Blade natural frequency identification method based on minimum variance power spectrum estimation

Abstract

The present invention discloses a method for identifying the natural frequency of a blade based on minimum variance power spectrum estimation. The method comprises the following steps: using finite element analysis to analyze the first-order natural frequency of the blade, determining the frequency estimation range and frequency estimation recognition rate of the blade, wherein the blade vibration is represented as the superposition of multiple complex frequency signals and complex Gaussian white noise signals, constructing a signal snapshot matrix based on the blade vibration, and calculating the approximation of the signal autocorrelation matrix according to the signal snapshot matrix; constructing a peak search vector according to the arrangement of blade tip timing sensors, the frequency estimation range and the frequency estimation resolution; selecting blade tip timing data of a low-speed non-resonance section, traversing all frequencies within the frequency estimation range to calculate the power spectrum amplitude of the signal; and estimating the asynchronous vibration frequency of the blade as the natural frequency of the blade based on a linear average smoothing algorithm.

Description

Technical Field

[0001] The invention belongs to the technical field of nondestructive detection of rotor blades of rotating machinery, and in particular to a method for identifying the natural frequency of blades based on minimum variance power spectrum estimation. Background Art

[0002] Modern aircraft pursue better performance and lower operating and maintenance costs. Rotating blades, as high-failure-rate components of the engine, are extremely prone to fatigue accidents. Therefore, blade health monitoring is of great significance to ensure the safety of aircraft engines. Blade tip timing, as a non-contact, non-destructive measurement method for blades, has the advantages of fast response, high signal-to-noise ratio, and strong robustness. However, due to the narrow internal space of the casing, the number and installation position of sensors are limited, resulting in the signal collected by the blade tip timing system being a typical non-uniform under-sampling signal. For this feature of the blade tip timing signal, the existing signal analysis methods need to effectively extract the natural frequency of the blade when the blade speed exceeds the critical value. However, in practical applications, in order to avoid damage to the blade caused by resonance, the resonance zone should be quickly crossed.

[0003] 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

[0004] In view of the problems existing in the prior art, the present invention proposes a blade natural frequency identification method based on minimum variance power spectrum estimation, which realizes the extraction of asynchronous resonance in the low-speed non-resonance zone of the blade. By introducing the minimum variance spectrum estimation method in the wave direction estimation, the present invention can effectively realize the estimation of asynchronous resonance in the non-resonance zone, and at the same time optimizes the calculation of the autocorrelation matrix to accelerate the calculation speed. Not only that, this method can effectively extract amplitude information on the basis of having excellent anti-aliasing ability, which is not available in the existing wave direction algorithms. Therefore, the improved minimum variance power spectrum estimation method proposed in the present invention has strong engineering significance and provides new support for online blade status monitoring based on blade tip timing.

[0005] The object of the present invention is achieved by the following technical scheme: a blade natural frequency identification method based on minimum variance power spectrum estimation comprises the following steps:

[0006] In the first step, the first-order natural frequency of the blade is analyzed by finite element analysis to determine the frequency estimation range and frequency estimation recognition rate of the blade, wherein the blade vibration is represented as a superposition of multiple complex frequency signals and complex Gaussian white noise signals;

[0007] In the second step, a signal snapshot matrix is ​​constructed based on the blade vibration, and a signal autocorrelation matrix is ​​calculated according to the signal snapshot matrix;

[0008] In the third step, a peak search vector is constructed according to the arrangement of the blade tip timing sensors, the frequency estimation range and the frequency estimation resolution;

[0009] In the fourth step, the blade tip timing data of the low-speed non-resonant section is selected, and the power spectrum amplitude of the signal is calculated by traversing all frequencies within the frequency estimation range;

[0010] In the fifth step, the blade asynchronous vibration frequency is estimated as the blade natural frequency based on a linear average smoothing algorithm.

[0011] In the method, in the first step,

[0012] Step S11, using a plurality of blade tip timing sensors arranged circumferentially to obtain the actual arrival time t of the blade along the circumferential direction, and obtaining the blade speed signal from the speed sensor, the physical size of the blade disk, and the installation positions of the plurality of blade tip timing sensors, to calculate the theoretical arrival time of each blade on the blade disk, in is the theoretical arrival time of the blade, f r is the rotation frequency of the blade disk, n is the number of revolutions of the blade, α i is the angle between the ith sensor and the first sensor, θ p is the angle between the pth blade and the first blade, and the theoretical arrival time of the blade is t exp The difference between the actual arrival time t is the blade tip timing data in the axial or radial direction.

