Gas path performance evaluation method based on blade surface key point normal vector included angle change

Abstract

The invention relates to the technical field of gas turbines, and particularly discloses a blade surface key point normal vector included angle change-based gas path performance evaluation method, which comprises the following steps of: acquiring a three-dimensional model for a series of life-up blades and normal blades through a high-precision scanning technology; establishing a mapping relation between a key point normal vector included angle and a blade fault mode through a model; when the gas turbine runs, monitoring parameters are input into the model, and the change of the blade key point normal vector included angle is deduced according to the change of thermodynamic parameters, so that the fault position, type and degree of the blade can be more accurately diagnosed. According to the method, the new index key point normal vector included angle is introduced to directly describe the blade phenotypic geometrical characteristics, the fluctuation and error conditions of blade gas path performance evaluation results caused by directly monitoring simple parameter indexes are greatly reduced, and blade fault diagnosis can be more accurately carried out.

Description

Technical Field

[0001] The invention relates to the technical field of gas turbines, and in particular to a method for evaluating gas path performance based on changes in normal vector angles of key points on a blade surface. Background Art

[0002] As a highly efficient energy conversion device, gas turbines play an important role in power generation, aviation, shipbuilding and other fields. With the growth of global energy demand and the improvement of environmental protection requirements, the performance and reliability requirements of gas turbines are getting higher and higher. The performance of its gas path components such as compressors and turbines is directly related to the overall efficiency and output power of gas turbines. Ensuring the stability of gas path performance is crucial to extending equipment life and reducing maintenance costs. As an important component in the gas path system, whether the blades can work normally and stably directly affects the performance of the whole machine. However, since the blades work in harsh environments for a long time, high temperature, high pressure, high speed, high load and exposure to air are prone to corrosion, cracks, surface pits and other damage to the blades, which will cause the blades to fail during operation and seriously affect the stability and safety of the operation. Therefore, it is very necessary to carry out research on the gas path performance evaluation of gas turbines.

[0003] In the gas path performance evaluation, the phenotypic geometric characteristics of the blade have a decisive influence on its performance. Even a slight change in the blade surface may lead to a significant decrease in aerodynamic performance. However, traditional evaluation methods mainly rely on empirical formulas or threshold judgments of performance parameters, including thermodynamic parameters and component degradation. These indicators will lead to large fluctuations in the evaluation results and even cause false alarms, which cannot meet the high accuracy requirements of modern gas path systems. At the same time, the selection of the above indicators can only monitor the occurrence of faults, but cannot know the specific type of fault. Therefore, it is necessary to explore new indicators that can reflect the specific location and type of faults. The existing technology can evaluate the gas path performance by selecting blade phenotypic geometric characteristics such as blade length and width, etc., according to the changes in blade geometric characteristics, but these selected indicators are too simple and cannot fully capture the complex geometric characteristics of the blade. Therefore, it is necessary to find new indicators to extract the complex characteristics of the blade more finely and establish an evaluation model for geometric characteristics to improve the accuracy of gas path evaluation.

[0004] Based on this, a method for evaluating air path performance based on the change of normal vector angles at key points on the blade surface is now provided, which can eliminate the drawbacks of existing devices. Summary of the invention

[0005] The purpose of the present invention is to provide a method for evaluating gas path performance based on the change of normal vector angles of key points on the blade surface, thereby solving the shortcomings of existing gas path fault diagnosis technologies.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for evaluating gas path performance based on the change of normal vector angles of key points on a blade surface comprises the following steps:

[0008] Step 1: Obtain a series of serviceable blades and normal blades with different fault information, and obtain the three-dimensional model of the blade through high-precision scanning equipment;

[0009] Step 2: Identify key points on the three-dimensional model of the blade, and calculate the angle between the legal vectors of the neighborhood point cloud at the key points at the same position of the normal blade and the blade at the end of its service life;

[0010] Step 3: Reconstruct the three-dimensional model obtained in step 1 into the service life blade and the normal blade, and conduct cascade experiments to obtain thermodynamic parameters. The experimental data are used to establish a relationship model between thermodynamic parameters and blade geometric characteristics;

[0011] Step 4: Analyze the correlation between the normal vector angles of key points of the service life blade and the normal blade and the thermodynamic parameters, and explore the blade failure mechanism caused by the change of thermodynamic parameters caused by the change of blade geometric characteristics;

[0012] Step 5: Establish a fault diagnosis model based on the mapping relationship between the blade normal vector angle change and the fault mode;

[0013] Step 6: Monitor the thermodynamic parameters of the gas turbine in real time during operation, input them into the diagnostic model in step 5 to determine the change in the normal vector angle of the key points of the blade, and diagnose the type and degree of fault according to the change in the geometric characteristics of the blade.

