Method and system for reconstructing timing undersampling vibration signal of rotating blade of aero-engine

Abstract

The invention relates to the technical field of aero-engine rotating blade health monitoring, and provides an aero-engine rotating blade timing undersampling vibration signal reconstruction method and system, and the method comprises the steps: constructing a reconstruction model and a recovery matrix; setting an initial residual error and a support set of the recovery matrix; calculating the inner product of the column vector of the recovery matrix and the initial residual error; the column vectors with the absolute values of the inner product values exceeding a threshold value are selected as matching vectors to be combined into a first set; projecting the first set to a parameter space; clustering is carried out by adopting a K-nearest neighbor algorithm, and column vectors with maximum inner products in each class are reserved to form a second set; adding the second set into a support set, and solving a sparse coefficient by adopting a least square method by taking the minimum initial residual error as a target; a new residual error is calculated, iteration is terminated until the support set is not updated any more, and a final vibration signal sparse coefficient is obtained; and calculating to obtain a blade end vibration signal of the rotating blade of the reconstructed aero-engine. According to the invention, the non-contact online real-time health monitoring effect of the blade can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of health monitoring of rotating blades of aero - engines, and particularly to a method and system for reconstructing the vibration signal of aero - engine rotating blades with timed undersampling. Background Technique

[0002] Aero - engine blades are in a harsh working environment of high speed and high pressure, and are at high risk of fatigue or even fracture under complex excitations such as centrifugal loads, high - pressure airflows, and rotor systems. In recent years, the Blade Tip Timing (BTT) method has gradually become a main method for real - time monitoring of the entire stage of blades. According to the principle of blade tip timing sampling, a single fiber optic sensor can sample each blade once when the blade rotates one week. Therefore, the sampling frequency of a single blade is related to the number of circumferentially installed fiber optic sensors and the rotor rotation frequency. Limited by the internal structure of the engine, the actual number of installed fiber optic sensors is limited, and the natural frequency of the engine blade itself is often much higher than the rotor rotation frequency. Therefore, the blade tip timing sampling data is undersampled data that does not satisfy the Nyquist sampling theorem. Using traditional signal processing methods to directly analyze it will result in spectral aliasing, and the dynamic frequency change of the blade cannot be distinguished. Currently, the traditional orthogonal matching pursuit algorithm can reconstruct the undersampled vibration signal of the rotating blade, but complex inner - product calculations are required for each iteration, resulting in low computational efficiency and poor reconstruction accuracy. Therefore, realizing the reconstruction of the blade tip timing undersampled vibration signal of aero - engine rotating blades while taking into account the signal reconstruction accuracy and real - time performance is a difficult point in carrying out blade health monitoring. Summary of the Invention

[0003] The purpose of the present invention is to solve at least one technical problem in the background technique, and provide a method and system for reconstructing the vibration signal of aero - engine rotating blades with timed undersampling.

[0004] To achieve the above - mentioned purpose, the present invention provides a method for reconstructing the vibration signal of aero - engine rotating blades with timed undersampling, including: S1. Construct a reconstruction model and a recovery matrix for the blade tip timing undersampled vibration signal of aero - engine rotating blades; S2. Set the blade tip timing undersampled vibration signal as the initial residual of the recovery matrix, and set the support set of the recovery matrix; S3. Calculate the inner product of the column vectors of the recovery matrix and the initial residual; S4. Select the column vectors of the recovery matrix corresponding to the inner - product values whose absolute values exceed the threshold among all the inner - product values as matching vectors and merge them to form the first set; S5. Project the first set into the parameter space , where , are the amplitudes of the sine and cosine vibration components, respectively, are the frequencies of the sine and cosine vibration components; The nearest neighbor algorithm is used for clustering, and the column vector with the largest inner product in each class is retained to form the second set; S6. Add the second set to the support set, and with the goal of minimizing the residual, use the least squares method to solve the sparse coefficients; S7. Calculate the new residual, and repeat steps S3 to S7 until the support set no longer updates, then terminate the iteration. At this time, the final sparse coefficients of the vibration signal are obtained; S8. Calculate the reconstructed aeroengine rotating blade tip vibration signal based on the reconstruction model, the recovery matrix, and the final sparse coefficients of the vibration signal.

