• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    Method for detecting obstructions (12) in a gas network (1), comprising: - sources (6); - consumers (7); sensors (9a, 9b); characterized in that the method comprises the following phases: a zeroing phase (15) in which an initial physical model is determined between the measurements of a first group and a second group of sensors (9a, 9b); - an operational phase (16) in which the physical model is reset the measurements of the first group and the second group of sensors (9a, 9b) at regular intervals using estimation algorithms to predict obstructions in the gas network (1); wherein the operational phase (16) comprises the following steps: - based on the readings from the sensors (9a, 9b, 9c), redetermining the physical model; - determining or calculating whether there is an obstruction (12) in the system based on the difference between the parameters of the physical model as determined during the or zeroing phase (15) and the operational phase (16).
    公开号:BE1026836B1
    申请号:E20195839
    申请日:2019-11-26
    公开日:2021-01-06
    发明作者:Philippe Geuens；Ebrahim Louarroudi
    申请人:Atlas Copco Airpower Nv；
    IPC主号:
    专利说明:

    Method of detecting obstructions in a | gas network under pressure or under vacuum and gas network. The present invention relates to a method 9 for detecting obstructions in a gas network under | pressure or under vacutim. | More specifically, the invention is intended to enable | 10 quantifying obstructions occurring in a gas network. For example, "gas" here, but not necessarily, refers to air, but nitrogen or natural gas is also possible, "obatruction" here means partial or partial blockage in the gas network or an increase in the resistance of a pipe .
    Methods for monitoring or controlling a pressurized gas network are already known, these methods being set up for long and straight pipelines, where the incoming flow is not necessarily equal to the outgoing flow due to the compressibility of the gas concerned. This method is based on a number of assumptions such as, for example, very long pipelines, straight pipelines, which are not suitable for complex gas networks under pressure where one or more compressor installations supply gas under pressure to a complete network of consumers.
    > BE2019 / 5839 | are both an individual end-user and a {so-called consumer zone or an aorcep of individual | end consumers, 9 5 However, the aforementioned method only relates to detection of leaks in the gas network. 9 Such known methods therefore have the disadvantage 9 that they do not suffer from leaks in the network of pipes. to detect obstructions between the source and the consumers.
    Moreover, the gas network itself forms a not to be underestimated source of obatructions. The present invention aims to provide a solution to this.
    The present invention aims at a method for detecting and quantifying obstructions in a gas network under pressure or vacuum, the gas network comprising: - one or more sources of compressed gas or of vacuum; - one or more consumers, compressed gas consumer zones or vacutim applications: - pipes or a network of pipes to transport the compressed gas or vacuum from the sources to the consumers, consumer zones or passages; - several sensors which determine one or more = physical parameters of the gas at different times and locations in the gas network; characterized in that the gas network is also provided with additional sensors indicating the position or
    | 3 BE2019 / 5839 status (e.g., on / off) of the sources, consumers and / or consumer zones and that the method follows the following
    | iasen includes:
    : - an eventual Start-up Phase in which the aforementioned
    9 5 sensors are calibrated before use;
    | - a baseline or nullinos phase in which an initial
    9 physical model or mathematical relationship is determined
    | between the measurements of a first group of
    9 sensors and a second group of sensors based on iD of {ysische werten with Dehulp var estimation algorithms;
    “An operational phase in which the physical model or mathematical relationship between the measurements of the first group of sensors and the second group of sensors is set back at regular time points using estimation algorithms to predict obstructions in the gas network;
    where the operational phase includes the following steps:
    - reading the first group and second group of sensors;
    - based on the readings from the sensors, reestimate, determine or calculate the {physical model or mathematical relationship;
    - determining or calculating whether an obstruction is present in the system based on the difference, and / or its elimination, between the parameters of the physical model or mathematical relationship as determined during the baseline zeroing phase and the cerational phase;
    - generating an alarm and / or generating a degree of obstruction and / or generating the
    ; A BE2019 / 5839; associated obstruction cost if an obstruction is detected, By 'the all guides' of the difference is meant any mathematical quantity that can be extracted from the 9 differences, for example, sum, cumulative sum, {weighted}: mean, least squares sum,… 9 By 'at regular times', oock meant continuous: 10 or nearly continuous, ie the regular times | in rapid succession.
