CN111853553A - Method and system for detecting fault of fluid pipeline - Google Patents
Method and system for detecting fault of fluid pipeline Download PDFInfo
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- CN111853553A CN111853553A CN202010751765.4A CN202010751765A CN111853553A CN 111853553 A CN111853553 A CN 111853553A CN 202010751765 A CN202010751765 A CN 202010751765A CN 111853553 A CN111853553 A CN 111853553A
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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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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
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Abstract
The invention provides a method and a system for detecting faults of a fluid pipeline, which are used for solving the problems that a leakage and blockage detection method in the prior art cannot be applied to any pipeline and is low in precision. The method comprises the following steps: on a specified leak detection and plugging detection pipe section, mapping the mutual constraint relation between the pipe and the fluid according to the energy balance of the fluid in the pipe, the flow rate of the fluid in the pipe and the pressure difference value of the front end and the rear end of the pipe section; continuously acquiring a pipeline on-way resistance value R and a fluidity characteristic coefficient n as normal reference samples in a machine self-learning mode; once the on-way resistance R and the fluidity characteristic coefficient n are changed, the pipeline is in an abnormal state; and capturing the time and degree of the occurrence quantity of the on-way resistance R and the fluidity characteristic coefficient n, and identifying the quality change fault state of the pipe section in blockage or leakage. Through the self-learning mode of the machine, the original parameters of pipeline erection condition, section surface shape, type of clear viscous medium, flow speed and the like do not need to be observed, and the application range is wide.
Description
Technical Field
The invention relates to the technical field of measurement and control, in particular to a method and a system for detecting a fault of a fluid pipeline.
Background
Liquid and gas media pass through the conveying pipeline for a long time, and deposit is accumulated on the pipe wall of the pipeline, so that the flowing resistance is gradually increased on the one hand, the electrochemical corrosion damage to the pipeline material is also caused, and the perforation leakage of the pipeline is accelerated.
Most of the initial leaks are peculiar in concealment, are not very intuitive and obvious, and are difficult to find particularly in trace leakage due to factors such as various erosion influences and unpredictable external force damage.
Once the continuous leakage accident occurs, the normal production is interfered, the economic loss is caused, the great potential safety hazard exists, and the serious environmental pollution is further formed. The current global problem is how to eliminate the threat to the security of the lives and properties and realize the leakage detection, blockage detection and forecast strategy which is mainly prevention and combined prevention and elimination.
Therefore, it is common knowledge to find a fixed and sensitive method for detecting leakage, detecting blockage and forecasting, quickly sense the current pipeline fault state and construct a self-adaptive and easily maintained detection system.
At present, the commonly adopted pipeline leakage point detection method comprises the following steps:
1. and (3) leak detection of an in-pipe detector: the method mainly comprises two types of leakage detection of an in-pipe detection ball and a pipeline crawler;
2. manual inspection outside the pipe: the method comprises the following steps of (1) judging whether leakage occurs or not by looking, smelling, listening or other ways by means of an experienced plumber to patrol the pipe;
3. leakage detection method for external cable: the cable leakage detection and the optical fiber leakage detection have the common characteristic that the leakage detection cable and the optical fiber are laid outside a pipeline, and leakage detection is carried out by analyzing leakage or analyzing the change of relevant parameters caused by the reaction of the leakage and a cable coating material;
4. thermal infrared imaging method: for a crude oil pipeline needing to be heated and conveyed, soil around the pipeline can increase the temperature due to the soaking of crude oil when leakage occurs;
5. flow difference leak detection method: detecting pipeline leakage by measuring and calculating the difference between the outflow quality and the inflow quality of the pipeline;
6. pressure gradient method: when the pipeline leaks, the expansion wave generated by pressure reduction is transmitted to the detection points at two sides to cause the pressure change at the point, and the break point is a leakage point;
7. negative pressure wave leak detection method: the fluid is equivalent to a leakage point, and a negative pressure wave which is transmitted at a certain speed is generated at the position where the fluid is quickly lost;
8. a statistical leak detection method comprises the following steps: the shell company provides that when the pipeline leaks, the relation between the flow and the pressure changes, a sequence probability ratio test method and a model identification technology are applied to analyze the actually measured flow and pressure values and calculate the probability of leakage, so that whether leakage occurs is judged;
9. a real-time model method: the pressure and the flow of the pipeline are monitored on line by establishing a pipeline model, and compared with the actual monitoring values of the pressure and the flow, the diagnosis of the leakage fault is carried out;
10. and (3) sound wave leak detection: when the processes of artificially removing a protective layer, installing a clamp, installing a valve, punching and the like on an oil pipeline are carried out, the generated sound waves can be transmitted along the steel pipe at a high speed;
11. magnetic flux leakage detection method: the pipe wall is corroded or has cracks, and part of magnetic force lines are bent and deformed, which is called magnetic leakage phenomenon;
12. ultrasonic detection: detecting defects of metal and non-metal materials;
13. a ground penetrating radar: when crude oil in the pipeline leaks, the electrical property of the medium around the pipeline can be changed;
14: gas imaging: when the laser is aligned with the leaked gas, the natural gas leakage can be detected by receiving the change of the reflection spectrum;
15. and (3) biological leak detection: an experienced technician carrying a detection instrument or a trained animal searches for abnormal phenomena of the pipeline and the periphery thereof through factors such as smell, sound, environmental conditions and the like;
16. tracer leak detection method: the liquid tracer is mixed in the fluid conveyed by the pipeline, and when the pipeline leaks, the tracer in the flowing-out fluid volatilizes and spreads;
17. a segmented pressure test method: the pipeline sectionally closes the stop valve, and the change condition of the pressure drop of the closed section is observed;
18. and (3) a radioactive substance detection method: putting radioactive markers such as smell, iodine, sodium and the like;
19. leakage characteristic monitoring method: paving a PVC plastic porous guide pipe along the pipeline, and detecting pipeline leakage by collecting steam leaked from the porous plastic guide pipe;
20. the artificial neural network technology comprises the following steps: and extracting the time-frequency domain characteristics, and establishing an artificial neural network capable of analyzing, detecting and positioning the leakage condition.
