A method for digital twin modeling and condition monitoring of electric actuators used in nuclear power plants

Abstract

This invention relates to the field of nuclear power technology and discloses a digital twin modeling and condition monitoring method for electric actuators used in nuclear power plants. During the operation and maintenance phase, this invention relies on a fault prediction model that integrates the operating characteristics of nuclear power plants, sensor monitoring, and a multi-factor coupled transmission efficiency calculation model. By collecting lubricating oil pressure, temperature, and motor operating parameters in real time, the actual transmission efficiency is accurately calculated, achieving online quantification of transmission efficiency, early warning of anomalies, and accurate fault identification. This breaks away from the traditional passive maintenance mode that relies on fixed durations or intuitive anomalies. This solution avoids downtime losses and material waste caused by "over-maintenance" and eliminates equipment failures, unplanned shutdowns, and significant operational losses caused by "under-maintenance," significantly improving the operational stability of electric actuators used in nuclear power plants and the safety of nuclear power operations.

Description

Technical Field

[0001] This invention relates to the field of nuclear power technology, and in particular to a method for digital twin modeling and condition monitoring of electric actuators used in nuclear power plants. Background Technology

[0002] Nuclear-grade electric actuators are core drive devices in the automation control of nuclear power systems. They are widely used in critical scenarios such as valve regulation in the main and auxiliary systems of the nuclear island, control of containment equipment, and drive of auxiliary machinery in the conventional island. The performance of their internal main drive unit directly determines the overall operating efficiency and energy consumption level of the nuclear power system, and is closely related to the safety and stability of nuclear facilities. Among them, worm gear drives are widely used in the transmission systems of electric actuators in various nuclear power plants due to their advantages such as large transmission ratio, compact structure, and ability to achieve reverse self-locking.

[0003] However, the sliding friction characteristics of worm gear drives result in generally low transmission efficiency. Moreover, this efficiency value is not a fixed parameter, but a dynamic variable affected by a combination of factors such as lubricant performance, material pairing characteristics, operating temperature, and manufacturing and assembly precision. In addition, the special operating conditions of nuclear power plants, such as high radiation, high temperature and high pressure, harsh operating conditions, and high requirements for continuous operation, further exacerbate the instability of transmission efficiency. This characteristic has become a core technical bottleneck restricting the efficiency improvement of electric actuators used in nuclear power plants. Furthermore, the high safety requirements of nuclear power equipment bring severe challenges to the operation and maintenance of the equipment throughout its entire life cycle.

[0004] Current maintenance strategies for electric actuators used in nuclear power plants typically rely on fixed operating times or obvious abnormalities such as unusual noises or jamming as trigger conditions. There are currently no effective technical means to conduct online assessments and trend predictions of the efficiency degradation process of the transmission chain. In the unique operating environment of nuclear power plants, maintenance personnel cannot know in real time whether the lubricating oil performance has deteriorated to a critical value due to radiation aging or high-temperature degradation, or whether the degree of tooth surface wear has significantly affected transmission efficiency. This leads to two types of unreasonable maintenance practices: First, "over-maintenance" increases the unnecessary downtime for nuclear facilities, which not only increases the cost of operation and maintenance materials, but also affects the continuous and stable operation of the nuclear power system. Second, "insufficient maintenance" can lead to a severe decline in transmission efficiency, a surge in equipment energy consumption, or even sudden malfunctions, causing problems such as failure of nuclear power pipeline valve regulation and abnormal auxiliary machine drive, thus touching the safety red line of nuclear facility operation and causing significant safety risks and economic losses.

[0005] In summary, the fundamental flaw in existing technologies lies in treating the transmission system of electric actuators used in nuclear power plants as a "black box." This makes it impossible to accurately predict transmission efficiency during the design and selection phase, taking into account the unique operating conditions of nuclear power plants, or to conduct real-time and effective online assessments of efficiency during long-term operation. At its core, the problem stems from the lack of an analytical model that can systematically quantify the changes in transmission efficiency under the coupled effects of multiple factors in the unique operating conditions of nuclear power plants. This model fails to provide a scientific and accurate technical basis for the design optimization, selection adaptation, and full life-cycle operation and maintenance management of electric actuators used in nuclear power plants.

