CN112100874A - Rotor blade health monitoring method and monitoring system based on digital twinning - Google Patents

Abstract

The invention discloses a method and system for rotor blade health monitoring based on digital twin. The method includes: establishing a three-dimensional model of a single blade, calculating the natural frequencies of various orders of the blade under different rotational speeds through finite element software, and using the rotor actually measured by the sensor. The difference between the blade vibration frequency and the natural frequencies of each order mode calculated by the finite element model is the objective function, and the material parameters and geometric parameters of the finite element model are used as design variables to construct the correction equation of the finite element model. The finite element reference model of the model is constructed; the model update sensitivity matrix is constructed to reflect the influence of the change of the element stiffness matrix on the natural frequency of the rotor blade; the sensitivity matrix is updated based on the model, and the real-time update equation of the digital twin model in service state is established; based on the real-time update equation , a sparse optimization model based on _lp norm is established; the element stiffness damage factor vector reflecting the damage position and degree of the rotor blade is obtained through the convex optimization method.

Description

Rotor blade health monitoring method and monitoring system based on digital twin

technical field

The invention belongs to the field of mechanical fault diagnosis, and relates to a rotor blade health monitoring method and monitoring system based on digital twin.

Background technique

Rotor blades are important components in aero-engines. The harsh working conditions such as high temperature, high pressure, and high speed during operation of aero-engines can easily cause the blades to vibrate, which in turn causes high-cycle fatigue of the blades, resulting in cracks and other damages to the blades. The damage and failure of aero-engine blades usually lead to changes in some vibration parameters of the blades, such as vibration frequency and amplitude. During the operation of the blade, by accurately monitoring its vibration parameters, locating the damage position of the blade, and evaluating the damage of the blade play an important role in reducing the operation and maintenance cost of the engine and ensuring the operation safety of the aero-engine.

The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art.

SUMMARY OF THE INVENTION

In view of the problems existing in the prior art, the present invention proposes a rotor blade health monitoring method and monitoring system based on digital twins. By exchanging data between the finite element reference model and the blade vibration parameters measured by the actual sensor, the blade damage location and the monitoring system can be realized. Damage assessment.

The invention establishes a digital twin model corresponding to the physical entity model, uses actual sensor measurement results and digital twin model calculation results for data association, realizes real-time update of the digital twin model, and accurately reflects the working state of the physical entity object. Traditional aero-engine fault detection is carried out by manual disassembly, which has a long diagnosis cycle and low detection efficiency, and cannot realize real-time rotor blade health monitoring. By installing the sensor on the actual engine, the vibration parameters of the blade can be measured, and the digital twin finite element model can be corrected by using the measurement results of the blade vibration parameters in the normal state to obtain the finite element reference model. Through the difference between the measured results and the finite element reference model, the digital twin model is updated in real time, and then the blade fault is located and the damage degree of the blade is evaluated, so as to realize the purpose of real-time rotor blade health monitoring.

The purpose of the present invention is to be achieved through the following technical solutions, a method for monitoring the health of rotor blades based on digital twins comprises the following steps:

In the first step, a three-dimensional model of the rotor blade is established, the natural frequency f _FE of each order mode of the blade at different speeds is calculated by finite element, and the finite element model is corrected according to the modal information measured in the initial crack-free state of the rotor blade. The finite element reference model for digital twin is obtained, in which the difference between the natural frequency f _FE of each order mode calculated by the finite element and the actual blade vibration frequency f _m measured by the sensor is used as the objective function, and the material parameters of the rotor blade are used as the objective function. M=[E ρ μ] and geometric parameter G=[lwh α] are design variables, with the supremum VHB and infimum VLB of material parameters and geometric parameters as constraints, the finite element model correction equation is constructed:

Among them, E is the elastic modulus of the material, ρ is the density, μ is the Poisson’s ratio, l is the length of the blade, w is the width of the blade, h is the thickness of the blade, α is the angle of attack of the blade, and the finite element model correction equation is continuously adjusted based on the evolutionary algorithm The material parameters and geometric parameters in the model can minimize the value of the objective function and obtain the finite element benchmark model for digital twins;

