CN110705186A - Real-time online instrument checking and diagnosing method through RBF particle swarm optimization algorithm - Google Patents
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Abstract
A real-time online instrument checking and diagnosing method through a RBF particle swarm optimization algorithm comprises the following steps: s1, building a flow network model; s2, iterating actual field measurement data, and calculating and determining parameters in the model through a RBF particle swarm optimization algorithm to enable the model to be usable; s3, periodically restarting the steps and optimizing the parameters; s4, checking the sampled variables one by using the model under the state of a stable flow field; s5, after the suspected failure point is eliminated, using the rest data to perform inverse iteration operation, and reversely deducing a theoretical calculation value of the suspected failure point; s6, eliminating process condition changes, comparing and analyzing actual instrument signals by using the theoretical calculation values, realizing verification and fault diagnosis, and determining the signal health level; and S7, recording the sampling signal and the calculation signal according to the measurement time, and alarming and positioning the fault according to the deterministic fault diagnosis condition. The invention can realize early discovery and early report of instrument faults, intelligently correct results and improve the working efficiency.
Description
Technical Field
The invention relates to a method for checking and diagnosing an on-line instrument.
Background
In recent years, the intellectualization and automation of industrial production are more and more emphasized. In smart manufacturing, the intelligence of a meter is an important component thereof. At present, the mainstream instruments are mainly detected one by adopting manual periodicity for judgment, and workers cannot accurately judge whether instrument measurement values are accurate in time, so that the opportunity for processing is delayed, and the whole production activity is influenced. When the instrument works, the intelligent diagnosis of the traditional instrument or electronic equipment only aims at the instrument, only open-loop self-check can be carried out, and the accuracy of data and whether the flow network system normally operates cannot be verified.
Disclosure of Invention
The invention aims to provide a method for checking and diagnosing an instrument on line in real time through a RBF particle swarm optimization algorithm
The aim of the invention can be realized by designing a real-time online instrument checking and diagnosing method through RBF particle swarm optimization algorithm, which comprises the following steps:
s1, building a flow network model including a flow channel model and an equipment assembly model through a fluid mechanics continuity equation, a momentum equation and an energy equation;
s2, iterating actual field measurement data, and calculating and determining parameters in the model through a RBF particle swarm optimization algorithm to enable the model to be usable;
s3, periodically restarting the steps, and optimizing the model parameters so as to adapt to new working condition conditions again and enable the model to learn and maintain autonomously;
s4, checking the sampled variables one by using the model obtained in the above step under the state of a stable flow field;
s5, after the suspected failure point is eliminated, using the rest data to perform inverse iteration operation, and reversely deducing a theoretical calculation value of the suspected failure point;
s6, eliminating process condition changes, comparing and analyzing actual instrument signals by using the theoretical calculation values, obtaining deviation parameters of the actual signals by adopting a predefined fault mode and deviation evaluation, realizing verification and fault diagnosis through threshold judgment, fuzzy logic and fault hypothesis verification, and determining the signal health level;
and S7, recording the sampling signal and the calculation signal according to the measurement time, and realizing alarming and fault positioning according to the diagnosis conditions of the flow network knowledge base and the instrument fault feature base.
Further, the flow equation is first simplified to F ═ 1-K0)*a1*(P1-P2-KZ)+K0*F1p
wherein ,
wherein ,
is the pressure from the last iteration, KZ ═ ρ g (Z)2-Z1) Where ρ is the density of the fluid, g is the acceleration of gravity, and Z is1Is the elevation at point 1, Z2The elevation at point 2; f1pThe value F obtained from the last iteration; k0A constant selectable by the user, by adjusting K0Obtaining the stability of numerical solution;
in the above formula, F, P1 and P2For unknown quantity, the height difference KZ is a system constant, and the other items are values obtained by the last iteration and can be regarded as known quantity;
a mass balance equation is also set, wherein the inflow node is a (+) sign and the outflow node is a (-) sign.
Further, according to the matrix equation set formed at step S1, pair F (F) will be formed3) The factors that influence the calculation of the value are used as model inputs and the F value is used as an output.
