Blade tip timing vibration parameter identification method and system based on multiple measurement vectors

Abstract

The invention discloses a blade tip timing vibration parameter identification method and system based on multiple measurement vectors. The method comprises the following steps: constructing a vibration parameter identification compressed sensing model of multiple measurement blade tip displacement signals based on a multiple measurement snapshot matrix; and solving the vibration parameter identification compressed sensing model of the multiple measurement blade tip displacement signals to obtain the vibration parameters of the rotor blade. According to the method, prior information such as excitation order is not needed, the relevance between continuous measurement vectors is utilized, and the robustness and the capability of filtering the rotation frequency and the frequency doubling of the rotation frequency are effectively improved.

Description

Technical Field

[0001] The invention belongs to the technical field of non-contact measurement of rotor blades of rotating machinery, and relates to a blade tip timing vibration parameter identification method and system based on multiple measurement vectors. Background Art

[0002] Rotating blades are an important part of turbine machinery systems. Operating under complex conditions will cause blade vibration, resulting in significant reduction in fatigue life and performance, and may cause serious accidents. Blade non-contact measurement technology represented by blade tip timing has attracted widespread attention from academia and industry.

[0003] The blade tip timing signal does not satisfy the Shannon sampling theorem due to its non-uniform undersampling characteristics, which makes the signal analysis method based on Fourier transform face challenges in processing such data. The application of sparse reconstruction method to reconstruct undersampled BTT signals helps to fully extract potential blade vibration information, thereby producing more accurate blade vibration parameter identification results. However, the traditional sparse reconstruction method of BTT signals based on single measurement vector (SMV) often has the problem of poor robustness. Summary of the invention

[0004] In order to overcome the above problems, the present invention proposes a blade tip timing vibration parameter identification method and system based on multiple measurement vectors, the method comprising: step 1, installing a blade tip timing sensor at a circumferential position of a rotor blade casing, and obtaining the actual time series of the rotor blade reaching the blade tip timing sensor; step 2, constructing a multi-measurement snapshot matrix according to the time difference between the ideal time and the actual time of the rotor blade reaching the blade tip timing sensor, the rotor blade rotation frequency and the rotation radius; step 3, constructing a vibration parameter identification compressed sensing model of a multi-measurement blade tip displacement signal based on the multi-measurement snapshot matrix; step 4, solving the vibration parameter identification compressed sensing model of the multi-measurement blade tip displacement signal using the MMV-OMP algorithm to obtain the vibration parameters of the rotor blade. The MMV-OMP algorithm is used to solve the constructed vibration parameter identification compressed sensing model of the multi-measurement blade tip displacement signal, without prior information such as the excitation order, and the correlation between continuous measurement vectors is used to effectively improve the robustness and the ability to filter out the rotation frequency and its multiple frequencies, providing a more reliable and accurate technical means for rotor blade vibration monitoring and diagnosis, which has important theoretical significance and broad practical application prospects, thereby completing the present invention.

[0005] Specifically, the purpose of the present invention is to provide the following aspects:

[0006] In one aspect, a method for identifying blade tip timing vibration parameters is provided, the method comprising:

[0007] A compressive sensing model for vibration parameter identification of multi-measurement blade tip displacement signals is constructed based on a multi-measurement snapshot matrix.

[0008] The compressed sensing model for vibration parameter identification of multiple measured blade tip displacement signals is solved to obtain the vibration parameters of the rotor blades.

[0009] Wherein, a time-frequency diagram of the rotor blade vibration signal is obtained according to the rotor blade vibration parameters.

[0010] Among them, the vibration parameters of the rotor blades are obtained based on the time-frequency diagram of the rotor blade vibration signal.

[0011] The parameters include vibration frequency and amplitude.

[0012] Wherein, the parameters also include the vibration mode number.

[0013] Among them, the multi-measurement snapshot matrix has the following representation:

[0014] D = [ d ( 1 ) ， d ( 2 ) ， … ， d ( l ) ] = [ d i , 1 1 d i , 1 n + 1 ⋯ d i , 1 ( l − 1 ) n + 1 ⋮ ⋮ ⋱ ⋮ d i , N p 1 d i , N p n + 1 ⋯ d i , N p ( l − 1 ) n + 1 ⋮ ⋮ ⋱ ⋮ d i , 1 n d i , 1 2 n ⋯ d i , 1 l n ⋮ ⋮ ⋱ ⋮ d i , N p n d i , N p 2 n ⋯ d i , N p l n ] ,

[0015] in, Indicates a single measurement snapshot vector, is the rotor blade serial number, Indicates the number of circles, Number of blade tip timing sensors.