其中，ψ_j为转子叶片的有限元模型第j阶质量归一化模态振型，

为所述的有限元基准模型中第i个单元的单元刚度矩阵，Q_i为有限元基准模型中单元刚度矩阵与总体刚度矩阵之间的关系矩阵，上标T表示矩阵或矢量的转置；In the second step, construct the sensitivity matrix:

where ψ _j is the normalized mode shape of the jth order mass of the finite element model of the rotor blade,

is the element stiffness matrix of the i-th element in the finite element reference model, Q _i is the relationship matrix between the element stiffness matrix and the overall stiffness matrix in the finite element reference model, and the superscript T represents the transposition of the matrix or vector;

其中，K(t_s)表示转子叶片有限元模型第t_s时刻对应的总体刚度矩阵，n_ele表示有限元模型中的单元数量，i表示第i个单元，θ_i(t_s)表示第t_s时刻，转子叶片第i个单元的损伤因子，Q_i为有限元基准模型中，单元刚度矩阵与总体刚度矩阵之间的关系矩阵,

为有限元基准模型中第i个单元的单元刚度矩阵，In the third step, a real-time update model of the digital twin in service state is established, wherein a parameterized stiffness damage model is established based on the overall stiffness matrix and element stiffness matrix of the rotor blade finite element reference model:

Among them, K(t _s ) represents the overall stiffness matrix corresponding to the t _s time of the finite element model of the rotor blade, n _ele represents the number of elements in the finite element model, i represents the ith element, and θ _i (t _s ) represents the t th At time _s , the damage factor of the ith element of the rotor blade, Q _i is the relationship matrix between the element stiffness matrix and the overall stiffness matrix in the finite element benchmark model,

is the element stiffness matrix of the ith element in the finite element benchmark model,

表示转子叶片损伤前后的模态频率变化量；f_d和f_u分别表示转子叶片损伤前后传感器测得的实际叶片模态频率，S为灵敏度矩阵，

是待求的单元刚度损伤因子矢量，ε为噪声矢量，n_f是传感器测得的实际叶片模态频率数量，n_ele是有限元模型中的单元数量；A real-time update model of the digital twin in service state is established based on the sensitivity matrix: Δf=Sθ+ε, where,

represents the variation of the modal frequency before and after the rotor blade is damaged; f _d and f _u represent the actual blade modal frequency measured by the sensor before and after the rotor blade is damaged, S is the sensitivity matrix,

is the element stiffness damage factor vector to be determined, ε is the noise vector, n _f is the number of actual blade modal frequencies measured by the sensor, and n _ele is the number of elements in the finite element model;

其中，

表示二范数的平方，||·||_p表示p范数，λ表示正则化参数，利用凸优化方法求解基于l_p范数的稀疏优化模型，得到唯一确定的单元刚度损伤因子矢量

根据θ中非零元素所在位置，对应转子叶片有限元模型中的损伤单元位置，非零元素的大小对应损伤单元的损伤严重程度。In the fourth step (S4), a sparse optimization model based on the norm of l _p (0≤p≤1) is established:

in,

represents the square of the second norm, ||·|| _p represents the _p -norm, and λ represents the regularization parameter. The convex optimization method is used to solve the sparse optimization model based on the lp-norm, and the uniquely determined element stiffness damage factor vector is obtained.

According to the location of the non-zero elements in θ, it corresponds to the position of the damaged element in the finite element model of the rotor blade, and the size of the non-zero element corresponds to the damage severity of the damaged element.

In the described method, the first step includes:

S101, obtaining a three-dimensional model of the rotor blade based on the proportional three-dimensional modeling of the shape of the rotor blade, and establishing a finite element model of the rotor blade based on the finite element calculation,

S102: Determine the maximum rotational speed Rm reached during the actual operation of the rotor blade, and use the finite element to calculate the modal natural frequencies of each order of the three-dimensional model of the rotor blade at rotational speeds ranging from 0 to Rm,

S103. Detect the rotor blades before they run to ensure that there is no fault before the actual blades run,

S104. Install sensors in the rotor blade casing and the surrounding operating environment, so that the rotor blade speed is increased from 0 to Rm and then decelerated to 0, and the data measured by all the sensors are obtained,

S105 , after the rotor blade operation is completed, check the blade. If a fault occurs after checking the actual blade operation, replace the blade and repeat steps S103 , S104 and S105 , until the actual blade operation does not contain a fault.

