State monitoring and fault diagnosis method based on digital twinborn sensing framework

Abstract

The invention relates to a state monitoring and fault diagnosis method based on a digital twinning sensing framework, which comprises an offline stage and an online stage, and the offline stage comprises the following steps: dividing a numerical calculation discrete model according to a physical structure of industrial equipment to be detected; based on the numerical calculation discrete model, determining a partial differential equation corresponding to the physical field, and collecting and obtaining a data set of the physical field; data analysis is carried out on the data set, real-time calculation of a high-dimensional physical field is realized, a reliability evaluation system, a physical filtering system and an abnormal working condition identification system are constructed respectively, and a digital twinning perception framework is formed; the on-line stage comprises the following steps of: acquiring actually measured data of industrial equipment to be measured, inputting the actually measured data into a digital twinning perception framework, and performing reliability evaluation, physical filtering and abnormal working condition identification. Compared with the prior art, the method is high in calculation efficiency, has interpretability, and can efficiently and accurately carry out state monitoring and fault diagnosis.

Description

Technical Field

[0001] The present invention relates to the field of equipment status monitoring, and in particular to a status monitoring and fault diagnosis method based on a digital twin perception framework. Background Art

[0002] The main goal of equipment operation and maintenance is to ensure long-term, stable and efficient performance while being able to respond quickly when faults occur. Key technologies in this area include condition monitoring and fault diagnosis, which have been widely used in industrial environments, especially in areas where equipment reliability and life are critical, such as petrochemicals, manufacturing, and power systems. In particular, for components operating under extreme conditions, the number of sensors is strictly limited, such as in high-temperature rotors, where the installation of sensors is extremely difficult, resulting in extremely sparse data. This makes it extremely challenging to obtain reliable, physically interpretable high-dimensional physical information in real time and to effectively diagnose complex faults. However, in industrial environments, components operating under extreme conditions are usually high-cost and high-risk, and it is these components that have the most urgent need for efficient and accurate condition monitoring and fault diagnosis.

[0003] At present, there are three main types of rapid assessment technologies for condition monitoring and fault diagnosis:

[0004] The first is the traditional condition monitoring and fault diagnosis technology. This technology uses sensors and data acquisition systems to collect key signals of the equipment (such as vibration, temperature, pressure and noise) in real time. It is worth noting that these signals are low-dimensional due to the limitation of the number of sensors. Then, data-driven algorithms or empirical methods are used to analyze and process the collected signals to generate reference low-dimensional data, thereby revealing the health status of the equipment. However, using low-dimensional reference data as the basis for condition monitoring and fault diagnosis is usually not reliable enough.

[0005] The second is a data-driven reduced-order model to achieve real-time state monitoring. The data-driven reduced-order model uses the numerical simulation results as the data set, and performs proper orthogonal decomposition (POD) on the data set to obtain the reduced-order orthogonal basis Φ of the physical field. In order to achieve the technology of inverting high-dimensional physical fields from low-dimensional sensor signals, there are two types of technical routes. The first type uses machine learning, Gaussian regression or related fitting techniques to establish a nonlinear relationship between low-dimensional sensor signals and low-dimensional orthogonal basis coefficients to achieve the mapping relationship between the two. Then, based on the theory of proper orthogonal decomposition, the process of inverting high-dimensional physical fields from low-dimensional sensor signals is realized. The second type uses low-dimensional sensor signals as constraints to find the best approximate physical field in the reduced-order orthogonal basis space through the least squares method. Although the two methods are driven by numerical solutions with physical knowledge as data sets, the solution process is still a purely data-driven processing method and still lacks reliability. In addition, these two methods will be unstable or unable to calculate when constrained by extremely sparse sensor data.

[0006] The third is a physically driven reduced-order model for real-time calculations. However, this method embeds a low-dimensional orthogonal basis into the physical equation to achieve dimensionality reduction of the equation. This has good physical interpretability and stability, but its essence is a forward calculation problem that requires clear boundary conditions. In order to achieve the inverse solution of sparse sensor data constraints, a forward iteration method can be used to minimize the minimum error between numerical simulation and sensor measurement and optimize the boundary conditions for solution, but this will greatly increase the amount of calculation. Especially for transient nonlinear problems, each time step needs to be iterated, which increases the time cost.

