CN110909503B - Prediction method for flange leakage of pipeline system - Google Patents
Prediction method for flange leakage of pipeline system Download PDFInfo
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- CN110909503B CN110909503B CN201911165669.5A CN201911165669A CN110909503B CN 110909503 B CN110909503 B CN 110909503B CN 201911165669 A CN201911165669 A CN 201911165669A CN 110909503 B CN110909503 B CN 110909503B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
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Abstract
The invention relates to the field of safety control of power pipelines, and discloses a method for predicting flange leakage of a pipeline system, which comprises the following steps: s1: establishing a pipeline modal analysis finite element model taking flange structural rigidity as an input and taking analog modal frequency as an output; s2: measuring to obtain the actual modal frequency of the pipeline system; s3: obtaining optimal solutions of all flange structural rigidity by adopting a genetic algorithm, so that the mean square error of the actual modal frequency and the simulated modal frequency is within a preset range; s4: establishing a BP neural network model of a mapping relation between the flange structural rigidity and the flange sealing surface pressure, and bringing the optimal solution into the BP neural network model to obtain the target flange sealing surface pressure; s5: judging whether the flange sealing pressure is smaller than the preset safety sealing pressure, if so, the flange has leakage risk, and if not, the flange does not have leakage risk. The flange inspection device effectively solves the problems that flange inspection is difficult to implement, low in efficiency and easy to miss in the prior art.
Description
Technical Field
The invention relates to the field of safety control of power pipelines, in particular to a prediction method for flange leakage of a pipeline system.
Background
The pipeline is an important component in the ship energy system, bears the transmission task of media such as steam, water, oil and the like, and is very important to a ship power system and other auxiliary systems. The flange is an important accessory of a pipeline system, is widely used for connecting pipelines, equipment and other pipeline accessories, and has wide distribution range and large use quantity.
Due to the severe working environment of the ship, the flange can not avoid the phenomena of bolt corrosion, ageing of sealing materials and the like, so that the sealing surface of the flange is loosened to leak, and the ship is adversely affected. In particular, some pipelines for transporting toxic and combustible media can generate serious safety accidents such as explosion, fire, poisoning and the like once leakage occurs, so that huge economic loss is caused.
At present, in order to prevent flange leakage, a regular manual overhaul mode is adopted on a ship. The method needs to carefully check each flange manually, is difficult to implement and low in efficiency, and is easy to leak and check, and hidden danger of leakage is buried; and only the flange that has been exposed to leakage can be found, which is not identifiable for loosening the flange at risk of leakage.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a prediction method capable of predicting flange leakage of a pipeline system, which effectively solves the problems of difficult implementation, low efficiency and easy occurrence of missed detection of flange inspection in the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method of predicting flange leakage in a piping system, comprising the steps of:
s1: establishing a finite element model for pipeline modal analysis, wherein the finite element model takes the rigidity of all flange structures as input and takes the simulation modal frequency of a pipeline system as output;
s2: measuring to obtain the actual modal frequency of the pipeline system;
s3: obtaining optimal solutions of all flange structural rigidity by adopting a genetic algorithm, so that the mean square error of the actual modal frequency and the simulated modal frequency of the pipeline system is within a preset range;
s4: establishing an error back propagation BP neural network model of a mapping relation between the flange structural rigidity and the flange sealing surface pressure, and bringing the optimal solutions of all the flange structural rigidity into the BP neural network model to obtain the target flange sealing surface pressure;
s5: judging whether the detected flange sealing pressure is smaller than the preset safety sealing pressure, if so, the flange has leakage risk, and if not, the flange does not have leakage risk.