In the described method, in step S104, the sensor used to measure the blade vibration parameter is a blade tip timing sensor, and the measurement of the blade vibration frequency includes the following steps:

S1041. Install the blade tip timing sensor on the engine casing, measure the time when the blade reaches the sensor, and use the time signal measured by the rotational speed sensor as a reference to calculate the blade tip vibration displacement:

y=2πf _r R _tip Δt, where Δt=t _expected -t _actual , _fr is the blade rotation frequency; R _tip is the distance from the rotor axis of rotation to the blade tip; t _expected is the time when the blade reaches the sensor when the blade does not vibrate ;t _actual is the time the blade actually reaches the sensor measured by the blade tip timing system,

S1042, construct a compressive sensing reconstruction model according to the measured blade vibration displacement y:

得到非欠采样重构信号为：Y＝Dα，其中，D为离散余弦字典，α为非欠采样重构信号Y在离散余弦字典D下的稀疏表示，Φ为观测矩阵与叶端定时传感器的安装位置相关，ε₁为容许误差，

The obtained non-undersampling reconstructed signal is: Y=Dα, where D is the discrete cosine dictionary, α is the sparse representation of the non-undersampling reconstructed signal Y under the discrete cosine dictionary D, and Φ is the difference between the observation matrix and the blade-end timing sensor. The installation position is related, ε ₁ is the allowable error,

S1043, according to the non-undersampling reconstruction signal Y, calculate the actual blade vibration frequency f _m :

f _m =FFT(Y), where FFT(.) represents the discrete Fourier transform.

In the described method, when θ _i =0, it means that the ith unit of the rotor blade is not damaged, and when θ _i =1, it means that the ith unit of the rotor blade is completely damaged.

In the method, in the third step, the noise vector ε includes the modal frequency measurement error and the model numerical calculation error.

According to another aspect of the present invention, a monitoring system implementing the method includes:

an entity measurement module, which includes,

The rotor blade under test,

a drive that drives the rotor blades to rotate,

The sensor, which measures the rotor blade to obtain the vibration parameters of the blade,

A digital twin module, which receives the blade vibration parameters obtained in the physical measurement module, the digital twin module includes a finite element reference model calculation unit, and the finite element reference model calculation unit is based on the sensor in the initial crack-free state of the rotor blade measured The vibration parameters of to generate a finite element benchmark model for the digital twin,

A data association module, which connects the digital twin module and the entity measurement module, and the data association module includes,

a sensitivity matrix calculation unit, which generates a sensitivity matrix based on the finite element reference model,

A digital twin real-time update model generation unit, which establishes a real-time update model of the digital twin in service state based on the sensitivity matrix,

The sparse optimization calculation unit is based on the convex optimization method to solve the sparse optimization model based on the _lp norm to obtain a uniquely determined element stiffness damage factor vector. The damage unit position of , and the size of non-zero elements corresponds to the damage severity of the damage unit.

Corresponding the position of the damaged unit in the finite element model with the rotor blade under test in the entity measurement module, and determining the damage position of the rotor blade under test.

beneficial effect

The present invention provides a method to correct a finite element model through an optimization algorithm, obtain a digital twin model corresponding to a physical entity, construct a model update sensitivity matrix, thereby establish a real-time update model of the digital twin in service state, and utilize the sparse optimization based on the _lp norm. Compared with the traditional manual detection, the method provided by the present invention can realize the real-time monitoring of the health status of the blades, reduce the downtime and maintenance time, and reduce the maintenance cost. Safety provides assurance.

Description of drawings

Various other advantages and benefits of the present invention will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings in the description are for the purpose of illustrating the preferred embodiments only, and are not to be considered as limiting the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative effort. Also, the same components are denoted by the same reference numerals throughout the drawings.

In the attached image:

Fig. 1 is the flow chart of the rotor blade health monitoring method based on digital twin;

Figure 2 is a schematic diagram of a digital twin based rotor blade health monitoring system.

The present invention will be further explained below in conjunction with the accompanying drawings and embodiments.

Detailed ways

Specific embodiments of the present invention will be described in more detail below with reference to FIGS. 1 to 2 . While specific embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present invention will be more thoroughly understood, and will fully convey the scope of the present invention to those skilled in the art.

It should be noted that certain terms are used in the description and claims to refer to specific components. It should be understood by those skilled in the art that the same component may be referred to by different nouns. The description and the claims do not use the difference in terms as a way to distinguish components, but use the difference in function of the components as a criterion for distinguishing. As referred to throughout the specification and claims, "comprising" or "including" is an open-ended term and should be interpreted as "including but not limited to". Subsequent descriptions in the specification are preferred embodiments for implementing the present invention, however, the descriptions are for the purpose of general principles of the specification and are not intended to limit the scope of the present invention. The scope of protection of the present invention should be determined by the appended claims.