[0007] Since abnormal working conditions are often outside the data set, the results of online status evaluation of abnormal working conditions will be biased to a certain extent or even completely deviate. This requires reliability evaluation of the calculation results, which the above three methods cannot do. Summary of the invention

[0008] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a state monitoring and fault diagnosis method based on a digital twin perception framework, a non-intrusive physically driven inverse hyper-reduced order model (NIPI-HROM) with extremely sparse sensor constraints, and a digital twin perception framework consisting of a reliability assessment system, a physical filtering system and an abnormal operating condition identification system is established with NIPI-HROM as the core.

[0009] The purpose of the present invention can be achieved by the following technical solutions:

[0010] A condition monitoring and fault diagnosis method based on a digital twin perception framework includes an offline stage and an online stage. The offline stage includes the following steps:

[0011] Divide the numerical calculation discrete model according to the physical structure of the industrial equipment to be tested;

[0012] Based on the numerical calculation discrete model, determine the partial differential equation corresponding to the physical field, and collect and obtain the data set of the physical field;

[0013] Performing eigendecomposition on the data set to obtain corresponding singular values ​​and eigenvectors, thereby calculating a reduced orthogonal basis of the data set;

[0014] Based on the reduced orthogonal basis, discrete empirical interpolation analysis is used to select key physical units to obtain a key physical unit index matrix and a key physical unit index matrix;

[0015] The numerical calculation discrete model, the reduced orthogonal basis, the key physical unit index matrix and the key physical unit index matrix are implanted into the partial differential equation of the physical field to realize the real-time calculation of the high-dimensional physical field, and then the reliability evaluation system, the physical filtering system and the abnormal condition identification system are respectively constructed to form a digital twin perception framework;

[0016] The online phase includes the following steps:

[0017] The measured data of the industrial equipment to be tested is obtained and input into the digital twin perception framework. The measured data is corrected in real time by the physical filtering system to obtain the measurement data after noise is eliminated. The error range of the physical field is calculated and evaluated by the reliability evaluation system. The abnormal working condition identification system is used to identify whether an abnormal working condition occurs.

[0018] Furthermore, the partial differential equation of the physical field is expressed as:

[0019]

[0020] In the formula, R t is the nonlinear residual of the equation at time t, u is the high-dimensional physical field, f is the nonlinear differential operator, g is the nonlinear source term, x is the sensor data value, and μ is the system parameter.

[0021] Furthermore, the partial differential equation of the physical field is iteratively solved using the Newton-Raphson iterative format until the nonlinear residual approaches 0, thereby obtaining the physical field at the current moment.

[0022] Furthermore, in the iterative solution process, the calculation expression at the kth iteration step is:

[0023]

[0024]

[0025] Where Δu is the increment of the physical field, and are the Jacobian matrix and nonlinear residual vector at the kth iteration step at time t,

[0026] Furthermore, the calculation expression for feature decomposition of the data set is:

[0027] M=U∑V

[0028] Where M is the correlation matrix composed of the physical field data set at different times and under different parameterization conditions, and the columns of U correspond to MM T The columns of V correspond to the eigenvectors of M T The eigenvectors of M, ∑ are the singular values ​​s1,...,s n A diagonal array.

[0029] Furthermore, the calculation expression of the reduced orthogonal basis is:

[0030]

[0031]

[0032] Where Φ is a reduced orthogonal basis, v∈V=[v1,...,v r ], r is the reduced orthogonal basis degree of freedom, ∈ is the weight coefficient.

[0033] Furthermore, the calculation expression of the key physical unit index matrix is:

[0034]

[0035]

[0036] In the formula, Z p is the key physical unit index matrix, is the identity matrix Middle n column vectors, where

[0037] Furthermore, the calculation form obtained by implanting the numerical calculation discrete model, the reduced orthogonal basis, the key physical unit index matrix and the key physical unit index matrix into the partial differential equation of the physical field is:

[0038]

[0039]

[0040] In the formula, the sharp corner symbol represents a low-dimensional variable, is the modal coefficient increment, and is the incomplete Jacobian matrix and nonlinear residual vector of the physical key unit set and sensor constraints, w sensor is the numerical weight of the sensor, Φ is the simplified orthogonal basis, Z p is the key physical unit index matrix, Z s is the sensor unit index matrix, and u is the high-dimensional physical field.

