CN114964367B - LNG tank fault prediction method and system based on time-varying parameters - Google Patents
LNG tank fault prediction method and system based on time-varying parameters Download PDFInfo
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Abstract
The invention discloses a LNG tank fault prediction method and system based on time-varying parameters, wherein the method comprises the following steps of S100: setting sampling frequency, collecting main dynamic monitoring parameters TVP such as liquid phase temperature, gas phase pressure, liquid level, acceleration and swing floating frequency, and collecting main static parameters t; s200: performing unit third-order linear regression analysis with t as an independent variable on the main dynamic monitoring parameter TVP; s300: and repeatedly performing three-order linear regression analysis by using long-time monitoring data to obtain a tank=M (D, b, e) value of the storage Tank in a certain given State (State=S ()), taking the value as a standard value, recording the value as a tank=Mstand (D, b, e) as a fingerprint characteristic of the storage Tank, comparing the actual monitoring Tank value with the Tank, analyzing, and judging the type of the fault by adopting a difference value caused by the RSS analysis. According to the method disclosed by the invention, LNG faults are accurately predicted in real time, and the safety of the full life cycle range of LNG is improved.
Description
Technical Field
The invention belongs to the technical field of fault prediction, and particularly relates to an LNG tank fault prediction method, system, terminal and storage medium based on time-varying parameters.
Background
The multi-mode intermodal transportation of the LNG tank is a third novel LNG logistics mode parallel to pipeline transportation and LNG bulk transportation ships, and is gradually changed from the previous test point to normal operation under the national strategic background of carbon emission reduction and carbon peak reaching. The LNG transportation also belongs to the field of dangerous goods transportation, and compared with the centralized large-batch transportation mode of pipeline transportation and ship bulk transportation, the LNG tank transportation is flexible and convenient, and meanwhile, has the characteristics of small quantity and multiple batches and very dispersed space-time distribution, so that the LNG tank safety monitoring brings great challenges.
At present, the following technical problems exist in LNG tank state information monitoring: (1) In order to monitor each physical parameter in the transportation process of the LNG tank, a plurality of monitoring sensors such as: temperature, pressure, liquid level height, acceleration sensor. Due to the factors such as the tank sea environment, the quality of the sensor and the like, the sensor is inevitably failed, so that data can not be acquired or the acquired data is unreliable; (2) LNG tank is the area pressure cryogenic equipment, and tank design initially adopts a series of technological means and equipment instrument to keep this kind of state, because the damage that product quality, maintenance state, transportation operating mode lead to all takes place, in order to guarantee tank transportation's safety, monitors, reports and latent trouble early warning to the trouble is indispensable in advance.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a LNG Tank fault prediction method based on time-varying parameters, which is characterized in that dynamic monitoring parameters such as liquid phase temperature, gas phase pressure, liquid level, acceleration, swing floating frequency and the like of an LNG Tank are collected, the dynamic monitoring parameters are subjected to unit third-order linear regression analysis taking constant parameters as independent variables, the long-time monitoring data are utilized to carry out regression to obtain a tank=M (D, b, e) value of the storage Tank in a certain given State (State=S ()), and the value is taken as a standard value and is recorded as the Tank stand =mstand (D, b, e) as "fingerprint" of the tank. Actual monitoring of the value of the Tankt and the Tankt stand And (3) performing comparative analysis, analyzing a difference value caused by faults by adopting RSS (sum of squares of residual errors), judging the type of the faults, accurately forecasting the faults of the LNG in real time, and improving the safety of the full life cycle range of the LNG.
In order to achieve the above object, according to a first aspect of the present invention, there is provided a method for predicting a fault of an LNG tank based on time-varying parameters, including the steps of:
s100: setting sampling frequency, collecting main dynamic monitoring parameters TVP such as liquid phase temperature, gas phase pressure, liquid level, acceleration and swing floating frequency, and collecting main static parameters t;
s200: performing unit third-order linear regression analysis with t as an independent variable on the main dynamic monitoring parameter TVP;
s300: repeatedly performing three-order linear regression analysis by using long-time monitoring data to obtain a tank=M (D, b, e) value of the storage Tank in a given State (State=S ()), and recording the value as a standard value of the Tank stand =mstand (D, b, e) as "fingerprint feature" of the Tank, will actually monitor Tank t The value of (A) and Tank stand And performing comparative analysis, and judging the type of the fault by adopting RSS to analyze the difference value caused by the fault.