[0016] Among them, the number of blade tip timing sensors is 4 to 10.

[0017] Among them, constructing a vibration parameter identification compressed sensing model for multiple measured blade tip displacement signals includes the following steps:

[0018] Convert the vibration displacement of the rotor blades into superimposed vibration displacements of each order of modalities;

[0019] According to the superimposed vibration displacements of each order mode, the sparse representation equation of the rotor blade vibration is obtained;

[0020] According to the sparse representation equation of the rotor blade vibration, a sparse transformation matrix is ​​obtained;

[0021] According to the sparse transformation matrix, the vibration parameter identification compressed sensing model of multi-measurement blade tip displacement signals is obtained.

[0022] Among them, the multi-measurement snapshot matrix is ​​constructed according to the vibration displacement of the rotor blades.

[0023] On the other hand, a blade tip timing vibration parameter identification system is provided, the system comprising:

[0024] A rotor blade vibration parameter identification module, which is used to construct a vibration parameter identification compressed sensing model of multi-measurement blade tip displacement signals based on a multi-measurement snapshot matrix;

[0025] The rotor blade vibration parameter identification module is used to solve the vibration parameter identification compressed sensing model of multiple measured blade tip displacement signals to obtain the vibration parameters of the rotor blade.

[0026] The beneficial effects of the present invention include:

[0027] (1) The blade tip timing vibration parameter identification method provided by the present invention is based on the key row sparse prior information that multiple snapshot vectors share the same sparse structure, and expands the compressed sensing of the blade tip timing single measurement vector to the category of multiple measurement vectors, that is, establishing a multi-measurement snapshot matrix, and further constructing a vibration parameter identification compressed sensing model of the multi-measurement blade tip displacement signal, which overcomes the deficiency of the existing compressed sensing model that only uses the frequency domain sparsity of the blade vibration signal.

[0028] (2) The blade tip timing vibration parameter identification method provided by the present invention uses the row sparseness of the sparse representation coefficient matrix as a constraint, and uses the MMV-OMP algorithm to solve the vibration parameter identification compressed sensing model of multiple measured blade tip displacement signals, thereby realizing blade tip timing vibration parameter identification and obtaining the vibration parameters of the rotor blade. This method does not require prior information such as the excitation order, and uses the correlation between continuous measurement vectors to effectively improve the robustness and the ability to filter out the rotation frequency and its multiple frequencies, thereby retaining more effective information.

[0029] (3) The blade tip timing vibration parameter identification method provided by the present invention uses the MMV-OMP algorithm to solve the vibration parameter identification compressed sensing model of the constructed multi-measurement blade tip displacement signal, which significantly improves the accuracy of BTT spectrum reconstruction and enhances the ability to resist noise interference. This innovative method not only improves the effect of signal processing, but also provides a more reliable and accurate technical means for rotor blade vibration monitoring and diagnosis, which has important theoretical significance and broad practical application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] By reading the detailed description of the preferred specific embodiments below, various other advantages and benefits of the present invention will become clear to those of ordinary skill in the art. The drawings in the specification are only used for the purpose of illustrating the preferred embodiments and are not considered to be limitations of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative work.

[0031] In the attached picture:

[0032] Figure 1 A flow chart of a blade tip timing vibration parameter identification method according to the present invention is shown;

[0033] FIG2 (a) shows a time domain signal diagram of simulated blade vibration displacement obtained based on the MMV-OMP method of the present invention at a constant speed;

[0034] Figure 2 (b) shows the time-frequency diagram of the rotor blade vibration signal obtained by analyzing the traditional method based on SMV at constant speed;

[0035] FIG2( c ) shows a time-frequency diagram of the vibration signal of the rotor blades obtained by analyzing the MMV-OMP method of the present invention at a constant speed;

[0036] FIG3 (a) shows a time domain diagram of a vibration signal obtained based on the MMV-OMP method of the present invention under variable speed;

[0037] Fig. 3 (b) shows the time-frequency diagram of the rotor blade vibration signal obtained by analyzing the traditional method based on SMV under variable speed;