[0013] Step S12, representing the blade vibration as a superposition of multiple complex frequency signals and complex Gaussian white noise signals: Where e is a natural constant, y(t) is the vibration displacement of the blade tip, are the amplitude, frequency and phase of the kth frequency component of the blade vibration signal, respectively. w(t) is the complex Gaussian white noise. The first-order natural frequency of the blade is obtained by finite element method to determine the frequency range of the blade tip timing signal [f min , f max ] and frequency recognition resolution Δf.

[0014] In the method, in the second step,

[0015] Step S21, using a spatial smoothing method to smooth the blade tip timing data Y i =[y(t0) y(t1) … y(t L )] T Divide, L is the leaf-end timing data length, and construct the signal snapshot matrix: where Y snapshotsis a signal snapshot matrix composed of Q leaf-end timing data sequences of length M, M is the length of a single snapshot captured, I is the number of sensors,

[0016] Step S22, calculate the signal snapshot matrix Y snapshots The autocorrelation matrix of in(·) H It means to find the conjugate transpose of a matrix.

[0017] In the method described above, in the third step, a peak search vector is constructed according to the sensor arrangement, the frequency range of the signal to be analyzed, and the frequency recognition resolution: Where M is the length of a single snapshot, f i is the frequency to be identified, j 2 =-1 represents an ordinal unit.

[0018] In the method, in the fourth step, all frequencies f within the frequency estimation range are traversed. i ∈[f min , f max ], calculate the signal autocorrelation matrix at each frequency f i Amplitude at: Get the leaf end timing data Y i Spectrum estimation S within the frequency estimation range i =[S IMVSE (f min )s IMVSE (f min+Δf ) … s IMVSE (f max )] T , select the blade tip timing data in the non-resonance region and obtain the minimum variance power spectrum estimate S in the time-frequency domain spectrogram =[S1 S2 … S N ], indicating that the complete leaf-end timing data is divided into N segments of data.

[0019] In the method, in the fifth step,

[0020] Step S51, introduce a linear average smoothing algorithm to estimate the blade asynchronous vibration frequency, the blade asynchronous vibration frequency is the first-order natural frequency of the blade at the corresponding speed, and select a frequency band W of the wide asynchronous vibration f , the frequency corresponding to the maximum amplitude in the frequency band

[0021] Step S52, setting the smoothing algorithm window length l, then the estimated value of the asynchronous vibration frequency is: in and is the closed-form solution of the least squares algorithm, t kIndicates the time corresponding to the kth point in the window length, indicating represents the mean time within the algorithm window length, f s (t k ) indicates that at time t k The frequency corresponding to the maximum amplitude in the asynchronous vibration frequency band.

[0022] Beneficial Effects

[0023] The method provided by the present invention avoids the resonance zone and realizes accurate estimation of the asynchronous resonance frequency of the blade in the low-speed non-resonance zone. At the same time, the calculation speed is fast and the algorithm is highly robust, which provides reliable support for blade status monitoring based on blade tip timing. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] By reading the detailed description of the preferred specific embodiments below, various advantages and benefits of the present invention will become clear to those skilled 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 skilled in the art, other drawings can be obtained based on these drawings without creative work. In addition, the same reference numerals are used to represent the same components throughout the drawings.

[0025] In the attached picture:

[0026] Figure 1 A schematic diagram of the steps of a blade natural frequency identification method based on blade tip timing and improved minimum variance power spectrum estimation according to an embodiment of the present invention;

[0027] Figure 2 A basic principle diagram of a blade tip timing measurement method of a blade natural frequency identification method based on blade tip timing and improved minimum variance power spectrum estimation according to an embodiment of the present invention;

[0028] Figure 3 A time domain diagram of a simulation signal of a blade natural frequency identification method based on blade tip timing and improved minimum variance power spectrum estimation according to an embodiment of the present invention;

