A method and system for diagnosing and optimizing circulation faults of a deepwater blowout preventer control system

Abstract

The present invention relates to a method and system for cyclic fault diagnosis and optimization of a deepwater blowout preventer control system, comprising the following steps: using the acquired real-time physical operation state parameters and working environment state parameters as the input of a digital twin model, simulating the real-time operation mode of the deepwater blowout preventer control system under different working conditions, and outputting fault diagnosis simulation results and virtual parameters; wherein the digital twin model is established based on the historical data of the physical operation state parameters and working environment state parameters of the physical entity of the deepwater blowout preventer control system; optimizing the simulation results and virtual parameters, and optimizing the parameters of the physical entity of the deepwater blowout preventer control system according to the obtained preferred parameters, while updating the established digital twin model of the deepwater blowout preventer control system; repeating the above steps, and continuously performing model optimization and parameter optimization. The present invention can be widely used in the field of fault diagnosis of deepwater blowout preventer control systems.

Description

Technical Field

[0001] The present invention relates to the field of fault diagnosis of deepwater blowout preventer control systems, and in particular to a method and system for cyclic fault diagnosis and optimization of deepwater blowout preventer control systems based on digital twins. Background Art

[0002] The deepwater BOP group is a vital safety equipment in deepwater drilling operations. Its main function is to control the wellhead pressure when a blowout or well kick occurs, to ensure that the drilling equipment can be safely evacuated and the wellhead closed in an emergency, to ensure the safety of personnel and equipment, and to avoid damage to the marine environment and oil and gas resources. The deepwater BOP control system is the command system for the opening and closing of the BOP, and is also the core device for the underwater BOP group. Experience has shown that once a BOP control system fails, it often causes serious consequences. Therefore, accurately determining the cause of the failure and promptly troubleshooting it are crucial to ensure the safety of deepwater drilling operations.

[0003] The hydraulic control system of the deepwater blowout preventer is the core part of the entire control system, which provides direct power for the opening and closing of various blowout preventers. Its key components such as valve bodies and pipelines must not only withstand the 30Mpa high pressure from deep sea water (taking 3000m as an example), but also withstand the high pressure of internal hydraulic oil exceeding 50Mpa. The harsh internal and external environment places high demands on the pressure bearing capacity, corrosion resistance and sealing performance of the hydraulic control system. The deepwater blowout preventer control system has a complex structure, a harsh working environment, and is difficult to diagnose faults. In the existing technology, fault diagnosis of the deepwater blowout preventer control system is often performed from a certain aspect, ignoring the coupling relationship of each operating component and less considering the process of mutual influence of each component; on the other hand, the control system data is not fully collected, resulting in the inability to conduct comprehensive evaluation and fault diagnosis.

[0004] In summary, how to conduct comprehensive and effective fault diagnosis and parameter optimization for deepwater BOP control systems is an urgent problem to be solved. Summary of the invention

[0005] In view of the above problems, the purpose of the present invention is to provide a deepwater blowout preventer control system cyclic fault diagnosis and optimization method and system based on digital twin, which can realize the cyclic fault diagnosis and optimization of the deepwater blowout preventer control system through continuous cyclic iteration.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for diagnosing and optimizing circulation faults in a deepwater blowout preventer control system, comprising the following steps:

[0008] The acquired real-time physical operation state parameters and working environment state parameters are used as inputs of the digital twin model to simulate the real-time operation mode of the deepwater blowout preventer control system under different working conditions, and output fault diagnosis simulation results and virtual parameters; wherein the digital twin model is established based on the historical data of the physical operation state parameters and working environment state parameters of the physical entity of the deepwater blowout preventer control system;

[0009] Optimize the simulation results and virtual parameters, and optimize the parameters of the physical entity of the deepwater BOP control system based on the obtained optimal parameters, while updating the established digital twin model of the deepwater BOP control system;

[0010] Repeat the above steps to continuously optimize the model and parameters.

[0011] Furthermore, the establishment of the digital twin model includes:

[0012] Obtain physical operation status parameters and working environment status parameters of the physical entity of the deepwater blowout preventer control system through sensors and data modules;

[0013] According to the physical operation state parameters and working environment state parameters combined with the transfer function, the physical operation state model and fault diagnosis model of the deepwater blowout preventer control system are established;

[0014] According to the state relationship and parameter transfer relationship among the physical entity, physical operation state model and fault diagnosis model of the deepwater blowout preventer control system, a corresponding digital twin model of the deepwater blowout preventer control system is established.

[0015] Furthermore, the physical operation state model includes a physical operation state model of an electric control system and a physical operation state model of a hydraulic control system; the inputs of the physical operation state model of an electric control system and the physical operation state model of a hydraulic control system are both physical operation state parameters and working environment state parameters of a physical entity, and the outputs are respectively the physical operation state parameters of the electric control system and the physical operation state parameters of the hydraulic control system.

