Compressor blade static and dynamic balance test method and system based on data analysis

Abstract

The invention relates to the technical field of machine static and dynamic balance, and discloses a compressor blade static and dynamic balance testing method and system based on data analysis, and the method comprises the steps: building a coating thickness distribution model, and calculating the coating thickness deviation; establishing an aerodynamic disturbance model of the spalling area; collecting a vibration signal in a blade operation process in real time, and extracting a high-frequency component of a peeling interference signal; adjusting and optimizing the dynamic compensation matrix; the technical problems that in the prior art, peeling interference signals are difficult to accurately extract under the complex working condition that a coating of a compressor blade peels off, and particularly under the static and dynamic balance testing environment of high rotating speed and high-frequency vibration, accurate modeling and compensation of disturbance of a peeling area cannot be achieved are solved. Through dynamic modeling of the coating stripping area and the dynamic compensation matrix, accurate adjustment and optimization of the static and dynamic balance test under the complex operation condition are realized, so that vibration signal distortion caused by coating stripping is avoided, and the efficiency and accuracy of the balance test are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of machine static and dynamic balance, and particularly relates to a static and dynamic balance test method and system for compressor blades based on data analysis. Background Art

[0002] At present, the application of machine static and dynamic balance technology under the complex working conditions of air compressor blades has deficiencies. For example, when the coating of the compressor blades of an air compressor peels off or wears, since the peeling area will disturb the air flow distribution and the dynamic characteristics of the blades, the prior art cannot accurately extract the interference signals caused by peeling, and it is difficult to achieve accurate modeling and dynamic compensation for the disturbance in the peeling area. Therefore, during the operation of the compressor blades, the vibration signals are easily distorted by complex interferences, and the effect of static and dynamic balance compensation is often poor. Most of the prior art conducts balance tests based on static working conditions, lacking adaptability to the changes in dynamic characteristics in high-speed and high-frequency vibration environments, and unable to fully meet the accuracy and efficiency requirements for static and dynamic balance tests under complex working conditions. Therefore, there is an urgent need for a method that can still accurately extract peeling interference signals under complex working conditions such as coating peeling, and can achieve accurate modeling and dynamic compensation for the disturbance in the peeling area, so as to improve the accuracy, efficiency and adaptability of static and dynamic balance tests. Summary of the Invention

[0003] Aiming at the above-mentioned technical deficiencies, the purpose of the present invention is to propose a static and dynamic balance test method for compressor blades based on data analysis, aiming to solve the technical problems in the prior art that it is difficult to accurately extract peeling interference signals under the complex working conditions of coating peeling of compressor blades, especially in the static and dynamic balance test environment of high speed and high frequency vibration, and it is impossible to achieve accurate modeling and compensation for the disturbance in the peeling area.

[0004] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a static and dynamic balance test method for compressor blades based on data analysis,

[0005] The static and dynamic balance test method for compressor blades based on data analysis includes:

[0006] Step S10: Obtain the three-dimensional point cloud data P(x, y, z) of the compressor blades of the machine through a laser scanning device, where (x, y, z) represents the three-dimensional point coordinates, and obtain the scanning coating thickness T of the compressor blades according to the three-dimensional point cloud data of the compressor blades of the machine scanned (x, y), and calculate the compressor blade coating thickness deviation matrix ΔT(x, y) by combining with the preset standard coating thickness T ideal (x, y) of the compressor blades;

[0007] Step S20: Preset the compressor blade thickness peeling threshold T th, based on the compressor blade thickness spalling threshold T th and the compressor blade coating thickness deviation matrix ΔT(x,y), determine the compressor blade coating spalling area Ω, and calculate the total area of the compressor blade spalling area according to the compressor blade coating spalling area Ω; obtain the air flow velocity when the compressor blade is running, and use the total area A of the compressor blade spalling area Ω as a global correction factor to establish a compressor blade aerodynamic disturbance model P Ω (x,y);

[0008] Step S30: Use a multi-sensor array to collect the multi-directional compressor blade vibration signal x(t) at time t during the operation of the compressor blade. Use the mixed signal separation method to decompose the multi-directional compressor blade vibration signal x(t) into the compressor blade spalling interference signal and other vibration signals, and introduce a spalling signal feature enhancement factor W k Extract the high-frequency component x Ω (t) of the compressor blade spalling interference signal;