To facilitate the understanding of the embodiments of the present invention, the following will take specific embodiments as examples for further explanation and description in conjunction with the accompanying drawings, and each accompanying drawing does not constitute a limitation to the embodiments of the present invention.

For better understanding, Figure 1 is a flow chart of a method for monitoring rotor blade health based on digital twin. As shown in Figure 1, the method for monitoring rotor blade health based on digital twin includes the following steps:

In the first step, a three-dimensional finite element model of the rotor blade is established, and the modal natural frequencies of each order at different speeds of the blade are calculated by the finite element software. amend;

In the second step, construct the model update sensitivity matrix;

In the third step, a real-time update model of the digital twin in service state is established;

In the fourth step, a sparse optimization model based on the l _p (0≤p≤1) norm is established, and the convex optimization method is used to solve it;

In the described method, in the first step, establishing a rotor blade model and revising the model includes the following steps:

S101 , according to the shape of the rotor blade in actual use, perform an isometric three-dimensional modeling. The actual blade is scanned by a contact aspheric measuring instrument, and a relatively accurate three-dimensional model of the rotor blade is obtained, and the finite element model of the blade is established in ANSYS.

S102 determines the maximum rotational speed Rm that the rotor blade can reach during the actual operation, and uses finite element analysis software to calculate the natural frequencies of each order of the three-dimensional model of the rotor blade at a rotational speed of 0-Rm.

S103. Detect the rotor blades before they run to ensure that there are no faults before the actual blades run.

S104 , a blade tip timing sensor is installed around the actual rotor blade, and a non-contact measurement method is used to detect the resonance frequency of the rotor blade. Let the rotor blade speed run up from 0-Rm and then down to 0. Then install a temperature sensor near the rotor blade to measure the temperature of the rotor blade when it is working, and save the data measured by the blade end timing sensor and the temperature sensor.

S105, after the rotor blades are finished running, check the blades again. If a fault occurs after checking the actual blade operation, replace the blade and repeat steps S103, S104 and S105 until the actual blade operation does not contain a fault.

S106. Substitute the rotating speed of the blade and the temperature measured by the temperature sensor into the finite element software to calculate the difference between the natural frequency f _FE of each order mode of the rotor blade and the actual blade vibration frequency f _m measured by the blade tip timing sensor as The objective function, with the material parameter M=[E ρ μ] and the geometric parameter G=[lwh α] of the rotor blade as the design variables, and the supremum VHB and the infimum VLB of the material parameters and geometric parameters as the constraints, is constructed. The finite element model correction equation:

subjected to VLB≤{M,G}≤VHB

Among them, E is the elastic modulus of the material, ρ is the density, μ is the Poisson's ratio, l is the length of the blade, w is the width of the blade, h is the thickness of the blade, and α is the angle of attack of the blade. The material parameters and geometric parameters in the correction equation of the finite element model are continuously adjusted by the evolutionary algorithm, so that the value of the objective function is minimized, and the finite element reference model for digital twin can be obtained.

In the method, in step S104, the sensor used to measure the blade vibration parameter is a blade tip timing sensor, and the measurement of the blade vibration frequency includes the following steps:

S1041. Install the blade tip timing sensor on the engine casing, measure the time when the blade reaches the sensor, and use the time signal measured by the rotational speed sensor as a reference to calculate the blade tip vibration displacement:

y=2πf _r R _tip Δt

Among them, Δt=t _expected -t _actual , f _r is the rotational frequency of the blade; R _tip is the distance from the rotor axis of rotation to the blade tip; t _expected is the time when the blade reaches the sensor under ideal conditions (that is, the blade does not vibrate); t _actual is the actual arrival time of the blade at the sensor as measured by the blade tip timing system.

S1042. According to the measured blade vibration displacement y, construct a compressive sensing reconstruction model:

The non-undersampled reconstructed signal is obtained as:

Y=Dα

Among them, D is the discrete cosine dictionary, α is the sparse representation of the non-undersampled reconstructed signal Y under the discrete cosine dictionary D, Φ is the correlation between the observation matrix and the installation position of the blade-end timing sensor, and ε is the allowable error.