[0038] FIG3 (c) shows a time-frequency diagram of the vibration signal of the rotor blades obtained by analyzing the MMV-OMP method of the present invention under variable speed;

[0039] Figure 4 A comparison chart of blade vibration amplitude reconstruction accuracy obtained by analyzing the MMV-OMP method of the present invention and the traditional SMV method is shown. DETAILED DESCRIPTION

[0040] The following will refer to the attached Figure 1 to Figure 4 Specific embodiments of the present invention are described in more detail. Although specific embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0041] It should be noted that certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that technicians may use different nouns to refer to the same component. This specification and claims do not use the difference in nouns as a way to distinguish components, but use the difference in the functions of the components as the criterion for distinction. As mentioned throughout the specification and claims, "including" or "comprising" is an open term, so it should be interpreted as "including but not limited to". The subsequent description of the specification is a preferred embodiment of the present invention, but the description is based on the general principles of the specification and is not intended to limit the scope of the present invention. The scope of protection of the present invention shall be determined by the attached claims.

[0042] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front", "rear", etc. indicate positions or positional relationships based on the working state of the present invention, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", and "fourth" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0043] To facilitate understanding of the embodiments of the present invention, further explanation will be given below by taking specific embodiments as examples in conjunction with the accompanying drawings, and each of the accompanying drawings does not constitute a limitation on the embodiments of the present invention.

[0044] In one aspect, a blade tip timing vibration parameter identification method is provided according to the present invention, the method comprising:

[0045] Step 1, installing a blade tip timing sensor at a circumferential position of a rotor blade casing, and obtaining an actual time series of the rotor blade reaching the blade tip timing sensor;

[0046] Step 2, constructing a multi-measurement snapshot matrix according to the time difference between the ideal time and the actual time when the rotor blade reaches the blade tip timing sensor, the rotor blade rotation frequency and the rotation radius;

[0047] Step 3, constructing a vibration parameter identification compressed sensing model of multi-measurement blade tip displacement signals based on the multi-measurement snapshot matrix;

[0048] Step 4: Use the MMV-OMP algorithm to solve the vibration parameter identification compressed sensing model of the multi-measurement blade tip displacement signal to obtain the vibration parameters of the rotor blade.

[0049] The blade tip timing vibration parameter identification method is described in detail below.

[0050] Step 1: Install a blade tip timing sensor at a circumferential position of a rotor blade casing, and obtain an actual time series of the rotor blade reaching the blade tip timing sensor.

[0051] In step 1, the number of excited modes is first determined and recorded as , determine the number of blade tip timing sensors according to the number of modes ,in ; Afterwards, The blade tip timing sensors are installed at the circumferential position of the rotor blade casing; then, the distribution angle and position of the blade tip timing sensors are determined based on the particle swarm optimization algorithm to ensure that the sampling points of the blade tip timing sensors can effectively cover all excited modes, thereby improving the accuracy and reliability of modal parameter identification. Through this process, an accurate and scientific blade tip timing sensor layout is established, laying a solid foundation for subsequent vibration monitoring and analysis.

[0052] Further, Usually 4~10.

[0053] In step 1, the collected pulse signal is preprocessed to obtain the actual time when the rotor blade reaches the blade tip timing sensor. , its physical meaning is The rotor blades are The actual arrival time of the circle The actual time of each blade tip timing sensor.

[0054] In step 1, the actual time when the rotor blade reaches the blade tip timing sensor is arranged into a series of data points according to the snapshot time sequence, so as to obtain the actual time series.

[0055] Furthermore, the preprocessing includes: collecting blade tip timing data, selecting a predetermined threshold for the original pulse signal, and screening the first The rotor blades are The actual arrival time of the circle The actual time of the blade tip timing sensor The threshold is generally chosen to be half of the pulse peak value.

[0056] Step 2: construct a multi-measurement snapshot matrix based on the time difference between the ideal time and the actual time when the rotor blade reaches the blade tip timing sensor, the rotor blade rotation frequency and the rotation radius.

[0057] In step 2, the ideal time for the rotor blade to arrive at the blade tip timing sensor is expressed as: ,in, is the rotor blade serial number, is the blade tip timing sensor number, Indicates the number of circles, Indicates the shaft rotation frequency. Indicates The installation angle of the blade tip timing sensor, Indicates the serial number is The ideal time series is understood as the ideal time for the rotor blade to reach the blade tip timing sensor arranged as a series of data points in snapshot time order.

