Gasket joint reliability evaluation system, method for evaluating reliability of gasket joint, and program

Abstract

To provide a system for evaluating reliability of a pipe connection part including a gasket.SOLUTION: The system includes: a measurement value acquisition unit configured to acquire measurement values measured by a sensor; a simulation unit configured to simulate behavior of a fluid flowing through the system on the basis of the measurement values and to calculate a state quantity of the fluid; a load analysis unit configured to analyze a load applied to a gasket on the basis of the measurement values and the state quantity, and to calculate a connection index of the gasket; a fatigue analysis unit configured to analyze a fatigue condition on the basis of specifications and conditions of the gasket and a gasket joint, and to calculate a deterioration index of the gasket; and a reliability evaluation unit configured to calculate reliability of the gasket joint on the basis of the measurement values, the state quantity, the connection index, the deterioration index, and a reliability evaluation model.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a gasket connection reliability evaluation system, a gasket connection reliability evaluation method, and a program. [Background technology]

[0002] In industries handling fluids such as oil and gas, maintaining fluid-carrying pipe connections is crucial. Flanges and gaskets are commonly used to connect one pipe to another or to other equipment. Because both flanges are rigid, they must be machined and aligned. When operating conditions change, their relative positions must be adjusted to maintain a tight seal. However, achieving a tight seal can be challenging, considering the properties of the alloys used in the equipment, the fluids, process variables (vibration, temperature fluctuations, pressure fluctuations, internal fluid surges, wear, chemical compatibility, etc.), and cost constraints (maintenance labor, product cost, downtime). In these cases, gaskets become crucial. Gaskets are softer than the rigid components they connect and fill the gap between the two components, preventing fluid leakage. The interaction of the clamping force and internal pressure between the flange and gasket fills the irregularities on the flange surface, achieving a tight seal. Gasket failure is a major maintenance issue for fluid-handling plants and other facilities. Gasket fatigue and leaks from connections can lead to fires and other accidents. To address gasket connection failures appropriately, gasket condition and integrity must be monitored. However, no such methods have been proposed, and gasket failures are often discovered after the fact. For example, industrial gaskets are used in a wide range of fields, including oil and gas production sites, gas processing plants, deepwater facilities, LNG (Liquefied Natural Gas) facilities, pipeline transportation facilities, refineries, and petrochemical plants. Different types of gaskets are used in each environment. Many of these gaskets are replaced not based on their condition or integrity during operation, but rather when systems are repaired or remodeled. However, depending on the operating conditions at the site, they may fail before the vendor's recommended lifespan.

[0003] As a related technique, Patent Document 1 discloses a technique for calculating the service life and service life probability of a pipe due to corrosion from the relationship between the service life of the pipe and the amount of thinning of the pipe, in relation to a piping system of a plant, etc. Patent Document 2 discloses a system that acquires sensor information representing the operating characteristics from machinery equipped in oil and gas production facilities and generates a digital model of the machinery based on user input via a GUI (Graphical User Interface). Patent Document 3 discloses an interactive monitoring system that visually displays the output values ​​of a digital model of machinery in oil and gas production facilities. Patent Documents 1 to 3 do not disclose a technique for evaluating the reliability of a piping connection, focusing on the connection. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2018-25497 [Patent Document 2] U.S. Patent No. 10,884,402 [Patent Document 3] U.S. Patent No. 10,746,015 Summary of the Invention [Problem to be solved by the invention]

[0005] There is a need for a method to monitor the condition of pipe connections and evaluate their reliability so that failures and other problems can be addressed in advance.

[0006] The present disclosure provides a gasket connection reliability evaluation system and a gasket connection reliability evaluation method that can solve the above problems. [Means for solving the problem]

[0007] The gasket connection reliability evaluation system of the present disclosure includes a measurement value acquisition unit that acquires measurement values ​​measured by sensors provided in a system including a pipe connection equipped with a gasket; a simulation unit that simulates the behavior of a fluid flowing through the system based on the measurement values ​​and calculates a state quantity related to the fluid; a load analysis unit that analyzes the load applied to the gasket based on the measurement values ​​and the state quantity and calculates a connection index of the gasket; a fatigue analysis unit that analyzes the fatigue state of the gasket based on the measurement values, the state quantity, the load applied to the gasket, and specifications and states of the gasket and the gasket connection that indicates the pipe connection; and a reliability evaluation unit that calculates the reliability of the gasket connection based on the measurement values, the state quantity, the connection index, the degradation index, and a predetermined reliability evaluation model.

[0008] The gasket connection reliability evaluation method disclosed herein includes the steps of acquiring measurement values ​​measured by a sensor provided in a system including a pipe connection having a gasket; simulating the behavior of a fluid flowing through the system based on the measurement values ​​and calculating a state quantity related to the fluid; analyzing the load applied to the gasket based on the measurement values ​​and the state quantity and calculating a connection index of the gasket; analyzing the fatigue state of the gasket based on the measurement values, the state quantity, the load applied to the gasket, and the specifications and state of the gasket connection indicating the gasket and the pipe connection and calculating a degradation index of the gasket; and calculating the reliability of the gasket connection based on the measurement values, the state quantity, the connection index, the degradation index, and a predetermined reliability evaluation model.

[0009] The program disclosed herein executes the following steps: acquiring measurement values ​​measured by a sensor provided in a system including a pipe connection with a gasket; simulating the behavior of a fluid flowing through the system based on the measurement values ​​and calculating a state quantity related to the fluid; analyzing the load applied to the gasket based on the measurement values ​​and the state quantity and calculating a connection index of the gasket; analyzing the fatigue state of the gasket based on the measurement values, the state quantity, the load applied to the gasket, and the specifications and state of a gasket connection indicating the gasket and the pipe connection, and calculating a degradation index of the gasket; and calculating the reliability of the gasket connection based on the measurement values, the state quantity, the connection index, the degradation index, and a predetermined reliability evaluation model. [Effects of the Invention]