S1043, according to the non-undersampling reconstruction signal Y, calculate the actual blade vibration frequency f _m :

f _m =FFT(Y)

where FFT(.) represents the discrete Fourier transform.

In the described method, in the second step, the constructed model update sensitivity matrix is:

为步骤S106所述的有限元基准模型中第i个单元的单元刚度矩阵，Q_i为有限元基准模型中，单元刚度矩阵与总体刚度矩阵之间的关系矩阵，上标T表示矩阵或矢量的转置。where ψ _j is the normalized mode shape of the jth order mass of the rotor blade finite element model,

is the element stiffness matrix of the i-th element in the finite element reference model described in step S106, Q _i is the relationship matrix between the element stiffness matrix and the overall stiffness matrix in the finite element reference model, and the superscript T represents the matrix or vector Transpose.

In the described method, in the third step, a real-time update model of the digital twin in service state is established, which mainly includes the following steps:

S301, using the change of the overall stiffness matrix K in the finite element reference model to reflect the damage of the blade. Based on the overall stiffness matrix and element stiffness matrix of the rotor blade finite element benchmark model, a parametric stiffness damage model is established:

为有限元基准模型中第i个单元的单元刚度矩阵。Among them, K(t _s ) represents the overall stiffness matrix corresponding to the t _s time of the finite element model of the rotor blade, n _ele represents the number of elements in the finite element model, i represents the ith element, and θ _i (t _s ) represents the t th At time _s , the damage factor of the ith unit of the rotor blade, when θ _i = 0, it means that the ith unit of the rotor blade is not damaged, when θ _i = 1, it means that the ith unit of the rotor blade is completely damaged, and Q _i is the finite element In the benchmark model, the relationship matrix between the element stiffness matrix and the overall stiffness matrix,

is the element stiffness matrix of the ith element in the finite element base model.

S302, establish a model update equation based on the sensitivity matrix:

Δf=Sθ+ε

表示转子叶片损伤前后的模态频率变化量，可用于判断转子叶片是否出现损伤；f_d和f_u分别表示转子叶片损伤前后传感器测得的实际叶片模态频率，S为灵敏度矩阵，

是待求的单元刚度损伤因子矢量，可表示单元损伤位置、程度，噪声矢量ε包含模态频率测量误差和模型数值计算误差，n_f是传感器测得的实际叶片模态频率数量，n_ele是有限元模型中的单元数量。in,

represents the variation of the modal frequency before and after the rotor blade is damaged, which can be used to judge whether the rotor blade is damaged; f _d and f _u represent the actual blade modal frequency measured by the sensor before and after the rotor blade is damaged, S is the sensitivity matrix,

is the element stiffness damage factor vector to be determined, which can represent the damage location and degree of the element. The noise vector ε includes the modal frequency measurement error and the model numerical calculation error, n _f is the number of actual blade modal frequencies measured by the sensor, and n _ele is The number of elements in the finite element model.

In the described method, in the fourth step, a sparse optimization model based on l _p (0≤p≤1) norm is established:

表示二范数的平方，||·||_p表示p范数，λ表示正则化参数。利用凸优化方法求解上述基于l_p范数的稀疏优化模型，可以得到唯一确定的单元刚度损伤因子矢量

根据θ中非零元素所在位置，即可对应转子叶片有限元模型中的损伤单元位置，非零元素的大小对应损伤单元的损伤严重程度。in,

represents the square of the two-norm, ||·|| _p represents the p-norm, and λ represents the regularization parameter. Using the convex optimization method to solve the above sparse optimization model based on the _lp norm, the uniquely determined element stiffness damage factor vector can be obtained.

According to the location of the non-zero elements in θ, it can correspond to the position of the damaged element in the finite element model of the rotor blade, and the size of the non-zero element corresponds to the damage severity of the damaged element.