In summary, the pipeline leakage detection technology has been widely applied and developed in engineering practice by knowing various pipeline leakage detection methods, but also reveals many shortcomings.
Although there are many methods for detecting leakage, the method for detecting leakage of a specific pipeline is selected comprehensively according to design parameters of the pipeline, properties of transmission media, economy of equipment and data communication capacity, and none of the methods can be applied to detecting leakage of any pipeline.
Secondly, the existing leakage detection and positioning technology is difficult to solve the problems of contradiction between field detection sensitivity and false alarm, low positioning precision and the like, and is particularly prominent in micro-flow leakage.
Therefore, it is urgently needed to improve the fixity and accuracy of the leakage detection and positioning system, solve the contradiction between good detection sensitivity and false alarm rate, that is, improve the significant correlation, and is an important standard for measuring the quality of a detection system.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention provides a method and a system for detecting a failure of a fluid pipeline, which are used to solve the problems that the method for detecting a leakage and a blockage in the prior art cannot be applied to any pipeline and is not high in precision.
To achieve the above and other related objects, the present invention provides a method for detecting a failure of a fluid pipeline, the method comprising the steps of:
step one, on an appointed leakage detection plugging detection pipe section, mapping a mutual constraint relation formula of a pipeline and fluid according to energy balance of the fluid in the pipeline, flow of the fluid in the pipeline and a pressure difference value of the front end and the rear end of the pipe section:
in the above formula, Q at a certain timei-fluid flow in the pipe; corresponds to Δ Pi-pressure difference between front and rear ends of pipe section, i-acquisition of QiAnd Δ PiThe number of matched data sets, the on-way resistance value R of the pipeline and the fluidity characteristic coefficient n;
continuously acquiring a pipeline on-way resistance value R and a fluidity characteristic coefficient n as normal reference samples in a machine self-learning mode;
thirdly, once the on-way resistance R and the fluidity characteristic coefficient n are changed, the pipeline is in an abnormal state; and capturing the time and degree of the occurrence quantity of the on-way resistance R and the fluidity characteristic coefficient n, and identifying the quality change fault state of the pipe section in blockage or leakage.
Alternatively to this, the first and second parts may,
the normal reference sample is that no matter how the normal pipeline is in a non-blocking and non-leakage state, the fluid flow Q in the pipeline and the pressure difference delta P at the front end and the rear end of the pipeline section interact with each other, and the on-way resistance R and the flowability characteristic coefficient n of the pipeline are in a relatively stable state under the conditions of energy balance and continuous mass constraint;
the R value of the on-way resistance of the pipeline is gradually increased, and the n value of the fluidity characteristic coefficient slightly fluctuates, so that the identification pipeline section is in a blocked quality-change fault state;
the pipeline along-the-way resistance R value has huge drop amplitude, the fluidity characteristic coefficient n value is reduced along with the drop, and the identification pipeline section is in a quality change fault state of leakage.
Optionally, the machine learning process is as follows:
the method comprises the following steps of firstly, establishing a machine self-learning algorithm, and solving a plurality of groups of fitting average values of fluidity characteristic coefficients n under different flow rates and fitting average values of on-way resistance R of a pipeline;
establishing a normal pipeline sample, obtaining a fitting average value of the fluidity characteristic coefficient n under the normal pipeline, and realizing the discrimination learning of the normal pipeline by the fitting average value of the on-way resistance R of the pipeline;
and in the third stage, establishing a blocked pipeline sample, obtaining a fitting average value of a fluidity characteristic coefficient n under the blocked pipeline, and realizing judgment and learning of the blocked pipeline by the fitting average value of the on-way resistance R of the pipeline:
and in the fourth stage, a leakage pipeline sample is established, the fitting average value of the fluidity characteristic coefficient n under the leakage pipeline is obtained, and the fitting average value of the on-way resistance R of the pipeline is obtained, so that the judgment and learning of the leakage pipeline are realized.
Optionally, in the third stage, the method specifically includes the following steps:
establishing a first sample of the blocked pipeline, obtaining a fitting average value of a fluidity characteristic coefficient n under the first sample, and comparing the fitting average value of the on-way resistance R of the pipeline with a normal pipeline sample, so as to realize one-time distinguishing learning of the blocked pipeline;
and establishing a second sample of the blocked pipeline aiming at the same leak detection blocked pipe section, and obtaining the fitting average value of the fluidity characteristic coefficient n and the fitting average value of the on-way resistance R of the pipeline under the second sample to be compared with the first sample, so as to realize the re-distinguishing learning of the blocked pipeline.
Optionally, in the fourth stage, the method specifically includes the following steps:
and establishing a sample of the slight leakage pipeline, and obtaining a fitting average value of the fluidity characteristic coefficient n under the sample of the slight leakage pipeline and a fitting average value of the on-way resistance R of the pipeline to be compared with a normal pipeline sample, so as to realize the discrimination learning of the slight leakage pipeline.
Optionally, in the fourth stage, the method further includes the following steps:
and establishing a dripping pipeline sample, obtaining a fitting average value of a fluidity characteristic coefficient n and a fitting average value of pipeline on-way resistance R of the dripping pipeline sample in the initial dripping and spraying ending states, and comparing the n and the R in the initial dripping and spraying ending states to realize the judgment and learning of the dripping pipeline.