Further, determining membership of the fuzzy equation;
let the system of fuzzy equations have c*A fuzzy group with the center of k, j being vk、vjThen the ith training sample XiMembership mu for fuzzy group kikComprises the following steps:
in the formula, n is a block matrix index required in the fuzzy classification process and is usually taken as 2; | is a norm expression;
using the above membership values or its variants to obtain a new input matrix;
for fuzzy group k, its input matrix is deformed as:
φik(Xi,μik)＝[1 func(μik)Xi]
wherein, func (mu)ik) Is a membership value muikDeformation function of, in general, take
φik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix.
Furthermore, an RBF neural network is used as a local equation of the fuzzy equation system, and each fuzzy group is subjected to optimization fitting; let the output of the kth RBF neural network fuzzy equation be,
wherein ,Clk and ωlkIs the k RBF neural network fuzzy equationCenter of l nodes and output weight, philk(‖Xi-Clk|) is the radial basis function of the ith node of the kth RBF neural network fuzzy equation, determined by:
wherein ,σlkIs the width of the kth gaussian member function of the l-th fuzzy rule.
Further, C of RBF neural network local equation in fuzzy equation is adjusted by adopting particle swarm optimizationlk、σlk、ωlkOptimizing, wherein the optimizing steps are as follows:
s201, determining the optimized parameter of the particle number as C of RBF neural network local equationlk、σlk、ωlkNumber of individual particle groups popsize, maximum number of cyclic optimization itermaxInitial position r of the p-th particlepInitial velocity vpLocal optimum value LbestpAnd the global optimum value Gbest of the whole particle swarm;
s202, setting an optimization objective function, converting the optimization objective function into fitness, and evaluating each local fuzzy equation; calculating a fitness function through a corresponding error function, considering that the fitness of the particle with large error is small, and expressing the fitness function of the particle p as follows:
fp＝1/(Ep+1)
in the formula ,EpIs an error function of the fuzzy equation,
in the formula ,
is the predicted output of a system of fuzzy equations, FiIs the target output of the fuzzy equation system;
s203, circularly updating the speed and the position of each particle according to the following formula,
vp(iter+1)＝ω×vp(iter)+m1a1(Lbestp-rp(iter))+m2a2(Gbest-rp(iter))；
rp(iter+1)＝rp(iter)+vp(iter+1)；
in the formula ,vpIndicates the velocity, r, of the update particle ppRepresenting the position of the update particle p, Lbest representing the individual optimum of the update particle p, Gbest representing the global optimum of the entire particle swarm, iter representing the number of cycles, ω being the inertial weight in the particle swarm algorithm, m1、m2Is the corresponding acceleration factor, a1、a2Is [0,1 ]]A random number 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: lbestp＝fp；
S205, if the individual optimal value Lbest of the particle ppIf the global optimum value Gbest of the particle swarm is greater than the original global optimum value Gbest, then Gbest is Lbestp；
S206, judging whether the performance requirements are met, if so, finishing the optimization to obtain a group of optimized local equation parameters of the fuzzy equation; otherwise, returning to step S203, continuing to iterate and optimize until reaching the maximum iteration number itermax。
Further, the regular period in step S3 is defined as monthly or quarterly or yearly.
Further, the variable in step S4 is a gauge signal; recording the measurement time, and comparing the calculated value with the 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 carried out for multiple times, the possibility of instrument failure is considered according to the deterministic fault diagnosis condition.
Further, theoretical calculation of suspected failure point PiThe formula of (a) is as follows,
wherein ,Pi、PjIndicates the pressure measured by the ith and the jth sensors, Zi、ZjIndicates the elevation at the ith and the j, FijRepresenting the mass flow rate between i, j, ρ representing the fluid density, g representing the gravitational acceleration, and a the flow coefficient.
Further, the predefined failure modes include drift, leakage, blockage, failure modes; the flow network knowledge base comprises energy transfer characteristics of flow network nodes and branches; the instrument fault feature library comprises numerical value drift, abnormal change rate, open circuit and short circuit fault features.
The invention adopts the combination of algorithm and computer intelligent analysis, replaces the traditional manual one-by-one inspection according to the month or the quarter, can realize early discovery, early report and intelligent correction of the result of the instrument fault, greatly saves manpower and material resources and improves the working efficiency. Meanwhile, when partial meters are maintained off-line due to faults, the invention can calculate the numerical value of the off-line monitoring point by utilizing the established flow network model and the reading of the 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 diagram of a fluid network in accordance with a preferred embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the following examples.
