CN112555689A - Multi-sensing pipeline state intelligent monitoring device - Google Patents
Multi-sensing pipeline state intelligent monitoring device Download PDFInfo
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- CN112555689A CN112555689A CN202011315984.4A CN202011315984A CN112555689A CN 112555689 A CN112555689 A CN 112555689A CN 202011315984 A CN202011315984 A CN 202011315984A CN 112555689 A CN112555689 A CN 112555689A
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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
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D3/00—Arrangements for supervising or controlling working operations
- F17D3/01—Arrangements for supervising or controlling working operations for controlling, signalling, or supervising the conveyance of a product
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D3/00—Arrangements for supervising or controlling working operations
- F17D3/18—Arrangements for supervising or controlling working operations for measuring the quantity of conveyed product
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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
Abstract
The invention belongs to the field of monitoring and predicting the running state of a pipeline system, and provides an intelligent monitoring device for the state of a multi-sensing pipeline. In the monitored pipeline system, a plurality of sets of multi-sensing intelligent pipe sections are arranged to monitor the vibration of the pipeline, the installation pressure of the pipeline, the pressure, the temperature and the flow of fluid in the pipeline, the corrosion of the inner wall of the pipeline and the like in real time, and whether the installation of the pipeline system is normal or not and whether the pipeline is leaked or blocked or not are judged through the analysis of related algorithms in a data analysis and prediction module, so that the operation state of the pipeline of the system is judged, a user can more quickly and accurately position a fault area, and accurate maintenance is realized.
Description
Technical Field
The invention belongs to the field of monitoring and predicting system running states, and relates to an intelligent monitoring device for multi-sensor pipeline states.
Background
Fluid transmission is a problem commonly encountered in various engineering fields, and is generally realized by fluid pipelines, wherein water system pipelines are widely applied to various industrial devices and daily life, and relate to the fields of ship engineering, ocean engineering, chemical industry, municipal water supply and drainage and the like. The abnormal vibration and leakage of the pipeline shorten the service life of the system pipeline if the system pipeline is in a light state, damage equipment and reduce economic benefits. Serious people cause the breakage of pipelines and valves, pollute the surrounding environment, destroy the system function, influence the economic benefit, damage the health of people and even cause personal injuries and deaths and equipment accidents.
Taking a ship system as an example, the ship system is an indispensable part of a ship, once the ship system breaks down, the transportation of various fluid media cannot be ensured, the normal work of the ship equipment and the ship system is met, and the safe navigation of the ship is ensured, any pipeline in the ship system leaks, a pump valve breaks down, and abnormal vibration can cause the abnormal use of related equipment, especially some high-pressure pipelines can cause very serious accidents such as water immersion once the leakage happens during the operation, the safe operation and installation navigation of the ship are affected, and the life of an operator is threatened. Therefore, how to take corresponding treatment measures for monitoring the pipeline state and predicting the fault in the first time can play an important role in the safety of the ship and crews and the positioning treatment after the system has the fault.
The pipeline monitoring means mainly comprises: the first type is a manual direct observation method, the method judges the system operation or the normal operation of equipment by using modes of watching, listening, smelling and the like through an experienced operator patrol device, and the method cannot continuously detect the system operation state, so the real-time monitoring on the system state operation is poor; the second type is a thickness measurement method, which utilizes an in-pipe leak detection method based on technologies such as acoustic emission, far-field eddy current, camera shooting and the like, and the method estimates the running state of the whole system by detecting the state of a certain point, so that the problems of high false alarm rate, incapability of realizing online monitoring and the like exist; the third type is an outside-pipe detection method for realizing leakage detection and positioning by directly detecting the existence of leakage substances through optical fibers, and the method is very sensitive and has good effects on small leakage and slow leakage, but the price and the construction cost are high, and the failure of the whole monitoring system is caused by one fracture; the fourth type is a parameter calculation method based on operation parameters such as pipeline pressure, flow and temperature, which can realize on-line detection, and is convenient for construction and maintenance, but has many defects for accurate fault positioning and micro positioning.