    An advantage is that such a method will allow to detect, detect and also quantify obstructions in the gas network itself. In other words, the obstructions detected with the aid of the method are not limited to obstructions in the sources or consumers. of compressed gas, ie in the compressor equipment and pneumatic tools, but may also be obstructions in the piping of the gas network itself.
    During both the baseline and the operational phase, a mathematical relationship is established between these sensors, based on known physical laws and using the measurements of the various sensors. An estimation cf echattino algorithm is used.
    It is assumed here that in the baseline or grading phase there are initially no noticeable obstructions.
    | the gas network, in other words it is assumed | of a normal situation of the gas network or a {so-called "baseline" or zeroing.
    They also include | determining the mathematical model assuming that there are no 9 & leaks in the gas network and that the topology of the | gas network does not change,
    : In this way a physical model, or mathematical model, can be drawn up that shows the relationship between the 9 different parameters that are measured by the sensors. This model can then be used for future measurements of the sensors. be able to immediately detect irregularities Le by comparing the baseline parameters and the parameters of the redetermined or calculated physical or mathematical model.
    In this way, coostructions can be detected very quickly and, upon detection of an obstruction, immediate action can be taken and the obstruction can be closed.
    Preferably, the aforementioned physical model or mathematical relationship is in the form of a matrix with parameters or constants, whereby the changes of these parameters or constants are monitored during the operational phase. This matrix will be a measure of the 'resistance', or its inverse. 'conductivity', of the network or rather a measure of the 'resistance' or 'conductivity' experienced by the gas in the gas network.
    Changes in the matrix indicate a change in the resistance. By following the changes in the matrix cop | By recalculating the parameters 9 of the matrix on the basis of new data from the sensors, changes in # 5 resistance can be detected and obstructions 9 can be detected. Preferably, at certain moments, the operational phase is temporarily interrupted or stopped, after which the baseline or zero phase is resumed to redetermine the physical model or mathematical relationship between the measurements from different sensors, before restarting the operational phase.
    It should be noted here that the process, ie the gas network with sources, pipes, consumers, and / or consumer zones, is not shut down, that is, only the method, In other words: when the operational phase is temporarily interrupted or stopped, the sources are still supply gas or vacuum to the consumers or consumer zones.
    Interrupting the operational phase and resuming the baseline phase has the advantage that the physical model or the mathematical relationship is updated or updated. This will allow taking into account the time-varying behavior of the gas network or system, so that it is taken into account. can be with modifications, repairs or additions to the gas network.
    The invention also relates to a gas network under pressure or under vacuum, which gas network is at least provided with: | «One or more sources of compressed gas or of | vacumz | 5 - no more consumers or consumer zones from | pressurized gas or applications of vacuum; 9 - pipes and network of pipes to transport the gas ci {vacuün from the sources to the consumers, 9 consumer zones or applications; 16 multiple sensors which determine one or more physical parameters of the gas at different times and locations in the gas network; characterized in that the gas network is also equipped with! - optionally one or more sensors which indicate the position or status (eg, on / off) of one or more sources, consumers and / or consumer zones; - a data acquisition control unit for collecting data from the sensors; - a calculation unit for carrying out the method according to the invention. Such a device can be used to apply a method according to the invention.
    With the insight to better demonstrate the characteristics of the invention, some preferred variants of a method and gas network according to the invention are described below, by way of example without any limitation, with reference to the accompanying drawings, in which!
    | 8: figure 1 schematically represents a device according to the invention; Figure 2 shows a schematic flow diagram of the method according to the invention.
    9 The gas network 1 from figure! mainly includes a | source side 2, a consumer side 2 and a network 4 of 9 pipes 5 between the two, 9 iD The gas network 1 is in this case a gas network 1 under pressure, ie, there is a pressure higher than the atmospheric pressure. The gas may be air, oxygen or nitrogen or some other non-toxic and / or hazardous gas or mixture of gases. The source side 2 comprises a number of compressors 6, in this case three, which generate compressed air. The consumer side 3 comprises a number of compressed lucnt consumers 7, in this case also three.
    It is also possible that the compressors 6 contain compressed air dryers. It cannot be ruled out that there may also be compressors 6 upstream of the gas network. This is referred to as so-called “boost compressors”.