[0010] According to the above-described gasket connection reliability evaluation system, gasket connection reliability evaluation method, and program, the reliability of a piping connection such as a gasket can be evaluated. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a diagram showing an outline of a piping system according to an embodiment; [Figure 2] FIG. 1 is a diagram illustrating an example of a reliability evaluation system according to an embodiment. [Figure 3] FIG. 10 is a diagram illustrating an example of processing by a data collection and selection unit according to the embodiment. [Figure 4] FIG. 10 is a diagram illustrating an example of processing by a simulation unit according to the embodiment. [Figure 5] FIG. 10 is a diagram illustrating an example of processing by a load analysis unit according to the embodiment. [Figure 6] FIG. 10 is a diagram illustrating an example of processing by a fatigue analysis unit according to the embodiment. [Figure 7A] FIG. 10 is a diagram illustrating an example of processing by an evaluation unit according to the embodiment. [Figure 7B] FIG. 10 is a diagram illustrating an example of a reliability evaluation process according to the embodiment. [Figure 8] FIG. 10 is a diagram illustrating an example of a change over time in an evaluation index according to an embodiment. [Figure 9] FIG. 10 is a diagram illustrating an example of a temperature change in an evaluation index according to the embodiment. [Figure 10] FIG. 10 is a diagram showing an example of a change over time in a gasket connection reliability LOF index value according to an embodiment. [Figure 11] 10 is a flowchart illustrating an example of a load analysis process according to the embodiment. [Figure 12] 10 is a flowchart illustrating an example of a reliability evaluation process according to the embodiment. [Figure 13] FIG. 1 is a diagram illustrating an example of a hardware configuration of a gasket reliability evaluation system according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] (System Configuration) FIG. 1 shows an outline of a piping system according to an embodiment. The piping system 1 includes a pipe 102, a flange 103, a gasket 104, a flange 105, a pipe 106, a real sensor 101, a virtual sensor 131, a virtual sensor 141, and a real sensor 107. The real sensors 101 and 107 are each a single real sensor or a group of multiple real sensors. For example, the actual real sensor 101 may be a simple pressure sensor, or may be a pressure sensor and a temperature sensor. Similarly, the virtual sensors 131 and 141 are each a single virtual sensor or a group of multiple virtual sensors. In the piping system 1, the pipe 102 and the pipe 106 are arranged coaxially. A flange 103 is provided at the end of the pipe 102. A flange 105 is provided at the end of the pipe 106. The flanges 103 and 105 are arranged opposite each other with a gasket 104 interposed therebetween. The gasket 104 seals the gap between the flanges. The gasket 104 and the joints between the gasket 104 and the flanges 103 and 105 are called gasket connection 108. In the piping system 1, a fluid flows in the direction indicated by the arrow. Real sensor 101 and virtual sensor 131 are provided upstream of gasket connection 108 in the fluid flow direction, and virtual sensor 141 and real sensor 107 are provided downstream. Real sensors 101 and 107 are physical sensors that measure the temperature, pressure, flow rate, and other properties of the fluid. Based on a simulation model that simulates the state and behavior of the piping system 1 and the measurements taken by real sensors 101 and 107, virtual sensors 131 and 141 calculate estimated values ​​of state quantities of the fluid at locations where real sensors cannot be installed, as well as state quantities of types that cannot be measured by real sensors. State quantities refer to the operating conditions, physical properties, and chemical composition of the fluid flowing through piping system 1. Operating conditions include, for example, the temperature, pressure, and flow rate of the fluid. Physical properties include, for example, the gas / liquid ratio, density, viscosity, and specific heat. The chemical composition is the content of H2O, CO2, N2, hydrocarbons, sand, and other impurities. The virtual sensors 131 and 141 calculate the conditions under which the estimated state quantities (hereinafter, sometimes referred to as virtual sensor values) are measured (calculated). For example, the virtual sensors 131 calculate the conditions under which the virtual sensor values ​​calculated by the virtual sensors 131 are certain values, such as the measured values ​​of the real sensors 101 and 107 and the openings of valves (not shown).In the piping system 1, actual sensors for measuring various state quantities at the gasket connection 108 are often not provided, which makes it difficult to evaluate and monitor the soundness of the gasket connection 108. By providing virtual sensors 131 and 141, the operating conditions and fluid characteristics near the gasket connection 108 can be clarified, making it possible to evaluate the reliability of the gasket connection 108. The positions and number of the actual sensors and virtual sensors in FIG. 1 are merely an example and are not limited to the illustrated embodiment. The piping system 1 may be provided with multiple gasket connection parts 108.

[0013] FIG. 2 illustrates an example of a reliability assessment system according to an embodiment. The gasket connection reliability assessment system 2 is composed of one or more computers. The gasket connection reliability assessment system 2 utilizes a digital twin to monitor and assess the reliability of a gasket connection 108 online or offline. A digital twin is a dynamic digital model of a physical system, designed for bidirectional information flow. This means that not only can results be simulated and referenced, but real-time state changes can also be displayed. This is the biggest difference from a simulation. This dynamic, bidirectional flow of information changes. The ultimate goal of twinning is to generate valuable insights. These insights can be applied to the original data objects. These insights are widely used in some industrial digital transformation applications. Four types of models are developed and used to predict the risk and reliability of a gasket connection 108 using a digital twin.

[0014] The first model is a process simulation model that simulates the process near the gasket connection 108. The simulation results are compared with measurements from the actual sensors 101, etc. Typical parameters used for comparison include fluid pressure, temperature, and flow rate. In some cases, engineering parameters such as density, velocity, and chemical composition, as well as occasional phase behavior, can also be included. This model also functions as a virtual sensor network model to create the required operating conditions at the gasket 104 connection location.

[0015] The second model is a finite element method (FEA), a reduced basis finite element method (RBFEA), and / or a computational fluid dynamics (CFD) model. The second model is used to analyze the loads applied to the gasket connection 108 and the fatigue state of the gasket 104 material. Analysis using the second model begins with selecting the gasket material (metal, non-metal, or semi-metal). The steady-state FEA and RBFEA models determine the loading state and quantify the restitution coefficient and leakage coefficient. A CFD model analyzes the dynamic effects of the fluid on the gasket connection 108 and quantifies the fatigue state of the gasket material, including the degradation and reduction coefficients. The second model is used to determine the material fatigue boundaries for the structure and operating range of the gasket connection system.

[0016] The third model is a series of machine learning (ML) models and artificial intelligence (AI) models that rely on the following (1) to (3). Hereinafter, the machine learning and artificial intelligence models will be referred to as ML / AI models: (1) a rigorous process simulation model for predicting process conditions and engineering parameters near the gasket connection location; (2) understanding the gasket loading conditions determined by FEA and RBFEA models and determining the boundaries of safe operation; and (3) identifying the fatigue limit due to the temperature effect of the gasket material using a CFD model. That is, the series of ML / AI models are: (1) a virtual sensor ML / AI model that corresponds to the rigorous process simulation model and predicts the process near the gasket connection 108 more quickly; (2) a load ML / AI model that predicts the restitution coefficient and leakage coefficient, etc., similar to the analysis results obtained by the FEA and RBFEA models; and (3) a fatigue ML / AI model that predicts the degradation coefficient, reduction coefficient, fatigue limit, safe operating range, etc., similar to the analysis results obtained by the CFD model. The virtual sensor ML / AI is constructed by learning the data input and output by the first model, the load ML / AI model is constructed by learning the relationship between the load analysis results by the second model and the input parameters used in the analysis, and the fatigue ML / AI model is constructed by learning the relationship between the analysis results of the fatigue state of the gasket material by the second model and the input parameters used in the analysis.

[0017] The fourth model is a calculation model for the gasket connection reliability LOF (Likelihood of Failure) index, which determines the overall safety guideline level for the gasket connection 108 and gasket material fatigue analysis. This model is a reliability ML / AI model based on the three ML / AI models described above and calculates the gasket connection reliability LOF index value in real time. For example, the reliability ML / AI model is constructed by learning the relationship between the output results of the virtual sensor ML / AI, load ML / AI, and fatigue ML / AI and the gasket connection reliability LOF value calculated using a predetermined calculation method.

[0018] Based on the above, we will now explain an example of the functions and configuration of the gasket connection reliability evaluation system 2. The gasket connection reliability evaluation system 2 includes a data collection and selection unit 200, a simulation unit 210, an analysis unit 220, and an evaluation unit 250.

[0019] The data collection and selection unit 200 acquires and selects data necessary for reliability evaluation of the gasket connection 108. The data collection and selection unit 200 includes an operation history database 201 and a gasket connection database 203. The operation history database 201 stores time history data such as measurement values ​​measured by the real sensors 101 and 107 and the operating state of the piping system 1. The operation history database 201 may also store time history data of virtual sensor values ​​calculated by the virtual sensors 131 and 141. The gasket connection database 203 stores information on the specifications and states of various gaskets and flanges. The specifications include material, size, etc. The states include usage history, accumulated fatigue, etc. The specification information is provided, for example, by the gasket manufacturer. The state information is updated as the piping system 1 operates. For example, a value related to the fatigue state of the gasket connection 108 calculated using the fatigue ML / AI model can be registered in the gasket connection database 203 as accumulated fatigue information. The data collection and selection unit 200 selects data from the gasket connection database 203 that matches or is closest to the specifications and conditions of the gasket 104 and flanges 103 and 105 .