[0006] Therefore, there is an urgent need in this field to propose a technical method that can break through the above-mentioned "black box" limitations, so as to achieve accurate prediction and efficient control of the transmission efficiency of electric actuators used in nuclear power plants, and meet the urgent needs of the nuclear power industry for energy saving, consumption reduction, safety and reliability, and long-term stable operation of equipment. Summary of the Invention

[0007] To address the above problems, this invention provides a digital twin modeling and condition monitoring method for electric actuators used in nuclear power plants, enabling accurate prediction and efficient control of transmission efficiency, and meeting the requirements of energy conservation, consumption reduction, safe and stable operation of nuclear power equipment.

[0008] The technical solution of this invention is: A method for digital twin modeling and condition monitoring of electric actuators used in nuclear power plants includes the following steps: Step 1: Constructing the transmission efficiency calculation model: A hierarchical calculation model covering the overall efficiency ηzj and the transmission efficiency ηcd is constructed. The overall efficiency ηzj is obtained by coupled calculation of the motor efficiency ηdj, the transmission efficiency ηcd, and the bearing efficiency ηzc. The three are in series coupled relationship, and the mathematical expression is: ηzj=ηdj*ηcd*ηzc. The transmission device uses a worm gear transmission pair, and its transmission efficiency ηcd is calculated from the meshing efficiency and the oil churning loss, and the expression is: ηcd=ηnh*η jy ; Step 2, Implementation of the Fault Prediction Model: The worm gear of the actuator is located in a closed housing and is submerged in oil. Pressure and temperature sensors are installed in the lubricating oil tank of the actuator to collect real-time data on lubricating oil pressure F, temperature T, and motor operating parameters. These data are then substituted into the transmission efficiency calculation model to obtain the actual transmission efficiency ηcd, which is then combined with a preset normal pressure limit F. req Normal temperature limit T req Based on the temperature change rate f(t), a fault judgment logic is constructed to realize abnormal early warning and fault judgment of the transmission device; the specific judgment process is as follows: The sensor collects lubricating oil pressure F and oil temperature T in real time. Combined with motor current and speed parameters, the actual transmission efficiency η is calculated using a transmission efficiency calculation model. cd ; When F > F req At that time, a warning signal is sent to the actuator control system to indicate "abnormal lubricating oil pressure"; When T>T req At that time, a warning signal is sent to the actuator control system, indicating "abnormal lubricating oil temperature," and the calculation of the rate of temperature change is initiated. f(t)=(△ra—△ra') / △t; Where, △ra = (T 当前 —T 上一时刻 ) / Δt, which is the rate of change of temperature over time; T 当前 T represents the current lubricating oil temperature. 上一时刻 This indicates the temperature of the lubricating oil sampled last time. The sampling period is 1 second. ra represents the temperature change over time, and ra' represents the temperature change over time at the previous moment. When F > F req And T > T req When f(t) > 0, the transmission device is considered to have a risk of failure.

[0009] Specifically, in step two, when F > F req And T > T req When f(t) > 0, the transmission device is considered to have a risk of failure. If η cd If the speed drops by more than 10% compared to the normal state, it is directly judged that the transmission device has failed. The control system will issue a buzzer alarm and display "Transmission device failure" on the remote control system interface. If η cd If the reading is similar to the normal state, it indicates "abnormal transmission device, close monitoring required".

[0010] Specifically, when the temperature T recovers to T req and below, pressure F returns to F req When the fault alarm signal is at or below the specified level, the fault alarm signal will be automatically reset.

[0011] Specifically, the meshing efficiency is calculated using the worm lead angle and the equivalent friction coefficient, and the mathematical expression is: etanh= tanγ / tan[γ +arctan(uv)], In the formula: ηnh is the meshing efficiency; γ is the lead angle of the worm; and uv is the equivalent friction coefficient.

[0012] Specifically, the function for calculating the worm gear lead angle γ is: γ = arctan(z*m / d); In the formula: z is the number of worm threads; m is the module; d is the pitch circle diameter of the worm.

[0013] Specifically, the equivalent friction coefficient uv is a composite function of the material, lubricating oil, and temperature, and its expression is: uv = k1 * k2 * k3 * u In the formula: u is the basic friction coefficient; k1 is the worm gear material correction coefficient; k2 is the load coefficient; k3 is the temperature coefficient.

[0014] Specifically, the basic coefficient of friction u = A + B / (v s +C) D , In the formula: A, B, C, and D are dimensionless parameters; v s This represents the average sliding speed of the worm gear meshing.