[0016] Further, the fault diagnosis model includes an input layer, a hidden layer and an output layer;

[0017] The input layer is used to obtain the physical operation state parameters of the physical entity, the electric control system and the hydraulic control system and the working environment state parameters of the physical entity;

[0018] The hidden layer includes a structural model and a parameter model. The structural model is used to obtain system information of the deepwater blowout preventer control system according to the software and hardware structure of the deepwater blowout preventer control system; the parameter model is used to analyze the acquired physical operation state parameters, working environment state parameters and system information to obtain the primary fault parameters of the deepwater blowout preventer control system, including the fault parameters of the electric control system and the fault parameters of the hydraulic control system.

[0019] Further, the digital twin model includes a mechanical structure model and an electro-hydraulic composite control system structure model, and the mechanical structure model and the electro-hydraulic composite control system structure model are used to output virtual parameters and simulation results according to the acquired physical operation state parameters, working environment state parameters, and first fault parameters;

[0020] The establishment of the mechanical structure model includes: first, building component models of various physical components included in the deepwater blowout preventer control system through modeling software; then, connecting and assembling the component models of various physical components so that the various physical components are connected into a mechanical system as the mechanical structure model of the deepwater blowout preventer;

[0021] The establishment of the electro-hydraulic composite control system structure model includes: first, importing the established mechanical structure model into maplesim through CADBox, setting the coordinate system and connection direction for each component and connection point of the mechanical structure, and using the mechanical elements and multi-body elements in maplesim to set the physical movement mode for the mechanical structure to realize the linkage of the mechanical system; secondly, using the hydraulic elements, electrical elements and signal modules in the maplesim component library to simulate the control mode, so as to realize the real-time operation of the deepwater blowout preventer control system driving the mechanical structure.

[0022] Further, the simulation results and virtual parameters are optimized, and the parameters of the physical entity of the deepwater blowout preventer control system are optimized according to the obtained preferred parameters, and the established digital twin model of the deepwater blowout preventer control system is updated, including:

[0023] According to the transfer function equation of the system and the working principle equations of each subsystem and component, the simulation results and virtual parameters are optimized in combination with the minimum particle algorithm to obtain the first-level optimal parameters;

[0024] The parameters of the physical entity of the deepwater blowout preventer control system are optimized according to the first-level optimal parameters, and the established digital twin model of the deepwater blowout preventer control system is updated.

[0025] Furthermore, the transfer function equation of the system and the working principle equations of each subsystem and component are combined with the minimum particle algorithm to optimize the simulation results and virtual parameters to obtain the first-level optimal parameters, including:

[0026] Based on the historical system status assessment data obtained in advance, the simulation results and virtual parameters are compared and analyzed to determine and display the operating status of the deepwater BOP control system;

[0027] According to the determined operating status of the deepwater BOP control system, the system transfer function equations and the working principle equations of the subsystems and components, the virtual parameters of the deepwater BOP control system are optimized by combining the minimum particle algorithm to obtain the first-level optimal parameters.

[0028] Furthermore, according to the determined operating state of the deepwater blowout preventer control system, the transfer function equation of the system and the working principle equations of the subsystems and components, the virtual parameters of the deepwater blowout preventer control system are optimized by combining the minimum particle algorithm to obtain the first-level optimal parameters, including:

[0029] ① Confirm and establish the objective function of the system and subsystem to be optimized, set the basic parameters of the particle swarm algorithm, and initialize the particle swarm in the feasible solution space;

[0030] ② Calculate the fitness of each particle according to the objective function, and select the local optimal solution and the group optimal solution according to the system relationship;

[0031] ③ Update the particle's speed and position according to the improved speed formula and position formula, and determine whether the speed and position are within the control boundary;

[0032] Among them, the velocity update formula and displacement update formula are:

[0033]

[0034] Among them, c1 and c2 are individual learning factors and group learning factors respectively; V is the particle velocity, X is the particle position, r1 and r2 are random numbers, is the local optimal solution, The optimal solution for the group;

[0035] ④ Update the inertia weight ω(k) according to the inertia weight formula;

[0036] Among them, the inertia weight update formula is:

[0037] ω(k)=ω start -(ω start -ω end )(T max -k) / T max

[0038] Among them, ω start is the initial inertia weight, ω end is the inertia weight when the iteration reaches the maximum number, k is the current iteration number, T maxis the maximum number of iterations;

[0039] ⑤ Determine whether the current number of iterations meets the termination condition. If the current number of iterations is greater than the maximum number of iterations m, stop the iteration and output the optimal solution; if the current number of iterations is less than the maximum number of iterations m, go to step ③ to continue searching for the optimal solution.

[0040] Further, the parameters of the physical entity of the deepwater blowout preventer control system are optimized according to the primary optimal parameters, and the established digital twin model of the deepwater blowout preventer control system is updated, including:

[0041] According to the first-level optimal parameters, the parameters of the deepwater blowout preventer control system are optimized to eliminate existing faults and deficiencies, improve the system working state, and at the same time update the physical operation state model and fault diagnosis model of the deepwater blowout preventer control system to obtain the second-level physical operation state model and the second-level fault diagnosis model;

[0042] Update the digital twin model of the deepwater BOP control system based on the state relationship and parameter transfer relationship between the secondary physical operation state model and the secondary fault diagnosis model;

[0043] The real-time physical operation state parameters of the physical entity, the working environment state parameters, and the secondary fault parameters output by the secondary fault diagnosis model are input into the updated digital twin model to obtain new virtual parameters and simulation results;

[0044] After optimizing and analyzing the new virtual parameters and simulation results, the algorithm optimization of the parameters of the deepwater BOP control system was performed again.