[0005] According to one aspect of the present invention, the reconstruction model is: , ; The recovery matrix is: ; In the formula, is to solve the norm, y represents the tip-timing undersampled vibration signal, and the sampled values included in y represent the vibration displacement measured by the p-th fiber optic sensor at the n-th revolution, x represents the sparse coefficient, represents the time when the blade passes through the p-th fiber optic sensor at the n-th revolution, is the set frequency resolution, N is the number of revolutions of the measured blade rotation, and M is the number of frequency points of the reconstructed signal spectrum.

[0006] According to one aspect of the present invention, the inner product of the column vector of the calculated recovery matrix and the initial residual is: , where r is the initial residual, is the column vector.

[0007] According to one aspect of the present invention, the selection of the column vectors of the recovery matrix corresponding to the inner product values whose absolute values exceed the threshold among all inner product values as the matching vectors and merging them to form the first set is: Select the column vectors of the recovery matrix corresponding to the inner product values whose absolute values exceed the threshold among all inner product values as the matching vectors and merge them to form the first set : ; In the formula, the threshold , T represents the signal length, and s is a coefficient.

[0008] According to one aspect of the present invention, the sparse coefficient is:

[0009] In the formula, is the sparse coefficient, is the support set, is the transpose of the support set.

[0010] According to one aspect of the present invention, the new residual is .

[0011] According to one aspect of the present invention, the reconstructed tip vibration signal of the aero-engine rotating blade calculated based on the reconstruction model, the recovery matrix, and the final vibration signal sparse coefficient is: According to calculate the reconstructed tip vibration signal of the aero-engine rotating blade .

[0012] To achieve the above object, the present invention also provides a system for reconstructing the time-undersampled vibration signal of the aero-engine rotating blade, including: A reconstruction model and recovery matrix construction module for constructing a reconstruction model and a recovery matrix for the time-undersampled vibration signal of the aero-engine rotating blade tip; An initial value setting module for setting the tip time-undersampled vibration signal as the initial residual of the recovery matrix and setting the support set of the recovery matrix; An inner product value calculation module for calculating the inner product of the column vector of the recovery matrix and the initial residual; A first set acquisition module for selecting the column vectors of the recovery matrix corresponding to the inner product values whose absolute values exceed the threshold among all the inner product values as matching vectors and combining them to form a first set; A second set formation module for projecting the first set into the parameter space , where , are the amplitudes of the sine and cosine vibration components respectively, is the frequency of the sine and cosine vibration components; perform clustering using the K-nearest neighbor algorithm, retain the column vector with the largest inner product in each class, and form a second set; A sparse coefficient solving module for adding the second set to the support set and solving the sparse coefficient using the least squares method with the minimum residual as the target; A sparse coefficient determination module for calculating the new residual and repeatedly executing the inner product value calculation module to the sparse coefficient determination module until the support set is no longer updated, then terminate the iteration, and at this time, obtain the final vibration signal sparse coefficient; A reconstructed vibration signal calculation module for calculating the reconstructed tip vibration signal of the aero-engine rotating blade based on the reconstruction model, the recovery matrix, and the final vibration signal sparse coefficient.

[0013] To achieve the above object, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the above-mentioned method for reconstructing the vibration signal of the rotating blade of an aeroengine with timing undersampling is implemented.

[0014] To achieve the above object, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method for reconstructing the vibration signal of the rotating blade of an aeroengine with timing undersampling is implemented.

[0015] According to the solution of the present invention, the present invention provides a method for reconstructing the vibration signal of the tip of a rotating blade of an aeroengine with timing undersampling based on clustering shrinkage matching pursuit. Compared with the traditional solution, this method can effectively improve the accuracy and real-time performance of reconstructing the vibration signal of the tip of a rotating blade of an aeroengine with timing undersampling, and can effectively improve the non-contact online real-time health monitoring effect of the blade.