[0029] Figure 4(a) to Figure 4(c) 4(a), 4(b) and 4(c) are spectrum estimation results of a blade natural frequency identification method based on blade tip timing and improved minimum variance power spectrum estimation according to an embodiment of the present invention; wherein FIG. 4(a), FIG. 4(b) and FIG. 4(c) are spectrum estimation results at three different grid accuracies of 1 Hz, 0.5 Hz and 0.1 Hz, respectively; wherein the cross represents the true value of the frequency estimation;

[0030] Figure 5(a) to Figure 5(c)5(a), 5(b) and 5(c) are spectrum estimation results of a blade natural frequency identification method based on blade tip timing and traditional minimum variance power spectrum estimation according to an embodiment of the present invention; wherein FIG. 5(a), FIG. 5(b) and FIG. 5(c) are spectrum estimation results at three different grid accuracies of 1 Hz, 0.5 Hz and 0.1 Hz, respectively; wherein the cross represents the true value of the frequency estimation;

[0031] Figure 6 The frequency estimation errors of seven methods, namely, improved minimum variance power spectrum estimation (IMVSE), traditional minimum variance power spectrum estimation (MVSE), orthogonal matching pursuit (OMP), subspace rotation invariance method (ESPRIT), improved multiple signal classification method (MUSIC), alternating direction multiplier method (ADMM) and iterative weighted least squares method (IRLS) under different signal-to-noise ratios according to a blade natural frequency identification method based on blade tip timing and improved minimum variance power spectrum estimation according to an embodiment of the present invention;

[0032] Figure 7 The frequency estimation errors of seven methods, namely, improved minimum variance power spectrum estimation (IMVSE), traditional minimum variance power spectrum estimation (MVSE), orthogonal matching pursuit (OMP), subspace rotation invariance method (ESPRIT), improved multiple signal classification method (MUSIC), alternating direction multiplier method (ADMM) and iterative weighted least squares method (IRLS), under different signal lengths according to a blade natural frequency identification method based on blade tip timing and improved minimum variance power spectrum estimation according to an embodiment of the present invention;

[0033] Figure 8 A time domain diagram of blade tip vibration displacement obtained by a blade tip timing non-contact measurement test bench of a blade natural frequency identification method based on blade tip timing and improved minimum variance power spectrum estimation according to an embodiment of the present invention;

[0034] Fig. 9 A Campbell diagram of a blade obtained based on ANSYS analysis according to a blade natural frequency identification method based on blade tip timing and improved minimum variance power spectrum estimation according to an embodiment of the present invention, indicating a trend of blade natural frequency changing with rotation speed;

[0035] Figure 10(a) to Figure 10(f)10(a), 10(b), 10(c), 10(d), 10(e) and 10(f) are respectively the improved minimum variance power spectrum estimation (IMVSE), the traditional minimum variance power spectrum estimation (MVSE), the improved multiple signal classification method (MUSIC), the orthogonal matching pursuit (OMP), the alternating direction multiplier method (ADMM) and the iterative weighted least squares method (IRLS); the dotted line indicates the natural frequency of the blade at the current speed obtained by ANSYS analysis;

[0036] Figure 11(a) to Figure 11(c) 11 is a time-frequency diagram of the blade tip vibration displacement in the acceleration section of the blade natural frequency identification method based on blade tip timing and improved minimum variance power spectrum estimation according to an embodiment of the present invention; wherein FIG11(a), FIG11(b) and FIG11(c) are improved minimum variance power spectrum estimation (IMVSE), traditional minimum variance power spectrum estimation (MVSE) and orthogonal matching pursuit (OMP), respectively;

[0037] Fig.12 The asynchronous resonance frequency extracted from the time-frequency diagram is obtained by the minimum variance power spectrum estimation of the blade tip vibration displacement in the acceleration section of the blade natural frequency identification method based on blade tip timing and improved minimum variance power spectrum estimation according to an embodiment of the present invention; wherein the dotted line represents the blade natural frequency obtained by ANSYS analysis.

[0038] The present invention is further explained below in conjunction with the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0039] The following reference Figures 1 to 12 The specific embodiments of the present invention will be described in more detail. Although the 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 set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0040] 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.

[0041] 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.