Further, in step S100, the data acquisition and fault prediction of the liquid phase temperature include:
s101: setting sampling frequency, collecting liquid phase temperature data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s102: judging whether the sum of squares of the residual errors exceeds a threshold range or not, if not, ending fault prediction, and if yes, turning to step S103;
s103: comparing the analysis result with the measured value, and judging whether the analysis result exceeds the temperature range of the liquid LNG within-165 ℃ to-140 ℃, if so, forecasting the fault of the temperature sensor; if not, go to step S104;
s104: judging whether the intercept D, the slope b and the error term e numbering trend of the analysis result are increased or decreased, and ending the fault prediction if D, b is decreased; if D, b increases, the forecast tank leakage rate increases.
Further, in step S100, the data collection and fault prediction of the gas phase pressure include:
s105: setting sampling frequency, collecting gas phase pressure data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s106: judging whether the sum of squares of residual errors exceeds a threshold range, if not, ending fault prediction; if so, go to step S107;
s107: comparing the analysis result with the actual measurement value, judging whether the analysis result exceeds a normal threshold range, and if so, forecasting the pressure touch sensor fault; if not, go to step S108;
s108: judging whether the intercept D, the slope b and the error term e numbering trend of the analysis result are increased or decreased, and ending the fault prediction if D, b is decreased; if D, b rises, the relief valve is predicted to take off.
Further, in step S100, the data collection and fault prediction of the liquid level include:
s109: setting sampling frequency, collecting liquid level data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s110: judging whether the sum of squares of residual errors exceeds a threshold range, if not, ending fault prediction; if yes, go to step S110;
s111: comparing the analysis result with the actual measurement value, and judging whether the analysis result exceeds a normal threshold range, if so, forecasting the tank overcharge or the touch sensor fault; and if not, ending the fault prediction.
Further, in step S100, the acceleration data acquisition and fault prediction include:
s112: setting sampling frequency, collecting acceleration data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s113: judging whether the sum of squares of the residual errors exceeds a threshold range, if not, ending fault prediction, and if yes, turning to step S114;
s114: comparing the analysis result with the actual measurement value, judging whether the analysis result exceeds a normal threshold range, and if so, forecasting the fault of the touch sensor; if the minimum value is 0, the prediction tank stands still.
Further, in step S100, the data collection and fault prediction of the wobble floating frequency include:
s115: setting sampling frequency, collecting pendulum floating frequency data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s116: judging whether the sum of squares of the residual errors exceeds a threshold range, if not, ending fault prediction, and if yes, turning to step S117;
s117: comparing the analysis result with the actual measurement value, judging whether the analysis result exceeds the normal threshold range, if so, forecasting the sensor fault, and if not, forecasting the tank to stand.
According to a second aspect of the present invention, there is provided an LNG tank fault prediction system based on time-varying parameters, comprising:
the data acquisition module is used for setting sampling frequency, acquiring main dynamic monitoring parameters TVP such as liquid phase temperature, gas phase pressure, liquid level, acceleration and swing floating frequency, and simultaneously acquiring main static parameters t;
the regression analysis module is used for carrying out unit third-order linear regression analysis with t as an independent variable on the main dynamic monitoring parameter TVP;
the fault prediction module is used for repeatedly carrying out three-order linear regression analysis by using long-time monitoring data to obtain a tank=M (D, b, e) value of the storage Tank in a given State (State=S ()), and taking the value as a standard value and recording the standard value as the Tank stand =mstand (D, b, e) as "fingerprint feature" of the Tank, will actually monitor Tank t The value of (A) and Tank stand And performing comparative analysis, and judging the type of the fault by adopting RSS to analyze the difference value caused by the fault.
According to a third aspect of the present invention, there is provided an electronic device comprising:
at least one processor, at least one memory, and a communication interface; wherein,
the processor, the memory and the communication interface are communicated with each other;
the memory stores program instructions executable by the processor that the processor invokes to perform the method of any of the above.
According to a fourth aspect of the present invention, there is provided a non-transitory computer readable storage medium, characterized in that the non-transitory computer readable storage medium stores computer instructions that cause the computer to perform the method of any one of the above.
In general, compared with the prior art, the above technical solution conceived by the present invention mainly has the following technical advantages:
1. the method of the invention collects the dynamic monitoring parameters such as the liquid phase temperature, the gas phase pressure, the liquid level, the acceleration, the swing floating frequency and the like of the LNG Tank, carries out unit third-order linear regression analysis taking the constant parameter as an independent variable on the dynamic monitoring parameters, obtains the value of rank=M (D, b, e) of the storage Tank in a certain given State (State=S ()) by regression through long-time monitoring data, and takes the value as a standard value to be recorded as rank stand =mstand (D, b, e) as "fingerprint" of the tank. Will actually monitor the Tank t The value of (A) and Tank stand And (3) performing comparative analysis, analyzing a difference value caused by faults by adopting RSS (sum of squares of residual errors), judging the type of the faults, accurately forecasting the faults of the LNG in real time, and improving the safety of the full life cycle range of the LNG.