[0058] Furthermore, the time difference between the theoretical arrival time of the rotor blade tip under the ideal condition without blade vibration and the actual arrival time measured in step 1 is obtained by the following formula: Arrange them into a series of data points in the order of snapshot time to get the sequence time difference.

[0059] In step 2, the time difference between the theoretical arrival time and the actual arrival time of the rotor blade tip is calculated. , rotor blade speed and the radius of rotation Obtain the vibration displacement of the rotor blade tip: Arranging them into a series of data points in the order of snapshot time can obtain a single measurement snapshot vector, i.e., the sequence number The rotor blades are in the order of Tip timing sensor rotation The vibration displacement data vector obtained from each cycle.

[0060] Furthermore, the single-measurement snapshot vector is constructed into a multi-measurement snapshot matrix, which is specifically expressed as:

[0061] D = [ d ( 1 ) ， d ( 2 ) ， … ， d ( l ) ] = [ d i , 1 1 d i , 1 n + 1 ⋯ d i , 1 ( l − 1 ) n + 1 ⋮ ⋮ ⋱ ⋮ d i , N p 1 d i , N p n + 1 ⋯ d i , N p ( l − 1 ) n + 1 ⋮ ⋮ ⋱ ⋮ d i , 1 n d i , 1 2 n ⋯ d i , 1 l n ⋮ ⋮ ⋱ ⋮ d i , N p n d i , N p 2 n ⋯ d i , N p l n ] ,in Indicates A single measurement snapshot vector.

[0062] Furthermore, the multi-measurement snapshot matrix is ​​understood to be the snapshot of all rotor blades over a period of time. The matrix composed of the vibration displacement data vectors measured by the blade tip timing sensors reflects the vibration state of the rotor blades in multiple rotation cycles.

[0063] Step 3: construct a vibration parameter identification compressed sensing model for multi-measurement blade tip displacement signals based on the multi-measurement snapshot matrix.

[0064] The inventors found that within a certain observation time, all snapshot vectors in the snapshot matrix have the same frequency domain sparse characteristics, and the non-zero point positions remain unchanged, that is, the support set of the vector remains fixed. Based on the key sparse prior information that multiple snapshot vectors share the same sparse structure, a compressed sensing model for vibration parameter identification of multi-measured blade tip displacement signals was established.

[0065] According to a preferred embodiment, step 3 comprises:

[0066] Step 3-1, converting the vibration displacement of the rotor blade into superimposed vibration displacements of each order of modal vibrations;

[0067] Step 3-2, obtaining a sparse representation equation of the rotor blade vibration according to the superimposed vibration displacements of each order of the modal vibrations;

[0068] Step 3-3, obtaining a sparse transformation matrix according to the sparse representation equation of the rotor blade vibration;

[0069] Step 3-4, based on the sparse transformation matrix, a vibration parameter identification compressed sensing model of multiple measured blade tip displacement signals is obtained.

[0070] In step 3-1, the vibration displacement of the rotor blade is converted into superimposed vibration displacements of each order mode, which is expressed as: , For rotor blade The modal vibration frequency, and Respectively is the coefficient of the blade vibration equation of the order, and m is the number of excited modes.

[0071] In step 3-2, the superimposed vibration displacements of each order are combined with the first Modal vibration frequency With The excitation order corresponding to the first mode The relationship between: , the vibration displacement expression is further expressed as: . Combine it with the relation: , and further obtain the sparse representation equation of the rotor blade vibration: , where M is the number of vibration modes of interest.