Optionally, the first stage comprises:
for the fluid mutual constraint relation in the pipeline:
taking logarithm on two sides, and unfolding:
log Qi＝n(logΔPi-logR)＝-n log R+n log ΔPi
with reference to a first order functional relationship: y ═ a + bX;
and (3) contraposition to obtain: y is log Qi，a＝-n log R，b＝n，X＝log ΔPi；
The front and the back of the pipe section are provided with a pressure gauge and a flowmeter, and the flow Q of the fluid in the pipeline is measured under different working conditionsiPressure difference delta P between front end and rear end of pipe sectioniMultiple groups of measured data are arranged in a matrix form as follows:
according to solving a first order function coefficient matrix: f ═ X+Y＝(XTX)-1XTY；
Fitting calculation
A coefficient matrix, i is not less than 2 because of two evidence obtaining coefficients of a and b;
after the F coefficient matrix is solved, the coefficients a and b are known, and the average value of the fluidity characteristic coefficient n is correspondingly obtained:
n＝b
obtaining the average value of the on-way resistance R of the pipeline:
a system comprises a fluid pipeline, wherein a leakage detection blocking section is arranged on the fluid pipeline, a flow meter is arranged in front of the leakage detection blocking section, a front pressure gauge and a rear pressure gauge are respectively arranged in front of and behind the leakage detection blocking section, and the leakage detection blocking section is not provided with an unfixed resistance part;
the fluid pipeline fault detection system further comprises a processor, wherein the processor is used for receiving data of the flow meter, the front pressure gauge and the rear pressure gauge and operating the fluid pipeline fault detection method.
Optionally, the non-stationary damage blocking component comprises a regulating valve and a bleeding branch.
Optionally, a fixed damage-resisting part is arranged on the leakage detection plugging section,
optionally, the fixed damage resisting part comprises a reducer head and a filter.
Optionally, the fluid pipeline is of a single pipeline structure, at least one section of the leakage detection blocking section is arranged on the fluid pipeline of the single pipeline structure, and when the leakage detection blocking section has multiple sections, the leakage detection blocking sections are connected in series;
or, the fluid pipeline comprises a main pipe and a branch pipe, the main pipe and/or the branch pipe is provided with at least one section of the leakage detection blocking section, and when the leakage detection blocking section on the main pipe or the branch pipe has multiple sections, the sections are connected in series.
As described above, the method and system for detecting a failure of a fluid line according to the present invention have the following advantageous effects:
firstly, fitting and calculating the hidden quantity of the actual working condition R and the medium flowing condition n of the mapping pipe section by means of a plurality of groups of detectable Q and delta P expression values; especially the on-way resistance R of the pipeline, and once the quantity becomes larger, the leakage detection and the blockage detection become simple and easy by analogy with the electrical engineering mechanism.
Secondly, through the self-learning mode of machine, need not to observe original parameters such as pipeline erection condition, section face shape, clear viscous medium type, velocity of flow speed, the wide range of adaptation, the commonality is strong.
And thirdly, the conventional flow and pressure detection does not change the original flow path system, does not need manual field operation, and does not influence the production process.
Drawings
FIG. 1 is a schematic view of a conventional fluid delivery line
FIG. 2 is a graph showing a comparison of parameters of a clogged pipe
FIG. 3 is a graph comparing leakage pipeline parameters
FIG. 4 is a schematic view of a series split leak detection plugging detection pipe section
FIG. 5 is a schematic view of a parallel split leak detection plugging detection pipe section
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
An embodiment of a method of fault detection of a fluid conduit, the method comprising the steps of:
step one, on an appointed leakage detection plugging detection pipe section, mapping a mutual constraint relation formula of a pipeline and fluid according to energy balance of the fluid in the pipeline, flow of the fluid in the pipeline and a pressure difference value of the front end and the rear end of the pipe section:
in the above formula, Q at a certain timei-fluid flow in the pipe; corresponds to Δ Pi-pressure difference between front and rear ends of pipe section, i-acquisition of QiAnd Δ PiNumber of paired data sets, resistance value R along the way of the pipe, flowA characteristic feature coefficient n;
continuously acquiring a pipeline on-way resistance value R and a fluidity characteristic coefficient n as normal reference samples in a machine self-learning mode;
thirdly, once the on-way resistance R and the fluidity characteristic coefficient n are changed, the pipeline is in an abnormal state; and capturing the time and degree of the occurrence quantity of the on-way resistance R and the fluidity characteristic coefficient n, and identifying the quality change fault state of the pipe section in blockage or leakage.
In this embodiment, in the normal reference sample, in a normal pipeline, that is, in a non-blocked and non-leakage state, no matter how the fluid flow Q in the pipeline interacts with the pressure difference Δ P between the front end and the rear end of the pipeline section, under the conditions of energy balance and continuous mass constraint, the on-way resistance R and the fluidity characteristic coefficient n of the pipeline are in a relatively stable state;
the R value of the on-way resistance of the pipeline is gradually increased, and the n value of the fluidity characteristic coefficient slightly fluctuates, so that the identification pipeline section is in a blocked quality-change fault state;
the pipeline along-the-way resistance R value has huge drop amplitude, the fluidity characteristic coefficient n value is reduced along with the drop, and the identification pipeline section is in a quality change fault state of leakage.
In this embodiment, optionally, the machine learning process is as follows:
the method comprises the following steps of firstly, establishing a machine self-learning algorithm, and solving a plurality of groups of fitting average values of fluidity characteristic coefficients n under different flow rates and fitting average values of on-way resistance R of a pipeline;
establishing a normal pipeline sample, obtaining a fitting average value of the fluidity characteristic coefficient n under the normal pipeline, and realizing the discrimination learning of the normal pipeline by the fitting average value of the on-way resistance R of the pipeline;
and in the third stage, establishing a blocked pipeline sample, obtaining a fitting average value of a fluidity characteristic coefficient n under the blocked pipeline, and realizing judgment and learning of the blocked pipeline by the fitting average value of the on-way resistance R of the pipeline:
and in the fourth stage, a leakage pipeline sample is established, the fitting average value of the fluidity characteristic coefficient n under the leakage pipeline is obtained, and the fitting average value of the on-way resistance R of the pipeline is obtained, so that the judgment and learning of the leakage pipeline are realized.