[0041] Furthermore, the reliability evaluation system uses the sum of the nonlinear residuals of the converged physical units obtained after performing real-time calculation of the high-dimensional physical field as the physical residual R p , taking the relative L2 norm error of the physical field as E L2 , thus based on the physical residual R p and the relative L2 norm error E L2 Draw a reliability characteristic curve to evaluate the error of the calculation results.

[0042] Furthermore, the calculation expression of the reliability characteristic curve is:

[0043]

[0044] The physical filtering system determines the stability of the sensor through the reliability evaluation system and gives greater weight to stable sensor data. sensor , giving smaller weight w to unstable sensor data sensor ;

[0045] The abnormal condition identification system is based on the physical residual R calculated by the reliability evaluation system. p The rate of change of the absolute value is compared with the preset stable change rate range to determine whether there is a drastic change, thereby determining whether an abnormal situation occurs.

[0046] Furthermore, the industrial equipment to be tested is a high-temperature rotor to be temperature monitored.

[0047] Compared with the prior art, the present invention has the following advantages:

[0048] (1) Reverse calculation of physical fields: Traditional methods require the iterative method of forward calculation to achieve reverse calculation. The present invention determines the accurate boundary conditions through Newton-Raphson iteration in the offline stage. In the online stage, the NIPI-HROM of the present invention can directly perform reverse calculation without the need to calculate the accurate boundary through iteration.

[0049] (2) Reliability evaluation system: The traditional method requires a few sensor data as the basis for calculating reliability evaluation, which wastes sensor information. The present invention uses physical residuals as the basis for reliability evaluation and does not require authentic sensor information.

[0050] (3) Physical filtering system: In traditional methods, the filtering algorithm of sensors is often based on statistical generalizations. Filtering algorithms based on different statistical preferences will produce different results, and the interpretability, reliability and physical significance of the filtering are relatively weak. The present invention uses a reliability assessment system driven by physical knowledge as the judgment standard, realizes the system-level filtering function, and has made substantial progress in interpretability and filtering objects.

[0051] (4) Abnormal working condition identification system: The traditional method uses vibration modal identification to analyze the working status of the current equipment. However, the vibration signal is single and cannot obtain the physical field changes inside the equipment, making it difficult to locate the specific fault. This paper uses the reliability evaluation system as the judgment standard for the abnormal working condition alarm system. It can not only give abnormal working condition reminders, but also output abnormal working condition physical fields with certain reference value, which is convenient for staff to make timely and correct rescue plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is an important component of a digital twin perception framework provided in an embodiment of the present invention;

[0053] Figure 2 An overall architecture diagram of an online transient thermal state evaluation based on a finite element reduced-order model provided in an embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram of the architecture of a condition monitoring and fault diagnosis method based on a digital twin perception framework provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0056] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0058] Example 1

[0059] like Figure 3 As shown, this embodiment provides a condition monitoring and fault diagnosis method based on a digital twin perception framework, including an offline stage and an online stage, wherein the offline stage includes the following steps:

[0060] S101: Divide the numerical calculation discrete model according to the physical structure of the industrial equipment to be tested;

[0061] Specifically: divide the numerical calculation discrete model based on the actual physical structure in the industrial equipment, define the unit properties and material properties, and set different types of boundary conditions according to the actual working conditions.

[0062] S102: determining a partial differential equation corresponding to a physical field based on the numerical calculation discrete model, and collecting a data set of the physical field;

[0063] Specifically: determine the partial differential equations of the relevant physical problems, establish and set parameterized sample operating intervals, and perform transient nonlinear numerical analysis. During the transient analysis, collect data sets of the physical field at several moments - [u(t)]. The basic form of the relevant partial differential equation is recorded as:

[0064]

[0065] In the formula, u represents the desired high-dimensional physical field, and f and g represent the above nonlinear differential operator and nonlinear source term, respectively. For the convenience of expression, equation (1) can be written as:

[0066]

[0067] In the formula, R t Represents the nonlinear residual of the equation at time t. The present invention adopts the Newton-Raphson iteration format to solve equation (2), wherein the kth iteration step is:

[0068]

[0069] Where Δu is the increment of the physical field, and are the Jacobian matrix and nonlinear residual vector at the kth iteration step at time t. Continuously iterate until When it approaches 0, the high-fidelity physical field at that moment is obtained and stored in the data set.