As shown in fig. 1, a method for real-time online meter verification and diagnosis by using a RBF particle swarm optimization algorithm comprises the following steps:
and S1, building a flow network model including a flow channel model and an equipment assembly model through a fluid mechanics continuity equation, a momentum equation and an energy equation.
And constructing a flow network model by using a node method through a fluid mechanics continuity equation, a momentum equation (a Navier-Stokes equation) and an energy equation. For a large-scale flow network, the large-scale flow network or system can be simplified into a plurality of small flow networks or systems, so that the modeling process is simplified.
To obtain an easily calculated model of the fluid network, it is assumed that the fluid flows uniformly only along the direction of the conduit and responds very rapidly to changes in boundary conditions. For compressible fluids, the node mass will increase or decrease depending on actual operating conditions, assuming that the mass of the incoming pipe is not equal to the mass of the outgoing pipe. Compressibility and mass balance terms are introduced into the equation.
Wherein: f is the mass flow rate ρ VA, ρ is the fluid density, V is the flow velocity, a is the pipe cross-sectional area, X is the pipe flow length, P is the node pressure, T is the node absolute temperature, α is the compression factor.
The conservation of momentum equation can be written over the length of the pipe L as:
wherein ：P1,P2Pressure at points 1,2, Z1,Z2Elevation at points 1,2, ρ fluid density, g gravitational acceleration, HLHead loss, v flow rate,
the head loss term HL, i.e. the sum of all major head losses due to friction effects and small head losses due to inlet, fittings, area changes, etc., can be expressed generally as being proportional to the square of the fluid: ρ gHL ═ F2/a2(3)
In the formula: a is calculated from the fluid flow rate, pressure drop and height difference.
Substituting (3) into (2) to obtain
Using quasi-stationary simplification, omitting the last term, the equation reduces to
The flow equation can be expressed as
F＝a[P1-P2-KZ]1/2(6)
Wherein: KZ ═ ρ g (Z)2-Z1) (7)
Equation (6) defines the relationship between the flow rate and pressure in the conduit.
A fluid network, such as the one shown in fig. 2, may be assumed to be a collection of closed conduits. Writing the equation as in equation (6) for each flow term results in a series of second order equations. To obtain the pressure and flow in the network, these equations and the node mass balance equations must be solved simultaneously. For this purpose, first of all, the second order equation has to be linearized.
Equation (6) can be linearized
F＝a1*[P1-P2-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 iteration results. To guide the stability of the numerical solution scheme, it is necessary to be in range
Introducing a relaxation factor Ko and modifying equation (8) as follows:
F＝a1*(P1-P2-KZ)-K0[a1*(P1-P2-KZ)-F1p](9)
wherein ：
F1pthe value F obtained from the last iteration
Simplify the above formula to obtain
F＝(1-K0)*a1*(P1-P2-KZ)+K0*F1p(10)
In practical application, K0Becomes a user selectable constant by adjusting K0And obtaining the stability of numerical solution. Reduction of K0Physically can be considered to introduce inertia into the system.
In formula (10), F, P1 and P2Is an unknown quantity. The height difference KZ is a system constant and the remaining terms are the values from the last iteration and can be considered as known quantities. KZ is usually ignored for simplicity of the calculation.
As with the flow network of fig. 2, equation (10) can be expressed as the following equation:
in addition to momentum balance, a mass balance equation is also required. Also, for the example problem in fig. 2, it can be given that:
F1+F2-F3＝0 (16)
F3-F4-F5＝0 (17)
in the above formula, the incoming node is denoted by a (+) sign, and the outgoing node is denoted by a (-) sign.
Equations (11) through (17) provide a complete set of seven equations for seven unknown arguments, i.e., F1，F2，F3，F4，F5，P1 and P2. In this problem, it is assumed that the boundary pressure P is givenBAre known. The system of equations in matrix form is shown below.
All FlpsAre the last iteration pass values, which are considered known at the current time step.