Drawings
Fig. 1 is a flowchart of an LNG tank fault prediction method based on time-varying parameters according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a liquid phase temperature failure prediction flow chart according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a gas phase pressure failure prediction flow in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a liquid level fault prediction flow in an embodiment of the present invention;
FIG. 5 is a schematic diagram of a acceleration fault prediction flow chart according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a flow chart of a pendulum frequency failure prediction in an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
As shown in fig. 1, the LNG tank fault prediction method based on time-varying parameters provided by the embodiment of the invention includes the following steps:
step one: and (5) parameter collection.
(1) Collecting main dynamic monitoring parameters, namely instant variable parameters (TVP):
a liquid phase temperature sensor T101 (°c);
a gas phase pressure sensor P102 (MPa);
level data m103 (kg);
acceleration a104 (m/s 2);
pendulum floating frequency s105 (HZ);
(2) The main static parameter, i.e. the constant parameter (t), is acquired with a frequency t=15 minutes.
Step two: and carrying out unit third-order linear regression analysis with t as an independent variable on the TVP, wherein the analysis formula is as follows:
TVP＝D+b 1 t 3 +b 2 t 2 +b 3 t+e
where D is the intercept, b is the slope, and e is the error term.
Step three: regression is performed using the long-time monitoring data to obtain a rank=m (D, b, e) value of the Tank in a given State (state=s ()), and the value is recorded as a rank using the value as a standard value stand =mstand (D, b, e) as "fingerprint" of the tank. Will actually monitor the Tank t The value of (A) and Tank stand And performing comparison analysis, and analyzing a difference value caused by the fault by adopting RSS (sum of squares of residual errors) to judge the type of the fault.
Specifically, as shown in fig. 2, the data acquisition and fault prediction of the liquid phase temperature include:
s101: setting sampling frequency, collecting liquid phase temperature data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s102: judging whether the sum of squares of the residual errors exceeds a threshold range or not, if not, ending fault prediction, and if yes, turning to step S103;
s103: comparing the analysis result with the measured value, and judging whether the analysis result exceeds the temperature range of the liquid LNG within-165 ℃ to-140 ℃, if so, forecasting the fault of the temperature sensor; if not, go to step S104;
s104: judging whether the intercept D, the slope b and the error term e numbering trend of the analysis result are increased or decreased, and ending the fault prediction if D, b is decreased; if D, b increases, the forecast tank leakage rate increases.
In the embodiment of the present invention, the liquid phase temperature sensor T101 failure determination: d is regression intercept and is mainly influenced by initial temperature of liquid filling; slope b123 reflects primarily the insulation capability of the tank itself, which may be affected as the tank ages; e is the error term. After the Tank is filled with LNG, data analysis is carried out, and the Tank is calculated to obtain the Tank state=MState (D, b, e) and the Tank under the current liquid filling working condition stand The values for equal D in Mstand (D, b, e) are compared, and if the bState and bstand deviate outside a given range, a fault is considered to occur.
As shown in fig. 3, the data collection and fault prediction of the gas phase pressure include:
s105: setting sampling frequency, collecting gas phase pressure data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s106: judging whether the sum of squares of residual errors exceeds a threshold range, if not, ending fault prediction; if so, go to step S107;
s107: comparing the analysis result with the actual measurement value, judging whether the analysis result exceeds a normal threshold range, and if so, forecasting the pressure touch sensor fault; if not, go to step S108;
s108: judging whether the intercept D, the slope b and the error term e numbering trend of the analysis result are increased or decreased, and ending the fault prediction if D, b is decreased; if D, b rises, the relief valve is predicted to take off.
In an embodiment of the present invention, the gas phase pressure sensor P102 failure determination: wherein D is a regression intercept and is mainly influenced by the initial temperature of the liquid filling; slope b123 is predominantly invertedThe thermal insulation capability of the tank itself may be affected as the tank ages; e is the error term. After the Tank is filled with LNG, data analysis is carried out, and the Tank is calculated to obtain the Tank state=MState (D, b, e) and the Tank under the current liquid filling working condition stand The values for equal D in Mstand (D, b, e) are compared, and if the bState and bstand deviate outside a given range, a fault is considered to occur.
