A method, device, controller and medium for evaluating the state performance of an emergency diesel engine

Abstract

The present invention discloses a method, device, controller and medium for evaluating the state performance of an emergency diesel engine. The method obtains the field measured data of the emergency diesel engine, models the emergency diesel engine according to the information of each component in the emergency diesel engine, and obtains the mechanism model of the emergency diesel engine. The mechanism model of the emergency diesel engine includes a whole-machine performance prediction mechanism model, a lubrication system mechanism model and a connecting rod assembly dynamic analysis mechanism model. The field measured data is input into the mechanism model of the emergency diesel engine for data calculation to obtain the state parameters of the connecting rod assembly in the emergency diesel engine. The state performance of the emergency diesel engine is evaluated based on the state parameters of the connecting rod assembly in the emergency diesel engine and the field measured data. It can be seen that by establishing the mechanism model of the emergency diesel engine, the present application can realize the real-time evaluation of the state performance of the emergency diesel engine quickly and accurately based on the field measured data, provide timely and accurate health status feedback to maintenance personnel, and improve the operational reliability of the emergency diesel engine.

Description

Technical Field

[0001] The present invention relates to the technical field of nuclear power emergency diesel engines, and in particular to an emergency diesel engine state performance evaluation method, device, controller and medium. Background Art

[0002] The reliability and continuous stable operation of emergency diesel engines are crucial to the operation of nuclear power plants. As one of the key controllers, the emergency diesel engine bears the key responsibility for power supply in various emergency situations. Therefore, real-time monitoring and health assessment of its operating status are particularly important. In the operation of emergency diesel generators, the connecting rod is a key component connecting the piston and crankshaft. Its working condition directly affects the reliability of the entire unit.

[0003] Currently, nuclear power plant emergency diesel engines primarily rely on periodic testing to verify their operational reliability, supplemented by monitoring engine status parameters. However, due to the lack of mechanistic models for these systems and components, real-time assessment and prediction of the performance parameters of key emergency diesel engine systems during rapid startup is difficult. Therefore, how to quickly and accurately assess the performance of emergency diesel engines in real time is a pressing issue for those skilled in the art. Summary of the Invention

[0004] Based on this, it is necessary to address the above technical problems. The embodiments of the present invention provide an emergency diesel engine status performance evaluation method, device, controller and medium, which can quickly and accurately evaluate the status performance of the emergency diesel engine in real time, and provide maintenance personnel with timely and accurate health status feedback, so as to timely discover potential problems and take preventive maintenance measures.

[0005] A first aspect of an embodiment of the present application provides an emergency diesel engine state performance evaluation method, the emergency diesel engine state performance evaluation method comprising:

[0006] Obtaining on-site measured data of the emergency diesel engine, wherein the on-site measured data includes the pressure before the lubricating oil filter, the pressure after the lubricating oil filter, the pre-lubricating oil pressure, the lubricating oil temperature, the lubricating oil pressure, the emergency diesel engine speed, and the generator active power;

[0007] Modeling is performed based on information of each component in the emergency diesel engine to obtain an emergency diesel engine mechanism model, wherein the emergency diesel engine mechanism model includes a first target model, a second target model, and a third target model, wherein the first target model is a whole-machine performance prediction mechanism model, the second target model is a lubrication system mechanism model, and the third target model is a connecting rod assembly dynamics analysis mechanism model;

[0008] Inputting the field measured data into the emergency diesel engine mechanism model to perform data calculation to obtain the state parameters of the connecting rod assembly in the emergency diesel engine;

[0009] The state performance of the emergency diesel engine is evaluated based on the state parameters of the connecting rod assembly in the emergency diesel engine and the on-site measured data.

[0010] A second aspect of an embodiment of the present application provides an emergency diesel engine state performance evaluation device, the emergency diesel engine state performance evaluation device comprising:

[0011] an acquisition module for acquiring on-site measured data of the emergency diesel engine, wherein the on-site measured data includes the pressure before the lubricating oil filter, the pressure after the lubricating oil filter, the pre-lubricating oil pressure, the lubricating oil temperature, the lubricating oil pressure, the emergency diesel engine speed, and the generator active power;

[0012] a modeling module, configured to perform modeling based on information of various components in the emergency diesel engine to obtain an emergency diesel engine mechanism model, wherein the emergency diesel engine mechanism model includes a first target model, a second target model, and a third target model, wherein the first target model is a whole-machine performance prediction mechanism model, the second target model is a lubrication system mechanism model, and the third target model is a connecting rod assembly dynamics analysis mechanism model;

[0013] A calculation module, configured to input the field measured data into the emergency diesel engine mechanism model to perform data calculations and obtain state parameters of the connecting rod assembly in the emergency diesel engine;

[0014] An evaluation module is used to evaluate the state performance of the emergency diesel engine according to the state parameters of the connecting rod assembly in the emergency diesel engine and the on-site measured data.

[0015] In a third aspect, a controller is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the emergency diesel engine status performance evaluation method as described in the first aspect is implemented.

[0016] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the emergency diesel engine status performance evaluation method as described in the first aspect is implemented.

[0017] In summary, the present invention provides an emergency diesel engine state performance evaluation method, device, controller and medium to obtain field measured data of the emergency diesel engine, wherein the field measured data includes the pressure before the lubricating oil filter, the pressure after the lubricating oil filter, the pre-lubricating oil pressure, the lubricating oil temperature, the lubricating oil pressure, the emergency diesel engine speed and the generator active power, and a model is built based on the information of each component in the emergency diesel engine to obtain an emergency diesel engine mechanism model, wherein the emergency diesel engine mechanism model includes a first target model, a second target model and a third target model, the first target model is a whole machine performance prediction mechanism model, the second target model is a lubrication system mechanism model, and the third target model is a connecting rod assembly dynamics analysis mechanism model, the field measured data is input into the emergency diesel engine mechanism model for data calculation, and the state parameters of the connecting rod assembly in the emergency diesel engine are obtained, and the state performance of the emergency diesel engine is evaluated based on the state parameters of the connecting rod assembly in the emergency diesel engine and the field measured data. It can be seen that by establishing an emergency diesel engine mechanism model, this application can realize real-time evaluation of the emergency diesel engine status performance based on on-site measured data, and provide maintenance personnel with timely and accurate health status feedback, so as to timely discover potential problems and take preventive maintenance measures to improve the operating reliability of the emergency diesel engine. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 This is a flow chart of a method for evaluating the state performance of an emergency diesel engine provided by one embodiment of the present invention;

[0020] Figure 2-3 1 is a schematic diagram of a study on the lubrication performance of a connecting rod assembly at 100% rated load using a third initial model in a method for evaluating state performance of an emergency diesel engine provided by one embodiment of the present invention;