And S2, iterating the actual field measurement data, and calculating and determining parameters in the model through an RBF particle swarm optimization algorithm to enable the model to be available. The calculation process is as follows:
according to the matrix equation system, pair F (F)3) Various factors (P) influencing the calculation of the value1、P2、PB、PC、PD、PESix modeling variables) as model inputs and F values as outputs.
The establishment of the fuzzy model comprises the following 3 parts:
(1) determining the membership degree of a fuzzy equation: let the system of fuzzy equations have c*A fuzzy group with the center of k, j being vk、vjThe ith training sample XiMembership mu for fuzzy group kikComprises the following steps:
in the formula, n is a block matrix index required in the fuzzy classification process, and is usually taken as 2, | · |, which is a norm expression.
Using the above membership values or its variants to obtain a new input matrix, for the fuzzy group k, its input matrix variant is:
φik(Xi,μik)＝[1 func(μik)Xi](18)
wherein func (. mu.)ik) Is a membership value muikDeformation function of, in general, take
Equal phiik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix.
(2) And the RBF neural network is used as a local equation of the fuzzy equation system, and each fuzzy group is subjected to optimization fitting. The output of the k-th RBF neural network fuzzy equation is set as follows:
wherein Clk and ωlkIs the center and output weight, phi, of the ith node of the kth RBF neural network fuzzy equationlk(‖Xi-Clk|) is the radial basis function of the ith node of the kth RBF neural network fuzzy equation, determined by:
(3) the particle swarm optimization module is used for adopting the particle swarm optimization to perform the C of the RBF neural network local equation in the fuzzy equationlk、σlk、ωlkThe optimization is carried out, and the specific implementation steps are as follows:
① determining the optimized parameter of the particle number as C of RBF neural network local equationlk、σlk、ωlkNumber of individual particle groups popsize, maximum number of cyclic optimization itermaxInitial position r of the p-th particlepInitial velocity vpLocal optimum value LbestpAnd a global optimum Gbest for the entire population of particles.
②, setting an optimization objective function, converting the optimization objective function into fitness, evaluating each local fuzzy equation, calculating the fitness function through a corresponding error function, and considering that the particle with large error has small fitness, wherein the fitness function of the particle p is expressed as:
fp＝1/(Ep+1) (21)
in the formula ,EpIs the error function of the fuzzy equation, expressed as:
in the formula ,
is the predicted output of a system of fuzzy equations, FiIs the target output of the fuzzy equation system;
③, the velocity and position of each particle is updated cyclically,
vp(iter+1)＝ω×vp(iter)+m1a1(Lbestp-rp(iter))+m2a2(Gbest-rp(iter))(23)
rp(iter+1)＝rp(iter)+vp(iter+1) (24)
in the formula ,vpIndicates the velocity, r, of the update particle ppRepresenting the position of the update particle p, Lbest representing the individual optimum of the update particle p, Gbest representing the global optimum of the entire particle swarm, iter representing the number of cycles, ω being the inertial weight in the particle swarm algorithm, m1、m2Is the corresponding acceleration factor, a1、a2Is [0,1 ]]A random number in between;
④ for the particle p, if the new fitness is greater than the original individual optimum, updating the individual optimum of the particle:
Lbestp＝fp(25)
⑤ if the individual optimum value Lbest of particle ppThe particle swarm global optimum value Gbest is larger than the original particle swarm global optimum value Gbest:
Gbest＝Lbestp(26)
⑥ judging whether the performance requirement is satisfied, if yes, ending the optimization to get a group of optimized fuzzy equation local equation parameters, otherwise returning to step ③, continuing the iterative optimization until reaching the maximum iterative times itermax。
And S3, restarting S1-S2 periodically (monthly/quarterly/yearly), and optimizing model parameters so as to adapt to new working condition conditions again and enable the model to learn and maintain autonomously.
And S4, checking the sampled variables (measuring instrument signals) one by using the model obtained in the step in a stable flow field state. And 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 and the like) of the deviation range. After the complete verification is carried out for multiple times, the possibility of instrument failure is considered according to the deterministic fault diagnosis condition.
And S5, after the suspected failure point is eliminated, performing inverse iteration 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、PjIndicates the pressure measured by the ith and the jth sensors, Zi、ZjIndicates the elevation at the ith and the j, FijRepresenting the mass flow rate between i, j.