[0070] S103: performing eigendecomposition on the data set to obtain corresponding singular values ​​and eigenvectors, thereby calculating a reduced orthogonal basis of the data set;

[0071] Singular Value Decomposition (SVD) is applied to perform data compression and feature decomposition on the data set collected in step S102 to obtain its singular value and feature vector, which are recorded as:

[0072] M=U∑V (4)

[0073] Where M is the correlation matrix of the data set in step S102 at different times and under different parameterization conditions, and the columns of U are the corresponding MM T The columns of V correspond to the eigenvectors of M T The eigenvectors of M, ∑ are the singular values ​​(s1, ..., s n ) is a diagonal matrix.

[0074] Obtaining a reduced orthogonal basis of the data set: The reduced orthogonal basis of the data set can be represented by the feature vector in step S103 and the data set in step S102:

[0075]

[0076] Where v∈V=[v1,...,v r ], the degree of freedom r of the reduced-order basis is usually dozens or several, which is much smaller than the discrete degree of freedom N (r<<N), and the weight coefficient ∈ is usually greater than 95%. Then the obtained reduced-order orthogonal basis Φ is used to realize the low-dimensional linear expression of high-dimensional physical variables in orthogonal space:

[0077]

[0078] S104: Select key physical units using Discrete Empirical Interpolation Methods (DEIM) based on the reduced orthogonal basis to obtain a key physical unit index matrix and a key physical unit index matrix;

[0079] Key physical unit index matrix [Z p The calculation expression of ] is:

[0080]

[0081] in is the identity matrix Middle n column vector, denoted as:

[0082]

[0083] S105: implanting the numerical calculation discrete model, the reduced orthogonal basis, the key physical unit index matrix and the key physical unit index matrix into the partial differential equation of the physical field to realize real-time calculation of the high-dimensional physical field;

[0084] Specifically, based on the numerical calculation discrete model obtained in step S101, the reduced-order orthogonal basis Φ, the key physical unit index Z p and the extremely sparse sensor unit index Z s Inserting into equation (2), we get the following form:

[0085]

[0086] Among them, the sharp corner symbols represent low-dimensional variables. is the modal coefficient increment, and is the incomplete Jacobian matrix and nonlinear residual vector of the physical key unit set and sensor constraints, w sensor is the numerical weight of the sensor. Combining equation (6) and equation (9) can realize the real-time calculation of high-dimensional physical field.

[0087] S106: Build a reliability assessment system, a physical filtering system, and an abnormal operating condition identification system to form a digital twin perception framework;

[0088] Specifically include:

[0089] Establish a reliability assessment system: define the sum of the nonlinear residuals of the physical unit after equation (9) converges as the physical residual R p , the relative L2 norm error of the physical field is E L2 . Based on a large number of calculations, it is found that |R p | and E L2 It satisfies the linear positive correlation, and this positive correlation is named the reliability characteristic curve:

[0090]

[0091] Therefore, even without relying on new sensor data, the error assessment of the calculation results can be realized. And it is essentially driven by physical knowledge and has physical interpretability.

[0092] Establishing a physical filtering system: Different from the traditional statistical filtering algorithm, the filtering system of the present invention is established under the reliability evaluation system of physical drive in step S106. sensor The basic idea is to use the reliability evaluation system to determine the stability of the sensor and give greater weight to stable sensor data, and vice versa. sensorThe calculation process of is essentially an optimization problem, which can be calculated using various optimization methods.

[0093] Establish an abnormal operating condition identification system: When | R p |When there is a drastic change, it means that an abnormal working condition has occurred. At this time, the physical field output by NIPI-HROM in real time is evaluated through the reliability evaluation system to determine the error range of the physical field, and then this is used as an important basis to find the location and cause of the component failure.

[0094] The online phase includes the following steps:

[0095] S201: Obtain the measured data of the industrial equipment to be tested, input it into the digital twin perception framework, and correct the measured data in real time through the physical filtering system to obtain the measurement data after noise is eliminated; calculate and evaluate the error range of the physical field through the reliability evaluation system; and identify whether an abnormal working condition occurs through the abnormal working condition identification system.