In this embodiment, optionally, in the third stage, the following steps are specifically included:
establishing a first sample of the blocked pipeline, obtaining a fitting average value of a fluidity characteristic coefficient n under the first sample, and comparing the fitting average value of the on-way resistance R of the pipeline with a normal pipeline sample, so as to realize one-time distinguishing learning of the blocked pipeline;
and establishing a second sample of the blocked pipeline aiming at the same leak detection blocked pipe section, and obtaining the fitting average value of the fluidity characteristic coefficient n and the fitting average value of the on-way resistance R of the pipeline under the second sample to be compared with the first sample, so as to realize the re-distinguishing learning of the blocked pipeline.
In this embodiment, optionally, in the fourth stage, the method specifically includes the following steps:
and establishing a sample of the slight leakage pipeline, and obtaining a fitting average value of the fluidity characteristic coefficient n under the sample of the slight leakage pipeline and a fitting average value of the on-way resistance R of the pipeline to be compared with a normal pipeline sample, so as to realize the discrimination learning of the slight leakage pipeline.
In this embodiment, optionally, in the fourth stage, the method further includes the following steps: and establishing a dripping pipeline sample, obtaining a fitting average value of a fluidity characteristic coefficient n and a fitting average value of pipeline on-way resistance R of the dripping pipeline sample in the initial dripping and spraying ending states, and comparing the n and the R in the initial dripping and spraying ending states to realize the judgment and learning of the dripping pipeline. The dripping of the dripping pipeline sample can be intermittent dripping, continuous dripping or jet dripping.
In this embodiment, optionally, the following relation is satisfied according to the principle of mutual restriction of fluids in the pipeline:
in the above formula, Q at a certain timei-fluid flow in the pipe; corresponds to Δ Pi-the pressure difference across the front and back of the tube section,i-Collection of QiAnd Δ PiNumber of paired data sets. The specific derivation process is as follows:
based on the classical fluid theory in the pipeline under the law of mass continuity and energy conservation:
equation of continuous mass of fluid in the pipeline:
the energy balance of the fluid in the pipeline-Bernoulli equation:
relationship between tube frictional resistance and flow rate:
based on the three classic fluid equations i, ii and iii, the following national standard recommended algorithm of the classic fluid theory of the pipeline is obtained by combination:
so far, the correlation between the electrical engineering mechanism, current, voltage and resistance
In the various flow manuals published so far, it is widely described that:
the method is characterized in that a classic national standard suggestion algorithm formula v is rewritten according to an electrical engineering mechanism formula, and the patent further discloses a mutual constraint relation formula of the fluid in the pipeline, which is convenient for the machine to learn by self:
Qiat a certain moment, setting the flow rate of the fluid in the pipeline, and obtaining the flow rate through detection of a flowmeter;
ΔPisetting the front and rear pressure gauges to detect and acquire the pressure difference between the front and rear ends of the pipe section corresponding to a certain moment;
i-Collection of QiAnd Δ PiThe number of the paired data sets must be i more than or equal to 2;
r is the on-way resistance value of the pipeline, maps the actual working condition in the pipeline section and is related to the pipe diameter d, the pipe length l, the pipe wall roughness, the local resistance coefficient xi and the like of the pipeline section between the front pressure gauge and the rear pressure gauge; the pipe section form is fixed, and the value is relatively fixed;
n is a fluidity characteristic coefficient, and the flow condition of the fluid medium is mapped and is related to the viscosity eta, the density rho, the flow velocity v and the like of the fluid; the fluid medium is fixed and the value is relatively fixed.
Realizes the collection of the fluid flow Q in the pipelineiPressure difference delta P between front end and rear end of pipe sectioniAnd fitting, calculating and obtaining two hidden quantities which are not directly detectable, namely the on-way resistance R and the fluidity characteristic coefficient n, by using a machine self-learning mode, and mapping the actual working condition of the pipe section and the medium flowing condition.
In the first stage, a machine self-learning algorithm is established:
and aiming at the fluid mutual constraint relation vi in the pipeline, taking logarithms at two sides and expanding:
log Qi＝n(logΔPi-logR)＝-n log R+n log ΔPi…………ⅶ
with reference to a first order functional relationship: y ═ a + bX
And (3) contraposition to obtain: y is log Qi，a＝-n log R，b＝n，X＝log ΔPi。
As shown in fig. 1, a schematic diagram of a common fluid conveying pipeline is provided, a flow meter F is arranged at an outlet of a supply pump to measure a fluid flow rate Q in a pipeline; on any long pipe section, a front pressure gauge P1 is arranged at the starting point, a rear pressure gauge P2 is arranged at the end point, and the pressure difference delta P between the front end and the rear end of the pipe section is measured.
Between the front pressure gauge P1 and the rear pressure gauge P2, the pressure gauge can detect the flow Q of the fluid in the pipelineiPressure difference delta P between front end and rear end of pipe sectioniAny length of line of value, i.e., designated as the leak detection plugged section. During the machine self-learning, in order to create comparable environmental conditions, the pipe section form must be fixed, all factors that change the flow and pressure, such as regulating valves, bleeding branches, etc., must be excluded from the leak detection of a blocked pipe section, and only the fixing of the damming parts, such as reducer heads, filters, etc., is permitted. In other words, there must be no changing energy consuming, shunting, etc. components within a given pipe section. If the conditions change, they must be learned again from scratch.