[0020] The simulation unit 210 includes a simulation result comparison unit 211 and a simulation model 213. When given predetermined parameters, the simulation model 213 simulates the behavior of a fluid flowing through the piping system 1 and the state that occurs in the piping system 1 due to the fluid flow. The simulation model 213 includes a strict process simulation model and a virtual sensor ML / AI model. The simulation unit 210 acquires historical data of measurement values ​​of the actual sensors 101 and 107 and the specifications and states of the gasket 104 and flanges 103 and 105 from the data collection and selection unit 200 and inputs them into the simulation model 213. The simulation unit 210 acquires information indicating accumulated fatigue of the gasket connection portion 108 from the analysis unit 220 and inputs it into the simulation model 213. When these parameters are input, the simulation unit 210 causes the simulation model 213 to simulate the state of the piping system 1. The simulation model 213 calculates various state quantities related to the piping system 1. For example, the simulation model 213 calculates the temperature, pressure, flow rate, etc. of the fluid flowing through the piping system 1. The calculation results of the simulation model 213 include state quantities calculated by the virtual sensors 131, 141. The simulation result comparison unit 211 screens the calculation results of the simulation model 213. For example, if there is any missing data, the simulation result comparison unit 211 performs a correction calculation or the like to fill in the missing data. The simulation unit 210 outputs the calculation results of the simulation model 213 and the filled in data to the analysis unit 220. The output data includes state quantities (virtual sensor values) of the fluid in the vicinity of the gasket connection portion 108.

[0021] The analysis unit 220 includes a load analysis unit 221 and a fatigue analysis unit 223. The load analysis unit 221 analyzes the load acting on the gasket 104 based on measurement values ​​from the actual sensors 101 and the like recorded in the operation history database 201 and virtual sensor values ​​from the virtual sensors 131 and the like calculated by the simulation unit 210, and calculates a connection index and the like for the gasket 104. The connection indexes are the coefficient of restitution and leakage coefficient of the gasket 104. The coefficient of restitution is a value obtained by indexing the repulsive force of the gasket against the tightening force using a predetermined formula, and the leakage coefficient is a value obtained by indexing the amount of fluid leaking from the gasket connection portion 108 using a predetermined formula. A larger value for the coefficient of restitution indicates a healthier state of the gasket connection portion 108 (a state in which the gasket 104 is free from failures and leaks), and a smaller value for the leakage coefficient indicates a healthier state of the gasket connection portion 108. The load analysis unit 221 performs simulations using FEA (Finite Element Method), RBFEA (Reduced Basis Finite Element Method), and CFD (Computational Fluid Dynamics) on gaskets of various specifications to create a gasket load database and library. The gasket load database and library store information such as load boundary values ​​(operating ranges), limit values ​​(upper limits), restitution coefficients, and leakage coefficients.

[0022] The fatigue analysis unit 223 analyzes the fatigue state of the gasket 104 based on the measurement values ​​from the actual sensors 101 and the like recorded in the operation history database 201, the virtual sensor values ​​from the virtual sensors 131 and the like calculated by the simulation unit 210, the load applied to the gasket 104, and the specifications and state of the gasket connection portion 108 (gasket connection portion database 203), and calculates a degradation index and a fatigue coefficient of the gasket 104. The degradation index is the degradation coefficient and reduction coefficient of the gasket 104. The degradation coefficient is a value obtained by indexing the degree of degradation of the sealing ability using a predetermined formula, and the reduction coefficient is a value obtained by indexing the amount of reduction in the materials constituting the gasket 104 using a predetermined formula. The degradation coefficient and reduction coefficient are factors related to temperature. A smaller degradation coefficient value indicates a healthier state of the gasket connection portion 108, and a larger reduction coefficient value indicates a healthier state of the gasket connection portion 108. The fatigue coefficient is a value that indicates the degree of fatigue of the gasket 104. The fatigue analysis unit 223 performs CFD analysis on gaskets of various specifications and creates a gasket material fatigue analysis database or library based on the analysis results and experimental results on deterioration conducted by vendors, etc. The gasket material fatigue analysis database or library stores the deterioration coefficient, reduction coefficient, fatigue limit (upper limit value of the fatigue coefficient), etc. The analysis unit 220 outputs the results of the load analysis and fatigue analysis of the gasket connection portion 108 to the evaluation unit 250.

[0023] The evaluation unit 250 integrates the processing results of the data collection and selection unit 200, the simulation unit 210, and the analysis unit 220 to evaluate the reliability of the gasket connection 108. The evaluation unit 250 includes a reliability evaluation unit 251, an IOW (Integrity Operating Windows) information integration unit 253, and an output unit 255. The reliability evaluation unit 251 calculates the gasket connection reliability LOF index based on measurement values ​​from the real sensors 101 and the like recorded in the operation history database 201, virtual sensor values ​​from the virtual sensors 131 and the like calculated by the simulation unit 210, the connection index calculated by the load analysis unit 221, the deterioration index calculated by the fatigue analysis unit 223, and the reliability ML / AL model (the fourth model described above). The IOW information integration unit 253 calculates the range of state quantities allowable in the piping system 1, the recommended range and limits of operation and handling, and the like, using the connection index, deterioration index, upper limits of load and fatigue coefficient, and the gasket connection reliability LOF index. The output unit 255 outputs the measurement values, virtual sensor values, connection indexes, degradation indexes, gasket connection reliability LOF indexes, upper limits of loads and fatigue coefficients, information on operating ranges and limitations, etc. to a display device, electronic file, etc.

[0024] FIG. 3 shows an example of processing performed by the data collection and selection unit 200 according to the embodiment. The time history data of the measured values ​​by the actual sensors 101 and 107 stored in the operation history database 201 is acquired through the interface 202, and data selection and qualification are performed (304). Data containing missing data or outliers is judged as unqualified. For data judged as unqualified, the measured values ​​by the actual sensors 101 and 107 are estimated using a predetermined data creation model. The data creation model is a model constructed by learning data judged as qualified. For example, the data creation model estimates the true value of the unqualified part from the data before and after the unqualified part in a chronological order. The data creation model creates the true value of the unqualified data (306). The data judged as qualified and the corrected qualified data judged as unqualified and created by the data creation model are output to the simulation result comparison unit 211 and the simulation model 213 (309). The data collection and selection unit 200 performs an IOW integrated display of the qualified data (the data judged as qualified and the data corrected by the data creation model) (311). For example, information indicating whether the temperature, pressure, flow rate, etc. measured by the actual sensors 101, 107 are within appropriate ranges is displayed on a display device or the like. Furthermore, the data collection and selection unit 200 displays the values ​​of the temperature, pressure, flow rate, etc. and various messages (for example, "The fluid condition is normal") on the display device (313). The information to be displayed may be selectable by the user.