[0016] According to the solution of the present invention, aiming at the problems of low efficiency and insufficient accuracy of the traditional orthogonal matching pursuit algorithm in reconstructing the vibration signal of the tip of a rotating blade of an aeroengine with timing undersampling, the present invention realizes the simultaneous selection of multiple column vectors of the recovery matrix in each iteration process by introducing a threshold criterion and a clustering shrinkage mechanism, and accurately deletes the redundant column vectors among them, significantly improving the algorithm efficiency and reconstruction accuracy. The present invention can realize the efficient and accurate reconstruction of the vibration signal of the tip of a rotating blade of an aeroengine with timing undersampling, providing a new solution for the online real-time health monitoring of the rotating blade of an aeroengine. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Schematically showing the flowchart of the method for reconstructing the vibration signal of the rotating blade of an aeroengine with timing undersampling according to an embodiment of the present invention; Figure 2 Schematically showing the tip timing undersampling vibration signal data to be analyzed according to Embodiment 1 of the present invention; Figure 3 and Figure 4 Schematically showing the reconstruction result diagram of the clustering shrinkage matching pursuit algorithm according to Embodiment 1 of the present invention; Figure 5 and Figure 6 Schematically showing the reconstruction result diagram of the orthogonal matching pursuit algorithm according to Embodiment 1 of the present invention; Figure 7 Schematically showing the Campbell diagram of blade vibration calculated by the finite element method according to Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] The content of the present invention will now be described with reference to exemplary embodiments. It should be understood that the described embodiments are only for enabling those of ordinary skill in the art to better understand and thus implement the content of the present invention, rather than implying any limitation on the scope of the present invention.

[0019] As used herein, the term "comprising" and its variants are to be construed as open-ended terms meaning "including but not limited to". The term "based on" is to be construed as "at least partially based on". The terms "one embodiment" and "an embodiment" are to be construed as "at least one embodiment".

[0020] Figure 1 A flowchart schematically showing a method for reconstructing a time-undersampled vibration signal of an aeroengine rotating blade according to an embodiment of the present invention. As Figure 1 shown, in the present embodiment, the method for reconstructing a time-undersampled vibration signal of an aeroengine rotating blade includes: S1. Construct a reconstruction model and a recovery matrix for the time-undersampled vibration signal at the tip of the aeroengine rotating blade; S2. Set the time-undersampled vibration signal at the tip as the initial residual of the recovery matrix, and set the support set of the recovery matrix; S3. Calculate the inner product of the column vectors of the recovery matrix and the initial residual (for example, if the recovery matrix has n column vectors, then n inner product values are obtained after taking the inner product with the initial residual respectively); S4. Select the column vectors of the recovery matrix corresponding to the inner product values whose absolute values exceed the threshold among all the inner product values as matching vectors and merge them to form a first set; S5. Project the first set into the parameter space , where , are the amplitudes of the sine and cosine vibration components respectively, is the frequency of the sine and cosine vibration components; Use the nearest neighbor algorithm for clustering, and retain the column vector with the largest inner product in each class to form a second set; S6. Add the second set to the support set, and use the least squares method to solve the sparse coefficients with the goal of minimizing the residual; S7. Calculate the new residual, and repeat steps S3 to S7 until the support set is no longer updated, then terminate the iteration. At this time, the final sparse coefficients of the vibration signal are obtained; S8. Calculate the reconstructed vibration signal at the tip of the aeroengine rotating blade based on the reconstruction model, the recovery matrix, and the final sparse coefficients of the vibration signal.

[0021] Furthermore, according to an embodiment of the present invention, the reconstruction model is: , ; The recovery matrix is: ; In the formula, is to solve norm, y represents the tip-timing undersampled vibration signal, and the sampled values included in y represent the vibration displacement measured by the p-th fiber optic sensor at the n-th revolution, x represents the sparse coefficient, represents the time when the blade passes through the p-th fiber optic sensor at the n-th revolution, is the set frequency resolution, N is the number of revolutions of the measured blade rotation, and M is the number of frequency points of the reconstructed signal spectrum.

[0022] Furthermore, according to an embodiment of the present invention, the inner product of the column vector of the recovery matrix and the initial residual is calculated as: , where r is the initial residual, is the column vector.

[0023] Furthermore, according to an embodiment of the present invention, the column vectors of the recovery matrix corresponding to the inner product values whose absolute values exceed the threshold among all the inner product values are selected as matching vectors and combined to form the first set as: Select the column vectors of the recovery matrix corresponding to the inner product values whose absolute values exceed the threshold among all the inner product values as matching vectors and combine them to form the first set : ; In the formula, the threshold , T represents the signal length, and s is a coefficient.