[0096] In this example, a typical high-temperature rotor is used as the object. In modern high-temperature mechanical systems, high-temperature rotors are key components widely used in steam turbines, gas turbines, turbochargers and other equipment. These rotor components are subjected to extreme thermal and mechanical loads under high temperature, high speed and high pressure operating conditions. In order to ensure the stability and reliability of these rotors under such harsh conditions, effective operation and maintenance are essential. Temperature monitoring plays a vital role in operation and maintenance, for example, early detection of potential overheating problems and thermal imbalance, thereby optimizing maintenance strategies and ensuring safe operation and long-term stability of equipment.

[0097] The high-speed rotation of the warm rotor and the high-temperature and high-pressure steam driving the rotor to work make it extremely difficult to arrange the sensors, making the number of sensors extremely sparse, and often only two or three or even one sensor point data can be obtained indirectly. Under this condition, in order to calculate a reliable high-dimensional physical field in real time and identify abnormal working conditions, this embodiment proposes the following Figure 1 The figure shows a NIPI-HROM constrained by extremely sparse sensors, and a digital twin perception framework consisting of a reliability evaluation system, a physical filtering system, and an abnormal condition identification system is established with NIPI-HROM as the core. Figure 3 As shown, the specific implementation of the method is mainly divided into two stages: offline data analysis and online temperature evaluation.

[0098] Offline data analysis phase:

[0099] The actual high-temperature rotor is physically modeled using CAD modeling software, and numerical mesh discretization is performed based on the finite element method. The discrete degrees of freedom are usually tens of thousands or even hundreds of thousands.

[0100] The different operating conditions during the operation of the high-temperature rotor are comprehensively considered, including basic operating parameters such as the steam temperature rise rate, the convective heat transfer coefficient between the high-temperature steam and the mechanical wall, and the test operating parameters under different loading parameters are obtained.

[0101] Conduct high-fidelity nonlinear heat transfer simulation based on finite element analysis, such as Figure 2 As shown, the third type of thermal boundary conditions in the operating conditions, namely the temperature of the steam and the convective heat transfer coefficient between the high-temperature steam and the wall, are fully considered. In each time step of the transient analysis process, the high-fidelity heat conduction matrix, heat capacity matrix and temperature field numerical results are collected in turn after the temperature field results converge through a nonlinear iterative process.

[0102] The collected matrix data set is the result physical quantity of finite element analysis, namely, temperature field T. The correlation matrix of the data set is subjected to singular value decomposition, and their singular values ​​and eigenvectors are obtained in turn. The simplified orthogonal basis of different data sets is obtained according to the weight of the singular value, which is denoted as Φ. The simplified degrees of freedom are usually a few or dozens, and the degrees of freedom are greatly reduced.

[0103] According to the specific sensor layout and discrete empirical interpolation analysis (DEIM), the key physical units are selected to obtain the sensor unit index matrix Z s and the key physical unit index matrix Z p .

[0104] Online thermal assessment stage:

[0105] During high-temperature rotor operation, the data measured by the sensor is input into NIPI-HROM, and the sensor weight is corrected in real time through the physical filtering system to output the global temperature field after noise elimination; the error range of the calculated physical field is evaluated through the reliability evaluation system; and the abnormal operating condition identification system is used to identify whether an abnormal operating condition occurs. It is worth noting that the transient thermal boundary conditions do not need to be input during the calculation process of NIPI-HROM, which enables fast and direct reverse calculation.

[0106] The offline phase in the last embodiment includes a lot of data collection, data analysis and data extraction. Good computing hardware and parallel software design can provide more convenient data processing and analysis methods. Its work content is the precondition for the online thermal state assessment. The work in the online phase is not sensitive to the computing hardware requirements and can be quickly implemented by relying on ordinary single-core computers.

[0107] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A condition monitoring and fault diagnosis method based on a digital twin perception framework, characterized in that: The method comprises an offline phase and an online phase, wherein the offline phase comprises the following steps: Divide the numerical calculation discrete model according to the physical structure of the industrial equipment to be tested; Based on the numerical calculation discrete model, determine the partial differential equation corresponding to the physical field, and collect and obtain the data set of the physical field; Performing eigendecomposition on the data set to obtain corresponding singular values ​​and eigenvectors, thereby calculating a reduced orthogonal basis of the data set; Based on the reduced orthogonal basis, discrete empirical interpolation analysis is used to select key physical units to obtain a key physical unit index matrix and a key physical unit index matrix; The numerical calculation discrete model, the reduced orthogonal basis, the key physical unit index matrix and the key physical unit index matrix are implanted into the partial differential equation of the physical field to realize the real-time calculation of the high-dimensional physical field, and then the reliability evaluation system, the physical filtering system and the abnormal condition identification system are respectively constructed to form a digital twin perception framework; The online phase includes the following steps: The measured data of the industrial equipment to be tested is obtained and input into the digital twin perception framework. The measured data is corrected in real time by the physical filtering system to obtain the measurement data after noise is eliminated. The error range of the physical field is calculated and evaluated by the reliability evaluation system. The abnormal working condition identification system is used to identify whether an abnormal working condition occurs.