[0045] In a second aspect, the present invention provides a deepwater blowout preventer control system circulation fault diagnosis and optimization system, comprising:

[0046] A model building module is used to build a digital twin model of a deepwater BOP control system based on the historical data of the physical operation state parameters and working environment state parameters of the physical entity of the deepwater BOP control system;

[0047] A simulation module, which is used to use the acquired real-time physical operation state parameters and working environment state parameters as inputs of the digital twin model, simulate the real-time operation mode of the deepwater blowout preventer control system under different working conditions, and output simulation results and virtual parameters; wherein the digital twin model is established based on the historical data of the physical operation state parameters and working environment state parameters of the physical entity of the deepwater blowout preventer control system;

[0048] The parameter optimization module is used to optimize the simulation results and virtual parameters, and optimize the parameters of the physical entity of the deepwater blowout preventer control system according to the obtained optimal parameters, and update the established digital twin model of the deepwater blowout preventer control system at the same time;

[0049] The loop optimization module is used to repeatedly run the simulation module and parameter optimization module to continuously perform model optimization and parameter optimization.

[0050] Due to the adoption of the above technical scheme, the present invention has the following advantages: the present invention has the characteristics of high synchronization and high consistency between the model and the equipment, high accuracy of the diagnostic results, high effective utilization rate of data, and strong algorithm adaptability, and can achieve the purpose of real-time detection of the equipment operating status, fault diagnosis of the equipment and system optimization.

[0051] Therefore, the present invention can be widely applied to the field of fault diagnosis of deepwater blowout preventer control systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Throughout the accompanying drawings, the same reference numerals are used to represent the same components. In the accompanying drawings:

[0053] Figure 1 It is a flow chart of a method for diagnosing and optimizing a deepwater blowout preventer control system cycle faults provided by an embodiment of the present invention;

[0054] Figure 2 It is a theoretical structural block diagram of a deepwater blowout preventer control system cyclic fault diagnosis and optimization method provided by an embodiment of the present invention;

[0055] Figure 3 It is a mechanical model diagram of a deepwater double-gate blowout preventer provided by an embodiment of the present invention;

[0056] Figure 4 It is a structural model of an electro-hydraulic composite control system provided by an embodiment of the present invention;

[0057] Figure 5 The invention provides a minimum particle optimization algorithm flow chart for optimizing the parameters of a deepwater blowout preventer control system. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0059] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0060] In some embodiments of the present invention, a method for cyclic fault diagnosis and optimization of a deepwater blowout preventer control system is provided, the method comprising: obtaining physical operation state parameters and working environment state parameters of the deepwater blowout preventer control system; determining a physical operation state model and a fault diagnosis model respectively according to the physical operation state parameters and the working environment state parameters; establishing a digital twin model of the deepwater blowout preventer control system according to the parameter transfer relationship between the physical operation state model and the fault diagnosis model; using the physical operation state parameters, the working environment state parameters and the fault parameters as inputs of the digital twin model, and outputting corresponding virtual parameters; performing data processing on the virtual parameters, determining and displaying the operation state of the deepwater blowout preventer control system, and optimizing the virtual parameters with an improved particle swarm algorithm to optimize the system working state, while updating the physical operation state model and the fault diagnosis model, and performing cyclic fault diagnosis processing. The present invention uses historical data and real-time data to establish a digital twin model, realizes the evaluation and fault diagnosis of the operation state of the deepwater blowout preventer control system and its subsystems and components, thereby completing the information fusion between multiple signals, fully considering the coupling relationship between system components, and performing more accurate and comprehensive fault diagnosis analysis and system state optimization.

[0061] Correspondingly, in some other embodiments of the present invention, a deepwater blowout preventer control system circulation fault diagnosis and optimization system, equipment and storage medium are provided.

[0062] Example 1

[0063] like Figure 1 , Figure 2 As shown, this embodiment provides a deepwater blowout preventer control system circulation fault diagnosis and optimization method, which includes the following steps:

[0064] (1) Based on the historical data of the physical operation state parameters and working environment state parameters of the physical entity of the deepwater BOP control system, a digital twin model of the deepwater BOP control system is established;

[0065] (2) Using the acquired real-time physical operation status parameters and working environment status parameters as the input of the digital twin model, the real-time operation mode of the deepwater blowout preventer control system under different working conditions is simulated, and the simulation results and virtual parameters are output;

[0066] (3) optimizing the simulation results and virtual parameters, and optimizing the parameters of the physical entity of the deepwater blowout preventer control system according to the obtained optimal parameters, and updating the digital twin model of the deepwater blowout preventer control system established in step (1);

[0067] (4) Repeat steps (2) to (3) to continue model optimization and parameter selection, forming a cyclic working mode for evaluating the working state and failure mode of the control system, eliminating system failures, and optimizing the working state of the control system.

