding new input matrix.
(2) And the RBF neural network is used as a local equation of a fuzzy equation system to perform optimization fitting on each fuzzy group. Let the output of the kth RBF neural network fuzzy equation be:
wherein Clk and ωlk Is the center and the output weight value of the kth node of the kth RBF neural network fuzzy equation, phi lk (‖X i -C lk II) is the radial basis function of the kth node of the fuzzy equation of the kth RBF neural network, and is determined by the following formula:
(3) The particle swarm optimization module is used for adopting a particle swarm optimization to perform C on a RBF neural network local equation in the fuzzy equation lk 、σ lk 、ω lk The method is optimized and comprises the following specific implementation steps:
(1) c for determining optimized parameter of particle number as RBF neural network local equation lk 、σ lk 、ω lk Particle swarm individual number pop ize, maximum cycle optimization number iter max Initial position r of the p-th particle p Initial velocity v p Local optimum value Lbest p And a global optimum Gbest for the entire population of particles.
(2) Setting an optimization objective function, converting the optimization objective function into fitness, and evaluating each local fuzzy equation; calculating a fitness function through the corresponding error function, and considering that the particle fitness with large error is small, and the fitness function of the particle p is expressed as:
f p ＝1/(E p +1) (21)
in the formula ,Ep Is an error function of the fuzzy equation, expressed as:
in the formula ,
is the predictive output of the fuzzy equation system, F i Target output for the fuzzy equation system;
(3) the velocity and position of each particle are updated cyclically, according to the following formula,
v p (iter+1)＝ω×v p (iter)+m 1 a 1 (Lbest p -r p (iter))+m 2 a 2 (Gbest-r p (iter)) (23)
r p (iter+1)＝r p (iter)+v p (iter+1) (24)
in the formula ,vp Representing the velocity of the update particles p, r p Lbest represents the individual optimum value of the updated particle p, gbest represents the global optimum value of the whole particle swarm, iter represents the number of cycles, ω is the inertia weight in the particle swarm algorithm, m 1 、m 2 Corresponding acceleration coefficient, a 1 、a 2 Is [0,1 ]]Random numbers in between;
(4) for particle p, if the new fitness is greater than the original individual optimum, the individual optimum of the particle is updated:
Lbest p ＝f p (25)
(5) if the individual optimum value Lbest of the particle p p Is greater than the original global optimum value Gbest of the particle swarm:
Gbest＝Lbest p (26)
(6) judging whether the performance requirement is met, if yes, ending the optimizing to obtain a set of local equation parameters of the optimized fuzzy equation; otherwise, returning to the step (3), and continuing the iterative optimization until the maximum iterative number item is reached max 。
S3, restarting the model in the step S1-S2 periodically (monthly/quarterly/annual), and optimizing the model parameters so as to adapt to new working conditions again, and enabling the model to learn and maintain autonomously.
S4, utilizing the model obtained in the step, and checking the sampled variables (measuring instrument signals) one by one in a stable flow field state. Recording the measurement time, and comparing the calculated value with the measured value corresponding to the measurement time to obtain the percentage (or variance, mean square error, etc.) of the deviation range. After the complete verification is performed a plurality of times, the instrument is considered to be possibly invalid according to the deterministic fault diagnosis condition.
S5, after the suspected failure point is eliminated, performing inverse iterative operation by using the rest data, and reversely deducing a theoretical calculation value of the suspected failure point.
From (5), it can be seen that:
wherein Pi 、P j Indicating the pressure measured by the ith and jth sensors, Z i 、Z j Representing the elevation at the i and j th positions, F ij Representing the mass flow rate between i, j.
S6, eliminating process condition changes, comparing and analyzing actual instrument signals by using the theoretical calculation value, obtaining deviation parameters of the actual signals by adopting a predefined fault mode and deviation evaluation, and realizing verification and fault diagnosis by threshold judgment, fuzzy logic and fault hypothesis verification to determine the signal health level. The predefined failure modes include drift, leakage, blockage, failure, etc. failure modes.