[0014] On the basis of the above technical solution, the present invention also provides the following optional technical solution:

[0015] In an optional solution: In step 1: the fault information includes blade surface wear, blade leading edge cracks, blade deformation, and blade corrosion.

[0016] In an optional scheme: In step 1: the specific scanning process is: the blade samples are fixed on the scanning table to ensure that they remain stable during the scanning process, and the blades are three-dimensionally scanned using a laser scanner with a resolution of 0.01 mm to ensure accurate capture of the blade surface details.

[0017] In an optional solution: the key points in step 2 come from characteristic points on the blade surface, including the leading edge, the trailing edge, the maximum thickness point, the minimum thickness point, the blade turning point, and significant characteristic points on the blade surface.

[0018] In an optional solution: Step 4 is specifically as follows: first, the normal vector angle features of the key points of the blade are extracted from the three-dimensional scanning data, then the thermodynamic parameter features of the blade surface are extracted from the experimental data, and finally a feature vector is created to combine the normal vector angle with the thermodynamic parameters; a machine learning algorithm is used to learn the relationship between the feature vector and the blade performance, and a historical data set is used to train the model to learn the correlation between the normal vector angle and the thermodynamic parameters.

[0019] In an optional solution: in step 5: based on a series of life blade data under different failure modes collected, including the damage type and severity of the blade, the collected data set is labeled with the fault type, location and severity, and the normal vector angle measured in step 2 is associated with the fault type to determine which faults are caused by the change in the blade geometric characteristics;

[0020] Specifically, a data set is created, which includes the geometric features of the blade, the normal vector angle and the corresponding failure mode. The data set is divided into a training set and a test set for model training and verification.

[0021] Specific principles of the model: Li is the change in blade geometric characteristics, that is, the normal vector angle between the faulty blade and the normal blade at a certain key point; the thermodynamic parameters are represented by corresponding symbols, such as flow, pressure, and temperature are represented by G, P, and T respectively; the monitored thermodynamic parameter is parameter1, and the normal thermodynamic parameter parameter2 is used as the input of the model. The output of the model is the change in the normal vector angle at a specific key point, that is, the change in the normal vector of the blade relative to the normal blade, which corresponds to the specific fault type; the location of the blade fault can be determined by the change in the normal vector at a certain key point; the type and severity of the blade fault are determined by the size and range of the change in the normal vector angle.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] The present invention provides a gas path performance evaluation method based on the change of the normal vector angle of the key points on the blade surface, and introduces the normal vector angle of the key points as a new geometric feature index to more accurately capture the slight changes on the blade surface. In order to reduce the volatility of the blade fault diagnosis results directly by monitoring the thermodynamic parameters during the operation of the gas turbine and improve the accuracy of fault diagnosis, the present invention introduces the normal vector angle of the key points to establish the relationship between the thermodynamic parameters and the blade fault type, and judges the change of the normal vector angle of the key points of the blade, that is, the geometric feature, by real-time monitoring of the changes in the thermodynamic parameters, and identifies the fault type and degree of the blade; F(parameter1, parameter2) = L i . BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the technical solution flow of the present invention.

[0025] Figure 2 It is a schematic diagram of the steps of the technical solution of the present invention.

[0026] Figure 3 It is a schematic diagram of the evaluation results of the classic degradation indicators of the present invention.

[0027] Figure 4 It is a schematic diagram of the evaluation results of the key point normal vector angle index of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] like Figure 1-4 As shown, an embodiment of the present invention provides a method for evaluating gas path performance based on the change of normal vector angles of key points on the blade surface, and the gas path performance evaluation method includes the following steps:

[0030] Step 1: Obtain a series of serviceable blades and normal blades with different fault types, and obtain the blade 3D model through high-precision scanning equipment.

[0031] Step 2: Identify key points on the three-dimensional model of the blade, and calculate the angle between the legal vector of the neighborhood point cloud at the key point at the same position of the normal blade and the blade at the end of its service life.