[0042] For a better understanding, Figure 1 to Figure 11(c) As shown, a blade natural frequency identification method based on blade tip timing and improved minimum variance power spectrum estimation includes:

[0043] Step 1: Select the blade spectrum estimation range and resolution based on the blade dynamics prior;

[0044] Step 2: Construct the signal snapshot matrix;

[0045] Step 3: Construct a peak search vector based on information such as sensor arrangement;

[0046] Step 4: traverse all frequencies within the frequency range to calculate the power spectrum amplitude;

[0047] Step 5: Select the data points of the speed-up section and use the linear average smoothing algorithm to extract the asynchronous resonance frequency.

[0048] The finite element analysis results are used as the blade dynamics prior to set a reasonable spectrum estimation resolution and estimation range; on the basis of the minimum variance power spectrum estimation method, the autocorrelation matrix calculation method of the traditional method is improved, the signal matrix is ​​calculated using the forward space smoothing algorithm, and the autocorrelation matrix of the non-uniformly sampled signal is obtained by the cross-product operation of the signal matrix; the peak search vector is constructed according to the physical size of the blade disk and other information; the data of the low-speed non-resonance section is selected, and all frequencies in the frequency search range are traversed to obtain the signal spectrum; the linear average smoothing algorithm is used to estimate the asynchronous vibration frequency of the blade.

[0049] Optionally according to the present invention, in step 1,

[0050] S11, measuring the arrival time of the blades by five circumferentially arranged optical fiber sensors, and one optical fiber sensor at the shaft is used as a rotation speed signal, and the vibration displacement of the blades is obtained by the rotation speed pulse;

[0051] S12, simplify the vibration of the blade into the superposition of multiple complex frequency signals and complex Gaussian white noise signals. Use finite element analysis to analyze the inherent characteristics of the blade, determine the frequency range of the vibration signal to be identified as [1, 500] Hz and the frequency identification resolution of 1 Hz,

[0052] Optionally, in step 2, the approximate calculation of the signal autocorrelation matrix includes the following steps:

[0053] s21, the spatial smoothing method is used to process the timing data of 2048 blade ends and construct the signal matrix Y snapshots , take the snapshot width as 32;

[0054] S22, calculate the signal snapshot matrix Y snapshots The autocorrelation matrix of

[0055] Optionally, in step three, a peak search vector is constructed according to the sensor arrangement, the frequency range of the signal to be analyzed, and the frequency recognition resolution:

[0056] Optionally, in step 4, the frequencies within [1, 500] Hz are traversed every 1 Hz, and the signal autocorrelation matrix is ​​calculated at each frequency f i The power spectrum amplitude at time: Get the blade vibration signal Y i The spectrum estimate S within the frequency search range i =[S IMVSE (1Hz) S IMVSE (2Hz) … S IMVSE (500Hz)] T , select the blade tip timing data of the speed-up section and obtain the minimum variance power spectrum estimate S in the time-frequency domain spectrogram =[S1 S2 … S 2000 ];

[0057] Optionally, in step 5, estimating the asynchronous vibration frequency using a linear average smoothing algorithm includes the following steps:

[0058] Step S51, select a frequency band W with a width of 10 Hz asynchronous vibration f , the maximum amplitude in the frequency band is

[0059] Step S52, setting the smoothing algorithm window length to 500, the estimated value of the asynchronous vibration frequency is: The extraction of asynchronous vibration of blades in non-resonance area is realized.

[0060] In a preferred embodiment of the method, in the first step, the angles between the blades and the angles between the sensors are calculated in a clockwise direction when viewed from the jet excitation side.

[0061] In a preferred embodiment of the method, the speed between each speed pulse is approximated to be a uniform speed.

[0062] In a preferred embodiment of the method, when the rotation speed variation within a snapshot is small, the structure of the peak search vector is approximated to be unchanged.

[0063] To further illustrate the method of the present invention, Figure 2 The basic principle diagram of converting the blade arrival pulse measured by the blade tip timing sensor into the blade vibration displacement is shown in this exemplary embodiment. Figure 2 As shown in the figure, the circumferentially arranged optical fiber is used to obtain the blade arrival time, and the optical fiber sensor arranged at the shaft is used to obtain the speed calibration signal. The actual radial arrival time t of the blade tip timing sensor in the blade is obtained. TOA , due to blade vibration, the theoretical arrival time of the blade is t exp The actual arrival time t TOA The difference is the blade tip timing data of the blade.