[0015] Specifically, the average sliding speed v of the worm gear meshing s The expression is: v s = πdn / (cosγ*60*1000) In the formula: d is the pitch circle diameter of the worm; n is the rotational speed of the worm.

[0016] Specifically, the expression for the load factor k2 is: K2 = 1 + 0.2 * log (Psj / Pe) In the formula: Psj is the actual load; Pe is the rated load.

[0017] Specifically, the mathematical expression for the temperature coefficient k3 is: K3 = [nd p +(1 / nd) q ] / 2; In the formula: nd is the ratio of actual kinematic viscosity to design viscosity; p is the high viscosity side index; q is the low viscosity side index.

[0018] In the operation and maintenance phase, this invention relies on a fault prediction model that integrates the operating conditions of nuclear power plants, sensor monitoring, and a multi-factor coupled transmission efficiency calculation model. By collecting lubricating oil pressure, temperature, and motor operating parameters in real time, it accurately calculates the actual transmission efficiency, achieving online quantification of transmission efficiency, early warning of anomalies, and precise fault identification. This breaks away from the traditional passive maintenance mode that relies on fixed durations or intuitive anomalies. This solution avoids downtime losses and material waste caused by "over-maintenance" while preventing equipment failures, unplanned shutdowns, and significant operational losses caused by "under-maintenance," significantly improving the operational stability of electric actuators used in nuclear power plants and the safety of nuclear power operations. Furthermore, by quantifying the coupling mechanism of multiple factors such as materials, lubricating oil viscosity, and temperature, the model has higher prediction accuracy and stronger adaptability to nuclear power plant application scenarios compared to existing technologies. This provides scientific and technological support for energy saving, consumption reduction, and full life-cycle optimized management of electric actuators used in nuclear power plants, promoting the upgrading of nuclear power operation and maintenance technology. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the fault diagnosis logic. Figure 2 This is a schematic diagram of the control system for electric actuators used in nuclear power plants, which embeds the transmission efficiency calculation model and fault judgment logic. Figure 3 This is a transmission efficiency curve at 40℃ under the same lubrication conditions; Figure 4 This is a transmission efficiency curve at 60℃ under the same lubrication conditions; Figure 5 It is a transmission efficiency curve at 70℃ under the same lubrication conditions; Figure 6 It is a transmission efficiency curve at 80℃ under the same lubrication conditions; Figure 7 It is a transmission efficiency curve at 100℃ under the same lubrication conditions; Figure 8 It is a comparison chart of the transmission efficiency of different materials at corresponding temperatures; Figure 9 This is a graph showing the relationship between the efficiency of a tin bronze worm gear transmission and temperature. Figure 10 This is a diagram showing the sensor placement locations; In the diagram, 1 is a pressure sensor, 2 is a liquid level sensor, 3 is a worm gear, 4 is a temperature sensor, 5 is a worm wheel, and 6 is a controller. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to specific embodiments. It should be noted that the scope of protection of the present invention is not limited to the following embodiments. Conventional modifications and substitutions made by those skilled in the art based on the technical solutions of the present invention are all within the scope of protection of the present invention. This embodiment discloses a digital twin modeling and condition monitoring method for electric actuators used in nuclear power plants. The core objective is to break through the "black box" limitation of traditional transmission systems of electric actuators used in nuclear power plants by constructing a transmission efficiency calculation model, a fault prediction model, and a digital twin simulation system. This enables accurate efficiency prediction during the R&D and design phase and early fault warning during the operation and maintenance phase, adapting to the special application scenarios of nuclear power plants with high radiation, high temperature and high pressure, limited operation and maintenance, and stringent requirements for safety, reliability, and long-term continuous operation.

[0021] Its specific implementation process includes the following three core modules: I. Construction of Transmission Efficiency Calculation Model Based on the structural characteristics and operating conditions of electric actuators used in nuclear power plants, this embodiment constructs a hierarchical calculation model covering both overall efficiency and transmission efficiency, achieving accurate quantification of transmission efficiency under the coupled effects of multiple factors. The specific model and parameter definitions are as follows: 1.1 Overall Efficiency Calculation Model The overall efficiency ηzj of the electric actuator used in nuclear power plants is jointly determined by the motor efficiency ηdj, the transmission efficiency ηcd, and the bearing efficiency ηzc. These three components are in series and coupled, and the mathematical expression is: ηzj = ηdj * ηcd * ηzc In the formula: ηzj is the overall machine efficiency; ηdj is the motor efficiency (which can be obtained from the performance parameter table provided by the motor manufacturer, or calculated by measuring the input / output power of the motor); ηcd is the transmission efficiency (a core calculation parameter, which needs to be derived through the sub-model below); ηzc is the bearing efficiency (a standard value is selected according to the bearing type, 0.98~0.99 for rolling bearings and 0.95~0.97 for sliding bearings).