[0009] Step S40: Combine the compressor blade aerodynamic disturbance model P Ω (x,y) and the high-frequency component x Ω (t) of the compressor blade spalling interference signal to generate a compressor blade dynamic compensation matrix; calculate the current vibration amplitude A(t) and the current vibration frequency f(t) according to the multi-directional compressor blade vibration signal x(t), and calculate the compressor blade vibration amplitude deviation |ΔA(t)| and the compressor blade frequency deviation |Δf(t)| by combining the preset standard vibration amplitude and standard vibration frequency;

[0010] Step S50: Introduce a balance amplitude and frequency optimization factor λ, set an optimization objective function F for the compressor blade dynamic compensation matrix, and dynamically adjust the compressor blade dynamic compensation matrix with the goal of minimizing the optimization objective function F of the compressor blade dynamic compensation matrix to obtain an optimized compressor blade dynamic compensation matrix M. Apply the optimized compressor blade dynamic compensation matrix M to the static and dynamic balance test of the blade; collect the compensated multi-directional compressor blade vibration signal x comp (t) in real time, compare it with the multi-directional compressor blade vibration signal x(t) before compensation, set the completion condition for the static and dynamic balance test adjustment, and determine that the static and dynamic balance test adjustment is completed if the completion condition for the static and dynamic balance test adjustment is met.

[0011] Preferably, in step S10, the calculation formula for the compressor blade coating thickness deviation matrix ΔT(x,y) is ΔT(x,y) = T ideal (x,y) - T scanned (x,y).

[0012] Preferably, in step S20, the compressor blade coating spalling area Ω is defined as the area that satisfies the condition |ΔT(x, y)| > T th That is, Ω = {(x, y) | |ΔT(x, y)| > T th}.

[0013] Preferably, in step S20, the aerodynamic disturbance model P Ω (x, y) of the compressor blade is given by the formula:

[0014]

[0015] where k is a preset aerodynamic force coefficient; A ref is the reference area of the compressor blade spalling area; v is the air flow velocity when the compressor blade is running.

[0016] Preferably, in step S30, the extraction formula for the high-frequency component x Ω (t) of the compressor blade spalling interference signal is:

[0017]

[0018] where IMF k (t) is the k-th intrinsic mode function, which is used to decompose the frequency components of the multi-direction compressor blade vibration signal x(t); n is the total number of preset intrinsic mode functions.

[0019] Preferably, in step S50, the completion condition for the static and dynamic balance test adjustment is:

[0020] If |ΔA(t)| < A max and |Δf(t)| > f min , it is determined that the balance adjustment is completed, where A max is the preset vibration amplitude deviation threshold of the compressor blade; f min is the frequency deviation threshold of the compressor blade.

[0021] Preferably, in step S40, the optimization objective function of the compressor blade dynamic compensation matrix is F = |ΔA(t)| + λ·|Δf(t)|.

[0022] The present invention also provides a static and dynamic balance test system for compressor blades based on data analysis, including:

[0023] A scanning and thickness deviation calculation module, which is used to obtain the three-dimensional point cloud data P(x, y, z) of the machine compressor blade through a laser scanning device, (x, y, z) represents the three-dimensional point coordinates, obtain the compressor blade scanning coating thickness T scanned (x, y) according to the three-dimensional point cloud data of the machine compressor blade, and combine the preset standard coating thickness T of the compressor blade ideal(x, y) calculates the compressor blade coating thickness deviation matrix ΔT(x, y);

[0024] Spalling area and aerodynamic disturbance modeling module, used to preset the compressor blade thickness spalling threshold T th , based on the compressor blade thickness spalling threshold T th and the compressor blade coating thickness deviation matrix ΔT(x, y) to determine the compressor blade coating spalling area Ω, and calculate the total area of the compressor blade spalling area according to the compressor blade coating spalling area Ω; obtain the air flow velocity when the compressor blade is running, and use the total area A of the compressor blade spalling area Ω as a global correction factor to establish the compressor blade aerodynamic disturbance model P Ω (x, y);

[0025] Vibration signal acquisition and interference signal extraction module, used to collect the multi-directional compressor blade vibration signal x(t) at time t during the operation of the compressor blade using a multi-sensor array, decompose the multi-directional compressor blade vibration signal x(t) into the compressor blade spalling interference signal and other vibration signals using the mixed signal separation method, and introduce the spalling signal feature enhancement factor W k Extract the high-frequency component x Ω (t) of the compressor blade spalling interference signal;