Based on the technical scheme, the measured modal frequencies of the front third order of the pipeline system are measured as follows: f (f) c1 ，f c2 And f c3 。
Based on the above technical solution, the step S3 specifically includes:
s31: bringing into an array of structural stiffness of all flanges according to genetic algorithm [ K ] i1 ,K i2 …K ii …K in ]Obtaining the front third-order simulation modal frequency [ f ] of the pipeline system by analyzing the finite element model to the pipeline modal i1 ,f i2 ,f i3 ]JudgingIf not, executing step S32, if not, executing step S33;
s32: determination of [ f i1 ,f i2 ,f i3 ]The corresponding array is the structural rigidity [ K ] of all flanges of the pipeline system i1 ,K i2 …K ii …K in ]；
S33: returning to step S31.
Based on the above technical solution, the preset value in step S31 is 0.1.
On the basis of the technical scheme, the BP neural network model for establishing the mapping relation between the flange structural rigidity and the flange sealing surface pressure specifically comprises the following steps:
respectively measuring the rigidity K of the flange structure of the flange connection structure under different sealing surface pressures F through experiments, and taking the rigidity K as a sample of the neural network;
and taking K as input and F as output, establishing a BP neural network model, and carrying out sample training on the BP neural network model to obtain a trained BP neural network model.
Based on the technical proposal, F in the sample takes a value of F which is m times of F e ，F e And the minimum allowable pressure is applied to the flange sealing surface.
On the basis of the technical scheme, m= {0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2.0}, the samples are 20 groups.
Based on the technical scheme, a mode test method is adopted to measure and obtain the actual measurement mode frequency of the pipeline system.
Compared with the prior art, the invention has the advantages that: according to the invention, the modal frequency of the pipeline is measured, so that the sealing surface pressure of each flange on the pipeline is converted, and the leakage risk of the flange is further predicted. The method comprises the steps of establishing a finite element model for pipeline modal analysis, wherein the finite element model takes all structural rigidity of flanges as input and takes the simulation modal frequency of a pipeline system as output; measuring to obtain the actual modal frequency of the pipeline system; obtaining optimal solutions of all flange structural rigidity by adopting a genetic algorithm, so that the mean square error of the actual modal frequency and the simulated modal frequency of the pipeline system is within a preset range; establishing a BP neural network model of a mapping relation between the flange structural rigidity and the flange sealing surface pressure, and bringing the optimal solutions of all the flange structural rigidity into the BP neural network model to obtain the target flange sealing surface pressure; judging whether the detected flange sealing pressure is smaller than the preset safety sealing pressure, if so, the flange has leakage risk, and if not, the flange does not have leakage risk. The method is simple and reliable, can rapidly position the flange which is likely to leak, greatly reduces the overhaul workload of the ship flange and improves the efficiency; meanwhile, the method can identify the flange which is not leaked but has leakage risk, reduce hidden danger and improve the safety of a pipeline system. In addition, the method of the invention has the advantages of convenient implementation, wide adaptability, low cost and better economic benefit.
Drawings
FIG. 1 is a flow chart of a method for predicting flange leakage in a piping system according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Fig. 1 is a flowchart of a method for predicting flange leakage of a pipeline system according to an embodiment of the present invention, and referring to fig. 1, the embodiment of the present invention provides a method for predicting flange leakage of a pipeline system, including the following steps:
s1: and establishing a finite element model for pipeline modal analysis, wherein the finite element model takes all structural rigidity of the flange as input and takes the simulation modal frequency of a pipeline system as output.
In this embodiment, according to the structure of the pipeline, a finite element model for pipeline modal analysis is established, and all the structural rigidity of the flange of the pipeline system is input into the model, so that the output first third-order simulation modal frequency can be obtained. The finite element model for modal analysis of the pipeline may be described by the equation f i1 ,f i2 ,f i3 ]＝F([K i1 ,K i2 ...K ii ..K in ]) And (5) expression. Wherein K is ii The i-th flange structural stiffness, f, being all flange structural stiffness in the i-th group i1 ,f i2 ,f i3 And analyzing the output first third-order simulation modal frequencies corresponding to the finite element model for inputting all flange structural rigidity in the ith group to the pipeline modal.
S2: the actual modal frequencies of the tubing are measured.