[0025] FIG. 4 shows an example of processing by the simulation unit 210 according to the embodiment. The simulation unit 210 performs a process simulation (400) that includes processing by the simulation model 213 and the simulation result comparison unit 211 described in FIG. 2. In the process simulation (400), information about the specifications of the gasket connection 108 is obtained from the gasket connection database 203, and the process of the piping system 1 is simulated. The simulation results are then output (402). Next, virtual sensor values ​​measured by the virtual sensors 131 and 141 are extracted from the simulation results, and quality analysis is performed (404). For example, the virtual sensor values ​​measured by the virtual sensors 131 and 141 are screened to remove and correct missing or outlying values. The corrected, qualified virtual sensor values ​​are sent to the virtual sensor ML / AI model creation process (408). Meanwhile, engineering parameters are created from the simulation results (405). The engineering parameters include fluid physical properties (gas / liquid ratio, density, viscosity, specific heat, etc.) and chemical composition (H2O, CO2, N2, hydrocarbons, etc.). In the virtual sensor ML / AI model creation (408), a virtual sensor ML / AI model is created by learning the simulation results from the process simulation (400), engineering parameters, and virtual sensor values. For example, the virtual sensor model is an ML / AI model constructed by learning the relationship between some parameters (e.g., state quantities at the positions of the real sensors 101 and 107) from the simulation results from the process simulation (400) and the engineering parameters (405) and the virtual sensor values ​​(404) from the virtual sensors 131 and 141. This ML / AI model is the virtual sensor ML / AI model (1) of the third model described above. Once the virtual sensor ML / AI model is created (408), the created virtual sensor ML / AI model can quickly and at low cost (low load) calculate operating conditions (e.g., virtual sensor values ​​from the virtual sensors 131 and 141) and bulk properties (engineering parameters such as speed and density) near the gasket connection portion 108. The simulation unit 210 performs IOW integrated display of the virtual sensor values ​​of the virtual sensors 131 and 141 calculated by the virtual sensor ML / AI model (411).For example, information indicating whether virtual sensor values ​​such as temperature, pressure, and flow rate are within appropriate ranges is displayed on a display device or the like. Furthermore, the simulation unit 210 displays (413) virtual sensor values ​​such as temperature, pressure, and flow rate, and various messages (e.g., "The fluid state is normal") on a display device. The information to be displayed may be selectable by the user. The virtual sensor ML / AI model creation (408) may be performed repeatedly as training data is accumulated to achieve sufficient accuracy. Although only one gasket connection 108 is illustrated in FIG. 1, if the piping system 1 includes multiple gasket connection portions, a virtual sensor model is created for each gasket connection portion.

[0026] FIG. 5 is a diagram illustrating an example of processing by the load analysis unit 221 according to the embodiment. In the gasket connection material analysis (501), the material of the gasket 104 is analyzed. For example, the gasket 104 is classified into one of three material categories: non-metallic (soft) gasket, semi-metallic gasket, and metallic gasket. In the gasket connection load analysis (503), the load applied to the gasket connection 108 is analyzed using FEA and / or RBFEA. In the gasket connection shape analysis (505), the connection shape and connection style of the gasket connection 108 are analyzed. For example, the connection shape category of the gasket connection 108 is determined based on the positional relationship of the connected pipes. In the gasket connection CFD simulation (507), the results of the material analysis, load analysis, and shape analysis are obtained and a CFD simulation is performed. Parameters that affect the gasket 104 include the following: These include operating conditions (temperature, pressure, flow rate), fluid properties (bulk properties and chemical composition), connection compression coefficient (tension pressure), allowable gasket leakage rate and leakage volume (leak coefficient), bolt torque, friction, and force (restitution coefficient), and laboratory analysis and limits for gasket material fatigue (reduction and degradation coefficients). The gasket connection CFD simulation (507) collects all of the above information based on the gasket 104 material, gasket connection 108 connection geometry, operating conditions, and loading state, and then performs a CFD analysis to determine the bolt tightening compression pressure range and boundaries based on various types of gasket materials and connection geometries. The load ranges and upper limits obtained from the CFD simulation (507) are registered in the boundary and limit value library 509. The gasket connection CFD simulation (507) is performed on numerous case studies.

[0027] The load analysis (502, 507) is performed from the perspectives of (a) load state, (b) load conditions, and (c) load history. (a) Load state refers to the compression level of the gasket due to flange tightening. The load state is steady and does not change rapidly over time. (b) Load conditions are determined by the relationship between the operating conditions of the gasket and the internal fluid and fluid properties such as corrosion and flow patterns, and change over time; therefore, they must be analyzed as dynamic behavior. (c) Load history refers to historical information, such as test data for various gasket materials and case studies of loading conditions that may be relevant to different ranges of load state analysis. (a) Load state analysis involves using FEA and / or RBFEA to determine the loads and tensions on the gasket. This generates parameters for determining gasket fatigue conditions, leakage conditions, and degradation conditions due to environmental conditions such as internal fluid conditions. (b) Load condition analysis involves performing simulations to calculate the loads on the gasket, taking into account changes in operating conditions such as fluid properties, fluid behavior and patterns, pressure, and temperature. Temperature changes affect gasket material life parameters such as degradation coefficient and reduction coefficient. For example, operation at high temperature and pressure may cause gaskets to deteriorate faster than during stable operation, and the same is true when the fluid contains corrosive compounds or high-hardness sand / solid particles. In the load condition analysis, we analyze the damage to the gasket based on the fluid properties and behavior. In (c) load history, we observe and compare long-term changes in the load state and loading conditions based on the time history data of the load state and loading conditions.

[0028] In load ML / AI model creation (511), the results of the analyses by FEA, RBFEA, and CDF are learned, and an ML / AI model is created that outputs the restitution coefficient, leakage coefficient, boundary and limit values ​​of load, etc. when, for example, the material of the gasket 104, the operating conditions of the fluid passing through the gasket connection 108, the fluid properties, the connection compression coefficient, the tightening torque, etc. are input. The model created is the load ML / AI model for predicting the restitution coefficient, leakage coefficient, etc. in the third model (2). Next, the restitution coefficient and leakage coefficient calculated using the load ML / AI model are compiled into a database or library (for example, load index library 513). The load index library 513 stores and associates the gasket material (metal, non-metal, semi-metal), size, connection geometry of the gasket connection portion 108, operating conditions (temperature, pressure, flow rate, etc.), fluid properties (bulk properties and chemical composition), connection compression coefficient (tension pressure), allowable gasket leakage rate, leakage coefficient (leakage amount), bolt torque, friction, and restitution coefficient (repulsive force). The load analysis unit 221 performs IOW integrated output (515) for the values ​​registered in the load index library 513. For example, information indicating the allowable ranges of the connection compression coefficient, leakage rate, leakage coefficient, torque, friction, and restitution coefficient is displayed on a display device or the like. Furthermore, the load analysis unit 221 displays the values ​​of the restitution coefficient, leakage coefficient, etc., as well as various messages (for example, "The restitution coefficient is normal") on a display device (517).