[0021] Figure 4 This is a polar coordinate diagram of the connecting rod big end bearing load in an emergency diesel engine state performance evaluation method provided by one embodiment of the present invention;

[0022] Figure 5-6 2. It is a schematic diagram of studying the lubrication performance of a connecting rod assembly at 75% of the rated load in a third initial model of an emergency diesel engine state performance evaluation method provided by one embodiment of the present invention;

[0023] Figure 7-82 is a schematic diagram of a study on the lubrication performance of a connecting rod assembly at 50% of the rated load in a third initial model of an emergency diesel engine state performance evaluation method provided by one embodiment of the present invention;

[0024] Figure 9 This is a schematic structural diagram of an emergency diesel engine state performance evaluation device provided by one embodiment of the present invention;

[0025] Figure 10 It is a structural diagram of a controller provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0027] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0028] It will also be understood that the term "and / or" used in the present description and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0029] As used in the present specification and the appended claims, the term “if” may be interpreted as “when” or “upon” or “in response to determining”, depending on the context. Similarly, the phrase “if it is determined” or “if compared to [described condition or event]” may be interpreted as meaning “upon determination” or “in response to determination” or “upon comparison to [described condition or event]” or “in response to comparison to [described condition or event]”, depending on the context.

[0030] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" in the present specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present invention. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0032] It should be understood that the order of execution of the steps in the following embodiments does not necessarily mean the order in which they are executed. The order in which each process is executed should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0033] In order to illustrate the technical solution of the present invention, specific embodiments are provided below.

[0034] See also Figure 1 , is a flow chart of a method for evaluating the state performance of an emergency diesel engine provided by one embodiment of the present invention, such as Figure 1 As shown, the emergency diesel engine state performance evaluation method can be implemented through the following steps.

[0035] S101: Acquire on-site measured data of the emergency diesel engine, wherein the on-site measured data includes lubricating oil filter front pressure, lubricating oil filter back pressure, pre-lubricating oil pressure, lubricating oil temperature, lubricating oil pressure, emergency diesel engine speed, and generator active power.

[0036] In step S101, the pre-filter pressure refers to the pressure of the lubricating oil before it enters the filter, reflecting the fuel supply status of the emergency diesel engine. The post-filter pressure refers to the pressure measured after the lubricating oil passes through the filter, which is usually lower than the pre-filter pressure and indicates the filter's resistance to oil flow. The pre-lubricating oil pressure refers to the lubricating oil pressure provided by the pre-lubrication system before the engine starts, ensuring timely lubrication of engine components at startup. The lubricating oil temperature refers to the temperature of the lubricating oil in the emergency diesel engine, which affects its viscosity and lubrication effect and should generally be maintained within an appropriate range. The lubricating oil pressure refers to the pressure of the lubricating oil as it flows through the emergency diesel engine, ensuring that the lubricating oil is effectively delivered to all components. The emergency diesel engine speed refers to the speed of the diesel engine in an emergency state and is generally required to be maintained within a certain range to ensure normal generator operation. The generator active power refers to the actual power output of the generator, usually measured in kilowatts (kW), reflecting its power supply capacity. These parameters are important indicators for ensuring the normal operation of the emergency diesel engine. This application installs pressure sensors before and after the lubricating oil filter to obtain the pressure before the lubricating oil filter, the pressure after the lubricating oil filter, the pre-lubricating oil pressure and the lubricating oil pressure, installs a temperature sensor in the lubricating oil pipeline to obtain the lubricating oil temperature, installs a speed sensor on the emergency diesel engine to obtain the speed of the emergency diesel engine, installs a power sensor or power meter at the generator output port to obtain the active power of the generator, connects all sensors to a data recorder or controller so that the data recorder or controller can obtain the on-site measured data of the emergency diesel engine fed back by all sensors, thereby accurately reflecting the actual working status of the emergency diesel engine.

[0037] In this embodiment, by obtaining on-site measured data of the emergency diesel engine, the operating status of the emergency diesel engine can be fully understood, so that if a failure or abnormal situation occurs later, it can be quickly and accurately located and measures can be taken to improve the emergency response capability.

[0038] S102: Modeling is performed based on information of each component in the emergency diesel engine to obtain an emergency diesel engine mechanism model, wherein the emergency diesel engine mechanism model includes a first target model, a second target model, and a third target model. The first target model is a whole-machine performance prediction mechanism model, the second target model is a lubrication system mechanism model, and the third target model is a connecting rod assembly dynamics analysis mechanism model.

[0039] In step S102, information about various components in the emergency diesel engine, such as the cylinder, crankshaft, connecting rod, turbocharger, and lubrication system, is obtained. After determining the information about each component in the emergency diesel engine, simulation modeling is performed using software tools (such as MATLAB / Simulink and ANSYS) based on the basic parameters of the diesel engine (such as power and speed), as well as the operating principles, characteristics, and operating conditions (such as load and ambient temperature) of each component. The mathematical models of each component are then integrated to form a complete emergency diesel engine mechanism model, ensuring that the model accurately reflects the actual operating conditions of the diesel engine. The emergency diesel engine mechanism model includes a first target model, a second target model, and a third target model. The first target model is a mechanism model for predicting overall engine performance, the second target model is a mechanism model for the lubrication system, and the third target model is a mechanism model for dynamic analysis of the connecting rod assembly.

[0040] In one embodiment of the invention, modeling is performed based on information of each component in the emergency diesel engine to obtain an emergency diesel engine mechanism model, including:

[0041] Component information of the emergency diesel engine of the nuclear power plant is divided according to a preset division method to obtain first, second, and third divided component information, wherein the first divided component information includes intake and exhaust pipes, the engine body, the intercooler, and the turbocharger; the second divided component information includes the main lubricating oil pump, the pre-lubricating oil pump, the heat exchanger, the lubricating oil filter, and pipelines including the lubricating oil pipelines entering various subsystems of the emergency diesel engine; and the third divided component information includes the connecting rod shaft, the connecting rod large and small end bearings, the crankshaft, and the piston pin;

[0042] Models are constructed for the first divided component information, the second divided component information, and the third divided component information in a simulation platform to obtain a first target model, a second target model, and a third target model.