[0024] Furthermore, according to an embodiment of the present invention, the sparse coefficient is: ; In the formula, is the sparse coefficient, is the support set, is the transpose of the support set.

[0025] Furthermore, according to an embodiment of the present invention, the new residual is .

[0026] Furthermore, according to an embodiment of the present invention, the reconstructed tip vibration signal of the aeroengine rotating blade is calculated based on the reconstruction model, the recovery matrix, and the final vibration signal sparse coefficient as: According to calculate the reconstructed tip vibration signal of the aeroengine rotating blade .

[0027] According to the above solution of the present invention, the present invention provides a method for reconstructing the vibration signal of the tip-timing undersampled rotating blade of an aero-engine based on clustering shrinkage matching pursuit. Compared with the traditional solution, this method can effectively improve the accuracy and real-time performance of reconstructing the vibration signal of the tip-timing undersampled rotating blade of an aero-engine, and can effectively improve the non-contact online real-time health monitoring effect of the blade.

[0028] According to the above solution of the present invention, aiming at the problems of low efficiency and insufficient accuracy of the traditional orthogonal matching pursuit algorithm in reconstructing the vibration signal of the tip-timing undersampled rotating blade of an aero-engine, the present invention realizes the simultaneous selection of column vectors of multiple recovery matrices in each iteration process by introducing a threshold criterion and a clustering shrinkage mechanism, and accurately deletes the redundant column vectors among them, significantly improving the algorithm efficiency and reconstruction accuracy. The present invention can realize the efficient and accurate reconstruction of the vibration signal of the tip-timing undersampled rotating blade of an aero-engine, providing a new solution for the online real-time health monitoring of the rotating blade of an aero-engine.

[0029] Furthermore, to achieve the above object, the present invention also provides a system for reconstructing the vibration signal of the tip-timing undersampled rotating blade of an aero-engine, including: A reconstruction model and recovery matrix construction module, which constructs a reconstruction model and a recovery matrix for the vibration signal of the tip-timing undersampled rotating blade of an aero-engine; An initial value setting module, which sets the tip-timing undersampled vibration signal as the initial residual of the recovery matrix and sets the support set of the recovery matrix; An inner product value calculation module, which calculates the inner product of the column vector of the recovery matrix and the initial residual; A first set acquisition module, which selects the column vectors of the recovery matrix corresponding to the inner product values whose absolute values exceed the threshold among all the inner product values as matching vectors and combines them to form a first set; A second set formation module, which projects the first set into the parameter space , where , are the amplitudes of the sine and cosine vibration components respectively, and is the frequency of the sine and cosine vibration components; the K-nearest neighbor algorithm is used for clustering, and the column vector with the largest inner product in each class is retained to form a second set; A sparse coefficient solving module, which adds the second set to the support set and uses the least squares method to solve the sparse coefficient with the minimum residual as the goal; A sparse coefficient determination module, which calculates the new residual and repeats the execution of the inner product value calculation module to the sparse coefficient determination module until the support set is no longer updated, then terminates the iteration, and at this time, the final sparse coefficient of the vibration signal is obtained; Reconstruct the vibration signal calculation module, and calculate the reconstructed tip vibration signal of the aero-engine rotating blade based on the reconstruction model, the recovery matrix, and the final vibration signal sparse coefficient.

[0030] Further, according to an embodiment of the present invention, the reconstruction model is: , ; The recovery matrix is: ; In the formula, is to solve norm, y represents the tip timing undersampled vibration signal, and the sampled values included in y represent the vibration displacement measured by the p-th fiber optic sensor at the n-th revolution, x represents the sparse coefficient, represents the time when the blade passes through the p-th fiber optic sensor at the n-th revolution, is the set frequency resolution, N is the number of revolutions of the measured blade rotation, and M is the number of frequency points of the reconstructed signal spectrum.

[0031] Further, according to an embodiment of the present invention, the inner product of the column vector of the recovery matrix and the initial residual is: , where r is the initial residual, is the column vector.