As shown in fig. 4, the data collection and fault prediction of the liquid level include:
s109: setting sampling frequency, collecting liquid level data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s110: judging whether the sum of squares of residual errors exceeds a threshold range, if not, ending fault prediction; if yes, go to step S110;
s111: comparing the analysis result with the actual measurement value, and judging whether the analysis result exceeds a normal threshold range, if so, forecasting the tank overcharge or the touch sensor fault; and if not, ending the fault prediction.
In the embodiment of the invention, the sampling frequency is set, the swing frequency data is collected and subjected to regression analysis, the analysis result is compared with the standard value to obtain the residual square sum, if the residual square sum exceeds the threshold range, the fault prediction is ended, if the residual square sum exceeds the threshold range, the analysis result is compared with the actual measurement value, if the analysis result exceeds the normal threshold range, the sensor fault is predicted, if the minimum value is 0, the output display tank is kept stand.
As shown in fig. 5, the acceleration data acquisition and fault prediction include:
s112: setting sampling frequency, collecting acceleration data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s113: judging whether the sum of squares of the residual errors exceeds a threshold range, if not, ending fault prediction, and if yes, turning to step S114;
s114: comparing the analysis result with the actual measurement value, judging whether the analysis result exceeds a normal threshold range, and if so, forecasting the fault of the touch sensor; if the minimum value is 0, the prediction tank stands still.
In the embodiment of the present invention, the acceleration sensor a104 failure determination: wherein D is a regression intercept and is mainly influenced by the position of the tank on the ship; slope b123 reflects mainly the tank motion acceleration change state; e is the error term. After the Tank is filled with LNG, data analysis is carried out, and the Tank is calculated to obtain the tankState=MState (D, b, e) and the transportation standard value Tank in the current transportation state of the Tank stand The values for equal D in Mstand (D, b, e) are compared, and if the bState and bstand deviate outside a given range, a fault is considered to occur. In addition, the fault determination can also compare and detect the data of other tank boxes in the same state (such as the tank box near the ship transportation position)
As shown in fig. 6, the data collection and fault prediction of the wobble floating frequency include:
s115: setting sampling frequency, collecting pendulum floating frequency data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s116: judging whether the sum of squares of the residual errors exceeds a threshold range, if not, ending fault prediction, and if yes, turning to step S117;
s117: comparing the analysis result with the actual measurement value, judging whether the analysis result exceeds the normal threshold range, if so, forecasting the sensor fault, and if not, forecasting the tank to stand.
In the embodiment of the invention, the swing frequency s105 failure determination: wherein D is a regression intercept and is mainly influenced by the position of the tank on the ship; slope b123 mainly reflects the motion swing and float change state of the tank; e is the error term. After the Tank is filled with LNG, data analysis is carried out, and the Tank is calculated to obtain the tankState=MState (D, b, e) and the transportation standard value Tank in the current transportation state of the Tank stand The values for equal D in Mstand (D, b, e) are compared, and if the bState and bstand deviate outside a given range, a fault is considered to occur. In addition, the fault determination can also compare and detect the data of other tank boxes in the same state (such as the tank box near the ship transportation position).
The method of the embodiment of the invention is realized by the electronic equipment, so that the related electronic equipment is necessary to be introduced. To this end, an embodiment of the present invention provides an electronic device including: at least one processor (processor), a communication interface (Communications Interface), at least one memory (memory) and a communication bus, wherein the at least one processor, the communication interface, and the at least one memory communicate with each other via the communication bus. The at least one processor may invoke logic instructions in the at least one memory to perform all or part of the steps of the methods provided by the various method embodiments described above.