The monitoring means is mainly biased to a long-distance conveying straight pipeline at present, the method has some defects, and further few researches are carried out on the overall state monitoring of the complex pipeline (the spatial arrangement is complex, the working condition is complex, and the number of branch pipes is large), so that the method has great practical significance in researching the state monitoring and prediction of the complex pipeline.
Disclosure of Invention
Aiming at the problems, the invention aims to overcome the defects of the prior art and provide an intelligent monitoring device for the state of a multi-sensing pipeline, which is used for acquiring, transmitting and analyzing state data of the pipeline, particularly the complex pipeline to obtain a relatively accurate pipeline running diagram and monitoring and predicting possible faults and the like of the pipeline.
In order to achieve the purpose, the technical scheme of the invention is as follows: the utility model provides a multisensory pipeline state intelligent monitoring device, includes multisensory intelligent pipeline section, data transmission module, power module, data analysis prediction module and state display and alarm module. The multi-sensing intelligent pipe section is a special pipe section with the functions of measuring internal fluid pressure, flow and temperature, measuring vibration, strain, corrosion and the like of the pipe section, is connected in the pipeline to be monitored in a distributed mode in series, and performs primary filtering, amplification and digital conversion on signals of pressure, flow, temperature, vibration, strain, corrosion and the like acquired by the multi-sensing intelligent pipe section through an internally integrated data acquisition module to form a multi-sensing information flow which is transmitted through a bus of a data transmission module so as to reduce the connection of cables; the data transmission module comprises one or more of a cable, an optical cable or a wireless network and is used for transmitting signals collected from the multi-sensor intelligent pipe section to the data analysis and prediction module; the power supply module comprises one or more of cable power supply, a storage battery, solar energy and wind energy and is used for supplying power to the multi-sensing intelligent pipe section, the data transmission module, the data analysis and prediction module and the state display and alarm module; the data analysis and prediction module comprises computer hardware and a corresponding data analysis and processing algorithm and is used for analyzing and processing data acquired and uploaded by the multi-sensor intelligent pipe section to obtain the running state of the pipeline; the state display and alarm module comprises a display and an optical alarm, and is used for visually displaying the running state of the pipeline on the display, when the current state of the pipeline section fails, the optical alarm gives an alarm by red light, and the display displays the fault type and the fault position, when the state of the pipeline section is predicted to fail, the optical alarm gives an alarm by yellow light, and the display displays the expected fault type, the expected fault position and the suggested processing method.
In the technical scheme, a plurality of multi-sensing intelligent pipe sections are dispersedly arranged in the monitored pipe sections, and are generally arranged in front of and behind a valve, a pump source and a branch pipe, so that more accurate monitoring and forecasting of the running state of the pipeline can be obtained.
In the above technical solution, the length of the multi-sensor intelligent pipe segment is a uniform standardized length, which is generally 200mm or 300 mm. The two end parts of the multi-sensing intelligent pipe section are connecting parts which comprise one of a flange, a threaded connector and a quick connector and are used for installing the multi-sensing intelligent pipe section in a pipeline in a matching mode.
In the above technical scheme, the middle part of the multi-sensing intelligent pipe section is a sensor mounting part, and comprises 8 immersion sensor interfaces which are circumferentially arranged, the immersion sensor interfaces are arranged in a front row and a rear row, the patch sensor interfaces are arranged in 8 axial directions, and all the sensor interfaces are standard interfaces.
In the technical scheme, the sensors installed on the multi-sensor intelligent pipe sections can be pressure, temperature and flow sensors for monitoring internal fluid, and can also be sensors for monitoring vibration, strain and corrosion of the pipeline, the monitoring positions and the monitoring quantity can be freely adjusted on the installation positions of the sensors, and redundant sensor interfaces can be sealed by the sensor blocks.
In the technical scheme, the data acquisition module is arranged in the middle of each multi-sensor intelligent pipe section, and a multi-sensor cable on each pipe section is connected to preliminarily and uniformly filter, amplify and digitally convert acquired information and transmit the information through a bus of the data transmission module so as to reduce the connection of the cables.