[0043] Specifically, the component information of the emergency diesel engine of the nuclear power plant is divided according to a preset division method to obtain first divided component information, second divided component information and third divided component information, wherein the first divided component information includes the intake and exhaust pipes, the engine body, the intercooler and the turbocharger; the second divided component information includes the main lubricating oil pump, the pre-lubricating oil pump, the heat exchanger, the lubricating oil filter and the pipelines including the lubricating oil pipelines of each subsystem entering the emergency diesel engine; the third divided component information includes the connecting rod body, the connecting rod large and small end bearings, the crankshaft, and the piston pin, and then the intake and exhaust pipes, the engine body, the intercooler and the turbocharger in the first divided component information are divided into the third divided component information. The engine, intercooler, and turbocharger were modeled on the simulation platform to create the first target model, the overall engine performance prediction mechanism model. The second-divided component information, including the main lubricating oil pump, pre-lubricating oil pump, heat exchanger, lubricating oil filter, and lubricating oil lines entering the various subsystems of the emergency diesel engine, was modeled on the simulation platform to create the second target model, the lubrication system mechanism model. The third-divided component information, including the connecting rod body, connecting rod large and small end bearings, crankshaft, and piston pin, was modeled on the simulation platform to create the third target model, the connecting rod assembly dynamic analysis mechanism model. By modeling each component of the emergency diesel engine in layers, not only can its performance, faults, and optimization paths be analyzed independently, but it also provides strong support for the integrated optimization of the entire emergency diesel engine, thereby improving the reliability and efficiency of the entire emergency diesel engine.

[0044] It should be noted that the preset division method can be set according to actual conditions, and this application does not impose any restrictions on this.

[0045] In one embodiment of the invention, the first divided component information, the second divided component information, and the third divided component information are respectively modeled in a simulation platform to obtain the first target model, the second target model, and the third target model, including:

[0046] Building models for the first divided component information, the second divided component information, and the third divided component information in a simulation platform to obtain a first initial model, a second initial model, and a third initial model;

[0047] The first initial model, the second initial model and the third initial model are simulated and calibrated respectively, and the calibrated first initial model, the second initial model and the third initial model are verified respectively to generate the first target model, the second target model and the third target model.

[0048] Specifically, the first divided component information, the second divided component information, and the third divided component information are respectively modeled in the simulation platform to obtain the first initial model, the second initial model, and the third initial model. For the first initial model, the first initial model is calibrated according to the provided test data, and the speed, power, explosion pressure, average exhaust temperature, intake pressure column A, intake pressure column B, and fuel consumption rate are calculated under 100%, 75%, 50%, and 25% load, respectively. Then, the calibrated first initial model is verified. When the verification and calibration results are successful, the first target model, that is, the whole machine performance prediction mechanism model, is generated. The main test data and simulation results of each working condition are shown in Table 1 below:

[0049]

[0050] Table 1

[0051] For the second initial model, the second initial model is calibrated based on the test data of the lubrication system at three different speeds. The pre-lubricating oil pressure, lubricating oil pressure, turbocharger oil pressure 1, turbocharger oil pressure 2, valve gear lubricating oil pressure, piston cooling oil pressure, lubricating oil temperature (because the lubricating oil temperature requires a longer calculation time to converge, the result will be confirmed after a longer calculation time), lubricating oil filter front pressure, lubricating oil filter back pressure and filter pressure difference are calculated at 1500rpm, 1000rpm and 500rpm respectively. Then, the calibrated second initial model is verified. When the verification and calibration results are successful, the second target model, i.e., the lubrication system mechanism model, is generated. The main test data and simulation results of each working condition are shown in Tables 2 and 3 below:

[0052]

[0053] Table 2

[0054]

[0055] Table 3

[0056] For the third initial model, the third initial model is calibrated according to the provided test data, and the lubrication performance and wear calculation of the connecting rod assembly are carried out at 100%, 75%, and 50% of the rated load, respectively. Then, the calibrated third initial model is verified. When the verification and calibration results are successful, the third target model is generated, that is, the connecting rod assembly dynamic analysis mechanism model.

[0057] In the study of the lubrication performance of the connecting rod assembly under 100% rated load, the bearing lubrication performance related parameters are calculated and analyzed through model simulation. The parameters include the minimum oil film thickness, maximum oil film pressure, etc., and one of the main indicators for measuring bearing lubrication performance is the minimum oil film thickness. The minimum oil film thickness of the connecting rod big end bearing changes with the crankshaft angle as shown in the figure. Figure 2 As shown in the figure, the minimum oil film thickness varies greatly at different crankshaft angles. The minimum oil film thickness decreases sharply from the combustion top dead center to 18°CA after the combustion top dead center, and decreases slowly from 18°CA to 81°CA after the combustion top dead center. The minimum oil film thickness reaches its minimum value at 81°CA after the combustion top dead center. The maximum oil film pressure of the connecting rod big end bearing changes with the crankshaft angle as shown in the figure. Figure 3 As shown, the maximum oil film pressure peak occurs at 13.5°CA after combustion top dead center. This is because 13.5°CA after combustion top dead center is located near the maximum explosion pressure, and the larger bearing load leads to a larger maximum oil film pressure. For example, when the minimum film thickness occurs at 81°CA after combustion top dead center, which corresponds to 0.129s in the model calculation, the bearing load applied during the calculation process still exists at 0.129s, which is about 1 / 6 of the peak load. That is, in the process from 13.5°CA after combustion top dead center to 81°CA after combustion top dead center, the force is always applied to the bearing, the load gradually decreases, the peak oil film pressure gradually decreases, but the minimum film thickness gradually decreases. Among them, the bearing load is obtained by extracting the fluid load in the bearing lubrication domain, and after coordinate transformation, the bearing load under the polar coordinate diagram of the connecting rod big end bearing load can be obtained, as shown in the figure. Figure 4 shown.

[0058] In the study of the lubrication performance of the connecting rod assembly under 75% of the rated load, the minimum oil film thickness of the connecting rod big end bearing changes with the crankshaft angle as follows Figure 5 As shown in Figure 1, in one cycle, the minimum oil film thickness at different crankshaft angles varies greatly. The oil film thickness decreases sharply after the explosion pressure, and the minimum oil film thickness decreases sharply from the combustion top dead center to 18°CA after the combustion top dead center. The minimum film thickness decreases slowly from 18°CA after the combustion top dead center to 81°CA after the combustion top dead center, and the minimum oil film thickness appears at 81°CA after the combustion top dead center. The maximum oil film pressure of the connecting rod big end bearing changes with the crankshaft angle as shown in Figure 1. Figure 6 As shown in the figure, unlike the rated operating conditions, the maximum oil film pressure does not occur near the explosion pressure, but occurs when the inertia force is the largest. 18°CA after top dead center of combustion is near the maximum explosion pressure, and the heavy bearing load causes a peak in the maximum oil film pressure. In the study of the lubrication performance of the connecting rod assembly under 50% rated load, the minimum oil film thickness of the connecting rod big end bearing changes with the crankshaft angle as shown below. Figure 7 As shown in Figure 2, in one cycle, the minimum oil film thickness at different crankshaft angles varies greatly. The maximum oil film pressure at the connecting rod big end bearing changes with the crankshaft angle as shown in Figure 2. Figure 8As shown in the figure, due to the low load, the oil film pressure generated by inertia near exhaust top dead center reaches its peak. Through the above steps, it is possible to build a model, perform simulation calibration, and verify the information of each component in the emergency diesel engine, ultimately generating accurate and reliable target models. These target models can be used for performance evaluation, fault prediction, and design optimization, providing strong support for the safe operation of emergency diesel engines.