[0072] In step 3-3, according to the sparse representation equation of the rotor blade vibration, the sparse transformation matrix is ​​obtained:

[0073] F = [ s i n ( D E O i 1 ) c o s ( D E O i 1 ) ⋯ s i n ( N D E O i 1 ) c o s ( N D E O i 1 ) ⋮ ⋮ ⋱ ⋮ ⋮ s i n ( D E O i N p ) c o s ( D E O i N p ) ⋯ s i n ( N D E O i N p ) s i n ( N D E O i N p ) ⋮ ⋮ ⋱ ⋮ ⋮ s i n ( D E O ( i 1 + 2 ∗ ( n − 1 ) π ) c o s ( D E O ( i 1 + 2 ∗ ( n − 1 ) π ) ⋯ s i n ( N D E O ( i 1 + 2 ∗ ( n − 1 ) π ) c o s ( N D E O ( i 1 + 2 ∗ ( n − 1 ) π ) ⋮ ⋮ ⋱ ⋮ ⋮ s i n ( D E O ( i N p + 2 ∗ ( n − 1 ) π ) c o s ( D E O ( i N p + 2 ∗ ( n − 1 ) π ) ⋯ s i n ( N D E O ( i N p + 2 ∗ ( n − 1 ) π ) c o s ( N D E O ( i N p + 2 ∗ ( n − 1 ) π ) ] ,in is the excitation order resolution of the reconstructed signal, is the number of frequency components of the reconstructed signal, then the identified frequency range is [ D E O , 2 D E O , ⋯ , N D E O ] , the maximum frequency component of the reconstructed signal is: .

[0074] In step 3-4, the coefficients of the sparse representation equation of the rotor blade vibration corresponding to each frequency component form a sparse representation coefficient matrix:

[0075] S = [ a 1 1 1 a 1 1 2 ⋯ a 1 1 l a 2 1 1 a 2 1 2 ⋯ a 2 1 l ⋮ ⋮ ⋱ ⋮ a 1 N 1 a 1 N 2 ⋯ a 1 N l a 2 N 1 a 2 N 2 ⋯ a 2 N l ] , then the multi-measurement snapshot matrix , sparse transformation matrix and the sparse representation coefficient matrix The relationship is: , which is the sparse representation equation for reconstructing undersampled signals.

[0076] Furthermore, through This equation is solved to reconstruct the undersampled BTT signal. But for the sparse representation coefficient matrix The solution of is an underdetermined problem, and the uniqueness of the solution is difficult to guarantee. Therefore, the present invention constructs a vibration parameter identification compressed sensing model of multi-measurement blade tip displacement signals according to the sparse representation equation of the reconstructed undersampled signal, which is expressed as:

[0077] ,in, ,

[0078] in Representation coefficient matrix No. OK, represents the power of a set, Represents the p-norm of a vector.

[0079] When satisfied , That is the coefficient matrix of the vibration equation The solution of the above model can also be called the row sparse recovery problem. The introduction of row sparse constraints makes it possible to solve the above underdetermined problem.

[0080] Step 4: Use the MMV-OMP algorithm to solve the compressed sensing model of the multi-measurement blade tip displacement signal to obtain the vibration parameters of the rotor blade.

[0081] In step 4, the orthogonal matching pursuit algorithm (OMP) in the greedy algorithm is used and expanded in the context of multiple measurement vectors (MMV) to form the MMV-OMP algorithm. Through the solution of this algorithm, the blade vibration coefficient matrix with row sparse characteristics is successfully obtained, thereby further obtaining the vibration parameters of the rotor blades.

[0082] In step 4, the vibration parameters include frequency, amplitude, and vibration mode number.

[0083] Furthermore, the inner product between each atom in the sparse transformation dictionary matrix and the current residual is first calculated, and the maximum value is selected to determine the index of the most relevant atom (i.e. the position of the non-zero rows in the sparse transformation matrix), and use the least squares method to estimate the sparse representation coefficient matrix to be reconstructed , thereby realizing the solution of the vibration parameter identification compressed sensing model of multiple measured blade tip displacement signals. The specific steps of the algorithm are:

[0084] Where K is the coefficient matrix of the vibration equation The row sparsity of For the The set of index numbers of all selected basis vectors updated after iterations, For the The updated residual after iterations, S ^ ( i ) [ L ] For the The sparse representation coefficient matrix output after iterations Estimated value, in order to solve , the above algorithm takes input and After that, we get Estimates. Includes the kth vibration equation coefficient , , thus obtaining the k-order vibration equation: , where the amplitude , is the vibration angular frequency, and the number of vibration frequency components is the number of vibration modes.

[0085] In seeking The vibration equations of the blades of each order can be obtained, thereby obtaining the vibration parameters of the rotor blades, which include the vibration frequency, amplitude and vibration mode number.

[0086] Furthermore, according to the vibration parameters of the rotor blades, a time-frequency diagram of the vibration signal of the rotor blades is obtained, and the above-mentioned vibration frequency and amplitude are obtained accordingly.