[0068] Furthermore, the above step (1) includes the following steps:

[0069] (1.1) Obtaining physical operation state parameters and working environment state parameters of the physical entity of the deepwater blowout preventer control system through sensors and data modules; wherein the physical operation state parameters and working environment state parameters include parameters of the deepwater blowout preventer control system as a whole and various components of the hydraulic control system and the electric control system;

[0070] (1.2) Based on the physical operation state parameters and working environment state parameters combined with the transfer function, the physical operation state model and fault diagnosis model of the deepwater BOP control system are established;

[0071] (1.3) Based on the state relationship and parameter transfer relationship between the physical entity, physical operation state model and fault diagnosis model of the deepwater blowout preventer control system, a corresponding digital twin model of the deepwater blowout preventer control system is established.

[0072] Furthermore, in the above step (1.2), the physical operation state model includes a physical operation state model of the electric control system and a physical operation state model of the hydraulic control system; wherein the inputs of the physical operation state model of the electric control system and the physical operation state model of the hydraulic control system are both physical operation state parameters and working environment state parameters of the physical entity, and the outputs are respectively the physical operation state parameters of the electric control system and the physical operation state parameters of the hydraulic control system, which are used to reflect the operation state of the electric control system and the hydraulic control system.

[0073] Among them, the initial values ​​of the physical operation state parameters and working environment state parameters of the physical entity are constants, and are updated in real time according to the data collected by the sensor and the data module.

[0074] Furthermore, in the above step (1.2), if Figure 3 As shown, the fault diagnosis model adopts a dynamic DBN model to summarize the possible fault types and fault causes of the deepwater blowout preventer control system based on the historical data of the deepwater blowout preventer control system.

[0075] Specifically, the fault diagnosis model includes an input layer, a hidden layer and an output layer, wherein the input layer is used to obtain the physical operation state parameters of the physical entity, the electric control system and the hydraulic control system and the working environment state parameters of the physical entity; the hidden layer includes a structural model and a parameter model, wherein the structural model is used to obtain the control mode, redundancy mode, transfer function, main parameter settings and other system information of the deepwater blowout preventer control system according to the software and hardware structure of the deepwater blowout preventer control system; the parameter model is used to analyze the obtained physical operation state parameters, working environment state parameters and system information to obtain the first-level fault parameters of the deepwater blowout preventer control system, including the fault parameters of the electric control system and the fault parameters of the hydraulic control system, which are used to describe the fault type, fault severity, fault probability, fault prediction parameters, fault isolation parameters, etc. of the deepwater blowout preventer control system, so as to feedback the impact of the fault on the system working process and determine the solutions that may need to be taken.

[0076] Among them, the initial values ​​of the physical operation status parameters and the working environment status parameters are constants, and are updated in real time according to the data collected by the sensors and data modules.

[0077] Furthermore, in the above step (1.3), the input of the digital twin model of the deepwater blowout preventer control system is the physical operation state parameters of the physical entity, the working environment state parameters and the primary fault parameters output by the fault diagnosis model, and the output is the digital twin system virtual parameters and simulation data, simulation curves and other simulation results used to reflect the working state of the deepwater blowout preventer control system. Among them, the digital twin system virtual parameters include the simulation values ​​of the control parameters, electric control system parameters, hydraulic control system parameters and construction parameters of the digital twin model of the deepwater blowout preventer control system.

[0078] Specifically, Figure 3 , Figure 4 As shown, the digital twin model includes a mechanical structure model and an electro-hydraulic composite control system structure model. The collected physical operation state parameters, working environment state parameters, and first fault parameters are used as input parameters of the mechanical structure model and the electro-hydraulic composite control system structure model, and the rated parameters and variable parameters in the virtual parameters are set according to the linkage relationship between the input parameters and the system.

[0079] Furthermore, the method for establishing the mechanical structure model includes: first, building a model of each physical component (such as metal rods, bolts, bases, etc.) included in the deepwater blowout preventer control system through modeling software such as CAD; then, connecting and assembling the models of each physical component so that the physical components are connected into a mechanical system, that is, the mechanical structure part of the deepwater blowout preventer.

[0080] Furthermore, the method for establishing the structural model of the electro-hydraulic composite control system includes: first, importing the established mechanical structure model into maplesim through CADBox, setting the coordinate system and connection direction for each component and connection point of the mechanical structure, and using the mechanical elements and multi-body elements in maplesim to set the physical movement mode for the mechanical structure to realize the linkage of the mechanical system; secondly, using the hydraulic elements (such as hydraulic pipelines, hydraulic valves, accumulators, etc.), electrical elements (such as motors, solenoid valves, etc.) and signal modules in the maplesim component library to simulate the control mode, so as to realize the real-time operation of the deepwater blowout preventer control system driving the mechanical structure.

[0081] Furthermore, the above step (3) includes the following steps:

[0082] (3.1) According to the transfer function equation of the system and the working principle equations of each subsystem and component, the simulation results and virtual parameters are optimized in combination with the minimum particle algorithm to obtain the first-level optimal parameters;

[0083] (3.2) Optimize the parameters of the physical entity of the deepwater blowout preventer control system according to the first-level optimal parameters, and update the digital twin model of the deepwater blowout preventer control system established in step (1).