[0059] Furthermore, when establishing a dynamic analysis mechanism model for a connecting rod assembly, component wear will occur. By calculating the corresponding component wear, the dynamic analysis mechanism model for the connecting rod assembly can be optimized, thereby enabling the model to output more accurate state parameters. For example, for the wear of the connecting rod big end bearing, wear calculations can be performed based on the Archard model. The Archard model assumes that the amount of wear between contact surfaces is proportional to the normal contact pressure and relative slip distance, and inversely proportional to the hardness of the material. The calculation formula is as follows:

[0060]

[0061] Where V is the total wear volume, S is the relative slip distance, is the dimensionless wear coefficient, is the normal load on the contact surface, H is the material hardness, divide both sides of the above formula by the contact area A, and express it in differential form:

[0062]

[0063] Where k is the dimensionless fit wear coefficient, h is the wear depth, and p is the bearing-journal mixed lubrication contact pressure. Based on the calculated bearing-journal mixed lubrication contact pressure, the wear amount of the bearing inner surface during one working cycle can be calculated using the following formula:

[0064]

[0065] in, is the total wear of the i-th bearing inner surface node in one working cycle, is the time of one working cycle, is the bearing-journal mixed lubrication contact pressure at node i at time t, is the relative speed between the bearing and the journal at the node i at time t. For example, according to the Archard model, the maximum wear amount of the inner surface of the bearing in one working cycle is According to the wear limit standard of sliding bearings, the bearing clearance increment is limited to 50% of the original clearance and the remaining thickness of the alloy layer is greater than 20% of the original thickness. The wear cycle number of the bearing bush is calculated to be , that is, the wear life is hours .

[0066] In this embodiment, an emergency diesel engine mechanism model is established so that the performance of the emergency diesel engine under different operating conditions can be accurately predicted and optimized, thereby improving the overall performance and operating efficiency of the emergency diesel engine, thereby providing better support for the operation and maintenance of the emergency diesel engine.

[0067] S103: Inputting the field measured data into the emergency diesel engine mechanism model to perform data calculation to obtain state parameters of the connecting rod assembly in the emergency diesel engine.

[0068] In step S103, the field measured data is cleaned and sorted to ensure the accuracy and completeness of the data, and the field measured data is converted into a format that can be recognized by the model, such as CSV, Excel or the data input format of specific software. The processed field measured data is then input into the emergency diesel engine mechanism model to ensure that the input data corresponds to the parameters and variables in the model so that accurate calculations can be performed to obtain the state parameters of the connecting rod assembly in the emergency diesel engine, such as the stress, deformation, lubrication performance, wear condition, etc. of the connecting rod assembly.

[0069] In one embodiment of the invention, the field measured data is input into the emergency diesel engine mechanism model for data calculation to obtain the state parameters of the connecting rod assembly in the emergency diesel engine, including:

[0070] Inputting the emergency diesel engine speed and the generator active power into the first target model to calculate the cylinder pressure curve of the emergency diesel engine in real time;

[0071] Inputting the lubricating oil filter front pressure, the lubricating oil filter rear pressure, the pre-lubricating oil pressure, the lubricating oil temperature, the lubricating oil pressure, and the emergency diesel engine speed into the second target model, and calculating the bearing lubricating oil pressure and bearing lubricating oil temperature of the connecting rod assembly in real time;

[0072] The cylinder pressure curve of the emergency diesel engine, the bearing lubricating oil pressure and the bearing lubricating oil temperature of the connecting rod assembly are input into the third target model, and the stress and strain parameters, bearing lubrication performance parameters and fatigue life parameters of the connecting rod assembly are calculated in real time.

[0073] Specifically, the emergency diesel engine speed and generator active power are input into the first target model (the overall engine performance prediction mechanism model), and the emergency diesel engine's cylinder pressure curve is calculated in real time. The lubricating oil filter pre-pressure, lubricating oil filter post-pressure, pre-lubricating oil pressure, lubricating oil temperature, lubricating oil pressure, and emergency diesel engine speed are then input into the second target model (the lubrication system mechanism model), and the connecting rod assembly's bearing lubricating oil pressure and bearing lubricating oil temperature are calculated in real time. Finally, the obtained emergency diesel engine cylinder pressure curve, connecting rod assembly bearing lubricating oil pressure, and bearing lubricating oil temperature are input into the third target model (the connecting rod assembly dynamic analysis mechanism model), and the connecting rod assembly's stress-strain parameters, bearing lubrication performance parameters, and fatigue life parameters are calculated in real time. The stress in the stress-strain parameters refers to the complex stress state to which the connecting rod assembly is subjected during operation, including alternating tensile, compressive, and bending stresses. The magnitude and direction of these stresses continuously change with the movement of the connecting rod. The strain refers to the change in the connecting rod's shape and size caused by the stress. Bearing lubrication performance parameters may include lubricating oil viscosity, lubricating film thickness, friction coefficient, etc. The selection and optimization of these parameters require comprehensive consideration of factors such as the bearing's operating conditions, materials, and lubrication method. Fatigue life parameters are typically determined through fatigue testing, which measures the number of cycles required for fatigue failure of the connecting rod assembly under a certain stress level. These parameters are influenced by a variety of factors, including the connecting rod's material, structure, manufacturing process, and operating environment. Real-time calculation of the connecting rod assembly's stress-strain parameters, bearing lubrication performance parameters, and fatigue life parameters facilitates timely detection of the connecting rod assembly's lubricating oil film state, connecting rod stress state, and damage such as wear and fatigue. This allows operators to take appropriate measures to prevent the occurrence or escalation of failures, thereby improving the operating efficiency and safety of emergency diesel engines.

[0074] Furthermore, the dynamic analysis mechanism model for the connecting rod assembly can be modeled and calculated using a single connecting rod dynamic assembly. To improve computational efficiency and minimize model convergence, only the flexible deformation of the object of interest is considered in the model, while all other components are treated as rigid bodies. The crankshaft and small end pin are set as rigid domains, while the remaining components are treated as flexible domains, saving significant computational time. To enable online deployment and computation of the model, a proxy model is constructed using a deep neural network (DNN). The DNN model consists of an input layer, a series of hidden layers, and an output layer, each composed of several nodes or neurons. Furthermore, for the fatigue life parameters of the connecting rod, this model uses the Basquin high-cycle fatigue criterion to predict fatigue life, assuming a power relationship between fatigue life and elastic stress. Since the connecting rod is in compression, it is primarily governed by the third principal stress, while the other two principal stresses are minimal. Therefore, the stress state in the critical area of the rod shaft can be considered uniaxial, and the third principal stress can be used as the amplitude stress in the Basquin relation.