Preferably, the measured modal frequencies of the pipeline system are measured by a modal test method. Of course, other ways of obtaining the measured modal frequencies of the tubing may be used.
Preferably, the measured modal frequencies of the first third order of the pipeline system are measured as follows: f (f) c1 ，f c2 And f c3 . The measured modal frequency of the front third order of the pipeline system can be measured to meet the accuracy requirement, and the calculation efficiency can be improved by taking the front third order.
S3: and obtaining optimal solutions of all the structural rigidity of the flange by adopting a genetic algorithm, so that the mean square error of the actual modal frequency and the simulated modal frequency of the pipeline system is within a preset range.
Preferably, the step S3 specifically includes:
s31: bringing into an array of structural stiffness of all flanges according to genetic algorithm [ K ] i1 ,K i2 …K ii …K in ]Obtaining the front third-order simulation modal frequency [ f ] of the pipeline system by analyzing the finite element model to the pipeline modal i1 ,f i2 ,f i3 ]JudgingIf the value is smaller than the predetermined value, step S32 is executed, and if not, step S33 is executed.
Preferably, the pre-value in step S31 is 0.1.
S32: determination of [ f i1 ,f i2 ,f i3 ]The corresponding array is the structural rigidity [ K ] of all flanges of the pipeline system i1 ,K i2 …K ii …K in ]。
S33: returning to step S31.
In the embodiment, the actual modal frequency of the pipeline is obtained by combining measurement, the finite element model is analyzed through the pipeline modal, and a genetic algorithm is adopted to gradually bring the actual modal frequency into an array [ K ] of the optimized flange structural rigidity i1 ,K i2 …K ii …K in ]And obtaining the simulation modal frequency which reaches a set threshold value with the mean square error of the actual modal frequency, so as to reversely deduce the bonding strength of the flange in the pipeline.
In the present embodiment, K in ∈[0,10 7 ]。
S4: and establishing a BP (back propagation) neural network model of a mapping relation between the flange structural rigidity and the flange sealing surface pressure, and bringing the optimal solutions of all the flange structural rigidity into the BP neural network model to obtain the target flange sealing surface pressure.
Preferably, establishing a BP neural network model of a mapping relation between the structural rigidity of the flange and the pressure of the sealing surface of the flange specifically comprises:
and respectively measuring the rigidity K of the flange structure of the flange connection structure under different sealing surface pressures F through experiments, and taking the rigidity K as a sample of the neural network.
And taking K as an input layer and F as an output layer, establishing a BP neural network model, and performing sample training on the BP neural network model to obtain a trained BP neural network model.
In the embodiment, the mapping relation of the training sealing surface pressure F corresponding to the flange structural rigidity K in the BP neural network model is adopted, so that calculation is simplified, and efficiency can be improved.
Preferably, the sampleF in F takes on a value m times e ，F e And the minimum allowable pressure is applied to the flange sealing surface. m= {0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2.0}, the samples are 20 groups.
In this embodiment, 20 sets of data are taken and respectively spaced at the minimum allowable pressure F e In this way, the mapping in the trained BP neural network model can be more accurate. This may make the corresponding calculation more accurate. Of course, in other embodiments, more sets of samples may be used to obtain a more accurate mapping.
S5: judging whether the detected flange sealing pressure is smaller than the preset safety sealing pressure, if so, the flange has leakage risk, and if not, the flange does not have leakage risk.
In this embodiment, the preset safety seal pressure is provided with a first safety value, and in order to avoid omission of detection, the safety seal pressure is generally set to be smaller than the actual safety seal pressure during actual detection, so that omission can be avoided.