For the leakage detection and blockage detection pipe section 1, a plurality of groups of flow Q are measurediFrom pressure difference Δ PiIn the value process, the method one: by means of an external regulating valve FV, each flow Q is at different openingsiValue corresponding to different pressure differences Δ PiAnd recording the measured data; the second method comprises the following steps: the same purpose is achieved by adjusting the output power of the supply pump; the third method comprises the following steps: waiting for the peak and valley consumption periods of the demand users, and extracting by utilizing the natural fluctuation of the load; or a combination of the three methods described above.
To flow rate QiFrom pressure difference Δ PiOrderly controlling, recording a plurality of groups of measured data, and arranging the measured data into the following array matrix form:
according to solving a first order function coefficient matrix expression: f ═ X+Y＝(XTX)-1XTY…………ⅷ
By means of self-learning of the machine
The coefficient matrix requires that the measured variable values X and Y are not less than two groups, i is more than or equal to 2, because two evidence-finding coefficients a and b exist;
after the F coefficient matrix is solved, the coefficients a and b are known, and a fitting average value of the flowability characteristic coefficient n, referred to as the flowability characteristic coefficient n, is correspondingly obtained:
n＝b…………ⅸ
similarly, obtaining a fitting average value of the on-way resistance R of the pipeline, which is called the on-way resistance R of the pipeline for short:
the patent further combines a mass equation i of continuity of the fluid in the pipeline, an energy balance of the fluid in the pipeline-Bernoulli equation ii, and a relationship iii between the friction resistance of the pipeline and the flow velocity and flow rate into a convenient machine self-learning formula, which is derived from the classical fluid theory:
the calculation substantially expresses the energy balance and mass continuity relation; the method is characterized by comprising the following steps of utilizing four parameters to represent the flow Q of fluid in a pipeline, the pressure difference delta P between the front end and the rear end of the pipeline section, the on-way resistance R of the pipeline and the flowability characteristic coefficient n together, and mapping the actual working condition and the medium flowing condition of the pipeline section by analogy of an electrical engineering mechanism.
Specifically, the double hidden quantity is a fixed value when the pipeline is normal; once the on-way resistance R and the fluidity characteristic coefficient n are changed, the pipeline is in an abnormal state; capturing the time and degree of the occurrence quantity of the double hiding quantity, identifying the pipe section in a blocking or leakage qualitative change state, and specifically executing the following flow.
And in the second stage, normal pipeline judgment:
measuring flow Q in normal pipe section, i.e. in non-blocked, non-leaking stateiFrom pressure difference Δ PiThe values i are 3 groups of measured data, which are arranged into an array, and the following table is obtained after the machine self-learning:
obtaining a machine self-learning algorithm expression 1 under the current normal pipeline non-blocking and non-leakage state:
in addition, the flow Q can be measured again under the normal pipe section, namely under the state of no blockage and no leakageiFrom pressure difference Δ PiThe values i are 3 groups of measured data, which are arranged into an array, and the following table is obtained after the machine self-learning:
aiming at the same leakage detection and blockage detection pipe section, the machine self-learning algorithm expression 2 is obtained under the current normal pipe non-blockage and non-leakage state:
compare normal tube sample 1 and normal tube sample 2:
| normal discriminating pipeline meter | |
Normal sample 2 | Amplitude and direction of wave |
| On-way resistance value R of pipeline | 591.27 | 592.14 | 0.2％ |
| Characteristic coefficient of fluidity n | 0.5787 | 0.5798 | 0.2％ |
And (3) analysis: similarly, the normal pipeline section obtains the on-way resistance R values of the normal pipeline in the front and back periods, the difference is only 0.2%, and the values are basically the same by considering measurement, calculation errors and the like.
The actual measurement shows that the flow Q of the fluid in the pipeline is found under the normal pipeline, namely, the non-leakage and non-blockage stateiPressure difference delta P between front end and rear end of pipe sectioniNo matter how the two are interacted, under the constraint conditions of energy balance and mass continuity, the on-way resistance R and the flowability characteristic coefficient n of the hidden pipeline are relatively fixed.
And in the third stage, judging a blocked pipeline:
after the operation for a long time, the roughness of the pipe wall is directly increased or the flow area is reduced due to the adhesion of scaling substances and the like; blocking pipe section, measuring flow rate Q in non-leakage stateiFrom pressure difference Δ PiThe values i are 4 groups of measured data, which are arranged into an array, and the following table is obtained after the machine self-learning:
aiming at the same leakage detection and blockage detection pipe section, obtaining a machine self-learning algorithm expression 3 under the current state that the rough blockage pipe section has no leakage:
compare normal tube sample 2 and coarse plugged tube sample 3:
| pipe blockage distinguishing gauge | Normal sample 2 | |
Amplitude and direction of wave |
| On-way resistance value R of pipeline | 592.14 | 701.61 | 18% × (resistance becomes large) |
| Characteristic coefficient of fluidity n | 0.5798 | 0.5304 | 8% ↓ (obstructed flow) |
And (3) analysis: the normal pipe section obtains a normal pipe on-way resistance R value 592.14, and the absolute value of the normal pipe on-way resistance R value is compared with the on-way resistance R value 701.61 of the rough pipe after scaling, which is reflected in that the R value is increased, namely the normal resistance R value is less than the rough resistance R value.
The normal pipeline fluidity characteristic coefficient n value 0.5798 and the rough pipeline fluidity characteristic coefficient n value 0.5304 after scaling are reflected in that the n value is reduced, namely the normal characteristic coefficient n value is larger than the rough characteristic coefficient n value.
The actual measurement shows that the roughness of the inner wall of the pipeline is increased due to the scaling substances and the like on the pipeline wall, the flowing energy consumption is increased, the on-way resistance R value of the hidden pipeline is gradually increased when the energy is out of initial balance but the quality is continuous, and the n value of the fluidity characteristic coefficient slightly fluctuates.