[0022] 1.2 Sub-model for calculating transmission efficiency ηcd In this embodiment, the transmission device adopts a worm gear transmission pair, and its transmission efficiency ηcd is determined by both the meshing efficiency and the oil churning loss, and is calculated as follows: 1.21, Meshing efficiency calculation: Meshing efficiency is related to the worm lead angle and the equivalent friction coefficient, and the mathematical expression is: ηnh = tanγ / tan[γ + arctan(uv)], where: ηnh is the meshing efficiency; γ is the lead angle of the worm; and uv is the equivalent friction coefficient.

[0023] The worm lead angle γ is calculated using the function: γ = arctan(z*m / d); where z is the number of worm threads; m is the module; and d is the pitch circle diameter of the worm (unit: mm).

[0024] The equivalent coefficient of friction uv is a composite function of material, lubricant, and temperature, and its expression is: uv = k1 * k2 * k3 * u In the formula: u is the basic friction coefficient; k1 is the worm gear material correction coefficient; k2 is the load coefficient; k3 is the temperature coefficient; The basic coefficient of friction u = A + B / (v) s +C) D , In the formula: A, B, C, and D are dimensionless parameters, obtained through experimental fitting of worm gear 5 and worm 3 transmission under nuclear power plant operating conditions, as shown in Table 1: Table 1: v s The average sliding speed of the worm gear meshing; the average sliding speed v of the worm gear meshing. s The expression is: v s = πdn / (cosγ*60*1000) In the formula: d is the pitch circle diameter of the worm (unit: mm); n is the rotational speed of the worm (unit: r / min).

[0025] The correction factor k1 for the worm gear material is selected based on the characteristics of the worm gear material. The values ​​of k1 for different worm gear materials are shown in Table 2 below (in this embodiment, a tin bronze worm gear is preferred): Table 2 Correction coefficients k1 for different worm gear materials: Worm Gear Material Reference correction factor k1 Reference hardness (HB) characteristic Tin bronze (ZCuSn10P1) 1.00 90 The benchmark material has the best overall performance. Zinc-based alloy (ZA-27) 1.15 120 Performance close to aluminum-iron-bronze Ductile iron (QT500-7) 1.25 170 Friction level between bronze and gray cast iron Load factor k2: Considering the impact of load fluctuations in nuclear power plant operating conditions on transmission efficiency, the expression is: K2 = 1 + 0.2 * log (Psj / Pe) In the formula: Psj is the actual load (obtained indirectly through motor current monitoring, current and torque are positively correlated, thus the actual load is derived); Pe is the rated load (determined by the design parameters of the transmission device), and K2 is taken as 1 under normal working conditions.

[0026] Temperature coefficient k3: Considering the changes in lubricating oil viscosity caused by ambient temperature fluctuations and transmission heat in nuclear power plants, which in turn affect the friction coefficient, the mathematical expression is: K3 = [nd p +(1 / nd) q ] / 2; In the formula: nd is the ratio of actual kinematic viscosity to design viscosity; p is the high viscosity side index (ranging from 0.05 to 0.15, and 0.1 in this example), which represents the sensitivity of the friction coefficient to increasing with increasing viscosity; q is the low viscosity side index (ranging from 0.3 to 0.8, and 0.8 in this embodiment), which characterizes the sensitivity of the friction coefficient to a sharp increase as the kinematic viscosity decreases.

[0027] p and q were obtained by fitting the measured efficiency data under simulated nuclear power plant operating conditions using a nonlinear regression method.

[0028] Model for the relationship between kinematic viscosity and temperature of lubricating oil: To accurately calculate nd, a model for the relationship between kinematic viscosity and temperature in logarithmic coordinates is constructed: log log(Z) = b – k*logT Where: Z is the kinematic viscosity (unit: mm² / s); T is the absolute temperature (unit: Kelvin K), T=t+273.15 (t is the actual temperature, unit: °C); b and k are constants related to the properties of the oil, which are determined by data in the lubricating oil product manual or by experimental measurements.