In summary, the method and the device for predicting the flange leakage risk based on the pressure of the sealing surface of each flange on the pipeline are used for measuring the modal frequency of the pipeline, and then the pressure of the sealing surface of each flange on the pipeline is converted. The method comprises the steps of establishing a finite element model for pipeline modal analysis, wherein the finite element model takes all structural rigidity of flanges as input and takes the simulation modal frequency of a pipeline system as output; measuring to obtain the actual modal frequency of the pipeline system; obtaining optimal solutions of all flange structural rigidity by adopting a genetic algorithm, so that the mean square error of the actual modal frequency and the simulated modal frequency of the pipeline system is within a preset range; establishing a BP neural network model of a mapping relation between the flange structural rigidity and the flange sealing surface pressure, and bringing the optimal solutions of all the flange structural rigidity into the BP neural network model to obtain the target flange sealing surface pressure; judging whether the detected flange sealing pressure is smaller than the preset safety sealing pressure, if so, the flange has leakage risk, and if not, the flange does not have leakage risk. The method is simple and reliable, can rapidly position the flange which is likely to leak, greatly reduces the overhaul workload of the ship flange and improves the efficiency; meanwhile, the method can identify the flange which is not leaked but has leakage risk, reduce hidden danger and improve the safety of a pipeline system. In addition, the method of the invention has the advantages of convenient implementation, wide adaptability, low cost and better economic benefit.
The invention is not limited to the embodiments described above, but a number of modifications and adaptations can be made by a person skilled in the art without departing from the principle of the invention, which modifications and adaptations are also considered to be within the scope of the invention. What is not described in detail in this specification is prior art known to those skilled in the art.
Claims (6)
1. A method for predicting flange leakage in a piping system, comprising the steps of:
s1: establishing a finite element model for pipeline modal analysis, wherein the finite element model takes the rigidity of all flange structures as input and takes the simulation modal frequency of a pipeline system as output;
s2: the actual modal frequencies of the pipeline system are obtained through measurement, and the actual modal frequencies of the front third order of the pipeline system are respectively: f (f) c1 ，f c2 And f c3 ；
S3: obtaining optimal solutions of all flange structural rigidity by adopting a genetic algorithm, so that the mean square error of the actual modal frequency and the simulated modal frequency of the pipeline system is within a preset range, wherein the step S3 specifically comprises the following steps:
s31: bringing into an array of structural stiffness of all flanges according to genetic algorithm [ K ] i1 ,K i2 …K ii …K in ]Obtaining the front third-order simulation modal frequency [ f ] of the pipeline system by analyzing the finite element model to the pipeline modal i1 ,f i2 ,f i3 ]JudgingIf not, executing step S32, if not, executing step S33;
s32: determination of [ f i1 ,f i2 ,f i3 ]The corresponding array is the structural rigidity [ K ] of all flanges of the pipeline system i1 ,K i2 …K ii …K in ]；
S33: returning to step S31;
s4: establishing an error back propagation BP neural network model of a mapping relation between the flange structural rigidity and the flange sealing surface pressure, and bringing the optimal solutions of all the flange structural rigidity into the BP neural network model to obtain the target flange sealing surface pressure;
s5: judging whether the detected flange sealing pressure is smaller than the preset safety sealing pressure, if so, the flange has leakage risk, and if not, the flange does not have leakage risk.
2. A method of predicting flange leakage in a ductwork system as set forth in claim 1, wherein the predetermined value in step S31 is 0.1.
3. The method for predicting flange leakage of a pipeline system according to claim 1, wherein the BP neural network model for establishing a mapping relationship between the flange structural rigidity and the flange sealing surface pressure is specifically provided with:
respectively measuring the rigidity K of the flange structure of the flange connection structure under different sealing surface pressures F through experiments, and taking the rigidity K as a sample of the neural network;
and taking K as input and F as output, establishing a BP neural network model, and carrying out sample training on the BP neural network model to obtain a trained BP neural network model.
4. A method of predicting flange leakage in a conduit system as set forth in claim 3, wherein the sample has a value F that is m times F e ，F e And the minimum allowable pressure is applied to the flange sealing surface.
5. The method of claim 4, wherein m= {0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2.0}, the samples are 20 groups.
6. The method for predicting flange leakage of a piping system according to claim 1, wherein the measured modal frequencies of the piping system are measured using a modal test method.
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