As shown in fig. 2, a comparison of the plugged line parameter curves is shown, with the parameter curves shown on the left side relative to normal line samples 1 and 2; rough pipe sample 3, with the parameter curve shown on the right;after the pipeline is blocked, the pipeline is opened,
the flow rate Q of the contrast fluid is slightly reduced, the fluidity characteristic coefficient n is slightly reduced, the on-way resistance R of the pipeline is increased, and the front-back pressure difference delta P is obviously increased. The energy consumption is increased significantly for the same volume of material to be delivered.
And in the fourth stage, judging the blocked pipeline again:
subsequently, on the basis of example 3, the flow rate Q was measured in the course of a roughly plugged pipe section, i.e. in the leak-free stateiFrom pressure difference Δ PiThe values i are 5 groups of measured data, which are arranged into an array, and the following table is obtained after the machine self-learning:
aiming at the same leakage detection and blockage detection pipe section, a machine self-learning algorithm expression 4 is obtained under the current state that the rough blockage pipe section has no leakage:
compare rough pipe sample 3 to rough pipe sample 4:
| normal discriminating pipeline meter | |
Coarse clogging sample 4 | Amplitude and direction of wave |
| On-way resistance value R of pipeline | 701.61 | 703.57 | 0.3％ |
| Characteristic coefficient of fluidity n | 0.5304 | 0.5316 | 0.2％ |
And (3) analysis: and in a similar time period, continuous self-learning of the machine shows that the obtained on-way resistance value R and the fluidity characteristic coefficient n of the pipeline are basically kept unchanged.
And in the fifth stage, judging a leakage pipeline:
the flow Q is measured when one water seepage point is found and the leakage is slightiFrom pressure difference Δ PiThe values i are 3 groups of measured data, which are arranged into an array, and the following table is obtained after the machine self-learning:
aiming at the same leakage detection and blockage detection pipe section, a current rough pipe is obtained, and under a slight leakage pipe section, a machine self-learning algorithm expression 5 is as follows:
compare normal no leak sample 4 with slight leak sample 5:
| leakage pipeline is distinguishedWatch (A) | Normal pipe sample 4 | Microleakage sample 5 | Amplitude and direction of wave |
| On-way resistance value R of pipeline | 703.57 | 88.98 | 87% ↓ ↓ (great drop) |
| Characteristic coefficient of fluidity n | 0.5316 | 0.438 | 17% ↓ (flow unsmooth) |
And (3) analysis: once micro leakage occurs, the original continuous relation between the energy balance and the mass represented by the four characteristics is invalid by using the flow Q, the pressure difference delta P, the on-way resistance R value of the pipeline and the flowability characteristic coefficient n, a new balancing and continuous relation process is found with hands, short-circuit pressure relief and shunting phenomena are generated on a leakage point, abnormal movement is caught by self-learning when the energy is unbalanced and the mass is not continuous, the hidden on-way resistance R value of the pipeline can drop greatly, and the situation that the current and the voltage meet the short-circuit point is similar to the situation that the current and the voltage meet the short-circuit point.
Meanwhile, the fluidity characteristic coefficient n obtained by self-learning is reduced along with the reduction, and it is understood that in the leakage occurrence process, a leakage point causes material exchange between the inside and the outside of the pipeline, when the internal fluid leaks out, peripheral external environment gas is sucked in, so that bubbles are mixed in the fluid, physical parameters such as viscosity eta, density rho, flow velocity v and the like of the original fluid are changed, the bubbles flow along with the fluid, expand or compress, split or polymerize, the original flow characteristic of the fluid is disturbed, and the fluidity characteristic coefficient n is caused to fluctuate and descend.
Analog electrical engineering
The current is transmitted at the speed of light, the index level of the current is higher than that of the fluid, the fluidity characteristic coefficient n of the current is 1 and is larger than that of the fluid, the fluidity characteristic coefficient n is approximately equal to 0.53-0.43, the larger the value of the fluidity characteristic coefficient n is, the larger the value is, the 0.53, the stronger the flowing capacity is, and the fluid in the pipeline flows smoothly; the smaller, e.g., 0.43, the less smooth the flow.
As shown in FIG. 3, a comparative graphical representation of a leakage pipeline parameter curve is shown on the left side relative to the normal spool piece sample 4; sample 5, slight leak, parametric curve shown on the right;
the comparative fluid flow Q is slightly reduced and basically kept flat, the fluidity characteristic coefficient n is reduced, the on-way resistance R of the pipeline is greatly reduced, and the pressure difference delta P between the front and the rear is obviously reduced. While energy consumption is significantly reduced, material is lost.
The actual measurement shows that once pipeline leakage occurs, short circuit pressure relief and shunt phenomena are generated on a leakage point, when the energy is unbalanced and the quality is discontinuous, the on-way resistance R value of the hidden pipeline is greatly reduced, and the fluidity characteristic coefficient n value is reduced along with the reduction.
In the sixth stage, a diversity sample is taken, a leak detection and blockage detection pipe section is replaced, one crude oil connecting line type leakage point is initially found for a crude oil conveying pipeline which is thousands of meters long, the connecting line type leakage point is characterized in that the initial state is an intermittent leakage state, the leakage state is changed from the intermittent type to a continuous linear type along with the aggravation of leakage, the leakage state is further changed from the continuous linear type to a jet type along with the aggravation of leakage, the leaked crude oil is weighed by connecting a barrel, the test is carried out from about 1 hour/ton to about 2 hours/ton until the leakage is gradually enlarged, actual measurement process parameters are recorded for a period of time, and the comparison result of the following table is obtained:
the actual measurement shows that in the development process from leakage to jet leakage, on the same leakage detection and blockage detection pipe section, along with the continuous expansion of pipeline leakage, the short circuit pressure relief and the more serious flow splitting are generated on a leakage point, and when the energy is more unbalanced and the quality is more discontinuous, the value of the on-way resistance R of the hidden pipeline can continuously and violently decline.