[0075]

[0076] in, is the stress amplitude, and b are material constants, is the number of cycles to failure.

[0077] For the fatigue life parameters of the connecting rod big end bearing, the Basquin formula based on shear stress is used to predict the fatigue life according to its multi-axial high-cycle fatigue cycle characteristics.

[0078]

[0079] in, is the shear stress amplitude, and b are material constants, is the number of cycles to failure.

[0080] In this embodiment, through the emergency diesel engine mechanism model, the state parameters of the connecting rod assembly in the emergency diesel engine can be quickly and accurately calculated, thereby achieving accurate prediction and optimization of the performance of the emergency diesel engine under different working conditions, so as to subsequently quickly and accurately evaluate the state performance of the emergency diesel engine and avoid the impact of different environmental conditions on the performance.

[0081] S104: Evaluate the state performance of the emergency diesel engine according to the state parameters of the connecting rod assembly in the emergency diesel engine and the field measured data.

[0082] In step S104, after the state parameters of the connecting rod assembly in the emergency diesel engine are calculated through the emergency diesel engine mechanism model, the state performance of the emergency diesel engine is evaluated based on the state parameters of the connecting rod assembly in the emergency diesel engine and the field measured data to obtain an evaluation result.

[0083] In one embodiment of the invention, the state performance evaluation of the emergency diesel engine is performed based on the state parameters of the connecting rod assembly in the emergency diesel engine and the field measured data, including:

[0084] Performing deviation analysis based on the state parameters of the connecting rod assembly in the emergency diesel engine and the field measured data to obtain an expected state deviation vector of the emergency diesel engine;

[0085] Conducting data tracing for the emergency diesel engine and establishing a performance evaluation space for the emergency diesel engine;

[0086] Calculating a performance evaluation index of the emergency diesel engine based on the state expectation deviation vector and the performance evaluation space;

[0087] Determining whether the performance evaluation index of the emergency diesel engine is less than a preset performance evaluation index;

[0088] If the performance evaluation index of the emergency diesel engine is less than the preset performance evaluation index, a performance warning signal is generated and an equipment maintenance plan is formulated, wherein the equipment maintenance plan is used to instruct staff to perform equipment maintenance on the emergency diesel engine.

[0089] Specifically, the state parameters of the connecting rod assembly in the emergency diesel engine and the field-measured data are vectorized to generate a real-time state vector and an expected state vector. Based on the expected state vector, deviations are identified for the real-time state vector to generate the expected state deviation vector for the emergency diesel engine. This involves comparing the state parameters of the connecting rod assembly in the emergency diesel engine with the field-measured data to identify differences between the two, resulting in the expected state deviation vector for the emergency diesel engine. Vectorization is a method that converts data into vector form for mathematical calculations and analysis. The deviation identification process can be performed using vector operations, specifically as follows: expected state deviation vector = real-time state vector - expected state vector. Deviation identification involves comparing the real-time state vector with the expected state vector, identifying differences between the two, and revealing performance deviations in the emergency diesel engine. By calculating the deviations of various parameters, it is possible to determine in which areas the emergency diesel engine's operating status deviates from its expected state. This helps accurately assess the emergency diesel engine's performance, promptly identify performance deviations, and implement appropriate maintenance or optimization measures. The data of the emergency diesel engine is traced, and a performance evaluation space for the emergency diesel engine is built to achieve all-round monitoring, performance optimization and anomaly detection of the operating status of the emergency diesel engine. The state expectation deviation vector is input into the performance evaluation space, and the performance evaluation index of the emergency diesel engine is calculated. This helps to understand the overall performance of the emergency diesel engine, detect performance deviations in a timely manner, and take corresponding maintenance or optimization measures. By pre-setting a performance evaluation index, it represents the minimum acceptable standard for the performance of the emergency diesel engine. If the performance evaluation index is lower than this preset value, it indicates that the emergency diesel engine may be in an abnormal state or insufficient performance, and there may be potential risks or failures. The performance evaluation index is a comprehensive indicator of the current performance calculated, reflecting the operating status and health of the emergency diesel engine. Determine whether the performance evaluation index is less than the preset performance evaluation index. If the performance evaluation index is lower than the threshold, it means that the performance of the emergency diesel engine has declined, and the operating status may be unstable or abnormal, and then an equipment maintenance plan needs to be formulated. Among them, the equipment maintenance plan is used to instruct the staff to perform equipment maintenance on the emergency diesel engine. At this time, a performance warning signal needs to be generated to inform the system or operator that the performance of the emergency diesel engine has dropped to an unsafe state and requires attention or intervention. After the warning signal is generated, the operator is reminded of the possible problem through an alarm or notification, and is required to check the status of the emergency diesel engine or take maintenance measures. By monitoring the performance evaluation index of the emergency diesel engine and generating a warning signal, effective measures can be taken in time when the performance of the emergency diesel engine deteriorates to reduce the risk of failure.

[0090] It should be noted that the preset performance evaluation index can be set according to actual conditions, and this application does not impose any limitation on this.

[0091] In one embodiment of the invention, data tracing is performed on the emergency diesel engine to establish a performance evaluation space for the emergency diesel engine, including:

[0092] Performing a global expected deviation search on the emergency diesel engine to obtain a state expected deviation vector record set;

[0093] Performing confidence evaluation processing on the state expectation deviation vector record set to obtain a performance evaluation record set;

[0094] The state expected deviation vector record set and the performance evaluation record set are input into the preset performance evaluation coordinate system to construct the performance evaluation space of the emergency diesel engine, wherein the preset performance evaluation coordinate system is constructed with the sample state expected deviation vector as the horizontal axis and the sample performance evaluation index as the vertical axis.

[0095] Specifically, by accessing the global state record database or historical data set, the global expected deviation retrieval and analysis of the operating history data of the emergency diesel engine are performed to obtain a state expected deviation vector record set, which includes multiple sample state expected deviation vectors. By performing confidence assessment processing on the state expected deviation vector record set, a performance evaluation record set is obtained, wherein the confidence assessment is to ensure that the data used for performance evaluation is high-quality and reliable by evaluating the accuracy and reliability of each record, and then the deviation vector and the performance index are mapped to a two-dimensional coordinate system with the sample state expected deviation vector as the horizontal axis and the sample performance evaluation index as the vertical axis, and a coordinate system for visualizing and analyzing the performance of the emergency diesel engine is established. The state expected deviation vector record set and the performance evaluation record set are input into the preset performance evaluation coordinate system, and a three-dimensional evaluation space is generated according to the performance evaluation index corresponding to different deviation vectors, reflecting the performance of the emergency diesel engine under various working conditions. By conducting data tracing and multi-dimensional analysis on emergency diesel engines and building a performance evaluation space for emergency diesel engines, we can intuitively see the relationship between performance and deviation, and achieve all-round monitoring of the operating status of emergency diesel engines, performance optimization and anomaly detection, thereby optimizing maintenance plans and strategies.