[0029] In this embodiment, L-CKE 320 lubricating oil for nuclear power plants (suitable for high temperature and high pressure environments) is preferred. According to the product manual, the viscosity is 320 mm² / s at 40℃ and 28 mm² / s at 100℃. Substituting these values ​​into the above model, we get k=3.358 and b=8.558. The design reference viscosity is selected as the kinematic viscosity at 70℃ (approximately 85 mm² / s).

[0030] The calculated kinematic viscosity and viscosity ratio at a certain temperature are shown in Table 3. Table 3: temperature Kinematic viscosity (cSt) Viscosity ratio nd 40℃ 320 3.764705882 60℃ 118 1.388235294 70℃ 85 1 80℃ 62 0.729411765 100℃ 28 0.329411765 The influence of temperature on transmission efficiency mainly includes the following aspects: High temperature zone (t>70℃): Temperature rises → viscosity decreases → oil film thins, lubrication state deteriorates from full film lubrication to mixed lubrication, friction is borne by "fluid shear" and "direct contact of micro-protrusions", the equivalent friction coefficient uv rises nonlinearly and sharply, resulting in a significant decrease in transmission efficiency; In the low-temperature region (t < 70℃): as the temperature decreases, the viscosity increases. Although this is beneficial for forming a thick oil film to maintain fluid lubrication, the oil churning loss increases significantly. Moreover, the negative impact of oil churning loss on efficiency is far greater than the positive impact of the thicker oil film, ultimately leading to a decrease in transmission efficiency.

[0031] 1.22 Calculation of oil stirring loss: Viscosity changes lead to power loss in fluid stirring, expressed as η. jy The churning loss coefficient of the lubricating oil is expressed by the following model expression: η jy = 1 / (1+0.05*nd) m ) In the formula: 0.05 is the basic coefficient for oil stirring loss; nd is the actual kinematic viscosity; m is the viscosity effect index (1.25 in this embodiment, determined through experimental fitting). In summary, the final formula for calculating the transmission efficiency ηcd is: ηcd = ηnh * η jy .

[0032] The calculated efficiency data of the worm gear transmission using Great Wall mineral oil L-CKE 320 at 70℃ are shown in Tables 4-1 and 4-2 below: Table 4-1: Number of worm threads Modulus Worm pitch circle diameter Lead angle Worm gear pitch circle diameter Number of teeth of worm gear Rotational speed (r / min) viscosity ratio Actual temperature Tin bronze 1 1.619 25.91 3.576 51.82 32 1400 1 70 Zinc-based alloys 1 1.619 25.91 3.576 51.82 32 1400 1 70 Ductile iron 1 1.619 25.91 3.576 51.82 32 1400 1 70 gray cast iron 1 1.619 25.91 3.576 51.82 32 1400 1 70 Table 4-2: Average sliding speed v (m / s) Basic coefficient of friction Material coefficient Load factor Oil churning loss Equivalent coefficient of friction Equivalent friction angle Transmission efficiency Tin bronze 1.903 0.058 1 1 0.952 0.055 3.159 0.504 Zinc-based alloys 1.903 0.058 1.15 1 0.952 0.063 3.632 0.471 Ductile iron 1.903 0.058 1.25 1 0.952 0.069 3.947 0.451 gray cast iron 1.903 0.058 1.35 1 0.952 0.075 4.262 0.432 Under the same lubrication conditions, the transmission efficiency of worm gears made of different materials at different temperatures is as follows: Figure 3 , 4 As shown in points 5, 6, 7, 8 and Table 5: Table 5: 40℃ 60℃ 70℃ 80℃ 100℃ Tin bronze 0.468 0.479 0.492 0.471 0.386 Zinc-based alloys 0.440 0.448 0.459 0.437 0.354 Ductile iron 0.424 0.430 0.439 0.417 0.335 gray cast iron 0.409 0.413 0.421 0.399 0.318 The transmission efficiency values ​​of the tin bronze worm gear under the same lubrication conditions and different temperatures are shown in Tables 6-1 and 6-2, and the transmission efficiency diagram is shown below. Figure 9 As shown: Table 6-1: Number of worm gear threads Modulus Worm pitch circle diameter Lead angle Worm gear pitch circle diameter Number of teeth of worm gear Rotational speed (r / min) viscosity ratio Actual temperature (°C) Average sliding speed v (m / s) 1 1.619 25.91 3.576334375 51.82 32 1400 3.764705882 40 1.903 1 1.619 25.91 3.576334375 51.82 32 1400 1.388235294 60 1.903 1 1.619 25.91 3.576334375 51.82 32 1400 1 70 1.903 1 1.619 25.91 3.576334375 51.82 32 1400 0.729411765 80 1.903 1 1.619 25.91 3.576334375 51.82 32 1400 0.329411765 100 1.903 Table 6-2: Number of worm gear threads Modulus Actual temperature (°C) Average sliding speed v (m / s) Basic coefficient of friction Material coefficient Load factor Oil churning loss Temperature coefficient Equivalent coefficient of friction Transmission efficiency 1 1.619 40 1.903 0.058 1 1 0.792 0.744 0.043 0.468 1 1.619 60 1.903 0.058 1 1 0.882 0.901 0.052 0.479 1 1.619 70 1.903 0.058 1 1 0.952 1.000 0.058 0.492 1 1.619 80 1.903 0.058 1 1 0.967 1.128 0.065 0.471 1 1.619 100 1.903 0.058 1 1 0.988 1.663 0.096 0.386 II. Fault Prediction Model and Algorithm Implementation for Transmission Devices In this case, the worm gear is located in a closed housing and is submerged in oil. The oil level sensor 2 detects the submersion position. Based on the above transmission efficiency calculation model and combined with the operation and maintenance requirements of electric actuators used in nuclear power plants, a fault prediction model is constructed, and an embedded algorithm is designed to be embedded in the actuator control system to achieve real-time monitoring and fault early warning. The specific implementation process is as follows: 2.1 Fault Prediction Model Parameter Setting Pressure sensor 1 and temperature sensor 4 are installed in the lubricating oil tank of the actuator to monitor the pressure value F and temperature value T of the lubricating oil in real time; a normal pressure limit F is preset. req and normal temperature limit T req This limit is determined based on the design parameters and operating experience of electric actuators used in nuclear power plants.