[0084] Furthermore, the above step (3.1) includes the following steps:

[0085] (3.1.1) Based on the historical system status evaluation data obtained in advance, the simulation results and virtual parameters are compared and analyzed to determine and display the operating status of the deepwater blowout preventer control system; the historical system status evaluation data can be obtained from the PLC terminal of the deepwater blowout preventer control system, which stores a large amount of historical working data. Based on the historical working data, various working states of the system (including normal working conditions, abnormal conditions, failures, shutdowns, etc.) can be determined, and the system and subsystem parameters corresponding to different working conditions are the historical system status evaluation parameters;

[0086] (3.1.2) According to the determined operating status of the deepwater BOP control system, the transfer function equation of the system and the working principle equations of the subsystems and components, the virtual parameters of the deepwater BOP control system are optimized by combining the minimum particle algorithm to obtain the first-level optimal parameters.

[0087] Furthermore, in the above step (3.1.2), if Figure 5 As shown, when the minimum particle algorithm is used to optimize the virtual parameters of the deepwater blowout preventer control system, the following steps are included:

[0088] ① Confirm and establish the objective function of the system and subsystem to be optimized, set the basic parameters of the particle swarm algorithm, and initialize the particle swarm in the feasible solution space;

[0089] Among them, the basic parameters of the particle swarm algorithm mainly include: population size N, maximum number of iterations m, particle position boundary X max and X min , velocity boundary V max and V min , inertia weight ω start and the final inertia weight ω end , the initial and final values ​​of the individual learning factor c1 and the group learning factor c2;

[0090] ② Calculate the fitness of each particle according to the objective function, and select the local optimal solution according to the system relationship And the optimal solution of the group

[0091] ③ Update the particle's speed and position according to the improved speed formula and position formula, and determine whether the speed and position are within the control boundary;

[0092] Among them, the velocity update formula and displacement update formula are:

[0093]

[0094] Among them, c1 and c2 are individual learning factors and group learning factors respectively; V is the particle velocity, X is the particle position, r1 and r2 are random numbers, is the local optimal solution, The optimal solution for the group;

[0095] ④ Update the inertia weight ω(k) according to the inertia weight formula;

[0096] Among them, the inertia weight update formula is:

[0097] ω(k)=ω start -(ω start -ω end )(T max -k) / T max

[0098] Among them, ω start is the initial inertia weight, ω end is the inertia weight when the iteration reaches the maximum number, k is the current iteration number, T max is the maximum number of iterations;

[0099] ⑤ Determine whether the current number of iterations meets the termination condition. If the current number of iterations is greater than the maximum number of iterations m, stop the iteration and output the optimal solution; if the current number of iterations is less than the maximum number of iterations m, go to step ③ to continue searching for the optimal solution.

[0100] Among them, the optimal solution of the objective function (s, d, Fe ), including control parameters s = [s1, s2, ..., s n ], valve core displacement d = [d1, d2, ..., d n ], electromagnetic force F e =[F e1 , F e2 , ..., F en ] and other parameters to achieve parameter optimization.

[0101] Furthermore, the above step (3.2) includes the following steps:

[0102] (3.2.1) Optimizing the parameters of the deepwater blowout preventer control system according to the first-level optimal parameters, eliminating existing faults and deficiencies, and improving the system working state. At the same time, updating the physical operation state model and fault diagnosis model of the deepwater blowout preventer control system in step (1.2) to obtain a second-level physical operation state model and a second-level fault diagnosis model;

[0103] The input of the secondary fault diagnosis model is the physical operation state parameters of the physical entity, the electronic control system and the hydraulic control system, and the working environment state parameters of the physical entity, and the output is the secondary fault parameters;

[0104] (3.2.2) Update the digital twin model of the deepwater BOP control system based on the state relationship and parameter transfer relationship between the secondary physical operation state model and the secondary fault diagnosis model;

[0105] (3.2.4) Inputting the real-time physical operation status parameters of the physical entity, the working environment status parameters, and the secondary fault parameters output by the secondary fault diagnosis model into the updated digital twin model to obtain new virtual parameters and simulation results;

[0106] (3.2.5) Using the same method as step (2), after optimizing and analyzing the new virtual parameters and simulation results, the algorithm optimization of the parameters of the deepwater blowout preventer control system is performed again.

[0107] Furthermore, in the above step (3.2.1), the algorithm is used to optimize the deepwater BOP control electro-hydraulic composite control system as a whole according to the historical system working status evaluation data, and then optimizes the local points or types of faults that often occur in the deepwater BOP control system, such as the solenoid valves, hydraulic valves, hydraulic pipelines, etc. that are often corroded and aged in the electro-hydraulic control system. Here, the solenoid valve is taken as the research object. The working environment of the deepwater BOP control system is usually located below 1,000 meters on the seabed. The seabed working environment is complex and has high uncertainty. The solenoid valve is very easy to fail due to mud, water pressure, impact, corrosion, friction, etc.

[0108] Specifically, when optimizing the deepwater BOP control system according to the first-level optimal parameters, the transfer function of the overall optimized electro-hydraulic composite control system is firstly:

[0109]

[0110] Among them, Q, τ, K1, K2 are constants, δ is the damping ratio of the system, ω n is the natural frequency of the undamped system.