[0029] FIG. 6 is a diagram showing an example of processing by the fatigue analysis unit 223 according to the embodiment. The fatigue analysis unit 223 acquires the load analysis results (601) from the load analysis unit 221, information (603) on the type of gasket material (metal, non-metal, or semi-metal), the gasket pressure (load) limit value (605) from the boundary value / limit value library 509 or the load index library 513, and the gasket temperature limit value (607) and degradation limit value (609) of the gasket material from the gasket connection database 203. The CFD comprehensive simulation (611) performs fluid analysis based on this data to calculate the fatigue state of the gasket material. In the CFD comprehensive simulation, a fatigue ML / AI model is constructed to calculate the fatigue state and fatigue limit of the gasket material, and calculations are performed using this model. This ML / AI model is constructed by learning the relationships between parameters such as the gasket material, operating conditions, load, and temperature profile and various parameters related to the fatigue of the gasket material, such as the reduction coefficient, degradation coefficient, and fatigue coefficient, based on laboratory test results and load analysis results. This ML / AI model is a fatigue ML / AI model for predicting (3) the degradation coefficient, reduction coefficient, fatigue limit, and safe operating range in the third model. In database creation (613), the calculation results of the fatigue state of the gasket material obtained by the CFD comprehensive simulation (611) are compiled into a database or library. This database or library records the load state of the gasket 104, the temperature profile (e.g., time history data of the fluid temperature), the fluid bulk properties, the restitution coefficient, the leakage coefficient, the material fatigue limit, the reduction coefficient, and the degradation coefficient in association with each other. In index creation (615), degradation indices (reduction coefficient, degradation coefficient) are created (615).

[0030] Next, the evaluation unit 250 (reliability evaluation unit 251) creates a reliability ML / AI model that calculates a gasket connection reliability LOF index (617). The input parameters for creating this ML / AI model include the operating conditions, gasket load analysis results, material category and fatigue analysis, gasket pressure boundary (load limit), temperature boundary (temperature limit), degradation coefficient, reduction coefficient, restitution coefficient, and leakage coefficient, all of which were obtained in the previous processes. The evaluation unit 250 creates a reliability ML / AI model that calculates a gasket connection reliability LOF index value between 0 and 100 when the above input parameters are given, with 100 representing a state in which parameters such as the load applied to the gasket, fluid temperature, degradation coefficient, reduction coefficient, restitution coefficient, and leakage coefficient exceed (or are close to exceeding) their limit values. A larger gasket connection reliability LOF index value indicates a higher probability of failure. This reliability ML / AI model is the fourth model described above. After creating the reliability ML / AI model, the evaluation unit 250 (reliability evaluation unit 251) calculates the gasket connection reliability LOF index for three gasket materials and a wide range of operating conditions, and stores the calculation results as a reliability LOF index library (619). The evaluation unit 250 performs an IOW integrated display based on the reliability LOF index library (621). The evaluation unit 250 outputs the gasket connection reliability LOF index limits, reduction coefficients, leakage coefficients, warning messages, etc. to a display device or the like (623).

[0031] FIG. 7 is a diagram illustrating an example of processing by the reliability evaluation unit according to the embodiment. The evaluation unit 250 acquires the analysis results of the gasket indices (701). The gasket indices include the reduction coefficient, the restitution coefficient, the degradation coefficient, the leakage coefficient, and the gasket connection reliability LOF index value. The evaluation unit 250 acquires the analysis results of the virtual process state (703). The evaluation unit 250 acquires the virtual sensor values ​​from the simulation unit 210. The evaluation unit 250 acquires the load analysis results for the gasket connection 108 from the load analysis unit 221 (705). The evaluation unit 250 acquires the limit value of gasket material degradation from the fatigue analysis unit 223 (707). For example, the evaluation unit 250 acquires the material fatigue limit value calculated by the CFD comprehensive simulation (611). The evaluation unit 250 performs an integrated evaluation according to the priority (709). For example, if there are multiple gasket connections 108, the evaluation unit 250 selects the gasket connection 108 with the highest priority from among the multiple gasket connections and evaluates the reliability of the selected gasket connection. For example, if there are three high-priority gasket connections, 108A to 108C, and the gasket connection reliability LOF index value of gasket connection 108A is A, the gasket connection reliability LOF index value of gasket connection 108B is B, and the gasket connection reliability LOF index value of gasket connection 108C is C, evaluation unit 250 performs an integrated evaluation of the high-priority gasket connections by calculating the gasket connection reliability LOF index value by (1-(1-α×A / 100)×(1-β×B / 100)×(1-γ×C / 100). α, β, and γ are weightings. The gasket connection reliability LOF index value of the gasket connection can be calculated using the series model shown in Figure 7B. In system 1A, gasket connections 108A to 108C are connected in series. If any of gasket connections 108A to 108C fails, system 1A will stop operating. Assuming that the gasket connection reliability LOF index value is similar to the failure rate, the probability that this system 1A will operate without failure can be calculated as (1-A / 100) x (1-B / 100) x (1-C / 100). The failure rate of system 1A can be calculated as 1-(1-A / 100) x (1-B / 100) x (1-C / 100).Considering that the gasket connection reliability LOF index value has the same meaning as the failure rate, the gasket connection reliability LOF index value can be calculated using the above formula, taking into account the weighting of the gasket connections 108A to 108C. A greater weighting can be assigned to important gasket connections 108. If the gasket connection 108A is important (e.g., if failure of the gasket connection 108A causes more serious damage), the failure rate of 108A can be estimated higher to ensure greater safety. In this case, a greater weighting can be assigned to the gasket connection 108. Next, the evaluation unit 250 performs a multiple integrated display (711). As a result of the integrated evaluation according to priority, the evaluation unit 250 displays a list of gasket connections 108 that are in a critical state (e.g., have a large gasket connection reliability LOF evaluation value) at the top of the list, and displays the cause of this. An alert may also be displayed to attract the operator's attention. Next, the evaluation unit 250 displays the analysis results and gasket connection reliability LOF index value thus far as information indicating the state of the gasket connection unit 108 based on the digital twin (713). Various displays, including maps showing the state of the gasket connection unit 108, are performed. For example, an operating range map of the gasket connection reliability LOF index value indicating the stable or unstable range, a predicted map of the gasket connection reliability LOF index value, a configuration diagram of the area around the gasket connection unit 108 displaying measured values ​​from actual sensors and virtual sensor values ​​from virtual sensors, alerts, analysis results and locations related to major faults, etc. are displayed. Furthermore, the evaluation unit 250 works in conjunction with a cloud application for monitoring the piping system 1, and outputs information obtained from the analysis so far to the cloud application (715).

[0032] As described above, the data collection and selection unit 200 identifies the gasket connection 108 to be evaluated and selects physical measurement information and time history data for that system from the operation history database 201. If the data does not exist or cannot be used, the simulation unit 210 calculates virtual sensor values ​​using a virtual sensor. To determine the engineering parameters required for the gasket connection 108, it is necessary to detect the operating conditions of the fluid near the gasket connection 108. However, since measurement values ​​of the gasket connection 108 in an actual facility are often not available, the simulation unit 210 calculates the state quantities and their time history data near the gasket connection 108. In this case, processing costs can be reduced by using a virtual sensor model (ML / AI model). The virtual sensor values ​​calculated by the virtual sensor model may be registered in the operation history database 201.

[0033] Once time history data on state quantities near the gasket connection 108 is obtained, load analysis and fatigue analysis of the gasket material are performed. For example, gasket materials are selected based on representative material categories, and FEA / RBFEA / CFD simulations are performed for a series of case studies. The FEA / RBFEA simulation simulates steady-state conditions and determines boundary conditions such as upper load (pressure) limits. The CFD simulation performs dynamic simulations to calculate the effects of operating conditions on the gasket 104. Simulations are performed under various conditions, varying operating conditions and other environmental factors, to calculate the degradation coefficient, reduction coefficient, rebound coefficient, leakage coefficient, and gasket connection reliability LOF index. The degradation coefficient and reduction coefficient are related to the gasket material, while the rebound coefficient and leakage coefficient are related to the connection. The gasket connection reliability LOF index value is a factor that predicts the overall health state of the gasket connection 108.