[0026] Dynamic compensation model construction and deviation calculation module, used to combine the compressor blade aerodynamic disturbance model P Ω (x, y) and the high-frequency component x Ω (t) of the compressor blade spalling interference signal to generate the compressor blade dynamic compensation matrix; calculate the current vibration amplitude A(t) and the current vibration frequency f(t) according to the multi-directional compressor blade vibration signal x(t), and calculate the compressor blade vibration amplitude deviation |ΔA(t)| and the compressor blade frequency deviation |Δf(t)| by combining the preset standard vibration amplitude and standard vibration frequency;

[0027] Real-time optimization and compensation application module, used to introduce the balance amplitude and frequency optimization factor λ, set the compressor blade dynamic compensation matrix optimization objective function F, dynamically adjust the compressor blade dynamic compensation matrix with the goal of minimizing the compressor blade dynamic compensation matrix optimization objective function F to obtain the optimized compressor blade dynamic compensation matrix M, and apply the optimized compressor blade dynamic compensation matrix M to the static and dynamic balance test of the blade; real-time collect the compensated multi-directional compressor blade vibration signal x comp (t), compare it with the uncompensated multi-directional compressor blade vibration signal x(t), set the completion condition of the static and dynamic balance test adjustment, and judge that the static and dynamic balance test adjustment is completed if the completion condition of the static and dynamic balance test adjustment is met.

[0028] The present invention also provides a computer program product, including a compressor blade static and dynamic balance test program based on data analysis. When the compressor blade static and dynamic balance test program based on data analysis is executed by a processor, the compressor blade static and dynamic balance test method based on data analysis as described above is implemented.

[0029] The beneficial effects of the present invention are as follows: Compared with the prior art, it is difficult to accurately extract the spalling interference signal under the complex working conditions of compressor blade coating spalling, especially in the static and dynamic balance test environment of high speed and high frequency vibration, and it is impossible to achieve accurate modeling and compensation of the disturbance in the spalling area. Through the dynamic modeling of the coating spalling area and the real-time optimization mechanism of the dynamic compensation matrix, the present application realizes the precise adjustment and optimization of the static and dynamic balance test under complex operating conditions, thereby avoiding the distortion of vibration signals caused by coating spalling and improving the efficiency and accuracy of the balance test. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0031] Figure 1 It is a schematic flowchart of the first embodiment of a compressor blade static and dynamic balance test method based on data analysis according to the present invention.

[0032] Figure 2 It is a schematic diagram of the equipment of a compressor blade static and dynamic balance test method based on data analysis according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0034] Embodiment 1: As Figure 1 shown, it is a schematic flowchart of the first embodiment of the compressor blade static and dynamic balance test method based on data analysis according to the present invention, and the first embodiment of the compressor blade static and dynamic balance test method based on data analysis according to the present invention is proposed.

[0035] In the first embodiment, the compressor blade static and dynamic balance test method based on data analysis includes:

[0036] Step S10: Obtain the three-dimensional point cloud data P (x, y, z) of the machine compressor blade through the laser scanning device, (x, y, z) represents the three-dimensional point coordinates, and obtain the compressor blade scanning coating thickness T according to the three-dimensional point cloud data of the machine compressor blade scanned (x,y), combined with the preset compressor blade standard coating thickness T ideal (x, y) calculates the compressor blade coating thickness deviation matrix ΔT(x, y);

[0037] It should be understood that in order to better identify the thickness deviation of the compressor blade surface coating, the three-dimensional point cloud data acquired by the laser scanning equipment can generate a coating thickness distribution model with extremely high resolution. Compared with the traditional contact measurement method, this non-contact scanning technology not only greatly improves the measurement efficiency, but also avoids potential mechanical damage to the blade surface; by calculating the coating thickness deviation matrix, the present invention can accurately locate the coating peeling area and its distribution characteristics, and provide reliable initial data support for subsequent static and dynamic balance adjustments. At the same time, the method is real-time and highly sensitive, and can effectively cope with complex blade surface geometries and small thickness changes, thereby significantly improving the accuracy and adaptability of static and dynamic balance tests of compressor blades.