    The compressed air is led via the network of pipes 5 from the compressors 6 to the consumers 7. This network 4 is in most cases a very complex network of pipes 5.
    | 3 | In figure 1 this network 4 is shown very schematically and in a simplified manner, Ock are associated termination and | bypass taps in the gas network 1 not explicitly shown to maintain simplicity in figure 1. 9 In most real situations, the network 4 of 9 lines 5 consists of very numerous lines 5 that the | connect consumers 7 in series and in parallel with each other and with the 9 compressors 6. It is not excluded that part of the network 4 assumes or comprises a ring structure.
    This is because the gas network 1 is often expanded over time with additional consumers 7 or compressors 6, whereby new pipes 5 must be laid between the pipes 5 already present, which leads to a tangle of pipes 5. The gas network is also 1 in this case, but not necessarily, provided with a pressure vessel 8, whereby all compressors 6 discharge on this pressure drop 8. It is not excluded that one or more pressure vessels 8 are located downstream of the gas network 1,
    In addition, components 17, such as filters, separators, atomizers, and / or regulators, can also be provided in the gas network.
    These components 17 can occur in various combinations and can be located both in the vicinity of the pressure vessel 8 and close to the individual consumers 7,
    : BE2019 / 5839 | 10; In the example shown, these components 17 are provided after the pressure vessel 8 and near the individual consumers 7, in the network 4 a number of sensors Sa, Sb, 9 Sc, Bd are also included, which are placed at different locations in the network € . | In this case, one flow sensor Yes has been placed, just after | 10 the dedicated pressure vessel 8, which represents the total flow, | supplied by all compressors 6, will measure, It is also not stated that the individual flow rates of the compressors 6 themselves are measured.
    Furthermore, the figure shows four pressure sensors 9b, which measure co different locations in the network 4 the pressure. Preferably also a pressure sensor% b is provided to measure the pressure in the pressure vessel & to measure the “mass-in-mass”. - from “Correcting principle Le for large, concentrated volumes. It is clear that more, or less, than four pressure sensors Db can also be provided. The number of flow sensors Yes is also not limiting for the invention.
    In addition to the flow sensors 9a or the pressure sensors Sb, it is possible to additionally, or alternatively, use sensors 9a, 9b 32 which determine one or more of the following physical parameters of the gas: differential pressure, gas velocity, temperature or humidity.
    n = BE2019 / 5839 {Furthermore, in addition to the aforementioned sensors 9a and 3b, which measure 9 physical parameters of the gas, there are also a number of 9 sensors Sc, or "state sensors 20", which are provided in the | near the compressors 5, consumers 7 Dt consumer zones.
    Preferably, these 9 sensors Sc form part of the consumers 7 themselves, one speaks | than of smart consumers, 9 as explained later, by using these # LO state sensors 3c, the position or state (e.g., on / off) of the compressors 6, consumers 7 or consumer zones can be taken into account, the noise sensitivity of the estimation algorithms are reduced so that these estimation algorithms become more reliable. It is also not excluded that at least some of the sensors Da, 3b, Sc are integrated in one module together with a source 6 and / or consumer 7.
    One then speaks of so-called 'smart connected pneumatic devices',
    It is also possible to use sensors 9a, 9b which measure the pressure or flow rate of the gas at the location of the consumers 7 or consumer zone.
    It is also possible to use sensors that measure the temperature of the gas at the location of the consumers 7 or consumer zone. The above-mentioned pressure difference sensors coming from the group of additional or alternative sensors 9a, Sb are preferably used via filter, separator, nebulizer, etc. and / or 32 regulator components 17 disposed.
    It goes without saying that the number of differential pressure sensors Sd can differ from what is indicated in Fiquur 1.
    The aforementioned humidity and temperature sensors coming
    9 from the group of additional or alternative sensors Yes, 9b
    9 are preferably placed at the inlet and / or outlet of the
    # compressors 6 and consumers 7 installed,
    9 In the example shown, the aforementioned additional or alternative sensors 2a, 9b are not all included in the gas network 1, but it goes without saying that this is also possible 19 Certainly in more extensive and comprehensive gas networks 1 such sensors 9a, 3b can be used. , as well as in networks 1 where only the volumerric flow is measured instead of the mass flow,
    According to the invention, the gas network 1 is further provided with a sensor-acoustic control unit 10 for collecting data from the aforementioned sensors Za, 3b and Gc,
    In other words, the sensors 9a, 9b, Sc determine or measure the physical parameters of the gas and the condition of the compressors 6, consumers 7 and / or consumer zones and send this data to the data acquisition control unit 10,
    According to the invention, the gas network 1 is further provided with a calculation unit 11 for processing the data from the sensors Fa, 9b, Sc, wherein the calculation unit 11 will use the method according to the invention for detecting and quantifying obstructions 12 in the gas network 1. can perform as explained below.