[0096] In one embodiment of the invention, calculating the performance evaluation index of the emergency diesel engine based on the state expected deviation vector and the performance evaluation space includes:

[0097] Inputting the state expected deviation vector into the performance evaluation space, calculating the distance between the state expected deviation vector and each sample state expected deviation vector in the performance evaluation space, and obtaining a plurality of vector target distances;

[0098] Determining whether the distances between the plurality of vector targets are less than a vector target distance threshold;

[0099] If any one of the plurality of vector target distances is less than the vector target distance threshold, generating an identification vector target distance;

[0100] Cluster calculation is performed based on the sample performance evaluation index corresponding to the identification vector target distance to generate the performance evaluation index of the emergency diesel engine.

[0101] Specifically, the state expected deviation vector is input into the performance evaluation space. The distance between the state expected deviation vector and each sample state expected deviation vector within the performance evaluation space is calculated to obtain multiple vector target distances. Common distance calculation methods include Euclidean distance and cosine similarity. The purpose of calculating distances is to identify the historical states that are most similar to the current state, thereby obtaining the corresponding performance evaluation index. The multiple vector target distances reflect the degree of difference between the input vector and each sample vector within the space, representing the similarity between the current state and the historical sample states. The smaller the distance, the closer the current state is to the state of a certain historical sample. The state expected deviation vector is a multidimensional vector that describes the difference between the current operating state of the emergency diesel engine and the expected state. The performance evaluation space is a previously established multidimensional space containing a large number of sample state expected deviation vectors and corresponding performance evaluation indices. A predefined critical value, the vector target distance threshold, is used to measure whether the current state is sufficiently close to the historical sample state. By determining whether each vector target distance is less than the threshold, it is determined whether the current state matches the state of a certain sample. If any of the multiple vector target distances is less than the vector target distance threshold, the input state expected deviation vector is considered similar to the corresponding sample state expected deviation vector, meaning that they are located close together in the performance evaluation space. In this case, a representation is generated to indicate that the input state expected deviation vector is similar to a sample vector in the performance evaluation space. This representation can be a simple logical value (such as True or False) or a more complex data structure to record specific similarity information. By identifying vectors that meet the criteria, the most valuable historical sample states are selected to avoid the influence of irrelevant data on the evaluation results. Once the historical sample vector closest to the current state is found, a clustering calculation is performed based on the performance evaluation index of this sample. Common clustering calculations include K-means clustering and hierarchical clustering. This results in a performance evaluation index for the emergency diesel engine, which identifies the overall performance of the emergency diesel engine under the current operating state. By inputting the state expected deviation vector into the performance evaluation space and calculating the distance between similar vectors, a performance evaluation index based on historical data is generated to reflect the current performance of the emergency diesel engine. This helps understand the overall performance of the emergency diesel engine, reduces errors in the evaluation results, and avoids interference from irrelevant data.

[0102] It should be noted that the vector target distance threshold can be set according to actual conditions, and this application does not impose any limitation on this.

[0103] In this embodiment, the state performance of the emergency diesel engine is evaluated based on the state parameters of the connecting rod assembly in the emergency diesel engine and the actual measured data on site, so as to perform real-time monitoring and early warning, thereby improving the accuracy of the state performance evaluation of the emergency diesel engine. Then, based on the evaluation results, a reasonable maintenance plan and management strategy are formulated to extend the service life of the emergency diesel engine and reduce maintenance costs, thereby improving the operating efficiency and overall performance of the emergency diesel engine.

[0104] In summary, the present invention provides an emergency diesel engine state performance evaluation method, device, controller and medium to obtain field measured data of the emergency diesel engine, wherein the field measured data includes the pressure before the lubricating oil filter, the pressure after the lubricating oil filter, the pre-lubricating oil pressure, the lubricating oil temperature, the lubricating oil pressure, the emergency diesel engine speed and the generator active power, and a model is built based on the information of each component in the emergency diesel engine to obtain an emergency diesel engine mechanism model, wherein the emergency diesel engine mechanism model includes a first target model, a second target model and a third target model, the first target model is a whole machine performance prediction mechanism model, the second target model is a lubrication system mechanism model, and the third target model is a connecting rod assembly dynamics analysis mechanism model, the field measured data is input into the emergency diesel engine mechanism model for data calculation, and the state parameters of the connecting rod assembly in the emergency diesel engine are obtained, and the state performance of the emergency diesel engine is evaluated based on the state parameters of the connecting rod assembly in the emergency diesel engine and the field measured data. It can be seen that by establishing an emergency diesel engine mechanism model, this application can realize real-time evaluation of the emergency diesel engine status performance based on on-site measured data, and provide maintenance personnel with timely and accurate health status feedback, so as to timely discover potential problems and take preventive maintenance measures to improve the operating reliability of the emergency diesel engine.

[0105] See also Figure 9 , Figure 9 This is a schematic diagram of the structure of the emergency diesel engine state performance evaluation device provided by an embodiment of the present invention. The emergency diesel engine state performance evaluation device corresponds to the emergency diesel engine state performance evaluation method in the above embodiment. Figure 9 as well as Figure 9 For the convenience of explanation, only the parts related to this embodiment are shown. Figure 9 The emergency diesel engine state performance evaluation device 90 includes: an acquisition module 91 , a modeling module 92 , a calculation module 93 , and an evaluation module 94 .

[0106] An acquisition module 91 is configured to acquire on-site measured data of the emergency diesel engine, wherein the on-site measured data includes the pressure before the lubricating oil filter, the pressure after the lubricating oil filter, the pre-lubricating oil pressure, the lubricating oil temperature, the lubricating oil pressure, the emergency diesel engine speed, and the generator active power;

[0107] a modeling module 92 for modeling the emergency diesel engine based on information about each component of the emergency diesel engine to obtain a mechanism model of the emergency diesel engine, wherein the mechanism model of the emergency diesel engine includes a first target model, a second target model, and a third target model, wherein the first target model is a mechanism model for predicting the performance of the entire engine, the second target model is a mechanism model for the lubrication system, and the third target model is a mechanism model for dynamic analysis of the connecting rod assembly;

[0108] A calculation module 93 is used to input the field measured data into the emergency diesel engine mechanism model to perform data calculations to obtain state parameters of the connecting rod assembly in the emergency diesel engine;

[0109] The evaluation module 94 is configured to evaluate the state performance of the emergency diesel engine according to the state parameters of the connecting rod assembly in the emergency diesel engine and the field measured data.