S7, recording sampling signals and calculation signals according to the measurement time, and realizing alarming and fault positioning according to diagnosis conditions of a flow network knowledge base and an instrument fault feature base (fault features such as numerical drift, abnormal change rate, open circuit, short circuit and the like). The flow network knowledge base includes energy transfer characteristics of the flow network nodes and branches. The instrument fault feature library comprises fault features such as numerical drift, abnormal change rate, open circuit, short circuit and the like.
The invention combines the algorithm and the computer intelligent analysis, replaces the traditional manual inspection by month or quarter, can realize early detection, early report and intelligent correction of the faults of the instrument, greatly saves manpower and material resources and improves the working efficiency. Meanwhile, when partial meters are maintained offline due to faults, the invention can calculate the numerical value of an offline monitoring point by using the built flow network model and the readings of a sensor which normally works, and the normal operation of the system is not influenced.
Claims (9)
1. The real-time on-line instrument checksum diagnosis method through the RBF particle swarm optimization algorithm is characterized by comprising the following steps of:
s1, constructing a flow network model comprising a flow channel model and an equipment assembly model through a hydrodynamic continuity equation, a momentum equation and an energy equation;
first the flow equation is reduced to f= (1-K) 0 )*a 1 *(P 1 -P 2 -KZ)+K 0 *F 1p
wherein ,
wherein ,
is the pressure from the last iteration, kz=ρg (Z 2 -Z 1 ) Wherein ρ is fluid density, g is gravitational acceleration, Z 1 For elevation at point 1, Z 2 Is the elevation at point 2; f (F) 1p Value F obtained by the last iteration; k (K) 0 For a user selectable constant, K can be adjusted by 0 Obtaining stability of numerical solution; a is the flow coefficient;
in the above, F, P 1 and P2 For the unknown quantity, the height difference KZ is a system constant, and the rest is a value obtained by the last iteration and can be considered as the known quantity;
a mass balance equation is also set, wherein the inflow node is (+) and the outflow node is (-);
s2, iterating on-site actual measurement data, and calculating and determining parameters in the model through an RBF particle swarm optimization algorithm to enable the model to be usable;
s3, restarting the steps regularly, and optimizing model parameters so as to adapt to new working conditions again, so that the model is automatically learned and maintained;
s4, utilizing the model obtained in the step, and checking the sampled variables one by one in a stable flow field state;
s5, after the suspected failure points are eliminated, performing inverse iterative operation by using the rest data, and reversely deducing theoretical calculation values of the suspected failure points;
s6, eliminating process condition changes, comparing and analyzing actual instrument signals by using the theoretical calculation value, obtaining deviation parameters of the actual signals by adopting a predefined fault mode and deviation evaluation, and realizing verification and fault diagnosis by threshold judgment, fuzzy logic and fault hypothesis verification to determine the signal health level;
s7, recording sampling signals and calculation signals according to the measurement time, and realizing alarming and fault positioning according to diagnosis conditions of a flow network knowledge base and an instrument fault feature base.
2. The method for on-line meter checksum diagnosis on-line by RBF particle swarm optimization algorithm according to claim 1, wherein: according to the matrix equation set formed in step S1, the pair F (F 3 ) Factors influencing the calculation of the values are taken as model inputs and F values are taken as outputs.
3. The method for on-line meter checksum diagnosis on-line by RBF particle swarm optimization algorithm according to claim 2, wherein: determining membership of a fuzzy equation;
set fuzzy equationThe system has c * The centers of the fuzzy groups k and j are v respectively k 、v j Then the ith training sample X i Membership μ for fuzzy group k ik The method comprises the following steps:
wherein n is a blocking matrix index required in the fuzzy classification process, and is usually taken as 2; II is a norm expression;
using the above membership value or its variants to obtain a new input matrix;
for the fuzzy group k, its input matrix is deformed as:
φ ik (X i ,μ ik )＝[1 func(μ ik )X i ]
wherein func (μ) ik ) For membership value mu ik Is generally taken as
φ ik (X i ,μ ik ) Representing the ith input variable X i And membership mu of its fuzzy group k ik The corresponding new input matrix.