[0064] In one embodiment of the present invention, preferably, the asynchronous vibration and synchronous vibration of the blade are considered at the same time, the blade speed is set to 4000RPM, and the sensor arrangement is [0°, 36°, 78°, 156°, 282°], and the following is obtained: Figure 3 The simulation signal shown is used to verify the improvement brought by the method of the present invention compared with the traditional method.

[0065] In one embodiment, Figure 4(a) to Figure 4(c) and shown as Figure 3The spectrum estimation results of the simulated signal at different grid sizes, the plus sign indicates the true value of the frequency and amplitude of the simulated signal. It can be seen that for the undersampled blade-end timing simulation signal, the method of the present invention is more effective and more robust than the traditional method. It improves the calculation speed while maintaining the spectrum estimation accuracy. It can be seen from Figure 5 (a) that when the grid size is 1Hz, the traditional method has a basis mismatch phenomenon and the spectrum estimation performance is seriously reduced. In contrast, the method has a higher spectrum estimation accuracy at different grid accuracy, and with the improvement of grid accuracy, the amplitude estimation accuracy is also significantly improved. Due to the non-uniform sampling problem of the blade-end timing signal itself, the accurate signal autocorrelation matrix cannot be obtained by directly calculating the signal cross product. The traditional minimum variance power spectrum estimation method uses the Vena-Schinchin theorem and iterative solution to calculate the signal autocorrelation matrix, which makes the traditional method time-consuming. The present invention introduces a forward space smoothing algorithm to construct a signal snapshot matrix, which reduces the computational complexity.

[0066] In one embodiment of the present invention, preferably, Figure 6 and Figure 7 As shown, six methods including traditional minimum variance power spectrum estimation (MVSE), orthogonal matching pursuit (OMP), improved multiple signal classification (MUSIC), subspace rotation invariance method (ESPRIT), alternating direction multiplier method (ADMM) and iterative weighted least squares method (IRLS) are used to compare with the method described in the present invention. A frequency of [1, 200] Hz is randomly generated, and the signal length is 512. Through 100 Monte Carlo simulations, the frequency estimation errors of the algorithms under different signal-to-noise ratios are compared. According to Figure 6 It can be seen that the method of the present invention has the best performance especially under low signal-to-noise ratio, which proves its robustness. Moreover, as a Boda algorithm, this method avoids the optimization solution of sparse algorithms, so it has a fast calculation speed. The frequency of [1, 200] Hz is randomly generated, and the signal-to-noise ratio is 15dB. Through 100 Monte Carlo simulations, the frequency estimation error of the algorithm under different signal lengths is compared. According to Figure 7 It can be seen that the method has the lowest frequency estimation error when the signal length is greater than 32.