[0110] Optionally, the modeling module 92 is specifically configured to:

[0111] Component information of the emergency diesel engine of the nuclear power plant is divided according to a preset division method to obtain first, second, and third divided component information, wherein the first divided component information includes intake and exhaust pipes, the engine body, the intercooler, and the turbocharger; the second divided component information includes the main lubricating oil pump, the pre-lubricating oil pump, the heat exchanger, the lubricating oil filter, and pipelines including the lubricating oil pipelines entering various subsystems of the emergency diesel engine; and the third divided component information includes the connecting rod shaft, the connecting rod large and small end bearings, the crankshaft, and the piston pin;

[0112] Models are constructed for the first divided component information, the second divided component information, and the third divided component information in a simulation platform to obtain the first target model, the second target model, and the third target model.

[0113] Optionally, the modeling module 92 is further configured to:

[0114] Building models for the first divided component information, the second divided component information, and the third divided component information in a simulation platform to obtain a first initial model, a second initial model, and a third initial model;

[0115] The first initial model, the second initial model and the third initial model are simulated and calibrated respectively, and the calibrated first initial model, the second initial model and the third initial model are verified respectively to generate the first target model, the second target model and the third target model.

[0116] Optionally, the calculation module 93 is specifically configured to:

[0117] Inputting the emergency diesel engine speed and the generator active power into the first target model to calculate the cylinder pressure curve of the emergency diesel engine in real time;

[0118] Inputting the lubricating oil filter front pressure, the lubricating oil filter rear pressure, the pre-lubricating oil pressure, the lubricating oil temperature, the lubricating oil pressure, and the emergency diesel engine speed into the second target model, and calculating the bearing lubricating oil pressure and bearing lubricating oil temperature of the connecting rod assembly in real time;

[0119] The cylinder pressure curve of the emergency diesel engine, the bearing lubricating oil pressure and the bearing lubricating oil temperature of the connecting rod assembly are input into the third target model, and the stress and strain parameters, bearing lubrication performance parameters and fatigue life parameters of the connecting rod assembly are calculated in real time.

[0120] Optionally, the evaluation module 94 is specifically configured to:

[0121] Performing deviation analysis based on the state parameters of the connecting rod assembly in the emergency diesel engine and the field measured data to obtain an expected state deviation vector of the emergency diesel engine;

[0122] Conducting data tracing for the emergency diesel engine and establishing a performance evaluation space for the emergency diesel engine;

[0123] Calculating a performance evaluation index of the emergency diesel engine based on the state expectation deviation vector and the performance evaluation space;

[0124] Determining whether the performance evaluation index of the emergency diesel engine is less than a preset performance evaluation index;

[0125] If the performance evaluation index of the emergency diesel engine is less than the preset performance evaluation index, a performance warning signal is generated and a controller maintenance plan is formulated, wherein the controller maintenance plan is used to instruct staff to perform controller maintenance on the emergency diesel engine.

[0126] Optionally, the evaluation module 94 is further configured to:

[0127] Performing a global expected deviation search on the emergency diesel engine to obtain a state expected deviation vector record set;

[0128] Performing confidence evaluation processing on the state expectation deviation vector record set to obtain a performance evaluation record set;

[0129] The state expected deviation vector record set and the performance evaluation record set are input into a preset performance evaluation coordinate system to construct the performance evaluation space of the emergency diesel engine, wherein the preset performance evaluation coordinate system is constructed with the sample state expected deviation vector as the horizontal axis and the sample performance evaluation index as the vertical axis.

[0130] Optionally, the evaluation module 94 is further configured to:

[0131] Inputting the state expected deviation vector into the performance evaluation space, calculating the distance between the state expected deviation vector and each sample state expected deviation vector in the performance evaluation space, and obtaining a plurality of vector target distances;

[0132] Determining whether the distances between the plurality of vector targets are less than a vector target distance threshold;

[0133] If any one of the plurality of vector target distances is less than the vector target distance threshold, generating an identification vector target distance;

[0134] Cluster calculation is performed based on the sample performance evaluation index corresponding to the identification vector target distance to generate the performance evaluation index of the emergency diesel engine.

[0135] It should be noted that the information interaction, execution process and other contents between the above-mentioned units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0136] Figure 10 This is a schematic diagram of the structure of a controller provided by an embodiment of the present invention. Figure 10 As shown, the controller of this embodiment includes: at least one processor ( Figure 10 Only one is shown), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned emergency diesel engine status performance evaluation method embodiments are implemented.

[0137] The controller may include, but is not limited to, a processor and a memory. Figure 10 This is merely an example of a controller and does not constitute a limitation on the controller. The controller may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input system.

[0138] In one embodiment, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor in a controller, the controller is enabled to perform the steps of any embodiment of the emergency diesel engine state performance assessment method disclosed herein, which are not repeated here. The computer-readable storage medium may be non-volatile or volatile.

[0139] The processor may be a CPU, other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0140] Memory includes readable storage media, internal memory, and other components. The internal memory can be the controller's internal memory, providing an environment for the operating system and computer-readable instructions stored in the readable storage medium. The readable storage medium can be the controller's hard drive. In other embodiments, it can also be an external storage controller, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory can include both the controller's internal storage unit and an external storage controller. The memory is used to store the operating system, associated applications, boot loaders, data, and other programs, such as computer program code. The memory can also be used to temporarily store data that has been output or is about to be output.

[0141] It is understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0142] Those skilled in the art can clearly understand that for the sake of convenience and brevity in description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0143] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, persons skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the state performance of an emergency diesel engine, characterized in that: include: Obtaining on-site measured data of the emergency diesel engine, wherein the on-site measured data includes the pressure before the lubricating oil filter, the pressure after the lubricating oil filter, the pre-lubricating oil pressure, the lubricating oil temperature, the lubricating oil pressure, the emergency diesel engine speed, and the generator active power; Modeling is performed based on information of each component in the emergency diesel engine to obtain an emergency diesel engine mechanism model, wherein the emergency diesel engine mechanism model includes a first target model, a second target model, and a third target model, wherein the first target model is a whole-machine performance prediction mechanism model, the second target model is a lubrication system mechanism model, and the third target model is a connecting rod assembly dynamics analysis mechanism model; Inputting the field measured data into the emergency diesel engine mechanism model to perform data calculation to obtain the state parameters of the connecting rod assembly in the emergency diesel engine; Performing a state performance evaluation on the emergency diesel engine according to the state parameters of the connecting rod assembly in the emergency diesel engine and the on-site measured data; The step of inputting the field measured data into the emergency diesel engine mechanism model for data calculation to obtain the state parameters of the connecting rod assembly in the emergency diesel engine includes: Inputting the emergency diesel engine speed and the generator active power into the first target model to calculate the cylinder pressure curve of the emergency diesel engine in real time; Inputting the lubricating oil filter front pressure, the lubricating oil filter rear pressure, the pre-lubricating oil pressure, the lubricating oil temperature, the lubricating oil pressure, and the emergency diesel engine speed into the second target model, and calculating the bearing lubricating oil pressure and bearing lubricating oil temperature of the connecting rod assembly in real time; The cylinder pressure curve of the emergency diesel engine, the bearing lubricating oil pressure and the bearing lubricating oil temperature of the connecting rod assembly are input into the third target model, and the stress-strain parameters, bearing lubrication performance parameters and fatigue life parameters of the connecting rod assembly are calculated in real time, wherein the fatigue life parameters are used to predict fatigue life based on the Basquin high-cycle fatigue criterion.