In the intelligent monitoring device for the state of the multi-sensing pipeline, the data analysis and prediction module processes the acquired signals in real time, and the prediction for judging whether the pipeline is blocked or leaked and the corresponding reasons comprises the following steps:
setting A1、A2、A3…AnFor a plurality of units distributed over the pipe being monitoredA sensing intelligent pipe section is provided with [ P ]1 F1 T1]Is A1Monitoring data of the fluid inside the pipe, wherein P1、F1、T1Filtered, amplified, digitally converted and normalized pressure, flow, temperature data.
Step S1-1: according to factors such as accuracy and reliability of the sensor adopted in each multi-sensing intelligent pipe section, the measured P is given1 F1 T1Is given a corresponding weighting factor alpha of 0.5 to 1, resulting in A1To AnProcessing the monitoring data [ alpha ] preliminarily1P1 α2F1 α3T1]…[αnPn αnFn αnTn]。
Step S1-2: according to the arrangement position of each multi-sensor intelligent pipe section on the monitored pipeline, a weighting coefficient beta of 0.3 to 1 corresponding to each group of measurement data is given, the selection of the weighting coefficient is determined according to the importance of the arrangement position, the multi-sensor intelligent pipe sections before and after important valves and pump sources are given the weighting coefficient of 0.8 to 1, the multi-sensor intelligent pipe sections at common valves and partial straight pipe sections are given the weighting coefficient of 0.6 to 0.8, and the weighting coefficient of 0.3 to 0.6 is given at the branching position and unstable flow position of the pipeline, so that A is obtained1To AnProcessing the final processed monitoring data beta1[α1P1 α2F1 α3T1]…βn[αnPn αnFn αnTn]。
Step S1-3: collecting monitored pipeline working condition M1、M2、M3…MnObtaining the running parameter set M of each working condition according to the information of each multi-sensing intelligent pipe section1(β1[α1P1 α2F1 α3T1])…Mn(βn[αn1Pn αn2Fn αn3Tn]). Operating mode MnThe working condition of normal operation of the pipeline can be adopted, and the working condition of the pipeline with faults can also be adopted.
Step S1-4: set of operating parameters M1(β1[α1P1 α2F1 α3T1])…Mn(βn[αn1Pn αn2Fn αn3Tn]) The classifier is used for training to obtain a working condition and parameter basic information base (including normal operation information and fault operation information), and can be one of an artificial neural network, a support vector machine and the like.
Step S1-5: after the classifier training is completed in the step S1-4, the real-time acquired parameter M of the operation of the monitored pipe section is acquired1(β1[α1P1 α2F1 α3T1])…Mn(βn[αn1Pn αn2Fn αn3Tn]) The classifier is sent to step S1-4 to obtain the operation status of the monitored pipeline in real time, such as a fault status, and the data analysis and prediction module identifies the fault type and locates the fault.
In the intelligent monitoring device for the state of the multi-sensing pipeline, the data analysis and prediction module processes the acquired signals in real time and predicts the faults of the pipeline caused by corrosion, improper installation and abnormal vibration, and comprises the following steps:
setting A1、A2、A3…AnSetting FS for multi-sensing intelligent pipe sections distributed on a monitored pipeline1 YL1 ZD1]Is A1Monitoring data of the pipeline itself, wherein FS1、YL1、ZD1The data are corrosion, stress and vibration acceleration data which are filtered, amplified, digitally converted and normalized.
Step S2-1: the method comprises the steps of collecting stress and vibration acceleration data of normal and abnormal vibration-free installation of a monitored pipeline, and collecting corrosion data of good corrosion state of the inner wall of the pipeline to obtain standard range parameters. Wherein the standard range parameter upper limit is [ FS ]s YLsZDs]With a lower limit of [ FS ]x YLx ZDx]。
Step S2-2: the real-time collected parameters A of the operation of the monitored pipe sectionn[FSn YLn ZDn]FS in (1)n YLnZDnThree parameters are respectively associated with [ FSs YLs ZDs]、[FSx YLx ZDx]For comparison, if FSx<FSn<FSsThen consider AnThe corrosion condition of the pipeline is good; if YLx<YLn<YLsThen consider AnThe installation is normal; if ZDx<ZDn<ZDsThen consider AnThe vibration condition of the pipeline is good; if the measured parameter A isn[FSn YLn ZDn]If not, the state display and alarm module displays the state and gives an early warning.