[0038] For example, the coating thickness of a compressor blade under design conditions is T ideal (x, y) = 2.0 mm, the actual coating thickness is measured by laser scanning equipment as T scanned (x, y) = 1.5 mm, then the coating thickness deviation at this position is ΔT(x, y) = T ideal (x,y)-T scanned (x, y) = 2.0-1.5 = 0.5 mm. By performing the same calculation on the entire blade surface, the thickness deviation matrix ΔT(x, y) can be obtained, as

[0039]

[0040] Step S20: Preset compressor blade thickness peeling threshold T th , based on the compressor blade thickness peeling threshold T th The compressor blade coating peeling area Ω is determined by the compressor blade coating thickness deviation matrix ΔT(x, y), and the total area of ​​the compressor blade peeling area Ω is calculated according to the compressor blade coating peeling area Ω; the airflow velocity when the compressor blade is running is obtained, and the total area of ​​the compressor blade peeling area A is calculated. Ω As a global correction factor, the compressor blade aerodynamic disturbance model P is established in combination with the airflow velocity when the compressor blade is running. Ω (x,y);

[0041] It should be understood that, in order to better describe the overall disturbance characteristics of the compressor blade spalling area, the present invention establishes a dynamic aerodynamic disturbance model through the combination of the thickness deviation matrix and the total area. Compared with the prior art, this model can not only reflect the local spalling characteristics, but also reflect the global impact of large-area spalling on the overall balance state of the blade; the aerodynamic disturbance model provides a quantitative evaluation of the impact of the spalling area on the aerodynamic performance and static and dynamic balance of the compressor blade, providing reliable data support for subsequent dynamic compensation.

[0042] For example, the coating thickness spalling threshold T th = 0.3 mm, and the thickness deviation matrix of a certain compressor blade is According to |ΔT(x,y)|>T th , the spalling area Ω includes coordinate points such as (x 2 ,y 1 ), (x 2 ,y 2 ), (x 2 ,y 3 ), (x 3 ,y 2 ), etc. Calculate the total area A Ω of the spalling area. Assuming that the grid area corresponding to each point is 1 mm 2 , then A Ω = 4 mm 2 , the air flow velocity v = 300 m / s, and the aerodynamic disturbance model is k = 0.02 Pa / mm·m 2 / s 2 , the reference area A ref = 10 mm 2 ; then for the point (x 2 ,y 2 ), |ΔT(x 2 ,y 2 )| = 0.7 mm, and the aerodynamic disturbance is Through this calculation method, the impact of the spalling area on the air flow disturbance can be accurately analyzed.

[0043] Step S30: Use a multi-sensor array to collect the multi-directional compressor blade vibration signal x(t) at time t during the operation of the compressor blade, decompose the multi-directional compressor blade vibration signal x(t) into the compressor blade spalling interference signal and other vibration signals by using the mixed signal separation method, and introduce the spalling signal feature enhancement factor W k Extract the high-frequency component x Ω (t) of the compressor blade spalling interference signal;

[0044] It should be understood that, in order to better extract the coating spalling interference signal, the present invention combines time-domain and frequency-domain analysis. After decomposing the vibration signal into multiple modal functions through a mixed signal separation method, a feature enhancement factor is introduced for the high-frequency mode to improve the accuracy of signal extraction. The extracted high-frequency component of the coating spalling is not only the input data for subsequent dynamic compensation, but also can effectively eliminate the influence of other low-frequency interference signals, thereby improving the accuracy of the static and dynamic balance test.

[0045] Step S40: Combine the compressor blade aerodynamic disturbance model P Ω (x,y) and the high-frequency component x Ω (t) of the compressor blade spalling interference signal to generate a compressor blade dynamic compensation matrix; calculate the current vibration amplitude A(t) and the current vibration frequency f(t) based on the multi-direction compressor blade vibration signal x(t), and calculate the compressor blade vibration amplitude deviation |ΔA(t)| and the compressor blade frequency deviation |Δf(t)| by combining the preset standard vibration amplitude and standard vibration frequency;

[0046] It should be understood that, in order to better optimize the static and dynamic balance state of the compressor blade, the present invention makes real-time adjustments to the blade through the dynamic compensation matrix. Combining the vibration amplitude deviation and frequency deviation, it realizes the dynamic feedback control of the vibration signal. By analyzing the change trend of the deviation value, the compressor blade dynamic compensation matrix can be adjusted in real time, so that the blade can still maintain a balanced state under complex operating conditions, improving the adaptability and accuracy of the static and dynamic balance test.