[0032] Further, according to an embodiment of the present invention, select the column vectors of the recovery matrix corresponding to the inner product values whose absolute values exceed the threshold among all the inner product values as the matching vectors and merge them to form the first set as: Select the column vectors of the recovery matrix corresponding to the inner product values whose absolute values exceed the threshold among all the inner product values as the matching vectors and merge them to form the first set : ; In the formula, the threshold , T represents the signal length, and s is the coefficient.

[0033] Further, according to an embodiment of the present invention, the sparse coefficient is: ; In the formula, is the sparse coefficient, is the support set, is the transpose of the support set.

[0034] Further, according to an embodiment of the present invention, the new residual is .

[0035] Further, according to an embodiment of the present invention, the reconstructed tip vibration signal of the rotating blade of the aero-engine is calculated based on the reconstruction model, the recovery matrix, and the final sparse coefficient of the vibration signal as follows: According to Calculate the reconstructed tip vibration signal of the rotating blade of the aero-engine .

[0036] According to the above solution of the present invention, the present invention provides a method for reconstructing the tip timing undersampled vibration signal of the rotating blade of the aero-engine based on clustering shrinkage matching pursuit. Compared with the traditional solution, this method can effectively improve the accuracy and real-time performance of reconstructing the tip timing undersampled vibration signal of the rotating blade of the aero-engine, and can effectively improve the non-contact online real-time health monitoring effect of the blade.

[0037] According to the above solution of the present invention, aiming at the problems of low efficiency and insufficient accuracy of the traditional orthogonal matching pursuit algorithm in reconstructing the tip timing undersampled vibration signal of the rotating blade of the aero-engine, the present invention realizes the simultaneous selection of multiple column vectors of the recovery matrix in each iteration process by introducing a threshold criterion and a clustering shrinkage mechanism, and accurately deletes the redundant column vectors among them, significantly improving the algorithm efficiency and reconstruction accuracy. The present invention can realize the efficient and accurate reconstruction of the tip timing undersampled vibration signal of the rotating blade of the aero-engine, providing a new solution for the online real-time health monitoring of the rotating blade of the aero-engine.

[0038] Further, to achieve the above object, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the method for reconstructing the tip timing undersampled vibration signal of the rotating blade of the aero-engine as described above.

[0039] Further, to achieve the above object, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by the processor, it implements the method for reconstructing the tip timing undersampled vibration signal of the rotating blade of the aero-engine as described above.

[0040] To make the purpose, technical solution and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only the best embodiments of the present invention, only used to explain the present invention, and do not limit the protection scope of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

[0041] Embodiment 1

[0042] Taking the scaled test piece of a certain type of aero-engine compressor rotor disk as the research object, an experimental analysis of the reconstruction of the under-sampled vibration signal of the rotating blade tip-timing of the aero-engine is carried out by using a high-speed rotating blade tip-timing vibration measurement test bench. The entire disk is made of 7050 aluminum alloy. The disk contains 34 blades with complex curved surface configurations. Its overall radius is 113.5 mm, the radius of the disk is 63.5 mm, and the length of the blade is 50 mm. A through crack with a width of 1 mm and a depth of 4.5 mm is prefabricated at the position 11 mm from the root of the leading edge of the 15th blade of the disk. The parameters of the disk test piece are shown in Table 1 below.

[0043] Table 1 Parameters of the disk test piece:

[0044] The resonance section tip-timing measurement data of each blade are reconstructed by using the clustering shrinkage matching pursuit algorithm (i.e., the method for reconstructing the under-sampled vibration signal of the rotating blade tip-timing of the aero-engine based on clustering shrinkage matching pursuit) and the orthogonal matching pursuit algorithm. Taking the 15th blade as an example for analysis, its tip-timing vibration data are as Figure 2 shown. The results of solving and reconstructing by using the clustering shrinkage matching pursuit algorithm and the orthogonal matching pursuit algorithm are respectively as Figure 3 and Figure 4 as well as Figure 5 and Figure 6 shown. As Figure 4 and Figure 6 shown, the first-order dynamic frequencies of the blade reconstructed by the two algorithms are 752.4 Hz and 752.3 Hz respectively.