[0034] Through analysis of the load on the gasket connection 108 and analysis of the fatigue state of the gasket material based on FEA / RBFEA / CFD simulations, two ML / AI models, a database or library for gasket connection boundary conditions such as the restitution coefficient, leakage coefficient, and load limit value, a database or library for degradation coefficients, reduction coefficients, and fatigue limit values, and a reliability ML / AI model are created. Once these models, databases, and libraries are created, online monitoring can use the various models, databases, and libraries to quickly evaluate the reliability of the gasket connection 108 being monitored with low processing cost. To maintain the accuracy of the various models, libraries, etc., the various models, databases, and libraries may be updated periodically or irregularly.

[0035] In online monitoring, the operating conditions near the gasket connection 108, gasket load analysis results, material category and fatigue analysis results, gasket pressure boundary (load limit), temperature boundary (temperature limit), degradation coefficient, reduction coefficient, restitution coefficient, and leakage coefficient are calculated based on the history of state quantities near the gasket connection 108 obtained using a virtual sensor model, various ML / AI models, databases, and libraries. These parameters are then input into a reliability ML / AI model to calculate the gasket connection reliability LOF index, which indicates the comprehensive soundness of the gasket connection 108. By referring to the gasket connection reliability LOF index, operators can understand the current soundness of the gasket connection 108 based on the history to date.

[0036] The simulation unit 210 makes it possible to predict the future behavior and state of the piping system 1. For example, future operating conditions and the like are given as parameters, and the simulation unit 210 is caused to calculate various state quantities near the gasket connection part 108 for a predetermined period in the future. Then, the analysis unit 220 predicts the deterioration coefficient, reduction coefficient, restitution coefficient, leakage coefficient, and the like after the predetermined period based on the predicted values ​​of the state quantities, and calculates a predicted value of the gasket connection reliability LOF index. This makes it possible to predict the future state and reliability of the gasket connection part 108.

[0037] FIG. 8 is a diagram illustrating an example of a change over time in the evaluation index according to the embodiment. The vertical axis of the graph in FIG. 8 represents the magnitude of the restitution coefficient and leakage coefficient, and the horizontal axis represents time. As described above, the simulation unit 210 can predict the future behavior and state of the piping system 1, thereby predicting the future restitution coefficient and leakage coefficient. Graph 81 in FIG. 8 shows the change in the restitution coefficient over a predetermined period from the past to the future. Graph 82 shows the change in the leakage coefficient over a predetermined period from the past to the future. The restitution coefficient and leakage coefficient values ​​in graphs 81 and 82 can be calculated based on a load ML / AI model. Time t1 represents the present. Time t3, where the restitution coefficient and leakage coefficient intersect, represents a limit point, and is the time when failure of the gasket connection 108 is predicted. The output unit 255 sets time t2, which is before time t3, as a period during which safe operation is possible, and displays the graph shown in FIG. 8. This allows the operator to understand that safe operation is possible from the present (time t1) to time t2 from the perspective of the restitution coefficient and leakage coefficient.

[0038] FIG. 9 is a diagram illustrating an example of a change in the evaluation index with temperature according to the embodiment. The vertical axis of the graph in FIG. 9 represents the magnitude of the reduction coefficient and the deterioration coefficient, and the horizontal axis represents temperature. The simulation unit 210 can predict the behavior and state of the piping system 1 by changing the temperature conditions of the fluid, thereby estimating the reduction coefficient and the deterioration coefficient at various temperatures. Graphs 91a to 91c in FIG. 9 show the transition of the reduction coefficient at various temperatures. The existence of three graphs is due to the fact that the reduction coefficient was calculated under different conditions other than temperature. The cross marks in the graphs represent the reduction coefficient values ​​calculated under actual environmental conditions. The reduction coefficient and deterioration coefficient values ​​of graphs 91a to 91c and 92 can be calculated based on a fatigue ML / AI model. The output unit 255 displays graphs 91a to 91c and 92. Since graph 91a shows the transition closest to the distribution of cross marks, the operator can understand the transition of the reduction coefficient according to temperature using graph 91a. Based on graph 91a, the operator can understand the temperature range in which the reduction coefficient does not decrease. Based on graph 92, the operator can understand the temperature range in which the deterioration coefficient does not increase. The operator can understand that safe operation is possible at temperatures below, for example, temperature T1, which is the temperature range in which the reduction coefficient does not decrease and the deterioration coefficient does not increase.

[0039] FIG. 10 is a diagram showing an example of a change over time in the gasket connection reliability LOF index value according to the embodiment. The vertical axis of the graph in FIG. 10 represents the magnitude of the gasket connection reliability LOF index value, and the horizontal axis represents time. The simulation unit 210 can predict the gasket connection reliability LOF index value by predicting the future behavior and state of the piping system 1. Graph 11 in FIG. 10 shows the transition of the gasket connection reliability LOF index value over a predetermined period from the past to the future. The gasket connection reliability LOF index value in Graph 11 can be calculated based on a reliability ML / AI model. The output unit 255 displays the graph illustrated in FIG. 10. Time t1 represents the present, and time t2 represents the time when the gasket connection reliability LOF index value reaches the safety limit. The operator can grasp the time when the reliability of the gasket connection 108 will deteriorate.

[0040] FIG. 11 shows the flow of the load analysis process. FIG. 11 is a flowchart showing an example of the load analysis process according to the embodiment. The load analysis unit 221 performs a load analysis of the gasket connection portion 108 in the following procedure: The material category of the gasket 104 is determined (step S1). Next, the connection shape category of the gasket connection portion 108 is determined (step S2). For example, the connection structure of the piping is determined. Next, the connection load mechanism of the gasket connection portion 108 is determined (step S3). For example, the mechanism by which a load is applied to the gasket connection portion 108 from the connection structure of the piping is determined. Next, FEA / RBFEA structural load analysis and / or CFD dynamic simulation is performed (step S4). Simulations are performed for multiple case studies to determine load boundaries (load ranges) and limits (step S5). It is determined whether the simulations are complete (step S6). If not, the process from step S4 is repeated. If multiple simulations are complete, a load ML / AL model is created (step S7). A database and library in which connection coefficients and load analysis results are registered are created (step S8).

[0041] A flow of a process (reliability evaluation process) for calculating a gasket connection reliability LOF index value is shown in Fig. 12. Fig. 12 is a flowchart showing an example of the reliability evaluation process according to the embodiment. The data collection and selection unit 200 determines the location of the gasket connection 108 (step S11). The data collection and selection unit 200 identifies the gasket connection 108 to be evaluated. Next, the data collection and selection unit 200 determines the physical process conditions of the gasket connection 108 (step S12). For example, the data collection and selection unit 200 acquires measurement values ​​measured by the physical real sensors 101 and 107. Next, the simulation unit 210 determines virtual sensor values ​​of the gasket connection 108 (step S13). For example, the simulation unit 210 calculates virtual sensor values ​​of the virtual sensors 131 and 141. Next, the simulation unit 210 determines the operating conditions and bulk properties of the fluid near the gasket connection 108 (step S14). Next, the analysis unit 220 determines the material category of the gasket 104 (step S15). Next, the analysis unit 220 determines the degradation coefficient (step S16). Next, the analysis unit 220 determines a reduction coefficient (step S17). Next, the analysis unit 220 determines a leakage coefficient (step S18). Next, the analysis unit 220 determines a restitution coefficient (step S19). Next, the evaluation unit 250 creates a reliability ML / AI model (step S20). Next, the evaluation unit 250 determines a fatigue coefficient based on the reliability ML / AI model (step S21). Next, the evaluation unit 250 determines a gasket connection reliability LOF index value of the gasket connection portion 108 to be evaluated based on the reliability ML / AI model (step S22). It is determined whether the reliability evaluation process is completed (step S23). If not completed, the process from step S20 is repeated. If the reliability evaluation process is completed, a database or library in which the gasket connection reliability LOF index value and fatigue coefficient are registered is created (step S24).