2. The emergency diesel engine state performance evaluation method according to claim 1, characterized in that: The step of modeling the emergency diesel engine based on the information of each component in the emergency diesel engine to obtain the mechanism model of the emergency diesel engine includes: Component information of the emergency diesel engine of the nuclear power plant is divided according to a preset division method to obtain first, second, and third divided component information, wherein the first divided component information includes intake and exhaust pipes, the engine body, the intercooler, and the turbocharger; the second divided component information includes the main lubricating oil pump, the pre-lubricating oil pump, the heat exchanger, the lubricating oil filter, and pipelines including the lubricating oil pipelines entering various subsystems of the emergency diesel engine; and the third divided component information includes the connecting rod shaft, the connecting rod large and small end bearings, the crankshaft, and the piston pin; Models are constructed for the first divided component information, the second divided component information, and the third divided component information in a simulation platform to obtain the first target model, the second target model, and the third target model.

3. The emergency diesel engine state performance evaluation method according to claim 2, characterized in that: The first divided component information, the second divided component information, and the third divided component information are respectively modeled in a simulation platform to obtain the first target model, the second target model, and the third target model, including: Building models for the first divided component information, the second divided component information, and the third divided component information in a simulation platform to obtain a first initial model, a second initial model, and a third initial model; The first initial model, the second initial model and the third initial model are simulated and calibrated respectively, and the calibrated first initial model, the second initial model and the third initial model are verified respectively to generate the first target model, the second target model and the third target model.

4. The emergency diesel engine state performance evaluation method according to claim 1, characterized in that: The state performance evaluation of the emergency diesel engine according to the state parameters of the connecting rod assembly in the emergency diesel engine and the on-site measured data includes: Performing deviation analysis based on the state parameters of the connecting rod assembly in the emergency diesel engine and the field measured data to obtain an expected state deviation vector of the emergency diesel engine; Conducting data tracing for the emergency diesel engine and establishing a performance evaluation space for the emergency diesel engine; Calculating a performance evaluation index of the emergency diesel engine based on the state expectation deviation vector and the performance evaluation space; Determining whether the performance evaluation index of the emergency diesel engine is less than a preset performance evaluation index; If the performance evaluation index of the emergency diesel engine is less than the preset performance evaluation index, a performance warning signal is generated and a controller maintenance plan is formulated, wherein the controller maintenance plan is used to instruct staff to perform controller maintenance on the emergency diesel engine.

5. The emergency diesel engine state performance evaluation method according to claim 4, characterized in that: The data tracing of the emergency diesel engine and the establishment of a performance evaluation space for the emergency diesel engine include: Performing a global expected deviation search on the emergency diesel engine to obtain a state expected deviation vector record set; Performing confidence evaluation processing on the state expectation deviation vector record set to obtain a performance evaluation record set; The state expected deviation vector record set and the performance evaluation record set are input into a preset performance evaluation coordinate system to construct the performance evaluation space of the emergency diesel engine, wherein the preset performance evaluation coordinate system is constructed with the sample state expected deviation vector as the horizontal axis and the sample performance evaluation index as the vertical axis.

6. The emergency diesel engine state performance evaluation method according to claim 4, characterized in that: The calculating the performance evaluation index of the emergency diesel engine based on the state expected deviation vector and the performance evaluation space includes: Inputting the state expected deviation vector into the performance evaluation space, calculating the distance between the state expected deviation vector and each sample state expected deviation vector in the performance evaluation space, and obtaining a plurality of vector target distances; Determining whether the distances between the plurality of vector targets are less than a vector target distance threshold; If any one of the plurality of vector target distances is less than the vector target distance threshold, generating an identification vector target distance; Cluster calculation is performed based on the sample performance evaluation index corresponding to the identification vector target distance to generate the performance evaluation index of the emergency diesel engine.

7. An emergency diesel engine state performance evaluation device, characterized in that: include: an acquisition module for acquiring on-site measured data of the emergency diesel engine, wherein the on-site measured data includes the pressure before the lubricating oil filter, the pressure after the lubricating oil filter, the pre-lubricating oil pressure, the lubricating oil temperature, the lubricating oil pressure, the emergency diesel engine speed, and the generator active power; a modeling module, configured to perform modeling based on information of various components in the emergency diesel engine to obtain an emergency diesel engine mechanism model, wherein the emergency diesel engine mechanism model includes a first target model, a second target model, and a third target model, wherein the first target model is a whole-machine performance prediction mechanism model, the second target model is a lubrication system mechanism model, and the third target model is a connecting rod assembly dynamics analysis mechanism model; A calculation module, configured to input the field measured data into the emergency diesel engine mechanism model to perform data calculations and obtain state parameters of the connecting rod assembly in the emergency diesel engine; An evaluation module, configured to evaluate the state performance of the emergency diesel engine based on the state parameters of the connecting rod assembly in the emergency diesel engine and the field measured data; The step of inputting the field measured data into the emergency diesel engine mechanism model for data calculation to obtain the state parameters of the connecting rod assembly in the emergency diesel engine includes: Inputting the emergency diesel engine speed and the generator active power into the first target model to calculate the cylinder pressure curve of the emergency diesel engine in real time; Inputting the lubricating oil filter front pressure, the lubricating oil filter rear pressure, the pre-lubricating oil pressure, the lubricating oil temperature, the lubricating oil pressure, and the emergency diesel engine speed into the second target model, and calculating the bearing lubricating oil pressure and bearing lubricating oil temperature of the connecting rod assembly in real time; The cylinder pressure curve of the emergency diesel engine, the bearing lubricating oil pressure and the bearing lubricating oil temperature of the connecting rod assembly are input into the third target model, and the stress and strain parameters, bearing lubrication performance parameters and fatigue life parameters of the connecting rod assembly are calculated in real time.

8. A controller comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the emergency diesel engine status performance evaluation method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the emergency diesel engine state performance evaluation method according to any one of claims 1 to 6 is implemented.

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