S6, eliminating process condition changes, comparing and analyzing actual instrument signals by using the theoretical calculation values, obtaining deviation parameters of the actual signals by adopting a predefined fault mode and deviation evaluation, realizing verification and fault diagnosis through threshold judgment, fuzzy logic and fault hypothesis verification, and determining the signal health level. Predefined failure modes include drift, leakage, blockage, failure, etc. failure modes.
And S7, recording the sampling signal and the calculation signal according to the measurement time, and realizing alarming and fault positioning according to the diagnosis conditions of the flow network knowledge base and the instrument fault characteristic base (numerical value drift, abnormal change rate, open circuit, short circuit and other fault characteristics). The flow network knowledge base comprises energy transfer characteristics of flow network nodes and branches. The instrument fault feature library comprises fault features such as numerical value drift, abnormal change rate, open circuit, short circuit and the like.
The invention adopts the combination of algorithm and computer intelligent analysis, replaces the traditional manual one-by-one inspection according to the month or the quarter, can realize early discovery, early report and intelligent correction of the result of the instrument fault, greatly saves manpower and material resources and improves the working efficiency. Meanwhile, when partial meters are maintained off-line due to faults, the invention can calculate the numerical value of the off-line monitoring point by utilizing the established flow network model and the reading of the sensor which normally works, and the normal operation of the system is not influenced.
Claims (10)
1. A real-time online instrument checking and diagnosing method through a RBF particle swarm optimization algorithm is characterized by comprising the following steps of:
s1, building a flow network model including a flow channel model and an equipment assembly model through a fluid mechanics continuity equation, a momentum equation and an energy equation;
s2, iterating actual field measurement data, and calculating and determining parameters in the model through a RBF particle swarm optimization algorithm to enable the model to be usable;
s3, periodically restarting the steps, and optimizing the model parameters so as to adapt to new working condition conditions again and enable the model to learn and maintain autonomously;
s4, checking the sampled variables one by using the model obtained in the above step under the state of a stable flow field;
s5, after the suspected failure point is eliminated, using the rest data to perform inverse iteration operation, and reversely deducing a theoretical calculation value of the suspected failure point;
s6, eliminating process condition changes, comparing and analyzing actual instrument signals by using the theoretical calculation values, obtaining deviation parameters of the actual signals by adopting a predefined fault mode and deviation evaluation, realizing verification and fault diagnosis through threshold judgment, fuzzy logic and fault hypothesis verification, and determining the signal health level;
and S7, recording the sampling signal and the calculation signal according to the measurement time, and realizing alarming and fault positioning according to the diagnosis conditions of the flow network knowledge base and the instrument fault feature base.
2. The method for on-line real-time instrument verification and diagnosis by RBF particle swarm optimization algorithm according to claim 1, wherein: first, the flow equation is simplified to
F＝(1-K0)*a1*(P1-P2-KZ)+K0*F1p
wherein ,
wherein ,is the pressure from the last iteration, KZ ═ ρ g (Z)2-Z1) Where ρ is the density of the fluid, g is the acceleration of gravity, and Z is1Is the elevation at point 1, Z2The elevation at point 2; f1pThe value F obtained from the last iteration; k0A constant selectable by the user, by adjusting K0Obtaining the stability of numerical solution;
in the above formula, F, P1 and P2For unknown quantity, the height difference KZ is a system constant, and the other items are values obtained by the last iteration and can be regarded as known quantity;
a mass balance equation is also set, wherein the inflow node is a (+) sign and the outflow node is a (-) sign.
3. The method for on-line real-time instrument verification and diagnosis through RBF particle swarm optimization algorithm according to claim 2, wherein: according to the matrix equation set formed in step S1, pair F (F)3) The factors that influence the calculation of the value are used as model inputs and the F value is used as an output.
4. The method for on-line real-time instrument verification and diagnosis by RBF particle swarm optimization algorithm according to claim 3, wherein: determining the membership degree of a fuzzy equation;
let the system of fuzzy equations have c*A fuzzy group with the center of k, j being vk、vjThen the ith training sample XiMembership mu for fuzzy group kikComprises the following steps:
in the formula, n is a block matrix index required in the fuzzy classification process and is usually taken as 2; | is a norm expression;
using the above membership values or its variants to obtain a new input matrix;
for fuzzy group k, its input matrix is deformed as:
φik(Xi,μik)＝[1 func(μij)Xi]
wherein, func (mu)ik) Is a membership value muikDeformation function of, in general, take
φik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix.