[0047] Step S50: Introduce a balance amplitude and frequency optimization factor λ, set an optimization objective function F for the compressor blade dynamic compensation matrix, and dynamically adjust the compressor blade dynamic compensation matrix with the goal of minimizing the optimization objective function F of the compressor blade dynamic compensation matrix to obtain an optimized compressor blade dynamic compensation matrix M, and apply the optimized compressor blade dynamic compensation matrix M to the static and dynamic balance test of the blade; collect the compensated multi-direction compressor blade vibration signal x comp (t) in real time, compare it with the multi-direction compressor blade vibration signal x(t) before compensation, set the completion condition for the static and dynamic balance test adjustment, and determine that the static and dynamic balance test adjustment is completed if the completion condition for the static and dynamic balance test adjustment is met.

[0048] It should be noted that the optimization objective function F of the compressor blade dynamic compensation matrix is F = |ΔA(t)| + λ·|Δf(t)|.

[0049] It should be understood that in order to better meet the dynamic balance requirements under complex working conditions, the present invention introduces a feedback mechanism to collect the vibration signals after compensation in real time, combines them with the optimization results of the objective function, and gradually adjusts the dynamic compensation matrix, so as to continuously optimize the static and dynamic balance of the blades during operation. This method effectively avoids the problem of balance failure caused by working condition changes in traditional static compensation methods and improves the dynamic adaptability of balance adjustment.

[0050] For example, the current vibration amplitude A(t) = 0.7 mm, and the designed reference vibration amplitude A design = 0.5 mm. Calculate the vibration amplitude deviation |ΔA(t)| = |A(t) - A design | = |0.7 - 0.5| = 0.2 mm; the current vibration frequency f(t) = 55 Hz, and the designed reference frequency f design = 50 Hz. Calculate the vibration frequency deviation |Δf(t)| = |f(t) - f design | = |55 - 50| = 5 Hz; assume the weight factor λ = 0.1, and the optimization objective function is F = |ΔA(t)| + λ·|Δf(t)| = 0.2 + 0.1·5 = 0.7. Adjust the dynamic compensation matrix M according to the optimization objective function F, and gradually reduce |ΔA(t)| and |Δf(t)|. After compensation, A(t) = 0.55 mm, f(t) = 51 Hz, then: |ΔA(t)| = |0.55 - 0.5| = 0.05 min, |Δf(t)| = |51 - 50| = 1 Hz, F = 0.05 + 0.1·1 = 0.15; if the preset threshold conditions |ΔA(t)| < A max = 0.1 mm and |Δf(t)| < f min = 2 Hz are met, it is determined that the static and dynamic balance adjustment is completed.

[0051] In addition, a static and dynamic balance test system for compressor blades based on data analysis provided by the present invention adopts a static and dynamic balance test method for compressor blades based on data analysis in the above embodiment, and can solve the technical problem of static and dynamic balance test for compressor blades based on data analysis. Compared with the prior art, the beneficial effects of a static and dynamic balance test system for compressor blades based on data analysis provided by the present invention are the same as those of a static and dynamic balance test method for compressor blades based on data analysis provided in the above embodiment, and other technical features in the static and dynamic balance test system for compressor blades based on data analysis are the same as the features disclosed in the above embodiment method, and will not be elaborated here.

[0052] The present invention provides a static and dynamic balance test device for compressor blades based on data analysis. Please refer to Figure 2, A compressor blade static and dynamic balance test device based on data analysis includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute a compressor blade static and dynamic balance test method based on data analysis in the first embodiment above. A compressor blade static and dynamic balance test device in an embodiment of the present invention may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description: tablet computers), PMPs (Portable Media Player: portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A compressor blade static and dynamic balance test device based on data analysis is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention. A compressor blade static and dynamic balance test device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of a compressor blade static and dynamic balance test device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow a compressor blade static and dynamic balance test device to communicate with other devices wirelessly or wiredly to exchange data. Although a compressor blade static and dynamic balance test device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the shown systems. Instead, more or fewer systems may be implemented or had.

[0053] The present invention also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of a method for static and dynamic balance testing of compressor blades based on data analysis as described above. The computer program product provided by the present invention can solve the technical problem of static and dynamic balance testing of compressor blades based on data analysis. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as those of the method for static and dynamic balance testing of compressor blades based on data analysis provided in the above embodiments, and will not be elaborated herein.