[0067] In a preferred embodiment of a blade crack identification method based on blade tip timing and improved multiple signal classification of the present invention, an optical fiber sensor is selected as a blade tip timing sensor, and the blade tip timing sensors are arranged circumferentially on the casing, with the angles between the sensors being [48°, 108°, 158°, 168°, 288°]. The initial speed is set to 3000RPM, and the speed is increased to 6000RPM. The motor speed is maintained at a constant speed of 6000RPM for 35 seconds. The blade disk adopts an integral aluminum alloy blade disk with 8 blades. The blade disk radius is 68mm, the blade thickness is 1mm, and the blade width is 20mm. Jet excitation is adopted, and 4 jet excitation nozzles are evenly distributed on the nozzle sensor bracket. 0.5Mpa high-pressure gas is sprayed as synchronous resonance excitation, and the arrival time of the rotating blades is obtained by blade tip timing sensing. The measured blade tip timing data is as follows: Figure 8 As shown. Using ANSYS to analyze the blade dynamic characteristics, we can get the following Fig. 9 From the Campbell diagram shown, it can be seen that the first-order natural frequency of the blade is approximately around 370 Hz. This is used as the dynamic prior of the blade to determine the frequency estimation range of the blade vibration signal to be [1, 500] Hz and the frequency identification resolution to be 1 Hz. 2048 data points in the uniform speed segment are selected, the uniform speed is approximately 100 Hz, the snapshot length is set to 32, and the intercepted signal of length 2048 is rearranged into a signal snapshot matrix. The experimental data of the uniform speed segment are analyzed by six methods: improved minimum variance power spectrum estimation (IMVSE), traditional minimum variance power spectrum estimation (MVSE), orthogonal matching pursuit (OMP), improved multiple signal classification method (MUSIC), alternating direction multiplier method (ADMM) and iterative weighted least squares method (IRLS), and the following is obtained: Figure 10(a) to Figure 10(f) From the results, it can be seen that the method of the present invention has the best performance, and the problems of weak asynchronous vibration and severe frequency aliasing existing in other methods are significantly overcome. In order to better compare the performance differences of several methods, the data in the resonance region are not selected, but the data in the constant speed region are selected. As shown in Figure 10(a), the improved minimum variance power spectrum estimation most clearly reflects the asynchronous resonance component and weakens all other frequency components. However, the traditional minimum variance power spectrum estimation almost eliminates the asynchronous vibration component and only retains the synchronous vibration frequency, as shown in Figure 10(b). The improved multiple signal classification method is affected by synchronous resonance, as shown in Figure 10(c). The spectrum estimation results of orthogonal matching pursuit, alternating direction multiplier method and iterative weighted least squares method are very similar, among which the estimation result of OMP is less disturbed and the asynchronous vibration component is clearer, as shown in Figure 10(d), Figure 10(e) and Figure 10(f).

[0068] In a preferred embodiment of a blade crack identification method based on blade tip timing and improved multiple signal classification of the present invention, according to Figure 11(a) to Figure 11(c)As shown in FIG. 1 , three representative methods, namely, improved minimum variance power spectrum estimation (IMVSE), orthogonal matching pursuit (OMP), and improved multiple signal classification (MUSIC), are compared to further demonstrate the superiority of the method of the present invention. The dotted line in the figure indicates the natural frequency of the blade at the current speed obtained by ANSYS analysis. Figure 8 The data of the acceleration section shown in the figure are analyzed. The data obtained from the acceleration area are divided into 2000 data segments with a signal length of 2048 and a step size of 20. The rotation speed in each data segment is regarded as a constant to simplify the calculation. The improved minimum variance power spectrum estimation, orthogonal matching pursuit and improved multiple signal classification are all gridding methods, so their frequency resolution is set to 1Hz. It can be noted that the results of the improved minimum variance power spectrum estimation are much better than those of the other two methods. In the entire time-frequency domain, the asynchronous resonance frequency of the blade can be identified, and the synchronous resonance of each order is greatly weakened but not eliminated. It is also observed that the vibration amplitude of the blade increases when the rotation speed approaches the resonance region. In the results obtained by the improved multiple signal classification, the asynchronous resonance frequency components in the resonance region can be roughly identified, and the synchronous resonance frequency components appear intermittently in the low-speed region. However, as shown in Figure 11(b), not all synchronous resonances and asynchronous resonances can be identified. In addition, a careful observation shows that pseudo peaks appear in both the high-frequency and low-frequency regions of the resonance region, indicating that the improved multiple signal classification cannot overcome frequency aliasing well. As shown in Figure 11(c), the results of orthogonal matching pursuit can only identify the asynchronous vibration frequency of the blade in the resonance region. Fig.12 The asynchronous resonance frequency is extracted from the time-frequency diagram obtained by the improved minimum variance power spectrum estimation of the blade tip vibration displacement in the acceleration section. The dotted line is the first-order natural frequency of the blade obtained by ANSYS simulation. It can be seen that due to the differences between the boundary conditions set during ANSYS analysis and the actual experimental environment, the actually measured asynchronous vibration frequency is slightly lower than the simulation result.

[0069] Although the embodiments of the present invention are described above in conjunction with the accompanying drawings, the present invention is not limited to the above specific embodiments and application fields, and the above specific embodiments are only illustrative and instructive, rather than restrictive. A person of ordinary skill in the art can also make many forms under the guidance of this specification and without departing from the scope of protection of the claims of the present invention, all of which belong to the protection of the present invention.