Compared with the prior art, the invention has the following beneficial effects:
1. real-time performance: the leakage and the blockage of the system pipeline can be detected in the shortest time, the fault point can be determined, and ship turbine managers can maintain the ship turbine in time conveniently, so that the loss is reduced.
2. Sensitivity: the leakage of a small leakage amount can be detected and positioned, and the leakage monitoring and positioning device can be suitable for ship pipeline leakage monitoring and positioning under various leakage conditions.
3. The accuracy is as follows: the detection system can timely and accurately detect the occurrence of the system pipeline fault condition, and the detection system is required to have lower false alarm rate, higher reliability and smaller positioning error.
Drawings
FIG. 1 is a system diagram of the intelligent monitoring device for the state of a multi-sensor pipeline.
FIG. 2 is a schematic diagram of an exemplary configuration of a multi-sensor smart pipe segment.
FIG. 3 is a simplified layout of a multi-sensing smart pipe in a complex pipe.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
the present embodiment is applied to complex marine system piping, which includes a pump source, a regulating valve, a shut-off valve, a strainer, a branch pipe, and the like, as shown in fig. 3. One set of many sensing pipeline state intelligent monitoring device of this embodiment includes: the intelligent pipeline comprises at least n sets of multi-sensing intelligent pipeline sections 1, at least n sets of data transmission modules 2, at least n sets of power supply modules 3, at least 1 set of data analysis and prediction module 4 and at least 1 set of display and alarm module 5, wherein n in the embodiment in fig. 3 is 5. The multi-sensing intelligent pipe section 1 is installed on a monitored pipeline in series, and is generally installed at the front and the rear of a pump source, a valve, a filter, a main pipe and a branch pipe. The multi-sensing intelligent pipe section 1 is a special pipe section with the functions of measuring internal fluid pressure, flow and temperature, measuring vibration, strain, corrosion and the like of the pipe section. The data transmission module 2 comprises one or more of a cable 21, an optical cable 22 or a wireless network 23 and is used for transmitting signals acquired from the multi-sensing intelligent pipe section 1 to the data analysis and prediction module 4; the power supply module 3 comprises one or more of a cable power supply 31 and a storage battery 32 and is used for supplying power to the multi-sensing intelligent pipe section 1, the data transmission module 2, the data analysis and prediction module 4 and the state display and alarm module 5; the data analysis and prediction module 4 comprises computer hardware 41 and a corresponding data analysis and processing algorithm 42, and is used for analyzing and processing data uploaded by the multi-sensor intelligent pipe section 1 to obtain the running state of the pipeline; the state display and alarm module 5 comprises a display 51 and an optical alarm 52, and is used for visually displaying the running state of the pipeline on the display, when the current state of the pipeline section fails, the optical alarm 52 gives an alarm in red light, the display 51 displays the fault type and the fault position, when the state of the pipeline section is predicted to fail, the optical alarm 52 gives an alarm in yellow light, and the display 51 displays the expected fault type, the expected fault position and the suggested processing method.
The typical structure of the multi-sensor intelligent pipe section 1 in this embodiment is shown in fig. 2, and two ends of the multi-sensor intelligent pipe section 1 are connection parts 18, including one of a flange, a threaded connector, and a quick connector, for installing the multi-sensor intelligent pipe section in a pipeline in a matching manner. The middle of the multi-sensing intelligent pipe section is provided with a sensor mounting part 112 which comprises 8 immersion sensor interfaces 113 arranged circumferentially, a front row and a back row of patch type sensor interfaces 114 arranged in 8 axial directions, and all the sensor interfaces are standard interfaces. The sensors of the multi-sensing intelligent pipe section installation 1 can be a pressure sensor 11, a temperature sensor 13 and a flow sensor 12 for monitoring internal fluid, and can also be a vibration sensor 16, a strain sensor 15 and a corrosion sensor 14 for monitoring the pipeline, the monitoring positions and the monitoring quantity can be freely adjusted on the sensor installation positions, and redundant sensor interfaces can be sealed by a sensor block 19.
In the embodiment, the data acquisition module 17 is arranged in the middle of the multi-sensor intelligent pipe section 1, and accesses a multi-sensor cable on each pipe section, and performs filtering, amplification and digital conversion processing on acquired information primarily and uniformly, and transmits the acquired information through a bus so as to reduce the connection of the cables.