[0111] The minimum particle optimization algorithm is combined with the transfer function of the electro-hydraulic control system to optimize the parameters, and the best preferred parameters are obtained after iterative optimization.

[0112] When optimizing local components, taking the solenoid valve as an example, the motion balance equation of the solenoid valve core is:

[0113]

[0114] Among them, F e is the electromagnetic force generated when the coil is energized; k is the spring coefficient; d is the valve core displacement; C v is the friction coefficient; C f is the viscous damping coefficient; m is the valve core mass.

[0115] The electromagnetic force equation generated when the coil is energized is:

[0116]

[0117] The parameters of the solenoid valve are optimized by combining the minimum particle algorithm with the formula to obtain the optimized optimal parameters.

[0118] Example 2

[0119] The above-mentioned embodiment 1 provides a deepwater blowout preventer control system fault diagnosis and optimization method. Correspondingly, this embodiment provides a deepwater blowout preventer control system fault diagnosis and optimization system. The system provided in this embodiment can implement the deepwater blowout preventer control system fault diagnosis and optimization method of embodiment 1. The system can be implemented by software, hardware, or a combination of software and hardware. For example, the system may include integrated or separate functional modules or functional units to execute the corresponding steps in each method of embodiment 1. Since the system of this embodiment is basically similar to the method embodiment, the process described in this embodiment is relatively simple. For relevant parts, please refer to the partial description of embodiment 1. The embodiment of the system provided in this embodiment is only illustrative.

[0120] The deepwater blowout preventer control system fault diagnosis and optimization system provided in this embodiment includes:

[0121] A model building module is used to build a digital twin model of a deepwater BOP control system based on the historical data of the physical operation state parameters and working environment state parameters of the physical entity of the deepwater BOP control system;

[0122] The simulation module is used to use the acquired real-time physical operation status parameters and working environment status parameters as the input of the digital twin model, simulate the real-time operation mode of the deepwater blowout preventer control system under different working conditions, and output the simulation results and virtual parameters;

[0123] The parameter optimization module is used to optimize the simulation results and virtual parameters, and optimize the parameters of the physical entity of the deepwater blowout preventer control system according to the obtained optimal parameters, and update the established digital twin model of the deepwater blowout preventer control system at the same time;

[0124] The loop optimization module is used to repeatedly run the simulation module and the parameter optimization module to continue model optimization and parameter optimization, forming a cyclic working mode for evaluating the working state and failure mode of the control system, eliminating system failures, and optimizing the working state of the control system.

[0125] Example 3

[0126] This embodiment provides a processing device corresponding to the deepwater blowout preventer control system fault diagnosis and optimization method provided in this embodiment 1. The processing device can be a processing device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of embodiment 1.

[0127] The processing device includes a processor, a memory, a communication interface and a bus, and the processor, the memory and the communication interface are connected through the bus to complete mutual communication. The memory stores a computer program that can be run on the processor, and the processor executes the deepwater blowout preventer control system fault diagnosis and optimization method provided in this embodiment 1 when running the computer program.

[0128] Preferably, the memory may be a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0129] Preferably, the processor may be a central processing unit (CPU), a digital signal processor (DSP) or other general-purpose processors of various types, which are not limited here.

[0130] Example 4

[0131] The deepwater blowout preventer control system fault diagnosis and optimization method of this embodiment 1 can be specifically implemented as a computer program product, which may include a computer-readable storage medium carrying computer-readable program instructions for executing the deepwater blowout preventer control system fault diagnosis and optimization method described in this embodiment 1.

[0132] Computer readable storage media can be tangible devices that hold and store instructions used by instruction execution devices. Computer readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination thereof.