Claims

1. A blade natural frequency identification method based on minimum variance power spectrum estimation, the method comprising the following steps: In the first step (S1), the first-order natural frequency of the blade is analyzed by finite element analysis to determine the frequency estimation range and frequency estimation recognition rate of the blade, wherein the blade vibration is represented as a superposition of multiple complex frequency signals and complex Gaussian white noise signals; In the second step (S2), a signal snapshot matrix is ​​constructed based on blade vibration, and a signal autocorrelation matrix is ​​calculated according to the signal snapshot matrix, wherein, based on the minimum variance power spectrum estimation method, the autocorrelation matrix calculation method of the traditional method is improved, the signal matrix is ​​calculated using the forward space smoothing algorithm, and the autocorrelation matrix of the non-uniformly sampled signal is obtained by using the signal matrix cross-product operation; In the third step (S3), a peak search vector is constructed according to the arrangement of the blade tip timing sensors, the frequency estimation range and the frequency estimation resolution; In the fourth step (S4), the blade tip timing data of the low-speed non-resonance section is selected, and the power spectrum amplitude of the signal is calculated by traversing all frequencies within the frequency estimation range; In the fifth step (S5), the blade asynchronous vibration frequency is estimated as the blade natural frequency based on a linear average smoothing algorithm.

2. The method according to claim 1, wherein: In the first step (S1), Step S11, using a plurality of blade tip timing sensors arranged circumferentially to obtain the actual arrival time of the blade along the circumferential direction , according to the speed signal of the blade obtained by the speed sensor, the physical size of the blade disk and the installation position of multiple blade tip timing sensors, the theoretical arrival time of each blade on the blade disk is calculated. ,in is the theoretical arrival time of the blade, is the rotation frequency of the blade disk, is the number of revolutions of the blade, For the The angle between the first sensor and the second sensor, For the The angle between the first blade and the first blade, the theoretical arrival time of the blade The actual arrival time The difference is the blade tip timing data in the axial or radial direction. Step S12, representing the blade vibration as a superposition of multiple complex frequency signals and complex Gaussian white noise signals: ,in is a natural constant, is the vibration displacement of the blade tip, They are the blade vibration signals The amplitude, frequency and phase of the frequency components, is a complex Gaussian white noise. The first-order natural frequency of the blade is obtained by finite element method to determine the frequency range of the blade tip timing signal. and frequency recognition resolution .

3. The method according to claim 2, wherein: In the second step (S2), Step S21: Use spatial smoothing method to smooth the blade tip timing data. To divide, For the leaf-end timing data length, construct the signal snapshot matrix: ,in for The length is The signal snapshot matrix composed of the leaf end timing data sequence, is the length of the captured single snapshot, is the number of sensors, Step S22, calculate the signal snapshot matrix The autocorrelation matrix of ,in It means to find the conjugate transpose of a matrix.

4. The method according to claim 3, wherein: In the third step (S3), a peak search vector is constructed according to the sensor arrangement, the frequency range of the signal to be analyzed, and the frequency recognition resolution: ,in is the length of the captured single snapshot, is the frequency to be identified, Indicates an ordinal unit.

5. The method according to claim 2, wherein: In the fourth step (S4), all frequencies within the frequency estimation range are traversed. , calculate the signal autocorrelation matrix at each frequency Amplitude at: , get the leaf-end timing data Spectrum estimation within the frequency estimation range , select the blade tip timing data in the non-resonance region and obtain the minimum variance power spectrum estimate in the time-frequency domain , indicating that the complete leaf-end timing data is divided into Segment data.

6. The method according to claim 5, wherein: In the fifth step (S5), Step S51, introduce a linear average smoothing algorithm to estimate the blade asynchronous vibration frequency, the blade asynchronous vibration frequency is the first-order natural frequency of the blade at the corresponding speed, and select a frequency band of wide asynchronous vibration , the frequency corresponding to the maximum amplitude in the frequency band , Step S52, setting the smoothing algorithm window length , then the estimated value of the asynchronous vibration frequency is: ,in and is the closed-form solution of the least squares algorithm, Indicates the window length The time corresponding to each point is expressed as represents the mean time within the algorithm window length, Indicates at time The frequency corresponding to the maximum amplitude in the asynchronous vibration frequency band.

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