[0087] In the present invention, in order to meet the needs of rotor blade vibration parameter identification, based on the key row sparse prior information that multiple snapshot vectors share the same sparse structure, the blade tip timing single measurement vector (SMV) compressed sensing is expanded to the multi-measurement vector (MMV) category, that is, a multi-measurement snapshot matrix is ​​established, and a vibration parameter identification compressed sensing model of multi-measurement blade tip displacement signals is further constructed, breaking through the deficiency of the existing compressed sensing model that only uses the frequency domain sparsity of blade vibration signals. Compared with the classic compressed sensing model under the SMV framework, the compressed sensing model under the MMV framework makes full use of the row sparse characteristics of the coefficient matrix under continuous sampling, which can reduce the probability of atomic misselection, suppress the rotation frequency and its multi-frequency interference components under variable speed conditions, improve the aggregation and robustness of the time-frequency reconstruction of blade vibration signals within the full operating speed range, and greatly reduce the signal reconstruction time when processing batch blade tip timing data.

[0088] Furthermore, the row sparseness of the sparse representation coefficient matrix is ​​used as a constraint, and the MMV-OMP algorithm is used to solve the vibration parameter identification compressed sensing model of multiple measured blade tip displacement signals, thereby realizing the blade tip timing vibration parameter identification and obtaining the vibration parameters of the rotor blades. This method does not require prior information such as the excitation order, and uses the correlation between continuous measurement vectors to effectively improve the robustness and the ability to filter out the rotation frequency and its multiples, thereby retaining more effective information.

[0089] On the other hand, a blade tip timing vibration parameter identification system is provided, the system comprising:

[0090] A blade tip timing sensor layout module, which is used to install blade tip timing sensors at circumferential positions of the rotor blade casing and obtain a time series of the rotor blades reaching the blade tip timing sensors;

[0091] A vibration displacement calculation module, which is used to construct a multi-measurement snapshot matrix based on the time difference between the ideal time and the actual time when the rotor blade reaches the blade tip timing sensor, the rotor blade rotation frequency and the rotation radius;

[0092] A rotor blade vibration parameter identification module, which is used to construct a vibration parameter identification compressed sensing model of multi-measurement blade tip displacement signals based on a multi-measurement snapshot matrix;

[0093] The rotor blade vibration parameter identification module is used to solve the vibration parameter identification compressed sensing model of multiple measured blade tip displacement signals using the MMV-OMP algorithm to obtain the vibration parameters of the rotor blades.

[0094] The present invention is further described below by means of specific examples.

[0095] In one embodiment, a simulation experiment is performed when the blades vibrate synchronously at a constant speed, and the number of excited modes is Based on the particle swarm optimization algorithm, the optimal layout angle of each blade tip timing sensor is determined as , shaft speed The frequency was set to 216.7 Hz, the excitation order was 3, and -15 dB Gaussian white noise was added. Half of the pulse peak value of the collected pulse signal was selected as the threshold to filter out the actual time.

[0096] The time difference between the theoretical arrival time and the actual arrival time of the rotor blade tip , rotor blade speed and the radius of rotation Obtain the vibration displacement of the rotor blade tip: , and then construct a single measurement snapshot vector, which is composed of The circular vibration displacement data vector is composed of 10 snapshots in total, and a multi-measurement snapshot matrix constructed by 10 single-measurement snapshot vectors is obtained, that is, a matrix with 80 rows and 10 columns is obtained.

[0097] The vibration displacement of the rotor blade is converted into superimposed vibration displacements of each order modal. According to the superimposed vibration displacements of each order modal, the sparse representation equation of the rotor blade vibration is obtained. According to the sparse representation equation of the rotor blade vibration, the sparse transformation matrix is ​​obtained in combination with the installation angles of each sensor, namely:

[0098] F = [ s i n ( D E O i 1 ) c o s ( D E O i 1 ) ⋯ s i n ( N D E O i 1 ) c o s ( N D E O i 1 ) ⋮ ⋮ ⋱ ⋮ ⋮ s i n ( D E O i N p ) c o s ( D E O i N p ) ⋯ s i n ( N D E O i N p ) s i n ( N D E O i N p ) ⋮ ⋮ ⋱ ⋮ ⋮ s i n ( D E O ( i 1 + 2 ∗ ( n − 1 ) π ) c o s ( D E O ( i 1 + 2 ∗ ( n − 1 ) π ) ⋯ s i n ( N D E O ( i 1 + 2 ∗ ( n − 1 ) π ) c o s ( N D E O ( i 1 + 2 ∗ ( n − 1 ) π ) ⋮ ⋮ ⋱ ⋮ ⋮ s i n ( D E O ( i N p + 2 ∗ ( n − 1 ) π ) c o s ( D E O ( i N p + 2 ∗ ( n − 1 ) π ) ⋯ s i n ( N D E O ( i N p + 2 ∗ ( n − 1 ) π ) c o s ( N D E O ( i N p + 2 ∗ ( n − 1 ) π ) ]

[0099] Excitation order resolution Set to 0.01, the number of frequency components of the reconstructed signal Set it to 1000, and you get a matrix with 80 rows and 2000 columns.

[0100] On the basis of the above, a compressive sensing model for vibration parameter identification of multiple blade tip displacement signals is established:

[0101] ,in, .

[0102] MMV-OMP is used to solve the model and obtain the sparse representation coefficient matrix , the coefficients of each order vibration equation of the rotor blades are obtained, and thus the blade vibration parameters are obtained.

[0103] Figure 2 (a) shows the simulated blade vibration displacement time domain signal, the rotation speed is constant at 216.7 Hz, the excitation order EO is 3, and the sampling points of the four sensors are marked in the figure, which are represented by S1, S2, S3, and S4 respectively; Figure 2 (b) shows the time-frequency diagram of the rotor blade vibration signal obtained by the traditional method based on SMV, and its vibration frequency is constant at 650.1 Hz; Figure 2 (c) shows the time-frequency diagram of the rotor blade vibration signal obtained by the MMV-OMP method of the present invention. It can be seen that Figure 2 (c) is almost not affected by noise.

[0104] In another embodiment, a simulation experiment of blade synchronous vibration under variable speed was conducted, and the rotational acceleration was set to 50 rpm / s. 2 , and also added -15dB Gaussian white noise, the number of excited modes Based on the particle swarm optimization algorithm, the optimal layout angle of each blade tip timing sensor is determined as , shaft speed From 10,000 Hz to 15,000 Hz, the excitation order is 3. Figure 3 (a) is the time domain diagram of the vibration signal. It can be seen that the blade resonates significantly around 63 seconds. Half of the pulse peak value of the collected pulse signal is selected as the threshold to filter out the actual time.

[0105] The time difference between the theoretical arrival time and the actual arrival time of the rotor blade tip , rotor blade speed and the radius of rotation Obtain the vibration displacement of the rotor blade tip: , and then construct a single measurement snapshot vector, which is composed of The circular vibration displacement data vector is composed of 5 snapshots in total, and a multi-measurement snapshot matrix constructed by 5 single-measurement snapshot vectors is obtained, that is, a matrix with 80 rows and 5 columns is obtained.

[0106] The vibration displacement of the rotor blade is converted into superimposed vibration displacements of each order modal. According to the superimposed vibration displacements of each order modal, the sparse representation equation of the rotor blade vibration is obtained. According to the sparse representation equation of the rotor blade vibration, the sparse transformation matrix is ​​obtained in combination with the installation angles of each sensor, namely:

[0107] F = [ s i n ( D E O i 1 ) c o s ( D E O i 1 ) ⋯ s i n ( N D E O i 1 ) c o s ( N D E O i 1 ) ⋮ ⋮ ⋱ ⋮ ⋮ s i n ( D E O i N p ) c o s ( D E O i N p ) ⋯ s i n ( N D E O i N p ) s i n ( N D E O i N p ) ⋮ ⋮ ⋱ ⋮ ⋮ s i n ( D E O ( i 1 + 2 ∗ ( n − 1 ) π ) c o s ( D E O ( i 1 + 2 ∗ ( n − 1 ) π ) ⋯ s i n ( N D E O ( i 1 + 2 ∗ ( n − 1 ) π ) c o s ( N D E O ( i 1 + 2 ∗ ( n − 1 ) π ) ⋮ ⋮ ⋱ ⋮ ⋮ s i n ( D E O ( i N p + 2 ∗ ( n − 1 ) π ) c o s ( D E O ( i N p + 2 ∗ ( n − 1 ) π ) ⋯ s i n ( N D E O ( i N p + 2 ∗ ( n − 1 ) π ) c o s ( N D E O ( i N p + 2 ∗ ( n − 1 ) π ) ]

[0108] Excitation order resolution Set to 0.01, the number of frequency components of the reconstructed signal Set it to 1000, and you get a matrix with 80 rows and 2000 columns.