[0033] 2.2 Fault diagnosis logic, such as Figure 1 As shown; 2.21 The sensor collects lubricating oil pressure F and oil temperature T in real time. Combined with parameters such as motor current and speed, the actual transmission efficiency η is calculated using the aforementioned transmission efficiency calculation model. cd ; 2.22, when F > F req At that time, a warning signal is sent to the actuator control system to indicate "abnormal lubricating oil pressure"; 2.23, when T > T req When the temperature rises, a warning signal is sent to the actuator control system, indicating "abnormal lubricating oil temperature", and the calculation of the rate of change of temperature rise is initiated: f(t) = (△ra - △ra') / △t; Where, △ra = (T 当前 —T 上一时刻 ) / Δt, which is the rate of change of temperature over time; T 当前 T represents the current lubricating oil temperature. 上一时刻 This indicates the temperature of the lubricating oil sampled last time. The sampling period is 1 second. ra represents the temperature change over time, and ra' represents the temperature change over time at the previous moment. 2.24, when F > F req And T > T req When f(t) > 0 (temperature continues to rise), the transmission device is considered to have a risk of failure: if η cd If the efficiency drops by more than 10% compared to the normal state (90% of design efficiency), a transmission device malfunction is directly diagnosed. The control system will issue a buzzer alarm and display "Transmission device malfunction" on the remote control system interface; if η cd If the reading is similar to the normal state, it indicates "Transmission device abnormal, close monitoring required"; 2.25, when temperature T recovers to T req and below, pressure F returns to F req When the fault alarm signal is at or below the specified level, the fault alarm signal will be automatically reset.

[0034] 2.3 Fault Prediction Algorithm Implementation Steps like Figure 2 As shown, the above transmission efficiency calculation model and fault judgment logic are embedded into the control system of the electric actuator used in nuclear power plants (using controller 6, adapted to the industrial bus protocol used in nuclear power plants); after the system starts, it collects operating parameters in real time, such as motor current (reflecting torque), motor speed, lubricating oil temperature T and lubricating oil pressure F; it calls the transmission efficiency calculation model to calculate the current actual transmission efficiency ηcd and the overall efficiency ηzj; the system continuously tracks the corresponding efficiency change trend and records the efficiency data curve; When the transmission efficiency drops significantly (in this case, a single drop exceeding 5% or a cumulative drop exceeding 8% after three consecutive data collections is preferred), it is initially judged to be an abnormality in the lubricating oil or abnormal wear of the transmission components. The system sends an early warning signal to the nuclear power ship's central control system, and simultaneously displays "abnormal lubricating oil" or "abnormal wear risk of transmission components" on the on-site display screen and the host computer, and pushes suggested maintenance measures (such as replacing the lubricating oil and checking the wear of the gear teeth).