[0032] Step 3: Reconstruct the three-dimensional model obtained in step 1 into the service life blade and normal blade, and conduct blade cascade experiments to obtain thermodynamic parameters. The experimental data are used to establish the relationship model between thermodynamic parameters and blade geometric characteristics.

[0033] Step 4: Analyze the correlation between the normal vector angles of key points of the service life blade and the normal blade and the thermodynamic parameters, and explore the blade failure mechanism caused by the change of thermodynamic parameters due to the change of blade geometric characteristics.

[0034] Step 5: Establish a fault diagnosis model based on the mapping relationship between the blade normal vector angle change and the fault mode.

[0035] Step 6: Real-time monitoring of thermodynamic parameters in the operation of the gas turbine, inputting them into the diagnostic model in step 5 to determine the change in the normal vector angle of the key points of the blade, and diagnosing the type and degree of the fault according to the change in the geometric characteristics of the blade;

[0036] Step 1: Obtain a series of blades with different fault types and normal blades, and obtain the three-dimensional model of the blade through high-precision scanning equipment.

[0037] A group of gas turbine blades running to the end of their service life under different working conditions were collected. These blades showed various types of faults, including blade surface wear, blade leading edge cracks, blade deformation, blade corrosion, etc. The blades were cleaned before scanning to remove oil and dust to ensure the accuracy of the scan data, and the detailed information of each blade was recorded, including its installation position in the gas turbine, operation history, historical maintenance records, etc. No faults occurred on normal blades.

[0038] The specific scanning process is as follows: fix the blade samples on the scanning table to ensure that they remain stable during the scanning process, and use a laser scanner with a resolution of 0.01 mm to perform a three-dimensional scan of the blade to ensure the accurate capture of the blade surface details. At the same time, this scanner is equipped with high-precision sensors and advanced data processing software. After starting the scanner, it will emit a laser beam and capture the laser points reflected from the blade surface. The scanner rotates around the blade, or the blade rotates on a precisely controlled rotating table to ensure that every angle of the blade is scanned. During the scanning process, the device will generate a large number of data points, which constitute the point cloud of the blade surface. After the scanning is completed, the point cloud data is processed using professional 3D reconstruction software. The software converts the point cloud data into a continuous 3D surface model through an algorithm. This model accurately reflects the actual geometric shape of the blade. The 3D model is post-processed to remove noise, fill in data missing areas, smooth the surface, etc. to improve the quality of the model, and finally generate a complete 3D model of the blade, including information such as the blade surface shape, size and surface roughness.

[0039] Step 2: Identify the key points on the 3D model of the blade, determine the resultant vector of the normal vectors of the point cloud in the neighborhood of the key points, and calculate the legal vector angle at the same key point position of the service life blade and the normal blade.

[0040] After obtaining the precise geometric model of the blade through three-dimensional scanning, computer-aided design software (CAD) is used to identify key points. These key points come from the characteristic points on the blade surface, including the leading edge, trailing edge, maximum (minimum) thickness point, blade turning point, and other significant characteristic points on the blade surface. These points have a significant impact on the aerodynamic performance. In the CAD software, these key points are marked and the coordinates of each point are recorded. For these key points, the point cloud within the neighborhood with a radius of r is determined, and the normal vectors of these points are calculated, that is, the vector perpendicular to the blade surface at this point, which can represent the degree and direction of the inclination of the blade surface at this point. Among them, the range of the neighborhood depends on the size of r, which is determined according to the experimental requirements and objectives. After the normal vectors including the key points in the field are determined, the resultant vector of these normal vectors is calculated, that is, the normal vector representing the position of the key point, which represents the geometric characteristics of the position. The angle between the normal vectors at the same key point position of the normal blade and the blade at the end of life is calculated using CAD software. By calculating the normal vector angle at the key point position, we can more accurately describe whether the geometric characteristics of the blade surface have changed and analyze the change in the angle. If the angle changes significantly, it indicates that the blade surface has deformed near these key points, which may be caused by wear, corrosion or other damage, and can be linked to blade failure.

[0041] Step 3: Reconstruct the three-dimensional model obtained in step 1 into the service life blade and normal blade, and conduct blade cascade experiments to obtain thermodynamic parameters. The experimental data are used to establish the relationship model between thermodynamic parameters and blade geometric characteristics.