The data analysis and prediction module 4 of this embodiment processes the acquired signals in real time, and the prediction of whether the pipeline is blocked or leaked and the corresponding reason includes the following steps:
setting A1、A2、A3…AnFor the multi-sensor intelligent pipe sections distributed on the monitored pipeline in the figure 3, setting P1 F1 T1]Is A1Monitoring data of the fluid inside the pipe, wherein P1、F1、T1Filtered, amplified, digitally converted and normalized pressure, flow, temperature data.
Step S1-1: according to factors such as accuracy and reliability of the sensor adopted in each multi-sensing intelligent pipe section, the measured P is given1 F1 T1Is given a corresponding weighting factor alpha of 0.5 to 1, resulting in A1To AnProcessing the monitoring data [ alpha ] preliminarily1P1 α2F1 α3T1]…[αnPn αnFn αnTn]。
Step S1-2: giving each group of measurement data corresponding 0.3 to 0.3 according to the arrangement position of each multi-sensing intelligent pipe section on the monitored pipelineThe weighting coefficient beta of 1 is determined according to the importance of the arrangement position, the weighting coefficients of 0.8 to 1 are given to multi-sensor intelligent pipe sections before and after important valves and pump sources, the weighting coefficients of 0.6 to 0.8 are given to multi-sensor intelligent pipe sections at common valves and partial straight pipe sections, and the weighting coefficients of 0.3 to 0.6 are given to branch positions and unstable flow positions of a pipeline to obtain A1To AnProcessing the final processed monitoring data beta1[α1P1 α1F1 α3T1]…βn[αnPn αnFn αnTn]。
Step S1-3: collecting monitored pipeline working condition M1、M2、M3…MnObtaining the running parameter set M of each working condition according to the information of each multi-sensing intelligent pipe section1(β1[α1P1 α2F1 α3T1])…Mn(βn[αn1Pn αn2Fn αn3Tn]). Operating mode MnThe working condition of normal operation of the pipeline can be adopted, and the working condition of the pipeline with faults can also be adopted.
Step S1-4: set of operating parameters M1(β1[α1P1 α2F1 α3T1])…Mn(βn[αn1Pn αn2Fn αn3Tn]) The classifier is used for training to obtain a working condition and parameter basic information base (including normal operation information and fault operation information), and can be one of an artificial neural network, a support vector machine and the like.
Step S1-5: after the classifier training is completed in the step S1-4, the real-time acquired parameter M of the operation of the monitored pipe section is acquired1(β1[α1P1 α2F1 α3T1])…Mn(βn[αn1Pn αn2Fn αn3Tn]) The classifier fed to step 2The operation state of the monitored pipeline can be obtained in real time, if a fault state occurs, the data analysis and prediction module identifies the fault type and positions the fault.
The data analysis and prediction module 4 of the embodiment processes the acquired signals in real time, and predicts the faults of the pipeline caused by corrosion, improper installation and abnormal vibration, and comprises the following steps:
setting A1、A2、A3…AnFor the multi-sensor intelligent pipe sections distributed on the monitored pipeline in FIG. 3, let [ FS ]1 YL1 ZD1]Is A1Monitoring data of the pipeline itself, wherein FS1、YL1、ZD1The data are corrosion, stress and vibration acceleration data which are filtered, amplified, digitally converted and normalized.
Step S2-1: the method comprises the steps of collecting stress and vibration acceleration data of normal and abnormal vibration-free installation of a monitored pipeline, and collecting corrosion data of good corrosion state of the inner wall of the pipeline to obtain standard range parameters. Wherein the standard range parameter upper limit is [ FS ]s YLsZDs]With a lower limit of [ FS ]x YLx ZDx]。
Step S2-2: the real-time collected parameters A of the operation of the monitored pipe sectionn[FSn YLn ZDn]FS in (1)n YLnZDnThree parameters are respectively associated with [ FSs YLs ZDs]、[FSx YLx ZDx]For comparison, if FSx<FSn<FSsThen consider AnThe corrosion condition of the pipeline is good; if YLs<YLn<YLsThen consider AnThe installation is normal; if ZDx<ZDn<ZDsThen consider AnThe vibration condition of the pipeline is good; if the measured parameter A isn[FSn YLn ZDn]If not, the state display and alarm module displays the state and gives an early warning.