Meanwhile, the fluidity characteristic coefficient n converges, a large number of air bubbles are mixed in the fluid to be saturated, and the capability of changing parameters such as viscosity eta, density rho, flow velocity v and the like of the fluid tends to limit saturation.
An embodiment of the system, includes the fluid pipeline, be equipped with on the fluid pipeline and detect stifled section, be equipped with the flowmeter before the stifled section of detection, be equipped with preceding manometer and back manometer around the stifled section of detection respectively, it does not have the non-fixed part that hinders to lose to detect stifled section. Optionally, the non-stationary damage blocking component comprises a regulating valve and a bleeding branch. Optionally, be equipped with fixed the part that decreases of hindering on the stifled section of leak testing, optionally, fixed the part that decreases includes reducing head and filter. The flow meter referred to here can transmit data to the computer equipment for data analysis, the front pressure gauge and the rear pressure gauge can transmit the acquired pressure data to the computer equipment for data analysis, the computer equipment here can be mobile or non-mobile electronic equipment with a data analysis function, or can transmit data to the cloud server in a wired or wireless manner, and then perform subsequent data analysis.
In this embodiment, optionally, the fluid pipeline is of a single pipeline structure, the leak detection blocking section is at least provided in one section of the fluid pipeline of the single pipeline structure, and when the leak detection blocking section has multiple sections, the leak detection blocking sections are connected in series.
In this embodiment, the fluid pipeline includes a main pipe and a branch pipe, the main pipe and/or the branch pipe is provided with at least one section of the leakage detecting block section, and when there are multiple sections of the leakage detecting block section on the single main pipe or the single branch pipe, the leakage detecting block sections are connected in series.
Referring to FIG. 4, a schematic diagram of a series split leak-test plugged pipe section is shown, based on the starting point P1 and the end point P2 of samples 1, 2, 3, 4 and 5Then, a terminal pressure gauge P3 is continuously arranged, namely a leakage detection and blockage detection pipe section 2 is appointed between P2 and a newly arranged P3; outlet flowmeter F of the feed pump, while still measuring the flow rate Q of the fluid in the pipei(ii) a On the leak detection and blockage detection pipe section 2, a pressure gauge P2 and a rear-point pressure gauge P3 are changed as a starting point, and the pressure difference delta P between the front end and the rear end of the pipe section is measuredi. By analogy, more leak detection plugging pipe sections … … can be designated later
As shown in fig. 5, a schematic diagram of a parallel split leak detection plugging detection pipe section is equivalent to parallel duplication on a branch pipe; the branch series of F1(P11-P12) forms a leakage detection plugging branch section 1, the branch series of F2(P21-P22) forms a leakage detection plugging branch section 2, and the branch series of F3(P31-P32) forms a leakage detection plugging branch section 3.
As shown in fig. 4 and 5, by arranging the flow meter and the pressure gauge as required, the leakage detection and blockage detection pipe section can be cut off as required for pipelines with any length and even complex pipe networks.
In short, the interactive fluid flow QiFrom pressure difference Δ PiIn an array, it is difficult to observe the relationship between the fluid and the constraint pipe.
The patent is derived from the energy balance and mass continuity relation of fluid in the pipeline, and the flow Q of the fluid in the pipeline can be detectediPressure difference delta P between front end and rear end of pipe sectioniThe method comprises the following steps of capturing fixed hiding amounts of on-way resistance R and a fluidity characteristic coefficient n of a pipeline in actual working conditions by a plurality of expression values, and further identifying the current state of a pipeline section:
firstly, the on-way resistance R of the pipeline is leveled, which indicates that the energy is balanced, the quality is continuous, and the pipeline is in a normal state; secondly, the on-way resistance R rises, which shows that the flow energy consumption is increased, the energy breaks the initial balance, the quality is continuous, and the blockage phenomenon occurs after the scaling substances are accumulated; when the pipeline leaks, the energy is unbalanced, the mass is discontinuous, short-circuit pressure relief and shunt phenomena are generated on a leakage point, the on-way resistance R can drop greatly by main identification parameters, and the secondary identification parameters and the fluidity characteristic coefficient n are also reduced; fourthly, along with the expansion of the leakage of the pipeline, the leakage is more serious, and the R value of the on-way resistance of the pipeline is also found to be seriously reduced.
In addition, in the leakage occurrence process, bubbles are generated due to material exchange, parameters such as viscosity eta, density rho and flow velocity v of the fluid are changed, the original flow characteristics of the fluid are disturbed, and the flow characteristic coefficient n is fluctuated.
The detection of leakage, blockage and judgment can be simply and easily completed by utilizing the mutual constraint relation of the fluids.
A method for detecting leakage and blockage of pipeline features that the on-way resistance R parameter is mainly recognized, the flowability characteristic coefficient n parameter is secondarily recognized, and the detection of leakage and blockage is simple and easy, and has no relation with the factors such as the erection state of pipeline, the shape of segment surface, the type of viscous medium, and the flow speed.