5. The method for on-line real-time instrument verification and diagnosis through RBF particle swarm optimization algorithm according to claim 4, wherein: taking an 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 ωlkIs the center and output weight, phi, of the ith node of the kth RBF neural network fuzzy equationlk(‖Xi-Clk|) is the radial basis function of the ith node of the kth RBF neural network fuzzy equation, determined by:
wherein ,σlkIs the width of the kth gaussian member function of the l-th fuzzy rule.
6. The method for on-line real-time instrument verification and diagnosis by RBF particle swarm optimization algorithm according to claim 5, wherein the particle swarm optimization algorithm is adopted to carry out the C of RBF neural network local equation in the fuzzy equationlk、σlk、ωlkOptimizing, wherein the optimizing steps are as follows:
s201, determining the optimized parameter of the particle number as C of RBF neural network local equationlk、σlk、ωlkNumber of individual particle groups popsize, maximum number of cyclic optimization itermaxInitial position r of the p-th particlepInitial velocity vpLocal optimum value LbestpAnd the global optimum value Gbest of the whole particle swarm;
s202, setting an optimization objective function, converting the optimization objective function into fitness, and evaluating each local fuzzy equation; calculating a fitness function through a corresponding error function, considering that the fitness of the particle with large error is small, and expressing the fitness function of the particle p as follows:
fp＝1/(Ep+1)
in the formula ,EpIs an error function of the fuzzy equation,
in the formula ,
is the predicted output of a system of fuzzy equations, FiIs the target output of the fuzzy equation system;
s203, circularly updating the speed and the position of each particle according to the following formula,
vp(iter+1)＝ω×vp(iter)+m1a1(Lbestp-rp(iter))+m2a2(Gbest-rp(iter))；
rp(iter+1)＝rp(iter)+vp(iter+1)；
in the formula ,vpIndicates the velocity, r, of the update particle ppRepresenting the position of the update particle p, Lbest representing the individual optimum of the update particle p, Gbest representing the global optimum of the entire particle swarm, iter representing the number of cycles, ω being the inertial weight in the particle swarm algorithm, m1、m2Is the corresponding acceleration factor, a1、a2Is [0,1 ]]A random number 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: lbestp＝fp；
S205, if the individual optimal value Lbest of the particle ppIf the global optimum value Gbest of the particle swarm is greater than the original global optimum value Gbest, then Gbest is Lbestp；
S206, judging whether the performance requirements are met, if so, finishing the optimization to obtain a group of optimized local equation parameters of the fuzzy equation; otherwise, returning to step S203, continuing to iterate and optimize until reaching the maximum iteration number itermax。
7. The method for on-line real-time instrument verification and diagnosis by RBF particle swarm optimization algorithm according to claim 1, wherein: the regular period in step S3 is defined as monthly or quarterly or yearly.
8. The method for on-line real-time instrument verification and diagnosis by RBF particle swarm optimization algorithm according to claim 1, wherein: the variable in step S4 is a gauge signal; recording the measurement time, and comparing the calculated value with the 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 carried out for multiple times, the possibility of instrument failure is considered according to the deterministic fault diagnosis condition.
9. The on-line meter verification and diagnosis by RBF particle swarm optimization algorithm on-line basis as claimed in claim 1The method is characterized in that: theoretical calculation of suspected failure point PiThe formula of (a) is as follows,
wherein ,Pi、PjIndicates the pressure measured by the ith and the jth sensors, Zi、ZjIndicates the elevation at the ith and the j, FijRepresenting the mass flow rate between i, j, ρ representing the fluid density, g representing the gravitational acceleration, and a the flow coefficient.
10. The method for on-line meter verification and diagnosis on-the-fly by adaptive support vector machine algorithm of claim 1, wherein: predefined failure modes include drift, leakage, blockage, failure modes; the flow network knowledge base comprises energy transfer characteristics of flow network nodes and branches; the instrument fault feature library comprises numerical value drift, abnormal change rate, open circuit and short circuit fault features.
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