[0054] Specifically, according to the embodiments disclosed by the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed by the present invention include a computer program product which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by a processing device 1001, it executes the above-mentioned functions defined in the methods of the embodiments disclosed by the present invention.

[0055] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0056] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A static and dynamic balance test method for compressor blades based on data analysis, characterized in that: Methods include: Step S10: Obtain the three-dimensional point cloud data P (x, y, z) of the machine compressor blade through the laser scanning device, (x, y, z) represents the three-dimensional point coordinates, and obtain the compressor blade scanning coating thickness T according to the three-dimensional point cloud data of the machine compressor blade scanned (x,y), combined with the preset compressor blade standard coating thickness T ideal (x, y) calculates the compressor blade coating thickness deviation matrix ΔT(x, y); Step S20: Preset compressor blade thickness peeling threshold T th , based on the compressor blade thickness peeling threshold T th The compressor blade coating peeling area Ω is determined by the compressor blade coating thickness deviation matrix ΔT(x, y), and the total area of ​​the compressor blade peeling area Ω is calculated according to the compressor blade coating peeling area Ω; the airflow velocity when the compressor blade is running is obtained, and the total area of ​​the compressor blade peeling area A is calculated. Ω As a global correction factor, the compressor blade aerodynamic disturbance model P is established in combination with the airflow velocity when the compressor blade is running. Ω (x,y); Step S30: Use a multi-sensor array to collect the multi-directional compressor blade vibration signal x(t) at time t during the operation of the compressor blade, use a mixed signal separation method to decompose the multi-directional compressor blade vibration signal x(t) into compressor blade peeling interference signals and other vibration signals, and introduce a peeling signal feature enhancement factor W k Extract the high frequency component x of the compressor blade peeling interference signal Ω (t); Step S40: Combine the compressor blade aerodynamic disturbance model P Ω (x,y) and the high-frequency component x of the compressor blade peeling interference signal Ω (t) generating a compressor blade dynamic compensation matrix; calculating the current vibration amplitude A(t) and the current vibration frequency f(t) according to the multi-directional compressor blade vibration signal x(t), and calculating the compressor blade vibration amplitude deviation |ΔA(t)| and the compressor blade frequency deviation |Δf(t)| in combination with the preset standard vibration amplitude and standard vibration frequency; Step S50: Introduce the balance amplitude and frequency optimization factor λ, set the compressor blade dynamic compensation matrix optimization objective function F, dynamically adjust the compressor blade dynamic compensation matrix to minimize the compressor blade dynamic compensation matrix optimization objective function F as the goal to obtain the optimized compressor blade dynamic compensation matrix M, and apply the optimized compressor blade dynamic compensation matrix M to the static and dynamic balance test of the blade; collect the compensated multi-directional compressor blade vibration signal x in real time comp (t), and compared with the multi-directional compressor blade vibration signal x(t) before compensation, the static and dynamic balance test adjustment completion conditions are set, and the static and dynamic balance test adjustment completion conditions are met, and it is determined that the static and dynamic balance test adjustment is completed.

2. A compressor blade static and dynamic balance test method based on data analysis as claimed in claim 1, characterized in that: In step S10, the calculation formula of the compressor blade coating thickness deviation matrix ΔT(x, y) is ΔT(x, y)=T ideal (x,y)-T scanned (x,y).

3. A compressor blade static and dynamic balance test method based on data analysis as claimed in claim 1, characterized in that: In step S20, the compressor blade coating peeling area Ω is defined as satisfying the condition |ΔT(x,y)| > T th for the region Ω = {(x, y) ∣ |ΔT(x, y)| > T th}.

4. A compressor blade static and dynamic balance test method based on data analysis as claimed in claim 1, characterized in that: In step S20, the compressor blade aerodynamic disturbance model P Ω The formula for (x,y) is: Where k is the preset aerodynamic coefficient; A ref is the reference area of ​​the compressor blade peeling area; v is the air flow velocity when the compressor blade is running.

5. A compressor blade static and dynamic balance test method based on data analysis as claimed in claim 1, characterized in that: In step S30, the high frequency component x of the compressor blade peeling interference signal Ω The extraction formula of (t) is: Among them, IMF k (t) is the kth intrinsic mode function, which is used to decompose the frequency components of the multi-directional compressor blade vibration signal x(t); n is the total number of preset intrinsic mode functions.