[0042] Load ML / AI model. The fatigue ML / AI model and reliability ML / AI model may be configured to input, as input parameters, data obtained by collecting samples and analyzing them in a laboratory (lab testing data) for data that is difficult to obtain online, in addition to the measurement values ​​of the actual sensors 101 and 107 and the virtual sensor values. This allows for more accurate predictions to be output. Furthermore, the output unit 255 may be configured to output, in cooperation with a monitoring system that monitors a plant including the piping system 1, the gasket connection reliability LOF index value, reduction coefficient, deterioration coefficient, leakage coefficient, restitution coefficient, fatigue coefficient, graphs such as those shown in Figures 8 to 10, and the like, to the monitoring system. The monitoring system may display the information output from the gasket connection reliability evaluation system 2 directly on a monitoring screen, or may use the gasket connection reliability LOF index value and the like to perform fault diagnosis of the entire plant 3 and evaluate the productivity and economic efficiency of products produced in the plant 3.

[0043] FIG. 13 is a diagram showing an example of the hardware configuration of the gasket connection reliability evaluation system 2 according to each embodiment. The computer 900 includes a CPU 901 , a main memory device 902 , an auxiliary memory device 903 , an input / output interface 904 , and a communication interface 905 . The gasket connection reliability evaluation system 2 described above is implemented in a computer 900. Each of the above-described functions is stored in the form of a program in an auxiliary storage device 903. A CPU 901 reads the program from the auxiliary storage device 903, loads it into a main storage device 902, and executes the above-described processing in accordance with the program. The CPU 901 allocates a storage area in the main storage device 902 in accordance with the program. The CPU 901 allocates a storage area in the auxiliary storage device 903 for storing data being processed in accordance with the program.

[0044] A program for implementing all or part of the functions of the gasket connection reliability evaluation system 2 may be recorded on a computer-readable recording medium, and the program may be loaded into a computer system and executed to perform processing by each functional unit. Here, the term "computer system" includes hardware such as an OS and peripheral devices. If a WWW system is used, the term "computer system" also includes the homepage provision environment (or display environment). A "computer-readable recording medium" refers to portable media such as CDs, DVDs, and USBs, as well as storage devices such as hard disks built into the computer system. If the program is distributed to the computer 900 via a communication line, the computer 900 may load the program into the main storage device 902 and execute the processing described above. The program may be for implementing part of the functions described above, or may be capable of implementing the functions described above in combination with a program already stored in the computer system.

[0045] As described above, several embodiments according to the present disclosure have been described, but all of these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as defined in the claims, as well as in the scope and spirit of the invention.

[0046] <Additional Notes> The gasket connection reliability evaluation system, the gasket connection reliability evaluation method, and the program described in each embodiment can be understood, for example, as follows.

[0047] (1) A gasket connection reliability evaluation system according to a first aspect includes a measurement value acquisition unit that acquires measurement values ​​measured by a sensor provided in a system including a pipe connection having a gasket; a simulation unit that simulates the behavior of a fluid flowing through the system based on the measurement values ​​and calculates a state quantity related to the fluid; a load analysis unit that analyzes the load applied to the gasket based on the measurement values ​​and the state quantity and calculates a connection index of the gasket; a fatigue analysis unit that analyzes the fatigue state of the gasket based on the measurement values, the state quantity, the load applied to the gasket, and the specifications and state of the gasket connection indicating the gasket and the pipe connection (flanges 103, 105) and calculates a degradation index of the gasket; and a reliability evaluation unit that calculates the reliability of the gasket connection based on the measurement values, the state quantity, the connection index, the degradation index, and a predetermined reliability evaluation model. This allows the reliability of the gasket connection portion 108 to be evaluated.

[0048] (2) A gasket connection reliability evaluation system according to a second aspect is a gasket connection reliability evaluation system according to (1), in which the reliability evaluation unit evaluates the reliability of the system based on the reliability of each of the gaskets included in the system. This makes it possible to evaluate the reliability of the gasket connections in the entire piping system, even if the piping system includes multiple gasket connections.

[0049] (3) A gasket connection reliability evaluation system according to a third aspect is a gasket connection reliability evaluation system according to (1) to (2), wherein the measured values ​​and the state quantities include at least one of the operating conditions, physical properties, and chemical composition of the fluid flowing through the system. Based on the operating conditions, physical properties, chemical composition, etc. of the fluid near the gasket connection, it is possible to perform load analysis and fatigue analysis on the gasket connection.

[0050] (4) A gasket connection reliability evaluation system according to a fourth aspect is a gasket connection reliability evaluation system according to any one of (1) to (3), wherein the operating conditions include at least one of temperature, pressure, and flow rate, the physical properties include at least one of gas / liquid ratio, density, viscosity, and specific heat, and the chemical composition includes at least one of H2O, CO2, N2, hydrocarbons, sand, and other impurities. Taking these factors into consideration, the state quantities (virtual sensor values) in the vicinity of the gasket connection can be estimated, and analysis of the load and fatigue applied to the gasket connection can be performed.

[0051] (5) A gasket connection reliability evaluation system according to a fifth aspect is the gasket connection reliability evaluation system according to any one of (1) to (4), wherein the connection index is the restitution coefficient and leakage coefficient of the gasket. This makes it possible to evaluate the reliability and soundness of the gasket connection.

[0052] (6) A gasket connection reliability evaluation system according to a sixth aspect is the gasket connection reliability evaluation system according to any one of (1) to (5), wherein the deterioration index is a deterioration coefficient and a reduction coefficient of the gasket. This makes it possible to evaluate the reliability and soundness of the gasket material.

[0053] (7) A gasket connection reliability evaluation system according to a seventh aspect is a gasket connection reliability evaluation system according to any one of (1) to (6), wherein the reliability evaluation unit further evaluates the operable period based on the time series change in the connection index. As shown in FIG. 8, the period during which the vehicle can be driven can be evaluated based on the transition of the connection index over time.

[0054] (8) The gasket connection reliability evaluation system according to the eighth aspect is a gasket connection reliability evaluation system according to any one of (1) to (7), wherein the reliability evaluation unit further evaluates the usable temperature range based on the deterioration index for each temperature. As shown in FIG. 9, the usable temperature range can be evaluated based on the temperature transition of the deterioration index.

[0055] (9) A gasket connection reliability evaluation system according to a ninth aspect is a gasket connection reliability evaluation system according to any one of (1) to (8), further comprising an output unit that outputs at least one of the state quantity calculated by the simulation unit, the connection index calculated by the load analysis unit, the degradation index calculated by the fatigue analysis unit, and the reliability calculated by the reliability evaluation unit. This allows the current state of the gasket connection to be monitored. For example, if any of the connection index, degradation index, or reliability evaluation results is approaching a critical point, the gasket replacement can be considered, preventing accidents due to fluid leakage, etc.