[0133] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0134] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0135] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0136] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for diagnosing and optimizing circulation faults in a deepwater blowout preventer control system, characterized in that: The following steps are involved: The acquired real-time physical operation state parameters and working environment state parameters are used as inputs of the digital twin model to simulate the real-time operation mode of the deepwater blowout preventer control system under different working conditions, and output fault diagnosis simulation results and virtual parameters; wherein the digital twin model is established based on the historical data of the physical operation state parameters and working environment state parameters of the physical entity of the deepwater blowout preventer control system; Optimize the simulation results and virtual parameters, and optimize the parameters of the physical entity of the deepwater BOP control system based on the obtained optimal parameters, while updating the established digital twin model of the deepwater BOP control system; Repeat the above steps to continuously optimize the model and parameters; The establishment of the digital twin model includes: obtaining the physical operation state parameters and working environment state parameters of the physical entity of the deepwater blowout preventer control system through sensors and data modules; establishing the physical operation state model and fault diagnosis model of the deepwater blowout preventer control system according to the physical operation state parameters and the working environment state parameters combined with the transfer function; establishing the corresponding digital twin model of the deepwater blowout preventer control system according to the state relationship and parameter transfer relationship between the physical entity, the physical operation state model and the fault diagnosis model of the deepwater blowout preventer control system; The digital twin model includes a mechanical structure model and an electro-hydraulic composite control system structure model, and the mechanical structure model and the electro-hydraulic composite control system structure model are used to output virtual parameters and simulation results according to the acquired physical operation state parameters, working environment state parameters, and first fault parameters; the establishment of the mechanical structure model includes: first, building component models of various physical components contained in the deepwater blowout preventer control system through modeling software; then, connecting and assembling the component models of various physical components so that the various physical components are connected into a mechanical system as the mechanical structure model of the deepwater blowout preventer; the establishment of the electro-hydraulic composite control system structure model includes: first, importing the established mechanical structure model into maplesim through CADBox, setting coordinate systems and connection directions for various components and connection points of the mechanical structure, and using mechanical components and multi-body components in maplesim to set physical motion modes for the mechanical structure to realize the linkage of the mechanical system; secondly, using hydraulic components, electrical components, and signal modules in the maplesim component library to simulate the control mode, so as to realize the real-time operation of the deepwater blowout preventer control system driving the mechanical structure; The optimization of simulation results and virtual parameters, and optimizing the parameters of the physical entity of the deepwater blowout preventer control system according to the obtained preferred parameters, while updating the established digital twin model of the deepwater blowout preventer control system, includes: optimizing the simulation results and virtual parameters according to the transfer function equation of the system and the working principle equations of each subsystem and component in combination with the minimum particle algorithm to obtain the first-level preferred parameters; optimizing the parameters of the physical entity of the deepwater blowout preventer control system according to the first-level preferred parameters, and updating the established digital twin model of the deepwater blowout preventer control system; The method of optimizing the parameters of the simulation results and virtual parameters according to the transfer function equation of the system and the working principle equations of each subsystem and component in combination with the minimum particle algorithm to obtain the first-level preferred parameters includes: comparing and analyzing the simulation results and virtual parameters according to the historical system status evaluation data obtained in advance, determining and displaying the operating status of the deepwater blowout preventer control system; optimizing the virtual parameters of the deepwater blowout preventer control system according to the determined operating status of the deepwater blowout preventer control system, as well as the transfer function equation of the system and the working principle equations of the subsystem and components in combination with the minimum particle algorithm to obtain the first-level preferred parameters; The method of optimizing the parameters of the physical entity of the deepwater blowout preventer control system according to the first-level preferred parameters and updating the established digital twin model of the deepwater blowout preventer control system includes: optimizing the parameters of the deepwater blowout preventer control system according to the first-level preferred parameters, eliminating existing faults and deficiencies, and improving the working state of the system, and updating the physical operation state model and fault diagnosis model of the deepwater blowout preventer control system to obtain a second-level physical operation state model and a second-level fault diagnosis model; updating the digital twin model of the deepwater blowout preventer control system according to the state relationship and parameter transfer relationship between the second-level physical operation state model and the second-level fault diagnosis model; inputting the real-time physical operation state parameters of the physical entity, the working environment state parameters and the second-level fault parameters output by the second-level fault diagnosis model into the updated digital twin model to obtain new virtual parameters and simulation results; after optimizing and analyzing the new virtual parameters and simulation results, performing algorithm optimization on the parameters of the deepwater blowout preventer control system again.

2. A deepwater blowout preventer control system circulation fault diagnosis and optimization method as claimed in claim 1, characterized in that: The physical operation state model includes a physical operation state model of an electric control system and a physical operation state model of a hydraulic control system; the inputs of the physical operation state model of an electric control system and the physical operation state model of a hydraulic control system are both physical operation state parameters and working environment state parameters of a physical entity, and the outputs are respectively the physical operation state parameters of the electric control system and the physical operation state parameters of the hydraulic control system.

3. A deepwater blowout preventer control system circulation fault diagnosis and optimization method as claimed in claim 2, characterized in that: The fault diagnosis model includes an input layer, a hidden layer and an output layer; The input layer is used to obtain the physical operation state parameters of the physical entity, the electric control system and the hydraulic control system and the working environment state parameters of the physical entity; The hidden layer includes a structural model and a parameter model. The structural model is used to obtain system information of the deepwater blowout preventer control system according to the software and hardware structure of the deepwater blowout preventer control system; the parameter model is used to analyze the acquired physical operation state parameters, working environment state parameters and system information to obtain the primary fault parameters of the deepwater blowout preventer control system, including the fault parameters of the electric control system and the fault parameters of the hydraulic control system.

4. A deepwater blowout preventer control system circulation fault diagnosis and optimization method as claimed in claim 1, characterized in that: According to the determined operating state of the deepwater BOP control system, the transfer function equation of the system and the working principle equations of the subsystems and components, the virtual parameters of the deepwater BOP control system are optimized by combining the minimum particle algorithm to obtain the first-level optimal parameters, including: ① Confirm and establish the objective function of the system and subsystem to be optimized, set the basic parameters of the particle swarm algorithm, and initialize the particle swarm in the feasible solution space; ② Calculate the fitness of each particle according to the objective function, and select the local optimal solution and the group optimal solution according to the system relationship; ③ Update the particle's speed and position according to the improved speed formula and position formula, and determine whether the speed and position are within the control boundary; Among them, the velocity update formula and displacement update formula are: in, and are the individual learning factor and the group learning factor respectively; V is the particle velocity, X is the particle position, , is a random number, is the local optimal solution, The optimal solution for the group; ④ Update the inertia weight according to the inertia weight formula ; Among them, the inertia weight update formula is: in, is the initial inertia weight, is the inertia weight when it is iterated to the maximum number of times, k is the current iteration number, is the maximum number of iterations; ⑤ Determine whether the current number of iterations meets the termination condition. If the current number of iterations is greater than the maximum number of iterations m, stop the iteration and output the optimal solution; if the current number of iterations is less than the maximum number of iterations m, go to step ③ to continue searching for the optimal solution.