    {The aforementioned calculation unit 11 can be a physical module {which is a physical part of the gas network 1, It is | it does not exclude that the computing unit 11 is not a physical module | 15, but a so-called cloud-based unit of account 11, | 5 which may or may not be wirelessly connected to the gas network 1. This means that the accounting 11 or the software | of the calculation unit 11 is situated in the "cloud", 9 in this case the gas network 1 is further provided with | 10 monitor 13 for displaying or signaling obstructions 12 detected using the method.
    The operation of the gas network 1 and the method according to the invention is very simple and as follows.
    Figure 2 schematically represents the method for detecting and quantifying obstructions 12 in the gas network 1 of Figure 1, in a first phase 1e, the start-up phase 14, the sensors Za, 9b and Bc are optionally calibrated before use. It goes without saying that if there are other sensors, they can also be calibrated before use. This is done once when the sensors are placed Yes, fb, Dc in the gas network 1. Of course it is not excluded that the sensors Ga, Db, 9c will be recalibrated after some time. Preferably the sensors Ga, 9b, Jc are calibrated by means of an in-situ self-calibration, This means that the sensors Da, Sb, 3c in the gas network 1, ie after they have been reached BE2019 / 5839 ; 14 | placed, calibrated, By “in operation” or i “in-situ” is meant: calibration without having to disassemble the sensor 5a, üb, Ge from the network 1, 9 3 In this way you can be sure that the placement: and / or any contamination of the sensors Yes, 9b, Sc itself 9 has no influence on their measurements, because the calibration will only be done after 9 placement of the sensors Yes, 9b, ÿc | whether the calibration will repeat after a certain period of time, 9 10 9 Then the second phase 15, or the baseline phase 15, 9, also called zeroing fzsse, starts.
    In this phase an Éysical model or mathematical relationship is determined between the measurements a first group of sensors Yes, 92, Jc and a second group of sensors Oa, 9b, 3c based on physical laws using estimation algorithms, By means of additional state sensors Sc (e.g., on / off} of the compressors 6, consumers 7 or consumer zones, the noise sensitivity of the sochattino algorithms can be reduced, so that these estimation algorithms become more reliable, Cp based on known physical laws, a model can be drawn up between a first group of sensors 9a, 9b and a second green of sensors Yes, 2b, This first group of sensors Yes, 9b preferably all measure the same physical parameter of the QUE, for example pressure p and / or pressure difference Ap, at different locations in the gas network 1. The second group of sensors
    | BE2019 / 5839 | da, 3b preferably also all measure the same physical parameter of the gas, for example the flow rate a.
    | The first group of sensors Yes, Sb in this case comprises 3 3 different pressure and / or differential pressure sensors% b on 9 different locations in the gas network 1 and the second F group of sensors Za, 2 in this case comprises one, and preferably at least one, flow sensor a, but this is not strictly necessary, as long as there are no common senzors in the two groups of sensors Sa, Sb, the approach remains intact, so the only condition is that the curve section of the two groups of sensors Yes, 3b ie must be, 15 The model comprises, for example, a mathematical relationship such as, for example, a matrix or the like, in which there are still a number of parameters or constants. The matrix will be a measure of the resistance, or its inverse conductivity, of the signal 1. These parameters or constants can be determined by selecting the appropriate sensors Ia, üb, Se and using estimation algorithms.