[0056] (10) A gasket connection reliability evaluation system according to a tenth aspect is a gasket connection reliability evaluation system according to any one of (1) to (9), wherein the simulation unit calculates the state quantities at a position upstream or downstream of the gasket where a sensor cannot be installed. Monitoring gasket connections requires detecting the state quantities of the fluid near the gasket connections, but in actual equipment, it is often not possible to measure using sensors near the gasket connections.By calculating the state quantities near the gasket connections using the simulation unit, it becomes possible to evaluate and monitor the state of the gasket connections.

[0057] (11) A gasket connection reliability evaluation system according to an eleventh aspect is a gasket connection reliability evaluation system according to any one of (1) to (10), wherein the simulation unit predicts the state quantity, the load analysis unit predicts the connection index based on the predicted value of the state quantity, the fatigue analysis unit predicts the degradation index based on the predicted value of the state quantity, and the reliability evaluation unit calculates a predicted value of the reliability of the gasket based on the predicted value of the state quantity, the predicted value of the connection index, the predicted value of the degradation index, and the reliability evaluation model. This makes it possible to predict the future state of the gasket connection and to know in advance when the gasket connection will become dangerous.

[0058] (12) A gasket connection reliability evaluation method according to a twelfth aspect includes the steps of acquiring measurement values ​​measured by a sensor provided in a system including a pipe connection having a gasket; simulating the behavior of a fluid flowing through the system based on the measurement values ​​and calculating a state quantity related to the fluid; analyzing the load applied to the gasket based on the measurement values ​​and the state quantity and calculating a connection index of the gasket; analyzing the fatigue state of the gasket based on the measurement values, the state quantity, the load applied to the gasket, and the specifications and state of the gasket connection indicating the gasket and the pipe connection and calculating a degradation index of the gasket; and calculating the reliability of the gasket connection based on the measurement values, the state quantity, the connection index, the degradation index, and a predetermined reliability evaluation model.

[0059] (13) A program according to a thirteenth aspect causes a computer to execute the following steps: acquiring measurement values ​​measured by a sensor provided in a system including a pipe connection having a gasket; simulating the behavior of a fluid flowing through the system based on the measurement values ​​and calculating a state quantity related to the fluid; analyzing the load applied to the gasket based on the measurement values ​​and the state quantity and calculating a connection index of the gasket; analyzing the fatigue state of the gasket based on the measurement values, the state quantity, the load applied to the gasket, and specifications and states of a gasket connection indicating the gasket and the pipe connection, and calculating a degradation index of the gasket; and calculating the reliability of the gasket connection based on the measurement values, the state quantity, the connection index, the degradation index, and a predetermined reliability evaluation model. [Explanation of symbols]

[0060] 1. Piping system 102 Piping 103 Flange 104 Gasket 105···Flange 106 Piping 101 Actual sensor 131 Virtual Sensor 141 Virtual Sensor 107 Actual sensor 2. Gasket joint reliability evaluation system 200 Data collection and selection section 201···Driving history database 203 Gasket Connection Database 210 Simulation Department 211···Simulation result comparison section 213···Simulation Model 220...Analysis Department 221...Load analysis department 223···Fatigue Analysis Section 250...Evaluation section 251···Reliability Evaluation Section 253···IOW Information Integration Department 255... Output section 900···Computer 901 CPU 902...Main memory 903...Auxiliary storage device 904 Input / Output Interface 905···Communication Interface

Claims

1. a measurement value acquiring unit that acquires a measurement value measured by a sensor provided in a system including a pipe connection portion equipped with a gasket; a simulation unit that simulates the behavior of the fluid flowing through the system based on the measurement values ​​and calculates a state quantity related to the fluid; a load analysis unit that analyzes a load applied to the gasket based on the measurement value and the state quantity and calculates a connection index of the gasket; a fatigue analysis unit that analyzes a fatigue state of the gasket based on the measurement value, the state quantity, the load applied to the gasket, and specifications and states of a gasket connection portion that indicates the gasket and the pipe connection portion, and calculates a degradation index of the gasket; a reliability evaluation unit that calculates the reliability of the gasket connection based on the measurement value, the state quantity, the connection index, the degradation index, and a predetermined reliability evaluation model; A gasket connection reliability evaluation system comprising:

2. the reliability evaluation unit evaluates the reliability of the system based on the reliability of each of the gaskets included in the system. The gasket connection reliability evaluation system according to claim 1 .

3. The measured values ​​and the state quantities include at least one of an operating condition, a physical property, and a chemical composition of a fluid flowing through the system. The gasket connection reliability evaluation system according to claim 1 .

4. the operating conditions include at least one of temperature, pressure, and flow rate; The physical properties include at least one of gas / liquid ratio, density, viscosity, and specific heat; The chemical composition includes at least one of H2O, CO2, N2, hydrocarbons, sand, and other impurities. The gasket connection reliability evaluation system according to claim 3 .

5. The connection index is the coefficient of restitution and the leakage coefficient of the gasket. The gasket connection reliability evaluation system according to claim 1 .

6. The deterioration index is a deterioration coefficient and a reduction coefficient of the gasket. The gasket connection reliability evaluation system according to claim 1 .

7. The reliability evaluation unit further evaluates a period during which the vehicle can be driven based on a transition of the connection index over time. The gasket connection reliability evaluation system according to claim 1 .

8. the reliability evaluation unit further evaluates a usable temperature range based on the deterioration index for each temperature. The gasket connection reliability evaluation system according to claim 1 .

9. an output unit that outputs at least one of the state quantity calculated by the simulation unit, the connection index calculated by the load analysis unit, the degradation index calculated by the fatigue analysis unit, and the reliability calculated by the reliability evaluation unit; The gasket connection reliability evaluation system according to claim 1 , further comprising:

10. the simulation unit calculates the state quantity at a position upstream or downstream of the gasket where a sensor cannot be provided. The gasket connection reliability evaluation system according to claim 1 .

11. the simulation unit predicts the state quantity, the load analysis unit predicts the connection index based on the predicted value of the state quantity; the fatigue analysis unit predicts the deterioration index based on the predicted value of the state quantity; the reliability evaluation unit calculates a predicted value of the reliability of the gasket based on the predicted value of the state quantity, the predicted value of the connection index, the predicted value of the degradation index, and the reliability evaluation model. The gasket connection reliability evaluation system according to claim 1 .

12. obtaining measurements taken by a sensor in a system including a pipe connection with a gasket; simulating the behavior of the fluid flowing through the system based on the measured values ​​and calculating a state quantity related to the fluid; a step of analyzing a load applied to the gasket based on the measurement value and the state quantity, and calculating a connection index of the gasket; a step of analyzing a fatigue state of the gasket based on the measurement value, the state quantity, the load applied to the gasket, and specifications and states of the gasket and a gasket connection portion indicating the pipe connection portion, and calculating a degradation index of the gasket; calculating the reliability of the gasket connection based on the measurement value, the state quantity, the connection index, the degradation index, and a predetermined reliability evaluation model; A gasket connection reliability evaluation method comprising:

13. On the computer, obtaining measurements taken by a sensor in a system including a pipe connection with a gasket; simulating the behavior of the fluid flowing through the system based on the measured values ​​and calculating a state quantity related to the fluid; a step of analyzing a load applied to the gasket based on the measurement value and the state quantity, and calculating a connection index of the gasket; a step of analyzing a fatigue state of the gasket based on the measurement value, the state quantity, the load applied to the gasket, and specifications and states of the gasket and a gasket connection portion indicating the pipe connection portion, and calculating a degradation index of the gasket; calculating the reliability of the gasket connection based on the measurement value, the state quantity, the connection index, the degradation index, and a predetermined reliability evaluation model; A program that executes the following.

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