[0042] Step 1: After scanning and obtaining the three-dimensional model of the blade, reconstruct the service life blade and the normal blade for the blade row experiment. The reconstruction method can be carried out by 3D printing and other methods. The aerodynamic performance of the blade is studied through the blade row experiment. In the experiment, by building a test bench for the influence of air path failure on the blade row, the blade is installed in a simulated blade row channel, and the conditions of the fluid flowing into the blade row are controlled. A test section simulating the operating conditions of the gas turbine is set up in the wind tunnel to simulate the working environment of the blade in actual operation. The specific preparation work is to install a series of blades with different types of failures and normal blades in the cascade channel and ensure that they are fixed in the appropriate position, configure the test section to provide stable and controllable airflow, install pressure sensors, temperature sensors, flow meters and other measuring equipment at the inlet and outlet of the cascade, install thermocouples and pressure sensors on the blade surface to obtain the local temperature and pressure distribution on the blade surface, and finally set the airflow velocity, pressure, temperature and other related parameters and start the experiment, so that the airflow passes through the cascade, and record the thermodynamic parameters under different working conditions, including total pressure, static pressure, total temperature, static temperature and mass flow, and collect the local temperature and pressure data on the blade surface. Analyze the data of all measuring devices, calculate the surface pressure, outlet velocity field, etc., use the data acquisition system to collect all measurement data, and perform preliminary processing such as filtering, averaging and normalization, analyze the distribution of thermodynamic parameters on the blade surface, and identify the relationship between key parameters and blade performance. Compare the differences in thermodynamic parameters between normal blades and blades that have reached their end of life, and establish the relationship between changes in thermodynamic parameters and blade failures.

[0043] Step 4: Analyze the correlation between the normal vector angles of key points of the service life blade and the normal blade and the thermodynamic parameters, and explore the blade failure mechanism caused by the change of thermodynamic parameters due to the change of blade geometric characteristics.

[0044] Based on steps 1, 2, and 3, blade data (3D scanning data, operating parameters, performance parameters, and thermodynamic parameters) of different working conditions, different fault types, and normal blades are collected. These data are first processed to remove outliers and incomplete records, and the data is standardized to ensure that data from different sources and types can be compared and analyzed on the same scale. Using the thermodynamic parameters obtained for different blades and different working conditions, the association law between the normal vector angle at the key point position obtained by extracting and calculating the blade geometric features is established. Specifically, a mathematical model is established to identify and process the complex relationship between the parameters to accurately reflect the changes in thermodynamic parameters caused by changes in blade geometric features. First, the normal vector angle features of the key points of the blade are extracted from the 3D scanning data, and then the thermodynamic parameter features of the blade surface are extracted from the experimental data. Finally, a feature vector is created to combine the normal vector angle with the thermodynamic parameters. A machine learning algorithm is used to learn the relationship between the feature vector and the blade performance, and a historical data set is used to train the model to learn the association law between the normal vector angle and the thermodynamic parameters.

[0045] Step 5: Establish a fault diagnosis model based on the mapping relationship between the blade normal vector angle change and the fault mode.

[0046] Based on a series of life-expiring blade data under different fault modes collected, including the damage type (such as cracks, wear, corrosion, etc.) and severity of the blades, the collected data sets are labeled with the fault type, location and severity, and the normal vector angle measured in step 2 is associated with the fault type to determine which faults are caused by changes in the blade's geometric features. Specifically, it includes first creating a data set that contains the blade's geometric features, normal vector angles and corresponding fault modes. The data set is divided into a training set and a test set for model training and verification. A machine learning algorithm suitable for the classification problem is selected, and the model is trained using the training set data to learn the mapping relationship between the normal vector angle of the blade's key points and the fault mode. Finally, the test set data is used to evaluate the model's prediction accuracy. The trained model is deployed to the gas turbine's monitoring system to achieve real-time monitoring of blade geometric feature changes. Fault diagnosis.

[0047] Specific principles of the model: Li is the change in the geometric characteristics of the blade, that is, the normal vector angle between the faulty blade and the normal blade at a certain key point; the thermodynamic parameters are represented by corresponding symbols, such as flow, pressure, and temperature are represented by G, P, and T respectively. The monitored thermodynamic parameter is parameter1, and the normal thermodynamic parameter parameter2 is used as the input of the model. The output of the model is the change in the normal vector angle at a specific key point, that is, the change in the normal vector of the blade relative to the normal blade, which corresponds to the specific fault type. The location of the blade fault can be determined by the change of the normal vector at a certain key point; the type and severity of the blade fault can be determined by the size and range of the change in the normal vector angle.