[0109] On the basis of the above, a compressive sensing model for vibration parameter identification of multiple blade tip displacement signals is established:

[0110] ,in, .

[0111] MMV-OMP is used to solve the model and obtain the sparse representation coefficient matrix , obtain the coefficients of each order vibration equation of the blade, and thus obtain the blade vibration parameters. Figure 3 (b) is a time-frequency diagram of the rotor blade vibration signal obtained by the traditional SMV method. It can be seen that at about 63s, the rotor blade obviously underwent synchronous vibration with an excitation order of 3 and a vibration frequency of 650.1Hz. Figure 3 (c) shows the time-frequency diagram of the rotor blade vibration signal obtained by the MMV-OMP method of the present invention. It can be seen that the method of the present invention also maintains good robustness under variable speed conditions, which is sufficient to prove the applicability of the method under various conditions.

[0112] Figure 4A comparison chart of blade vibration amplitude reconstruction accuracy obtained by analyzing the MMV-OMP method of the present invention and the traditional SMV method is shown. It can be seen that the blade vibration amplitude reconstructed based on the method of the present invention is closer to the true value and has a smaller error.

[0113] The present invention is described in detail above in combination with preferred embodiments and exemplary examples. However, it should be noted that these specific embodiments are only illustrative explanations of the present invention and do not constitute any limitation on the protection scope of the present invention. Without exceeding the spirit and protection scope of the present invention, various improvements, equivalent substitutions or modifications may be made to the technical content of the present invention and its embodiments, which all fall within the protection scope of the present invention. The protection scope of the present invention shall be subject to the attached claims.

Claims

1. A blade tip timing vibration parameter identification method, characterized in that: The method comprises: A compressive sensing model for vibration parameter identification of multi-measurement blade tip displacement signals is constructed based on a multi-measurement snapshot matrix. The compressed sensing model for vibration parameter identification of multiple measured blade tip displacement signals is solved to obtain the vibration parameters of the rotor blades.

2. The method according to claim 1, characterized in that Preferably, a time-frequency diagram of the rotor blade vibration signal is obtained based on the rotor blade vibration parameters.

3. The method according to claim 2, characterized in that Based on the time-frequency diagram of the rotor blade vibration signal, the vibration parameters of the rotor blade are obtained.

4. The method according to claim 2, characterized in that: The parameters include vibration frequency and amplitude.

5. The method according to claim 1, characterized in that The parameters also include the number of vibration modes.

6. The method according to claim 1, characterized in that The multi-measurement snapshot matrix has the following representation: ， in, Indicates a single measurement snapshot vector, is the rotor blade serial number, Indicates the number of circles, Number of blade tip timing sensors.

7. The method according to claim 6, characterized in that The number of blade tip timing sensors is 4 to 10.

8. The method according to claim 6, characterized in that The construction of a compressed sensing model for vibration parameter identification of multiple measured blade tip displacement signals includes the following steps: Convert the vibration displacement of the rotor blades into superimposed vibration displacements of each order of modalities; According to the superimposed vibration displacements of each order mode, the sparse representation equation of the rotor blade vibration is obtained; According to the sparse representation equation of the rotor blade vibration, a sparse transformation matrix is ​​obtained; According to the sparse transformation matrix, the vibration parameter identification compressed sensing model of multi-measurement blade tip displacement signals is obtained.

9. The method according to claim 8, characterized in that A multi-measurement snapshot matrix is ​​constructed based on the vibration displacements of the rotor blades.

10. A blade tip timing vibration parameter identification system, characterized in that: The system comprises: A rotor blade vibration parameter identification module, which is used to construct a vibration parameter identification compressed sensing model of multi-measurement blade tip displacement signals based on a multi-measurement snapshot matrix; The rotor blade vibration parameter identification module is used to solve the vibration parameter identification compressed sensing model of multiple measured blade tip displacement signals to obtain the vibration parameters of the rotor blade.

Images