    This is based on a kind of baseline situation, or a normal situation of the gas network 1 without obstructions 12. The data acquisition control unit 10 will read the sensors 9a, Ob, Sc and send this data to the computing unit 11, where the necessary calculations will be performed to determine the aforementioned parameters or constants,
    | BE2019 / 5839 16 | Once the parameters or constants have been determined, the {physical model is determined in the form of a mathematical | pledged between the two groups of sensors Je, 9b. | Sa Subsequently, the third phase 16 or the operational phase 16 | in which the physical model or mathematical relationship 9 Loops the measurements of the first group of sensors 9 Sa, Db and the second group of sensors Sa, 3b is set up again using estimation algorithms to predict obstructions 12 in the gas network 1 , in this phase 16 the following steps are performed: selecting the first group and the second group of sensors Sa, 9b, Sc; - based on the readings of the sensors Yes, 9b, So, redetermine, estimate or calculate the physical model or mathematical relationship; = determining or calculating whether there is an obstruction 12 in the system based on the difference between the parameters of the physical model or mathematical relationship as determined during the case-line phase 15 and the overational phase 16; = generating an alarm if an obstruction is detected with possibly the associated obstruction degree and / or the obstruction Xost.
    In order to determine an obstruction 12 in the gas network 1, it will be checked in the foremost step whether the aforementioned difference exceeds a determined threshold value.
    This then indicates an obstruction 12 in the gas network 1,
    | _ BE2019 / 5839 | This threshold value can be set in advance or chosen empirically. 9 When an obstruction 12 is detected, sen | 3 alarm will be generated. In this case, this is done with the aid of the monitor 13 on which the alarm is displayed | displayed, 9 The user of the gas network 1 will notice this alarm and | 10 appropriate steps can be taken by an entrepreneur. These steps of the cpsratic phase 16 are preferably repeated sequentially and regularly at a certain time interval.
    As a result, obstructions 12 can be detected and traced during the entire operational period of the gas network 1, and not, for instance, only once at or shortly after the start-up of the gas network 1.
    The aforementioned time interval can be selected and set depending on the gas network 1, in a preferred variant of the invention the operational phase 16 will be temporarily interrupted or stopped at certain moments, after which the baseline or zeroing phase 15 is resumed to start the physical model. or to redetermine mathematical relationship between the measurements of different sensors, before resuming operational phase 16,
    ; BE2019 / 5839
    : 18
    9 "At certain times" should be interpreted here
    9 as moments which are preset, for example
    : once a week, a month or a year or as moments
    | which can be chosen by the user like the
    # 3 suits the user best,
    9 The Time span in which the baseline phase returns to 15
    9 is much larger compared to the time span in which the physical model or mathematical relationship during the
    1G operational phase is drawn back.
    Or in other words,
    the update time step during the baseline phase 15, to accommodate variations in the network 1, is much longer than the update time step during the operational phase 16,
    This will update the physical model, so that any time-varying behavior of the system is taken into account.
    This includes, for example, obstructions 12 in the network 4 which are subject to replacement of the relevant parts or taps, or adjustments or expansions of the network 4 which alter the aforementioned 'baseline' situation of the gas network 1,
    Although in the example of figure 1, it concerns a gas network i under pressure, it can also be a gas network 1 under vacuum,
    The source side 2 then comprises a number of sources of vacuum,
    i.e. vacuum pumps or the like.
    | The consumers 7 or consumer zones are in this case [replaced by applications requiring vacuum.