The embodiment is only one of the combinations of the above technical solutions, and in the actual application process, different combinations of different sensor combinations and numbers, different power modules, different signal transmission systems, different signal display modes, and the like are all within the protection scope of the patent.
Details not described in this specification are within the skill of the art that are well known to those skilled in the art.
Claims (7)
1. The utility model provides a many sensing pipeline state intelligent monitoring device which characterized in that: the intelligent pipeline monitoring system comprises a multi-sensing intelligent pipeline section, a data transmission module, a data analysis and prediction module, a power supply module and a state display and alarm module; the data transmission module comprises one or more of a cable, an optical cable or a wireless network and is used for transmitting signals collected from the multi-sensor intelligent pipe section to the data analysis and prediction module; the data analysis and prediction module comprises a computer and a corresponding data analysis and processing algorithm and is used for analyzing and processing multi-sensing information flow data acquired and uploaded by the multi-sensing intelligent pipe section to obtain the running state of the pipeline; the power supply module comprises one or more of a cable, a storage battery, a solar energy and wind energy power supply device and is used for supplying power to the multi-sensing intelligent pipe section, the data transmission module, the data analysis and prediction module and the state display and alarm module; the state display and alarm module comprises a display and an alarm; the multi-sensor intelligent pipe section is a pipe section with functions of measuring fluid parameters in the pipe section and measuring state signals of the pipe section, the multi-sensor intelligent pipe sections are connected in series in a distributed mode in a pipeline needing state monitoring, a data acquisition module is integrated in the multi-sensor intelligent pipe section, the data acquisition module is used for connecting a plurality of sensor cables in the pipe section, preliminarily filtering, amplifying and digitally converting acquired information into a multi-sensor information flow, and the multi-sensor information flow is transmitted to the data analysis and prediction module through a bus of the data transmission module.
2. The intelligent monitoring device for the state of the multi-sensor pipeline according to claim 1, characterized in that: the multi-sensing intelligent pipe section comprises two end parts and a middle part, wherein the two end parts are connecting parts and comprise one of a flange, a threaded connector and a quick connector, and the multi-sensing intelligent pipe section is matched and installed in a pipeline; the middle part is a sensor mounting part and comprises a plurality of immersion type sensor interfaces which are circumferentially arranged and a plurality of patch type sensor interfaces which are axially arranged, and all the sensor interfaces are standard interfaces.
3. The intelligent monitoring device for the state of the multi-sensing pipeline according to claim 2, characterized in that: the immersion type sensor interface with SMD sensor interface all sets up 8, and all sensor interfaces are standard interface.
4. The intelligent monitoring device for the state of the multi-sensing pipeline according to claim 2, characterized in that: the sensors installed on the multi-sensing intelligent pipe section are one or more of pressure sensors, flow sensors and temperature sensors for monitoring fluid in the pipe section and one or more of sensors for monitoring vibration, strain and corrosion of the pipe section, the monitoring positions and the monitoring number can be adjusted on the sensor interfaces, and the sensor interfaces which are not used are sealed by the blocking blocks.
5. The intelligent monitoring device for the state of multiple sensing pipelines according to any one of claims 1-4, wherein: the alarm is an optical alarm, when the multi-sensing intelligent pipe section has a fault, the optical alarm gives an alarm by red light, and meanwhile, the display displays the corresponding fault category and fault position; when the multi-sensor intelligent pipe section is predicted to be out of order, the light alarm warns with yellow light, and meanwhile, the display displays the corresponding expected failure category and the expected failure position.