In another aspect, a monitoring system implementing the method includes:

an entity measurement module, which includes,

The rotor blade under test,

a drive that drives the rotor blades to rotate,

The sensor, which measures the rotor blade to obtain the vibration parameters of the blade,

The digital twin module, which receives the blade vibration parameters obtained in the entity measurement module, the digital twin module includes a finite element reference model calculation unit, and the finite element reference model calculation unit is based on the sensor measured in the initial crack-free state of the rotor blade. The vibration parameters generate a finite element reference model for the digital twin,

A data association module, which connects the digital twin module and the entity measurement module, and the data association module includes,

a sensitivity matrix calculation unit, which generates a sensitivity matrix based on the finite element reference model,

A digital twin real-time update model generation unit, which establishes a real-time update model of the digital twin in service state based on the sensitivity matrix,

The sparse optimization calculation unit is based on the convex optimization method to solve the sparse optimization model based on the _lp norm to obtain a uniquely determined element stiffness damage factor vector. The damage unit position of , and the size of non-zero elements corresponds to the damage severity of the damage unit.

Further, in one embodiment, as shown in FIG. 2 , a monitoring system for implementing the method includes:

The physical entity module mainly includes the real rotor blade, the driving device that drives the blade to rotate, and the sensor that measures the operating state of the blade, and uses the signal measured by the sensor to identify the vibration parameters of the blade, providing data for the digital twin module.

The digital twin module mainly includes a three-dimensional finite element model constructed based on physical entities, and through the data of fault-free rotor blades measured by the physical entity module, the three-dimensional finite element model is corrected by an optimization method to obtain a finite element reference model.

The data association module uses the element stiffness matrix, the overall stiffness matrix and the mode shape of the finite element model to construct the sensitivity matrix, and the stiffness damage model. During the service process of the rotor blade, the real-time data measured by the sensor of the physical entity module is compared with the finite element model. Then, the variation of vibration parameters can be obtained by comparing with the basic reference model, and the sparse optimization model based on the l _p (0≤p≤1) norm is used to solve the model update equation, and the finite element model of the digital twin module is updated with the obtained results. Monitor rotor blade health in the Physical Solids module.

Although the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments and application fields, and the above-mentioned specific embodiments are only illustrative and instructive, rather than restrictive . Those of ordinary skill in the art can also make many forms under the inspiration of this specification and without departing from the scope of protection of the claims of the present invention, which all belong to the protection of the present invention.

Claims (6)

1. A method for monitoring rotor blade health based on digital twin, the method comprising the steps of: In the first step, a three-dimensional model of the rotor blade is established, the natural frequency f _FE of each order mode of the blade at different speeds is calculated by finite element, and the finite element model is corrected according to the modal information measured in the initial crack-free state of the rotor blade. The finite element reference model for digital twin is obtained, in which the difference between the natural frequency f _FE of each order mode calculated by the finite element and the actual blade vibration frequency f _m measured by the sensor is used as the objective function, and the material parameters of the rotor blade are used as the objective function. M=[E ρ μ] and geometric parameter G=[lwh α] are design variables, with the supremum VHB and infimum VLB of material parameters and geometric parameters as constraints, the finite element model correction equation is constructed:

Among them, E is the elastic modulus of the material, ρ is the density, μ is the Poisson’s ratio, l is the length of the blade, w is the width of the blade, h is the thickness of the blade, α is the angle of attack of the blade, and the finite element model correction equation is continuously adjusted based on the evolutionary algorithm The material parameters and geometric parameters in the model can minimize the value of the objective function and obtain the finite element benchmark model for digital twins; In the second step, construct the sensitivity matrix:

where ψ _j is the normalized mode shape of the jth order mass of the finite element model of the rotor blade,

is the element stiffness matrix of the i-th element in the finite element reference model, Q _i is the relationship matrix between the element stiffness matrix and the overall stiffness matrix in the finite element reference model, and the superscript T represents the transposition of the matrix or vector; In the third step, a real-time update model of the digital twin in service state is established, wherein a parameterized stiffness damage model is established based on the overall stiffness matrix and element stiffness matrix of the rotor blade finite element reference model:

Among them, K(t _s ) represents the overall stiffness matrix corresponding to the t _s time of the finite element model of the rotor blade, n _ele represents the number of elements in the finite element model, i represents the ith element, and θ _i (t _s ) represents the t th At time _s , the damage factor of the ith element of the rotor blade, Q _i is the relationship matrix between the element stiffness matrix and the overall stiffness matrix in the finite element benchmark model,

is the element stiffness matrix of the ith element in the finite element benchmark model, A real-time update model of the digital twin in service state is established based on the sensitivity matrix: Δf=Sθ+ε, where,

represents the variation of the modal frequency before and after the rotor blade is damaged; f _d and f _u represent the actual blade modal frequency measured by the sensor before and after the rotor blade is damaged, S is the sensitivity matrix,

is the element stiffness damage factor vector to be determined, ε is the noise vector, n _f is the number of actual blade modal frequencies measured by the sensor, and n _ele is the number of elements in the finite element model; In the fourth step, a sparse optimization model based on l _p (0≤p≤1) norm is established:

in,

represents the square of the second norm, ||·|| _p represents the _p -norm, and λ represents the regularization parameter. The convex optimization method is used to solve the sparse optimization model based on the lp-norm, and the uniquely determined element stiffness damage factor vector is obtained.