4. The method for on-line meter checksum diagnosis on-line by RBF particle swarm optimization algorithm according to claim 3, wherein: taking the RBF neural network as a local equation of a fuzzy equation system, and performing optimization fitting on each fuzzy group; let the output of the kth RBF neural network fuzzy equation be,
wherein ,Clk and ωlk Is the center and the output weight value of the kth node of the kth RBF neural network fuzzy equation, phi lk (‖X i -C lk II) is the radial basis of the kth node of the fuzzy equation of the kth RBF neural networkA function, determined by:
wherein ,σlk Is the width of the kth gaussian membership function of the ith fuzzy rule.
5. The method for on-line meter checksum diagnosis in real time by RBF particle swarm optimization according to claim 4, wherein the particle swarm optimization is adopted for the C of the RBF neural network local equation in the fuzzy equation lk 、σ lk 、ω lk Optimizing, wherein the optimizing steps are as follows:
s201, determining the optimized parameter of the particle number as C of RBF neural network local equation lk 、σ lk 、ω lk Particle swarm individual number pop ize, maximum cycle optimization number iter max Initial position r of the p-th particle p Initial velocity v p Local optimum value Lbest p And a global optimum Gbest for the entire population of particles;
s202, setting an optimization objective function, converting the optimization objective function into fitness, and evaluating each local fuzzy equation; calculating a fitness function through the corresponding error function, and considering that the particle fitness with large error is small, and the fitness function of the particle p is expressed as:
f p ＝1/(E p +1)
in the formula ,Ep Is an error function of the fuzzy equation,
in the formula ,
is the predictive output of the fuzzy equation system, F i Target output for the fuzzy equation system;
s203, circularly updating the speed and the position of each particle according to the following formula,
v p (iter+1)＝ω×v p (iter)+m 1 a 1 (Lbest p -r p (iter))+m 2 a 2 (Gbest-rp p (iter))；
rp p (iter+1)＝r p (iter)+v p (iter+1)；
in the formula ,vp Representing the velocity of the update particles p, r p Lbest represents the individual optimum value of the updated particle p, gbest represents the global optimum value of the whole particle swarm, iter represents the number of cycles, ω is the inertia weight in the particle swarm algorithm, m 1 、m 2 Corresponding acceleration coefficient, a 1 、a 2 Is [0,1 ]]Random numbers in between;
s204, for the particle p, if the new fitness is larger than the original individual optimal value, updating the individual optimal value of the particle: lbest p ＝f p ；
S205, if the individual optimum value Lbest of particle p p If the particle swarm is larger than the original global optimal value Gbest, gbest=Lbest p ；
S206, judging whether the performance requirement is met, if yes, ending the optimizing to obtain a set of local equation parameters of the optimized fuzzy equation; otherwise, returning to the step S203, continuing the iterative optimization until the maximum iterative number item is reached max 。
6. The method for on-line meter checksum diagnosis on-line by RBF particle swarm optimization algorithm according to claim 1, wherein: the period in step S3 is defined as monthly or quarterly or annually.
7. The method for on-line meter checksum diagnosis on-line by RBF particle swarm optimization algorithm according to claim 1, wherein: the variable in the step S4 is a measuring instrument signal; recording measurement time, and comparing the calculated value with a measured value corresponding to the measurement time to obtain the percentage or variance or mean square error of the deviation range; and after the complete verification is performed for a plurality of times, judging that the instrument fails according to the deterministic fault diagnosis condition.
8. The method for on-line meter checksum diagnosis on-line by RBF particle swarm optimization algorithm according to claim 1, wherein: theoretical calculated value of suspected failure point P i The formula of (c) is given by,
wherein ,Pi 、P j Indicating the pressure measured by the ith and jth sensors, Z i 、Z j Representing the elevation at the i and j th positions, F ij The mass flow rate between i and j is represented, ρ is represented by the fluid density, g is represented by the gravitational acceleration, and a is the flow coefficient.
9. The method for on-line meter checksum diagnosis on-line by RBF particle swarm optimization algorithm according to claim 1, wherein: predefined failure modes include drift, leakage, blockage, and failure modes; the flow network knowledge base comprises energy transfer characteristics of flow network nodes and branches; the instrument fault feature library comprises numerical drift, abnormal change rate, open circuit and short circuit fault features.
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