2. According to a condition monitoring and fault diagnosis method based on a digital twin perception framework according to claim 1, it is characterized in that: The partial differential equation of the physical field is expressed as: In the formula, R t is the nonlinear residual of the equation at time t, u is the high-dimensional physical field, f is the nonlinear differential operator, g is the nonlinear source term, x is the sensor data value, and μ is the system parameter.

3. A condition monitoring and fault diagnosis method based on a digital twin perception framework according to claim 2, characterized in that: The partial differential equation of the physical field is iteratively solved using the Newton–Raphson iterative format until the nonlinear residual approaches 0, thereby obtaining the physical field at the current moment.

4. According to a condition monitoring and fault diagnosis method based on a digital twin perception framework according to claim 2, it is characterized in that: In the iterative solution process, the calculation expression at the kth iteration step is: Where Δu is the increment of the physical field, and are the Jacobian matrix and nonlinear residual vector at the kth iteration step at time t, 5. The condition monitoring and fault diagnosis method based on the digital twin perception framework according to claim 1 is characterized in that: The calculation expression for feature decomposition of the data set is: M=U∑V Where M is the correlation matrix composed of the physical field data set at different times and under different parameterization conditions, and the columns of U correspond to MM T The columns of V correspond to the eigenvectors of M T The eigenvectors of M, Σ are the singular values ​​s1,…,s n A diagonal array.

6. A condition monitoring and fault diagnosis method based on a digital twin perception framework according to claim 5, characterized in that: The calculation expression of the reduced orthogonal basis is: Where Φ is a reduced orthogonal basis, v∈V=[v1,…,v r ], r is the reduced orthogonal basis degree of freedom, ∈ is the weight coefficient.

7. A condition monitoring and fault diagnosis method based on a digital twin perception framework according to claim 6, characterized in that: The calculation expression of the key physical unit index matrix is: In the formula, Z p is the key physical unit index matrix, is the identity matrix Middle n A column vector.

8. The condition monitoring and fault diagnosis method based on the digital twin perception framework according to claim 1 is characterized in that: The calculation form obtained by implanting the numerical calculation discrete model, the reduced orthogonal basis, the key physical unit index matrix and the key physical unit index matrix into the partial differential equation of the physical field is: In the formula, the sharp corner symbol represents a low-dimensional variable, is the modal coefficient increment, and is the incomplete Jacobian matrix and nonlinear residual vector of the physical key unit set and sensor constraints, w sensor is the numerical weight of the sensor, Φ is the simplified orthogonal basis, Z p is the key physical unit index matrix, Z s is the sensor unit index matrix, and u is the high-dimensional physical field.

9. The condition monitoring and fault diagnosis method based on the digital twin perception framework according to claim 1 is characterized in that: The reliability evaluation system uses the sum of the nonlinear residuals of the converged physical units obtained after real-time calculation of the high-dimensional physical field as the physical residual R p , taking the relative L2 norm error of the physical field as E L2 , thus based on the physical residual R p and the relative L2 norm error E L2 Draw a reliability characteristic curve to evaluate the error of the calculation results.

10. A condition monitoring and fault diagnosis method based on a digital twin perception framework according to claim 9, characterized in that: The calculation expression of the reliability characteristic curve is: The physical filtering system determines the stability of the sensor through the reliability evaluation system and gives greater weight to stable sensor data. sensor , giving smaller weight w to unstable sensor data sensor ; The abnormal condition identification system is based on the physical residual R calculated by the reliability evaluation system. p The rate of change of the absolute value is compared with the preset stable change rate range to determine whether there is a drastic change, thereby determining whether an abnormal situation occurs.

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