[0045] Figure 7 is the Campbell diagram of the blade vibration calculated by using the finite element method for the disk model. Figure 7 In it, EO represents the blade vibration order. Further, as Figure 7 shown, when the rotational speed is 7503 revolutions per minute (125.05 Hz), the first-order natural frequency line of the blade intersects with the 6-fold excitation frequency line, exciting the resonance of the first-order mode of the blade. The corresponding first-order dynamic frequency of the blade is 750.3 Hz. Comparing the first-order dynamic frequencies (752.4 Hz and 752.3 Hz) calculated by the clustering shrinkage matching pursuit algorithm and the orthogonal matching pursuit algorithm with the first-order dynamic frequency (750.3 Hz) analyzed by the Campbell diagram, the first-order dynamic frequency deviations are 2.1 Hz and 2 Hz respectively, and the deviation rates are 0.28% and 0.27% respectively, and the deviation is very small. Due to the machining and manufacturing errors of each blade, there will be a small deviation between the actual first-order dynamic frequency of each blade and the analysis result of the Campbell diagram. Therefore, it can be considered that both algorithms have achieved accurate reconstruction of the blade vibration. Further comparing and analyzing the performance of the clustering shrinkage matching pursuit algorithm and the orthogonal matching pursuit algorithm, when the blade resonates, the excited first-order dynamic frequency energy amplitude should be the largest component in the signal spectrum. However, fromFigure 5 and Figure 6 As can be seen from the reconstructed signal spectrogram of the orthogonal matching pursuit algorithm shown in Figure 6 , the vibration amplitude at the first-order dynamic frequency is significantly low, indicating that the orthogonal matching pursuit algorithm underestimates the reconstruction of the blade vibration component amplitude. And Figure 3 and Figure 4 the reconstruction results of the clustering shrinkage matching pursuit algorithm shown in Figure 4 are more in line with the actual situation. In addition, the reconstruction time of the orthogonal matching pursuit algorithm is 0.16 s, and the reconstruction time of the clustering shrinkage matching pursuit algorithm is 0.06 s, saving 62.5% in time. In summary, compared with the orthogonal matching pursuit algorithm, the reconstruction of the tip-timing undersampled vibration signal based on the clustering shrinkage matching pursuit algorithm has a greater improvement in both accuracy and efficiency, and can improve the non-contact online real-time health monitoring effect of the blade.

[0046] Those of ordinary skill in the art can realize that the modules and algorithm steps described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0047] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated herein.

[0048] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces, and the indirect coupling or communication connection of the devices or modules can be in electrical, mechanical or other forms.

[0049] The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they can be located in one place, or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention.

[0050] In addition, each functional module in the embodiments of the present invention may be integrated into one processing module, may exist physically alone for each module, or two or more modules may be integrated into one module.

[0051] If the above functions are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method for sending / receiving energy-saving signals in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0052] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the technical solution formed by mutually replacing the above features with (but not limited to) technical features with similar functions disclosed in the present application.

[0053] It should be understood that the magnitudes of the sequence numbers of the steps in the content of the present invention and the embodiments do not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

Claims

1. A method for reconstructing the timing under-sampling vibration signal of an aircraft engine rotating blade, characterized in that: include: S1. Construct the reconstruction model and recovery matrix of the timing under-sampling vibration signal of the rotating blade tip of the aircraft engine; S2. The blade tip timing under-sampling vibration signal is set as the initial residual of the recovery matrix, and the support set of the recovery matrix is ​​set; S3. Calculate the inner product of the column vector of the recovery matrix and the initial residual; S4. Select the column vectors of the recovery matrix corresponding to the inner product values ​​whose absolute values ​​exceed the threshold among all the inner product values ​​as matching vectors and merge them into a first set; S5. Project the first set into parameter space ,in are the amplitudes of the sine and cosine vibration components, respectively. is the frequency of the sine and cosine vibration components; clustering is performed using the K-nearest neighbor algorithm, and the column vector with the largest inner product in each class is retained to form the second set; S6. adding the second set to the support set, and using the least square method to solve the sparse coefficients with the goal of minimizing the residual; S7. Calculate the new residual and repeat steps S3 to S7 until the support set is no longer updated and the iteration is terminated. At this time, the final vibration signal sparse coefficient is obtained; S8. The reconstructed aviation engine rotating blade tip vibration signal is obtained based on the reconstruction model, the recovery matrix and the final vibration signal sparse coefficient calculation.