According to the location of the non-zero elements in θ, it corresponds to the position of the damaged element in the finite element model of the rotor blade, and the size of the non-zero element corresponds to the damage severity of the damaged element. 2. The method according to claim 1, wherein, preferably, the first step comprises: S101, obtaining a three-dimensional model of the rotor blade based on the proportional three-dimensional modeling of the shape of the rotor blade, and establishing a finite element model of the rotor blade based on the finite element calculation, S102: Determine the maximum rotational speed Rm reached during the actual operation of the rotor blade, and use the finite element to calculate the modal natural frequencies of each order of the three-dimensional model of the rotor blade at rotational speeds ranging from 0 to Rm, S103. Detect the rotor blades before they run to ensure that there is no fault before the actual blades run, S104. Install sensors in the rotor blade casing and the surrounding operating environment, so that the rotor blade speed is increased from 0 to Rm and then decelerated to 0, and the data measured by all the sensors are obtained, S105 , after the rotor blade operation is completed, check the blade. If a fault occurs after checking the actual blade operation, replace the blade and repeat steps S103 , S104 and S105 , until the actual blade operation does not contain a fault. 3. The method according to claim 2, wherein, in step S104, the sensor for measuring the blade vibration parameter is a blade tip timing sensor, and its measurement of the blade vibration frequency comprises the following steps: S1041. Install the blade tip timing sensor on the engine casing, measure the time when the blade reaches the sensor, and use the time signal measured by the rotational speed sensor as a reference to calculate the blade tip vibration displacement: y=2πf _r R _tip Δt, where Δt=t _expected -t _actual , _fr is the blade rotation frequency; R _tip is the distance from the rotor axis of rotation to the blade tip; t _expected is the time when the blade reaches the sensor when the blade does not vibrate ;t _actual is the time the blade actually reaches the sensor measured by the blade tip timing system, S1042, construct a compressive sensing reconstruction model according to the measured blade vibration displacement y:

The obtained non-undersampling reconstructed signal is: Y=Dα, where D is the discrete cosine dictionary, α is the sparse representation of the non-undersampling reconstructed signal Y under the discrete cosine dictionary D, and Φ is the difference between the observation matrix and the blade-end timing sensor. The installation position is related, ε ₁ is the allowable error, S1043, according to the non-undersampling reconstruction signal Y, calculate the actual blade vibration frequency f _m : f _m =FFT(Y), where FFT(.) represents the discrete Fourier transform. 4 . The method of claim 1 , wherein when θ _i =0, it means that the ith unit of the rotor blade is not damaged, and when θ _i =1, it means that the ith unit of the rotor blade is completely damaged. 5. The method according to claim 1, wherein, in the third step, the noise vector ε includes the modal frequency measurement error and the model numerical calculation error. 6. A monitoring system implementing the method of any one of claims 1-5, the monitoring system comprising: an entity measurement module, which includes, The rotor blade under test, a drive that drives the rotor blades to rotate, The sensor, which measures the rotor blade to obtain the vibration parameters of the blade, The digital twin module, which receives the blade vibration parameters obtained in the entity measurement module, the digital twin module includes a finite element reference model calculation unit, and the finite element reference model calculation unit is based on the sensor measured in the initial crack-free state of the rotor blade. The vibration parameters generate a finite element reference model for the digital twin, A data association module, which connects the digital twin module and the entity measurement module, and the data association module includes, a sensitivity matrix calculation unit, which generates a sensitivity matrix based on the finite element reference model, A digital twin real-time update model generation unit, which establishes a real-time update model of the digital twin in service state based on the sensitivity matrix, The sparse optimization calculation unit is based on the convex optimization method to solve the sparse optimization model based on the _lp norm to obtain a uniquely determined element stiffness damage factor vector. The damage unit position of , and the size of non-zero elements corresponds to the damage severity of the damage unit.

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