6. A compressor blade static and dynamic balance test method based on data analysis as claimed in claim 1, characterized in that: In step S50, the static and dynamic balance test adjustment completion conditions are: If |ΔA(t)| max and |Δf(t)|>f min , then the balance adjustment is completed, where A max is the preset compressor blade vibration amplitude deviation threshold; f min is the compressor blade frequency deviation threshold.​ 7. A compressor blade static and dynamic balance test method based on data analysis as claimed in claim 1, characterized in that: In step S40, the compressor blade dynamic compensation matrix optimizes the objective function F=|ΔA(t)|+λ·|Δf(t)|.

8. A compressor blade static and dynamic balance test system based on data analysis, characterized in that: The compressor blade static and dynamic balance test system based on data analysis includes: The scanning and thickness deviation calculation module is used to obtain the three-dimensional point cloud data P (x, y, z) of the machine compressor blade through the laser scanning device, (x, y, z) represents the three-dimensional point coordinates, and obtain the compressor blade scanning coating thickness T according to the three-dimensional point cloud data of the machine compressor blade scanned (x,y), combined with the preset compressor blade standard coating thickness T ideal (x, y) calculates the compressor blade coating thickness deviation matrix ΔT(x, y); The peeling area and aerodynamic disturbance modeling module is used to preset the compressor blade thickness peeling threshold T th , based on the compressor blade thickness peeling threshold T th The compressor blade coating peeling area Ω is determined by the compressor blade coating thickness deviation matrix ΔT(x, y), and the total area of ​​the compressor blade peeling area Ω is calculated according to the compressor blade coating peeling area Ω; the airflow velocity when the compressor blade is running is obtained, and the total area of ​​the compressor blade peeling area A is calculated. Ω As a global correction factor, the compressor blade aerodynamic disturbance model P is established in combination with the airflow velocity when the compressor blade is running. Ω (x,y); The vibration signal acquisition and interference signal extraction module is used to use a multi-sensor array to collect the multi-directional compressor blade vibration signal x(t) at time t during the operation of the compressor blade, and use the mixed signal separation method to decompose the multi-directional compressor blade vibration signal x(t) into the compressor blade peeling interference signal and other vibration signals, and introduce the peeling signal feature enhancement factor W k Extract the high frequency component x of the compressor blade peeling interference signal Ω (t); Dynamic compensation model construction and deviation calculation module, used to combine the compressor blade aerodynamic disturbance model P Ω (x,y) and the high-frequency component x of the compressor blade peeling interference signal Ω (t) generating a compressor blade dynamic compensation matrix; calculating the current vibration amplitude A(t) and the current vibration frequency f(t) according to the multi-directional compressor blade vibration signal x(t), and calculating the compressor blade vibration amplitude deviation |ΔA(t)| and the compressor blade frequency deviation |Δf(t)| in combination with the preset standard vibration amplitude and standard vibration frequency; The real-time optimization and compensation application module is used to introduce the balance amplitude and frequency optimization factor λ, set the compressor blade dynamic compensation matrix optimization objective function F, dynamically adjust the compressor blade dynamic compensation matrix to minimize the compressor blade dynamic compensation matrix optimization objective function F, and obtain the optimized compressor blade dynamic compensation matrix M. The optimized compressor blade dynamic compensation matrix M is applied to the static and dynamic balance test of the blade; real-time acquisition of the compensated multi-directional compressor blade vibration signal x comp (t), and compared with the multi-directional compressor blade vibration signal x(t) before compensation, the static and dynamic balance test adjustment completion conditions are set, and the static and dynamic balance test adjustment completion conditions are met, and it is determined that the static and dynamic balance test adjustment is completed.

9. A static and dynamic balance test equipment for compressor blades based on data analysis, characterized in that: The data analysis-based static and dynamic balancing test equipment for compressor blades comprises: a memory, a processor, and a data analysis-based static and dynamic balancing test program for compressor blades stored in the memory and executable on the processor. When the data analysis-based static and dynamic balancing test program for compressor blades is executed by the processor, the data analysis-based static and dynamic balancing test method for compressor blades described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product includes a compressor blade static and dynamic balancing test program based on data analysis, and when the compressor blade static and dynamic balancing test program based on data analysis is executed by a processor, it implements the compressor blade static and dynamic balancing test method based on data analysis described in any one of Claims 1 to 7.

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