2. The method for reconstructing the timing under-sampling vibration signal of an aircraft engine rotating blade according to claim 1, characterized in that: The reconstructed model is: , ; The recovery matrix for: ; In the formula, To solve norm, y represents the blade tip timing under-sampling vibration signal, and y contains the sampling values represents the vibration displacement measured by the pth optical fiber sensor at the nth circle, x represents the sparsity coefficient, represents the time when the blade passes the pth optical fiber sensor during the nth circle, is the set frequency resolution, N is the measured number of blade rotations, and M is the number of frequency points of the reconstructed signal spectrum.

3. The method for reconstructing the timing under-sampling vibration signal of an aircraft engine rotating blade according to claim 2, characterized in that: The inner product of the column vector of the calculated recovery matrix and the initial residual is: , where r is the initial residual, is a column vector.

4. The method for reconstructing the timing under-sampling vibration signal of an aircraft engine rotating blade according to claim 3, characterized in that: The column vectors of the recovery matrix corresponding to the inner product values ​​whose absolute values ​​exceed the threshold among all the inner product values ​​are selected as matching vectors to be combined to form the first set: Select all inner product values ​​whose absolute value exceeds the threshold The column vector of the recovery matrix corresponding to the inner product value of As matching vectors, merge into the first set : ; In the formula, the threshold , represents the signal length and s is the coefficient.

5. The method for reconstructing the timing under-sampling vibration signal of an aircraft engine rotating blade according to claim 4, characterized in that: The sparse coefficient is: ； In the formula, is the sparse coefficient, For the support set, is the transpose of the support set.

6. The method for reconstructing the timing under-sampling vibration signal of an aircraft engine rotating blade according to claim 5, characterized in that: The new residual is .

7. The method for reconstructing the timing under-sampling vibration signal of an aircraft engine rotating blade according to claim 6, characterized in that: The reconstructed aero-engine rotating blade tip vibration signal calculated based on the reconstruction model, the restoration matrix and the final vibration signal sparse coefficient is: according to Calculate and reconstruct the vibration signal of the rotating blade tip of the aero-engine .

8. A system for reconstructing the timing under-sampling vibration signal of rotating blades of an aircraft engine, characterized in that: include: Reconstruction model and recovery matrix building module, to build the reconstruction model and recovery matrix of the timing under-sampling vibration signal of the rotating blade tip of the aircraft engine; An initial value setting module sets the blade tip timing under-sampling vibration signal as the initial residual of the recovery matrix and sets the support set of the recovery matrix; Inner product value calculation module, calculates the inner product of the column vector of the recovery matrix and the initial residual; A first set acquisition module selects the column vectors of the restoration matrix corresponding to the inner product values ​​whose absolute values ​​exceed the threshold among all the inner product values ​​as matching vectors and merges them into a first set; The second set forms a module that projects the first set into the parameter space ,in , are the amplitudes of the sine and cosine vibration components, respectively. is the frequency of the sine and cosine vibration components; clustering is performed using the K-nearest neighbor algorithm, and the column vector with the largest inner product in each class is retained to form the second set; The sparse coefficient solving module adds the second set to the support set and solves the sparse coefficient using the least square method with the goal of minimizing the residual error; The sparse coefficient determination module calculates the new residual and repeatedly executes the inner product value calculation module to the sparse coefficient determination module until the support set is no longer updated, then the iteration is terminated, and the final vibration signal sparse coefficient is obtained; The reconstructed vibration signal calculation module calculates the reconstructed vibration signal of the tip of the rotating blade of the aero-engine based on the reconstruction model, the recovery matrix and the final vibration signal sparse coefficient.

9. An electronic device, characterized in that The invention comprises a processor, a memory and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the method for reconstructing the timing under-sampling vibration signal of the rotating blade of an aircraft engine as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for reconstructing the timing under-sampling vibration signal of an aircraft engine rotating blade according to any one of claims 1 to 7 is implemented.

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