    | Furthermore, the method is the same as described above. The present invention is by no means limited to the embodiments described as # example = and shown in the figures, but such a method and G gas network according to the invention can be realized according to 19 different variants without departing from the scope of the invention. within the scope of the invention,
    权利要求:
    Claims (1)
    [1]
    | Conclusions. | 1.- Method for detecting and quantifying structures (12) in a gas network {1} under pressure or | vacuum, the gas network (1) comprising: | - one or more sources (6) of compressed gas or of vacuum:; # - one or more consumers (7), consumer zones of | 10 compressed gas or vacuum applications; | - lines {5} or network {4} of lines {5} on | to transport the compressed gas or vacuum from the sources (6) to the consumers (73, consumer zones or applications; multiple sensors (Da, 35) which determine one or more physical parameters of the gas at different times and locations in the gas network { 1}; characterized in that the gas network (1) is further optionally = 0 provided with one or more sensors (20) which can register the position or status of one or more sources {6), consumers {7}, or consumer zones and that the method includes the following [asen:
    - any start-up phase (14) in which the aforementioned sensors (5a, 9b, Sc) are calibrated before use;
    - a Dbaseline or nullinos phase {15} in which an initial physical model or mathematical relationship is determined between the measurements of a first group of sensors (Ba, 9b) and a second group of sensors (9a, 90) based on physical laws with using estimation algorithms :;
    | BE2019 / 5839 21 | «An operational phase (16) in which the physical model or mathematical relationship between the measurements of the first group of sensors (Sa, 9b) and the second group of sensors (Da, SD] at regular 3 5 points in time is set up again with help from of 9 estimation algorithms to predict structures in the gas network (1}; 9 where the operational phase (16) includes the following steps: 9 10 - reading the first group and second group of sensors (Sa, 9b, Sc) = on the basis of the readings of the sensors (Yes, Db, Sc} the physical model or mathematical relationship to redetermine, estimate or calculate; + determine or calculate whether an obstruction (125 is present in the system based on the difference and / or its derivatives between the parameters of the physical model or mathematical relationship as determined during the pasein or zero phase (15) and the operational phase (16): «generating an alarm and / or generating a zen structure and / or generating the associated obstruction if an obstruction {12} is detected, 258
    Method according to claim 1, characterized in that the aforementioned Évsic model or mathematical relationship is in the form of a matrix with parameters or constants, the changes of these parameters or constants being monitored during the operational phase (16),
    | BE2019 / 5839 22 3.7 The method according to claim 1 or 2, characterized in that; that the first green of sensors (Ca, 9b, Sc} comprises several: pressure and / o pressure differential sensors {915} at different locations in the gas network (1} and possibly: 5 several sensors {9c} that the sources (6) F can determine consumers {7} or consumer zones, and the second group of sensors (9a, 9b, 96} comprises at least one 9 flow rate sensors (9a), 8, - Method according to one of the preceding claims, characterized in that the sensors (Sa, 9b, Sc) are calibrated by means of in-situ self-calibration,
    5. ” Method according to any one of the preceding claims, characterized in that said sensors (Sa, Ob) can measure one or more of the following physical parameters of the gas: pressure, differential pressure, temperature, flow rate, humidity.
    Method according to any of the preceding claims, characterized in that at certain times, the operational phase (16) is temporarily interrupted or stopped, after which the baseline or zeroing phase {15} is resumed in order to determine the physical model or mathematical relationship between the measurements from different sensors (% a, 3b, Sc} to be determined again before the operational phase (16) is restarted.
    Method according to any one of the preceding claims, characterized in that the steps of the operational phase (16) are repeated sequentially with per determined time interval,
    S.- Gas network under pressure or under vacuum, which gas network à {1} is at least provided with:
    [- one or more sources (6) of compressed gas or of | vacuum;
    9 5 - one or more consumers {7} cf consumer zones of | compressed gas or vacuum applications;
    9 - pipes (5) or network (4) of pipes (5) to | to transport the gas or vacuum from the sources (6) to the 9 consumers (7), consumer zones or applications: 20:
    - several sensors (9a, Jp} which determine one or more physical parameters of the gas, ie different times and locations in the gas network {1};
    characterized in that the gas network {1} is further provided with:
    - possibly one or more sensors (Sc) which can register the position or condition of one or more sources (6), consumers {7} or consumer zones;
    - a data acquisition control unit (10) for collecting cevens from the sensors (Ca, So, Sc);
    - a computing unit (11) for performing the method according to any one of the preceding claims, $. Gas network according to claim ©, characterized in that at least some of the sensors ({(93a, 9b, Sc) together with a source ( 6) and / or consumer (7) are integrated in one module,
    | 10. Gas network according to claim B or 9, thereby | characterized in that the gas network (1} is further provided with: monitor {13} for displaying or signaling: obstructions, degree of obstruction, obstruction costs and optionally location (12), 9 il. Gas network according to any one of the preceding claims & 9 to 9 to 10, characterized in that the sensors (fc) which can register the position or state of a consumer {7} are part of the consumers (7) themselves,
    Gas network according to any of the preceding claims 8 to 10, characterized in that the computing unit {11} is a cloud-based computing unit (11), which may or may not be wirelessly connected to the gas network (1).