[0035] III. Digital Twin Simulation Implementation in the Design Phase During the research and design phase of electric actuators for nuclear power plants, a virtual simulation environment was constructed based on digital twin technology. The aforementioned transmission efficiency calculation model was used for simulation testing to optimize and select design schemes. The specific implementation steps are as follows: 3.1 Digital Twin Model Construction: A digital twin model of the electric actuator transmission system for nuclear power plants is built on the computer. The model parameters are completely consistent with the physical entity, including: worm gear structural parameters (number of threads z, number of teeth, pitch circle diameter d, module m, etc.), material parameters (worm gear / worm material type, elastic modulus, coefficient of friction, etc.), lubricating oil parameters (type, kinematic viscosity-temperature characteristics, etc.), and load parameters (rated load, fluctuation range, etc.).

[0036] 3.2 Parameter Input and Scene Settings: Input different combinations of design parameters into the digital twin model and set up various typical operating conditions for nuclear power plants (such as high temperature and high pressure). 3.3 Efficiency Simulation Calculation: The transmission efficiency calculation model is invoked to calculate the transmission efficiency ηcd and the overall efficiency ηzj under different parameter combinations and operating conditions, generating transmission efficiency variation curves, thereby improving the reliability of the design scheme.

[0037] This invention possesses significant technical advantages and practical value, adapting to the special application scenarios of high pressure and high temperature in nuclear power plants. It effectively solves the "black box" problem of transmission efficiency control in traditional electric actuators used in nuclear power plants, resulting in outstanding comprehensive benefits. During the R&D and design phase, by combining digital twin simulation with a hierarchical transmission efficiency calculation model, repeated prototype manufacturing and testing are unnecessary. It can quickly simulate transmission efficiency under different parameter combinations and operating conditions, accurately select the optimal design scheme, and efficiently evaluate the application effects of new materials and lubricants. This significantly shortens the R&D cycle, reduces trial-and-error costs, and helps products quickly adapt to the customized needs of nuclear power.

[0038] During the operation and maintenance phase, relying on fault prediction models and real-time sensor monitoring, the system enables online calculation of transmission efficiency, early warning of anomalies, and accurate fault identification. This breaks away from the traditional maintenance model that relies on fixed durations or intuitive anomalies, avoiding downtime and material waste caused by "over-maintenance," while preventing equipment failures, unplanned shutdowns, and significant operational losses caused by "under-maintenance," thus improving the operational stability and safety of electric actuators used in nuclear power plants. Furthermore, the model fully considers the impact of nuclear power plant operating conditions on transmission efficiency, quantifying the multi-factor coupling mechanism. Compared to existing technologies, it offers higher prediction accuracy and greater applicability, providing a scientific basis for energy saving, consumption reduction, and optimized management of electric actuators used in nuclear power plants, and promoting the upgrading of industrial automation equipment operation and maintenance technology in nuclear power plants.

[0039] Regarding the information disclosed in this case, the following points need to be clarified: (1) The accompanying drawings of the embodiments disclosed in this case only involve the structures involved in the embodiments disclosed in this case. Other structures can refer to the general design. (2) Where there is no conflict, the embodiments and features disclosed in this case can be combined with each other to obtain new embodiments; The above are merely specific embodiments disclosed in this case, but the scope of protection of this disclosure is not limited thereto. The scope of protection disclosed in this case shall be determined by the scope of protection of the claims.