Further, the logic instructions in at least one of the memories described above may be implemented in the form of a software functional unit and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. The LNG tank fault prediction method based on the time-varying parameters is characterized by comprising the following steps of:
s100: setting sampling frequency, collecting main dynamic monitoring parameters TVP such as liquid phase temperature, gas phase pressure, liquid level, acceleration and swing floating frequency, and collecting main static parameters t;
s200: performing unit third-order linear regression analysis with t as an independent variable on the main dynamic monitoring parameter TVP;
s300: repeatedly performing three-order linear regression analysis by using long-time monitoring data to obtain a tank=M (D, b, e) value of the storage Tank in a given state, and recording the value as a standard value of the Tank stand =mstand (D, b, e) as "fingerprint feature" of the Tank, will actually monitor Tank t The value of (A) and Tank stand Performing contrast analysis, namely adopting RSS to analyze a difference value caused by the fault and judging the type of the fault;
in step S100, the data acquisition and fault prediction of the liquid phase temperature include:
s101: setting sampling frequency, collecting liquid phase temperature data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s102: judging whether the sum of squares of the residual errors exceeds a threshold range or not, if not, ending fault prediction, and if yes, turning to step S103;
s103: comparing the analysis result with the measured value, and judging whether the analysis result exceeds the temperature range of the liquid LNG within-165 ℃ to-140 ℃, if so, forecasting the fault of the temperature sensor; if not, go to step S104;
s104: judging whether the intercept D, the slope b and the error term e numbering trend of the analysis result are increased or decreased, and ending the fault prediction if D, b is decreased; if D, b increases, the forecast tank leakage rate increases.
2. The LNG tank fault prediction method based on time-varying parameters according to claim 1, wherein in step S100, the data acquisition and fault prediction of the gas phase pressure comprises:
s105: setting sampling frequency, collecting gas phase pressure data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s106: judging whether the sum of squares of residual errors exceeds a threshold range, if not, ending fault prediction; if so, go to step S107;
s107: comparing the analysis result with the actual measurement value, judging whether the analysis result exceeds a normal threshold range, and if so, forecasting the pressure touch sensor fault; if not, go to step S108;
s108: judging whether the intercept D, the slope b and the error term e numbering trend of the analysis result are increased or decreased, and ending the fault prediction if D, b is decreased; if D, b rises, the relief valve is predicted to take off.
3. The LNG tank fault prediction method based on time-varying parameters of claim 1, wherein in step S100, the data acquisition and fault prediction of the liquid level comprises:
s109: setting sampling frequency, collecting liquid level data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s110: judging whether the sum of squares of residual errors exceeds a threshold range, if not, ending fault prediction; if yes, go to step S110;
s111: comparing the analysis result with the actual measurement value, and judging whether the analysis result exceeds a normal threshold range, if so, forecasting the tank overcharge or the touch sensor fault; and if not, ending the fault prediction.
4. A method for predicting LNG tank failure based on time-varying parameters according to any one of claims 1-3, wherein in step S100, the acceleration data acquisition and failure prediction comprises:
s112: setting sampling frequency, collecting acceleration data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s113: judging whether the sum of squares of the residual errors exceeds a threshold range, if not, ending fault prediction, and if yes, turning to step S114;
s114: comparing the analysis result with the actual measurement value, judging whether the analysis result exceeds a normal threshold range, and if so, forecasting the fault of the touch sensor; if the minimum value is 0, the prediction tank stands still.
5. A method for predicting LNG tank failure based on time-varying parameters according to any one of claims 1-3, wherein in step S100, the data acquisition and failure prediction of the swing frequency comprises:
s115: setting sampling frequency, collecting pendulum floating frequency data, carrying out regression analysis, and comparing an analysis result with a standard value to obtain a residual square sum;
s116: judging whether the sum of squares of the residual errors exceeds a threshold range, if not, ending fault prediction, and if yes, turning to step S117;
s117: comparing the analysis result with the actual measurement value, judging whether the analysis result exceeds the normal threshold range, if so, forecasting the sensor fault, and if not, forecasting the tank to stand.
6. An LNG tank fault prediction system based on time-varying parameters for implementing the method of any of claims 1-5, comprising:
the data acquisition module is used for setting sampling frequency, acquiring main dynamic monitoring parameters TVP such as liquid phase temperature, gas phase pressure, liquid level, acceleration and swing floating frequency, and simultaneously acquiring main static parameters t;
the regression analysis module is used for carrying out unit third-order linear regression analysis with t as an independent variable on the main dynamic monitoring parameter TVP;
the fault prediction module is used for repeatedly carrying out three-order linear regression analysis by using long-time monitoring data to obtain a tank=M (D, b, e) value of the storage Tank in a given state, and taking the value as a standard value and recording the standard value as the Tank stand =mstand (D, b, e) as "fingerprint feature" of the Tank, will actually monitor Tank t The value of (A) and Tank stand And performing comparative analysis, and judging the type of the fault by adopting RSS to analyze the difference value caused by the fault.
7. An electronic device, comprising:
at least one processor, at least one memory, and a communication interface; wherein,
the processor, the memory and the communication interface are communicated with each other;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-5.
8. A non-transitory computer readable storage medium storing computer instructions that cause the computer to perform the method of any one of claims 1 to 5.
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