6. The intelligent monitoring device for the state of multiple sensing pipelines according to any one of claims 1-4, wherein: the data analysis and prediction module carries out data processing on the collected parameters of the fluid in the pipe section and judges whether the pipe is blocked or leaked according to the following steps:
setting A1、A2、A3...AnSetting P for multi-sensor intelligent pipe section distributed on the monitored pipeline1 F1 T1]Is A1Monitoring data of the fluid inside the pipe, wherein P1、F1、T1The pressure, flow and temperature data are filtered, amplified, digitally converted and normalized;
step S1-1: according to the factors of the precision and the reliability of the sensor adopted in each multi-sensing intelligent pipe section, the measured P is given1 F1 T1Is given a corresponding weighting factor alpha of 0.5 to 1, resulting in A1To AnProcessing the monitoring data [ alpha ] preliminarily1P1 α2F1 α3T1]...[αnPn αnFn αnTn]；
Step S1-2: according to the arrangement position of each multi-sensor intelligent pipe section on the monitored pipeline, a weighting coefficient beta of 0.3 to 1 corresponding to each group of measurement data is given, the selection of the weighting coefficient is determined according to the importance of the arrangement position, the multi-sensor intelligent pipe sections before and after important valves and pump sources are given the weighting coefficient of 0.8 to 1, the multi-sensor intelligent pipe sections at common valves and partial straight pipe sections are given the weighting coefficient of 0.6 to 0.8, and the weighting coefficient of 0.3 to 0.6 is given at the branching position and unstable flow position of the pipeline, so that A is obtained1To AnProcessing the final processed monitoring data beta1[α1P1 α2F1 α3T1]...βn[αnPn anFn αnTn]；
Step S1-3: collecting monitored pipeline working condition M1、M2、M3...MnObtaining the running parameter set M of each working condition according to the information of each multi-sensing intelligent pipe section1(β1[α1P1 α2F1 α3T1])...Mn(βn[αn1Pn αn2Fn αn3Tn]) Operating mode MnCan be a pipelineThe working condition of normal operation can also be the working condition of a pipeline with a fault;
step S1-4: set of operating parameters M1(β1[a1P1 α2F1 α3T1])...Mn(βn[αn1Pn αn2Fn αn3Tn]) Training through a classifier to obtain a working condition and parameter basic information base which comprises normal operation information and fault operation information, wherein the classifier is one of an artificial neural network and a support vector machine;
step S1-5: after the classifier training is completed in the step S1-4, the real-time acquired parameter M of the operation of the monitored pipe section is acquired1(β1[α1P1 α2F1 a3T1])...Mn(βn[αn1Fn αn2Fn αn3Tn]) And (4) sending the data to the classifier in the step S1-4, acquiring the running state of the monitored pipeline in real time, and identifying the fault type and positioning the fault by the data analysis and prediction module if the fault state occurs.
7. The intelligent monitoring device for the state of multiple sensing pipelines according to any one of claims 1-4, wherein: the data analysis and prediction module carries out data processing on the acquired state signals of the pipe sections and judges whether the pipe is corroded, improperly installed and abnormally vibrated according to the following steps:
setting A1、A2、A3...AnSetting FS for multi-sensing intelligent pipe sections distributed on a monitored pipeline1 YL1 ZD1]Is A1Monitoring data of the pipeline itself, wherein FS1、YL1、ZD1The data are corrosion, stress and vibration acceleration data which are filtered, amplified, digitally converted and normalized;
step S2-1: collecting the stress and vibration acceleration data of normal and abnormal vibration-free installation of the monitored pipeline, and collecting the corrosion data of good corrosion state of the inner wall of the pipeline to obtain a standard rangeParameter, wherein the standard range parameter upper limit is [ FS ]s YLsZDs]With a lower limit of [ FS ]x YLx ZDx]；
Step S2-2: the real-time collected parameters A of the operation of the monitored pipe sectionn[FSn YLn ZDn]FS in (1)n YLn ZDnThree parameters are respectively associated with [ FSs YLs ZDs]、[FSx YLx ZDx]For comparison, if FSx＜FSn＜FSsThen consider AnThe corrosion condition of the pipeline is good; if YLx＜YLn＜YLsThen consider AnThe installation is normal; if ZDx＜ZDn＜ZDsThen consider AnThe vibration condition of the pipeline is good; if the measured parameter A isn[FSn YLn ZDn]If not, the state display and alarm module displays the state and gives an early warning.
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Cited By (7)
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| CN113155017A (en) * | 2021-04-23 | 2021-07-23 | 西南石油大学 | Mountain area pipeline strain online monitoring system and method |
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