    类似技术:
    公开号 | 公开日 | 专利标题
    JP2008008291A|2008-01-17|System and method for detecting undesirable operation of turbine
    WO2014091953A1|2014-06-19|Cavitation detection device, cavitation detection method, and cavitation detection program
    BE1026836B1|2021-01-06|A method for detecting obstructions in a gas network under pressure or under vacuum and gas network
    US20220056933A1|2022-02-24|Method for detecting obstructions in a gas network under pressure or under vacuum and gas network
    JP6245653B2|2017-12-13|Liquid leakage detection device, liquid leakage detection method, and recording medium
    BE1026852B1|2020-09-28|Method for detecting leaks in a gas network under pressure or under vacuum and gas network
    BE1026843B1|2020-07-07|Gas network and method for detecting obstructions in a gas network under pressure or under vacuum
    BE1026848B1|2020-07-09|Gas network and method for detecting leaks in a gas network under pressure or under vacuum
    BE1026849B1|2020-07-09|Gas network and method for simultaneously detecting leaks and obstructions in a gas network under pressure or under vacuum
    BE1026966B1|2020-08-13|Method for determining and monitoring gas consumption in a gas network under pressure or under vacuum and gas network
    WO2020136476A1|2020-07-02|Method for determining and monitoring the gas consumption in a gas network under pressure or under vacuum and gas network
    WO2020136475A1|2020-07-02|Method for detecting leaks in a gas network under pressure or under vacuum and gas network
    JP2001184121A|2001-07-06|Sensor deterioration detection device
    JPH08152345A|1996-06-11|Method for measuring flow rate using pressure sensor
    JPH0612775U|1994-02-18|Pump monitoring device
    同族专利:
    公开号 | 公开日
    EP3903019A1|2021-11-03|
    BE1026836A1|2020-06-30|
    KR20210107732A|2021-09-01|
    BE1026836A9|2021-01-12|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US5272646A|1991-04-11|1993-12-21|Farmer Edward J|Method for locating leaks in a fluid pipeline and apparatus therefore|
    US20030187595A1|2002-03-29|2003-10-02|Hiroshi Koshinaka|Compressed air monitor system for monitoring leakage of compressed air in compressed air circuit|
    DE202008013127U1|2008-10-01|2009-11-12|Boge & Co. Maschinenhandelsgesellschaft Gmbh & Co. Kg|Device for detecting leakage of a compressed air system|
    DE202010015450U1|2010-11-17|2011-02-24|Boge & Co. Maschinenhandelsgesellschaft Gmbh & Co. Kg|Compressor and compressed air system with at least two compressors|
    US20180356046A1|2015-07-17|2018-12-13|The University Of Adelaide|Method and system for pipeline condition analysis|
    US20180196399A1|2017-01-10|2018-07-12|Sensus Spectrum Llc|Systems and methods for subnetwork hydraulic modeling|
    EP3409953A1|2017-05-31|2018-12-05|ABB Schweiz AG|Method in a compressed air system|
    CN108361560A|2018-03-21|2018-08-03|天津科技大学|A kind of pipe safety recognition methods being used for natural gas line safety monitoring assembly based on wavelet packet|
    WO2020040800A1|2018-08-20|2020-02-27|Halliburton Energy Services, Inc.|Method and system for non-intrusively determining cross-sectional variation for a fluidic channel|
    法律状态:
    2021-03-19| FG| Patent granted|Effective date: 20210106 |
    优先权:
    申请号 | 申请日 | 专利标题
    US201862785253P| true| 2018-12-27|2018-12-27|CN201980070015.6A| CN114072612A|2018-12-27|2019-11-28|Method for detecting blockage in gas pipe network under pressure or vacuum and gas pipe network|
    JP2021531371A| JP2022516402A|2018-12-27|2019-11-28|Methods and Gas Supply Networks for Detecting Obstacles in Gas Supply Networks Under Pressurization or Vacuum|
    KR1020217022373A| KR20210107732A|2018-12-27|2019-11-28|Methods and gas networks for detecting occlusion in a gas network under pressure or under vacuum|
    US17/418,146| US20220056933A1|2018-12-27|2019-11-28|Method for detecting obstructions in a gas network under pressure or under vacuum and gas network|
    PCT/IB2019/060292| WO2020136477A1|2018-12-27|2019-11-28|Method for detecting obstructions in a gas network under pressure or under vacuum and gas network|
    EP19839410.8A| EP3903019A1|2018-12-27|2019-11-28|Method for detecting obstructions in a gas network under pressure or under vacuum and gas network|
    [返回顶部]