The method comprises the steps of fitting and calculating the hidden quantity of the actual working condition R and the medium flow condition n of a mapping pipe section by means of multiple groups of detectable Q and delta P expression values; especially the on-way resistance R of the pipeline, and once the quantity becomes larger, the leakage detection and the blockage detection become simple and easy by analogy with the electrical engineering mechanism. Through the self-learning mode of the machine, original parameters such as the erection condition of the pipeline, the shape of the section surface, the type of the sticky and sticky medium, the flow speed and the like do not need to be observed, the application range is wide, and the universality is strong. The conventional flow and pressure detection does not change the original flow path system, does not need manual field operation, and does not influence the production process.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and it is apparent that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method of fault detection of a fluid conduit, the method comprising the steps of:
step one, on an appointed leakage detection plugging detection pipe section, mapping a mutual constraint relation formula of a pipeline and fluid according to energy balance of the fluid in the pipeline, flow of the fluid in the pipeline and a pressure difference value of the front end and the rear end of the pipe section:
in the above formula, Q at a certain timei-fluid flow in the pipe; corresponds to Δ Pi-pressure difference between front and rear ends of pipe section, i-acquisition of QiAnd Δ PiThe number of matched data sets, the on-way resistance value R of the pipeline and the fluidity characteristic coefficient n;
continuously acquiring a pipeline on-way resistance value R and a fluidity characteristic coefficient n as normal reference samples in a machine self-learning mode;
thirdly, once the on-way resistance R and the fluidity characteristic coefficient n are changed, the pipeline is in an abnormal state; and capturing the time and degree of the occurrence quantity of the on-way resistance R and the fluidity characteristic coefficient n, and identifying the quality change fault state of the pipe section in blockage or leakage.
2. The method of detecting a failure of a fluid conduit according to claim 1, wherein:
the normal reference sample is that no matter how the normal pipeline is in a non-blocking and non-leakage state, the fluid flow Q in the pipeline and the pressure difference delta P at the front end and the rear end of the pipeline section interact with each other, and the on-way resistance R and the flowability characteristic coefficient n of the pipeline are in a relatively stable state under the conditions of energy balance and continuous mass constraint;
the R value of the on-way resistance of the pipeline is gradually increased, and the n value of the fluidity characteristic coefficient slightly fluctuates, so that the identification pipeline section is in a blocked quality-change fault state;
the pipeline along-the-way resistance R value has huge drop amplitude, the fluidity characteristic coefficient n value is reduced along with the drop, and the identification pipeline section is in a quality change fault state of leakage.
3. The method of detecting a failure of a fluid conduit according to claim 2, wherein: the machine learning process is as follows:
the method comprises the following steps of firstly, establishing a machine self-learning algorithm, and solving a plurality of groups of fitting average values of fluidity characteristic coefficients n under different flow rates and fitting average values of on-way resistance R of a pipeline;
establishing a normal pipeline sample, obtaining a fitting average value of the fluidity characteristic coefficient n under the normal pipeline, and realizing the discrimination learning of the normal pipeline by the fitting average value of the on-way resistance R of the pipeline;
and in the third stage, establishing a blocked pipeline sample, obtaining a fitting average value of a fluidity characteristic coefficient n under the blocked pipeline, and realizing judgment and learning of the blocked pipeline by the fitting average value of the on-way resistance R of the pipeline:
and in the fourth stage, a leakage pipeline sample is established, the fitting average value of the fluidity characteristic coefficient n under the leakage pipeline is obtained, and the fitting average value of the on-way resistance R of the pipeline is obtained, so that the judgment and learning of the leakage pipeline are realized.
4. The method of detecting a failure of a fluid conduit according to claim 3, wherein: in the third stage, the method also comprises the following steps:
establishing a first sample of the blocked pipeline, obtaining a fitting average value of a fluidity characteristic coefficient n under the first sample, and comparing the fitting average value of the on-way resistance R of the pipeline with a normal pipeline sample, so as to realize one-time distinguishing learning of the blocked pipeline;
and establishing a second sample of the blocked pipeline aiming at the same leak detection blocked pipe section, and obtaining the fitting average value of the fluidity characteristic coefficient n and the fitting average value of the on-way resistance R of the pipeline under the second sample to be compared with the first sample, so as to realize the re-distinguishing learning of the blocked pipeline.
5. The method of detecting a failure of a fluid conduit according to claim 3, wherein: in the fourth stage, the method specifically comprises the following steps:
and establishing a sample of the slight leakage pipeline, and obtaining a fitting average value of the fluidity characteristic coefficient n under the sample of the slight leakage pipeline and a fitting average value of the on-way resistance R of the pipeline to be compared with a normal pipeline sample, so as to realize the discrimination learning of the slight leakage pipeline.
6. The method of detecting a failure of a fluid conduit of claim 5, wherein: in the fourth stage, the method also comprises the following steps:
establishing a dripping pipeline sample, obtaining a fitting average value of a flowability characteristic coefficient n and a fitting average value of pipeline on-way resistance R of the dripping pipeline sample under two states of initial dripping and spraying termination, comparing the n and the R under the two states of initial dripping and spraying termination, realizing the judgment and learning of the dripping pipeline, and obtaining the average value of the pipeline on-way resistance R:
7. a system, characterized by: the leakage detection device comprises a fluid pipeline, wherein a leakage detection plugging section is arranged on the fluid pipeline, a flowmeter is arranged in front of the leakage detection plugging section, a front pressure gauge and a rear pressure gauge are respectively arranged in front of and behind the leakage detection plugging section, and the leakage detection plugging section is not provided with an unfixed resistance part;
the fluid pipeline fault detection system further comprises a processor for receiving data of the flow meter, the front pressure gauge and the rear pressure gauge and operating the fluid pipeline fault detection method according to any one of claims 1 to 6.
8. The system of claim 7, wherein: the non-fixed damage resisting component comprises a regulating valve and a diffusing branch pipe.
9. The system of claim 8, wherein: the leakage detection plugging section is provided with a fixed resistance loss part, and the fixed resistance loss part comprises a reducing head and a filter.
10. The system of claim 8, wherein: the fluid pipeline is of a single pipeline structure, at least one section of the leakage detection plugging section is arranged on the fluid pipeline of the single pipeline structure, and when the leakage detection plugging section has multiple sections, the leakage detection plugging sections are connected in series;
or, the fluid pipeline comprises a main pipe and a branch pipe, the main pipe and/or the branch pipe is provided with at least one section of the leakage detection blocking section, and when the leakage detection blocking section on the main pipe or the branch pipe has multiple sections, the sections are connected in series.
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