5. A deepwater blowout preventer control system cycle fault diagnosis and optimization system, characterized in that: include: A model building module is used to build a digital twin model of a deepwater BOP control system based on the historical data of the physical operation state parameters and working environment state parameters of the physical entity of the deepwater BOP control system; A simulation module, which is used to use the acquired real-time physical operation state parameters and working environment state parameters as inputs of the digital twin model, simulate the real-time operation mode of the deepwater blowout preventer control system under different working conditions, and output simulation results and virtual parameters; wherein the digital twin model is established based on the historical data of the physical operation state parameters and working environment state parameters of the physical entity of the deepwater blowout preventer control system; The parameter optimization module is used to optimize the simulation results and virtual parameters, and optimize the parameters of the physical entity of the deepwater blowout preventer control system according to the obtained optimal parameters, and update the established digital twin model of the deepwater blowout preventer control system at the same time; The loop optimization module is used to repeatedly run the simulation module and parameter optimization module to continuously optimize the model and parameters; The establishment of the digital twin model includes: obtaining the physical operation state parameters and working environment state parameters of the physical entity of the deepwater blowout preventer control system through sensors and data modules; establishing the physical operation state model and fault diagnosis model of the deepwater blowout preventer control system according to the physical operation state parameters and the working environment state parameters combined with the transfer function; establishing the corresponding digital twin model of the deepwater blowout preventer control system according to the state relationship and parameter transfer relationship between the physical entity, the physical operation state model and the fault diagnosis model of the deepwater blowout preventer control system; The digital twin model includes a mechanical structure model and an electro-hydraulic composite control system structure model, and the mechanical structure model and the electro-hydraulic composite control system structure model are used to output virtual parameters and simulation results according to the acquired physical operation state parameters, working environment state parameters, and first fault parameters; the establishment of the mechanical structure model includes: first, building component models of various physical components contained in the deepwater blowout preventer control system through modeling software; then, connecting and assembling the component models of various physical components so that the various physical components are connected into a mechanical system as the mechanical structure model of the deepwater blowout preventer; the establishment of the electro-hydraulic composite control system structure model includes: first, importing the established mechanical structure model into maplesim through CADBox, setting coordinate systems and connection directions for various components and connection points of the mechanical structure, and using mechanical components and multi-body components in maplesim to set physical motion modes for the mechanical structure to realize the linkage of the mechanical system; secondly, using hydraulic components, electrical components, and signal modules in the maplesim component library to simulate the control mode, so as to realize the real-time operation of the deepwater blowout preventer control system driving the mechanical structure; The optimization of simulation results and virtual parameters, and optimizing the parameters of the physical entity of the deepwater blowout preventer control system according to the obtained preferred parameters, while updating the established digital twin model of the deepwater blowout preventer control system, includes: optimizing the simulation results and virtual parameters according to the transfer function equation of the system and the working principle equations of each subsystem and component in combination with the minimum particle algorithm to obtain the first-level preferred parameters; optimizing the parameters of the physical entity of the deepwater blowout preventer control system according to the first-level preferred parameters, and updating the established digital twin model of the deepwater blowout preventer control system; The method of optimizing the parameters of the simulation results and virtual parameters according to the transfer function equation of the system and the working principle equations of each subsystem and component in combination with the minimum particle algorithm to obtain the first-level preferred parameters includes: comparing and analyzing the simulation results and virtual parameters according to the historical system status evaluation data obtained in advance, determining and displaying the operating status of the deepwater blowout preventer control system; optimizing the virtual parameters of the deepwater blowout preventer control system according to the determined operating status of the deepwater blowout preventer control system, as well as the transfer function equation of the system and the working principle equations of the subsystem and components in combination with the minimum particle algorithm to obtain the first-level preferred parameters; The method of optimizing the parameters of the physical entity of the deepwater blowout preventer control system according to the first-level preferred parameters and updating the established digital twin model of the deepwater blowout preventer control system includes: optimizing the parameters of the deepwater blowout preventer control system according to the first-level preferred parameters, eliminating existing faults and deficiencies, and improving the working state of the system, and updating the physical operation state model and fault diagnosis model of the deepwater blowout preventer control system to obtain a second-level physical operation state model and a second-level fault diagnosis model; updating the digital twin model of the deepwater blowout preventer control system according to the state relationship and parameter transfer relationship between the second-level physical operation state model and the second-level fault diagnosis model; inputting the real-time physical operation state parameters of the physical entity, the working environment state parameters and the second-level fault parameters output by the second-level fault diagnosis model into the updated digital twin model to obtain new virtual parameters and simulation results; after optimizing and analyzing the new virtual parameters and simulation results, performing algorithm optimization on the parameters of the deepwater blowout preventer control system again.

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