Claims

1. A method for digital twin modeling and condition monitoring of electric actuators used in nuclear power plants, characterized in that, Includes the following steps: Step 1: Constructing the transmission efficiency calculation model: A hierarchical calculation model covering the overall efficiency ηzj and the transmission efficiency ηcd is constructed. The overall efficiency ηzj is obtained by coupled calculation of the motor efficiency ηdj, the transmission efficiency ηcd, and the bearing efficiency ηzc. The three are in series coupled relationship, and the mathematical expression is: ηzj=ηdj*ηcd*ηzc. The transmission device uses a worm gear transmission pair, and its transmission efficiency ηcd is calculated from the meshing efficiency and the oil churning loss, and the expression is: ηcd=ηnh*η jy ; Step 2, Implementation of the Fault Prediction Model: The worm gear of the actuator is located in a closed housing and is submerged in oil. Pressure and temperature sensors are installed in the lubricating oil tank of the actuator to collect real-time data on lubricating oil pressure F, temperature T, and motor operating parameters. These data are then substituted into the transmission efficiency calculation model to obtain the actual transmission efficiency ηcd, which is then combined with a preset normal pressure limit F. req Normal temperature limit T req Based on the temperature change rate f(t), a fault judgment logic is constructed to realize abnormal early warning and fault judgment of the transmission device; the specific judgment process is as follows: The sensor collects lubricating oil pressure F and oil temperature T in real time. Combined with motor current and speed parameters, the actual transmission efficiency η is calculated using a transmission efficiency calculation model. cd ; When F > F req At that time, a warning signal is sent to the actuator control system to indicate "abnormal lubricating oil pressure"; When T>T req At this time, a warning signal is sent to the actuator control system, indicating "abnormal lubricating oil temperature," and the calculation of the rate of temperature change is initiated. f(t)=(△ra—△ra') / △t; Where, △ra = (T 当前 —T 上一时刻 ) / △t; T 当前 T represents the current lubricating oil temperature. 上一时刻 This indicates the temperature of the lubricating oil sampled last time. The sampling period is 1 second. ra represents the temperature change over time, and ra' represents the temperature change over time at the previous moment.

2. The method for digital twin modeling and condition monitoring of electric actuators for nuclear power plants according to claim 1, characterized in that, In step two, when F > F req And T > T req When f(t) > 0, the transmission device is considered to have a risk of failure. If η cd If the speed drops by more than 10% compared to the normal state, it is directly judged that the transmission device has failed. The control system will issue a buzzer alarm and display "Transmission device failure" on the remote control system interface. If η cd If the condition is similar to the normal state, the message "Transmission device abnormal, close monitoring required" will be displayed.

3. The method for digital twin modeling and condition monitoring of electric actuators for nuclear power plants according to claim 2, characterized in that, When temperature T returns to T req and below, pressure F returns to F req When the fault alarm signal is at or below the specified level, the fault alarm signal will be automatically reset.

4. The method for digital twin modeling and condition monitoring of electric actuators for nuclear power plants according to claim 1, characterized in that, The meshing efficiency is calculated using the worm lead angle and the equivalent friction coefficient, and the mathematical expression is: etanh= tanγ / tan[γ +arctan(uv)], In the formula: ηnh is the meshing efficiency; γ is the lead angle of the worm; and uv is the equivalent friction coefficient.

5. The method for digital twin modeling and condition monitoring of electric actuators for nuclear power plants according to claim 4, characterized in that, The function for calculating the worm gear lead angle γ is: γ = arctan(z*m / d); In the formula: z is the number of worm threads; m is the module; d is the pitch circle diameter of the worm.

6. The method for digital twin modeling and condition monitoring of electric actuators for nuclear power plants according to claim 4, characterized in that, The equivalent friction coefficient uv is a composite function of material, lubricating oil, and temperature, and its expression is: uv = k1 * k2 * k3 * u; In the formula: u is the basic friction coefficient; k1 is the worm gear material correction factor; k2 is the load factor; k3 is the temperature factor.

7. The method for digital twin modeling and condition monitoring of electric actuators for nuclear power plants according to claim 6, characterized in that, The basic coefficient of friction u = A + B / (v) s +C) D , In the formula: A, B, C, and D are dimensionless parameters; v s This represents the average sliding speed of the worm gear meshing.

8. The method for digital twin modeling and condition monitoring of electric actuators for nuclear power plants according to claim 7, characterized in that, The average sliding speed v of the worm gear meshing s The expression is: v s = πdn / (cosγ*60*1000); In the formula: d is the pitch circle diameter of the worm; n is the rotational speed of the worm.

9. A method for digital twin modeling and condition monitoring of electric actuators for nuclear power plants according to claim 6, characterized in that, The expression for the load factor k2 is: K2 = 1 + 0.2 * log(Psj / Pe); In the formula: Psj is the actual load; Pe is the rated load.

10. A method for digital twin modeling and condition monitoring of electric actuators for nuclear power plants according to claim 6, characterized in that, The mathematical expression for the temperature coefficient k3 is: K3 = [nd p +(1 / nd) q ] / 2; In the formula: nd is the ratio of actual kinematic viscosity to design viscosity; p is the high viscosity side index; q is the low viscosity side index.

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