[0048] F(parameter1, parameter2)=L i Step 6: Monitor the thermodynamic parameters of the gas turbine in real time during operation, input them into the diagnostic model in step 5 to determine the change in the normal vector angle of the key points of the blade, and diagnose the type and degree of fault according to the change in the geometric characteristics of the blade.

[0049] The above principles are applied to the condition monitoring of actual gas turbines. High-precision sensors, including temperature sensors, pressure sensors and flow meters, are installed at key locations of gas turbines to monitor the inlet and outlet temperatures, pressures and flow rates of blades in real time. These sensors are connected to the central monitoring system through a data acquisition system and can transmit data to the control room in real time. When the gas turbine is running, the thermodynamic parameters of the blades are monitored online, and these thermodynamic parameters are input into the diagnostic model to determine whether the normal vector angle of the key points of the blades changes. The model uses an algorithm to analyze the relationship between the thermodynamic parameters and the normal vector angle and predict the geometric changes of the blades. Once the change in the normal vector angle is detected, the type and degree of the fault can be diagnosed. Based on the range and pattern of the change in the normal vector angle, it is possible to identify whether the blade has a fault, as well as the type and degree of the fault.

[0050] The limitation of the present invention patent is the integrity and reliability of the collected blade data. The more blades with different fault types are obtained, the more accurate the data acquisition equipment is, the closer the relationship between thermodynamic parameters, blade geometric characteristics, fault type and degree is, and the more accurate the final fault diagnosis result is.

[0051] In the traditional blade fault diagnosis method, thermodynamic indicators are generally selected for evaluation, such as efficiency degradation, flow degradation and other classic degradations. However, the selection of these indicators fluctuates severely in the gas path performance evaluation results, which may cause the risk of false alarms and missed alarms. At the same time, the abnormality of the monitored thermodynamic parameters cannot determine the specific fault type and severity. Therefore, it is necessary to construct new indicators to accurately reflect the location, type and severity of blade faults.

[0052] In the present invention, the geometric characteristics of the blade phenotype are accurately described by constructing the change in the angle of the normal vector at the key point position. The actual manifestation of the blade failure is the change in geometric characteristics, such as blade deformation, corrosion, cracks, etc. When the gas turbine is actually running, only real-time thermodynamic parameters can be monitored. This patent constructs a mapping relationship between thermodynamic parameters-blade geometric characteristics-blade failure mode. It only needs to determine whether the blade phenotype geometric characteristics have changed according to the change in thermodynamic parameters, and identify the location, type and degree of blade failure according to the size and range of the change in the normal vector angle.

[0053] The key point of the invention is the construction of the blade normal vector angle, i.e., the geometric characteristic index, and the establishment of a fault diagnosis model based on the connection between thermodynamic index, geometric characteristic index, and fault mode to accurately identify faults. The specific method for establishing the geometric characteristic index and the fault diagnosis model is as follows:

[0054] The 3D model of the blade is obtained by a high-precision 3D scanning device with a resolution of at least 0.01 mm to ensure accurate capture of the blade surface details. At the same time, multiple scans at different angles are performed to generate a complete 3D model of the blade, including information such as the blade surface shape, size and surface roughness.

[0055] After obtaining the precise geometric model of the blade through 3D scanning, a dedicated geometric analysis tool is used to identify key points. These key points come from the characteristic points on the blade surface, including the leading edge, trailing edge, maximum (minimum) thickness point, blade turning point and other locations that have a significant impact on aerodynamic performance. For these key points, the surface normal vector of the cloud computing of its neighborhood point cloud is determined, that is, the vector perpendicular to the blade surface at that point, which can represent the degree and direction of the inclination of the blade surface at that point, and the resultant vector of the normal vector of the neighborhood point cloud is obtained. The angle between the normal vectors at the same position of a normal blade and a faulty blade can be calculated using CAD software. By calculating the normal vector angle of the key points on the blade, the changes in the geometric characteristics of the blade surface can be described more accurately.

[0056] The blades are reconstructed through the obtained three-dimensional model of the blades, and the aerodynamic performance of the blades is studied through the cascade experiment. In the experiment, by building a test bench for the influence of air path faults on the cascade, the blades are installed in a simulated cascade channel, and the conditions of the fluid flowing into the cascade are controlled to simulate the working environment of the blades in actual operation. The specific preparation work is to install a series of blades with different types of failures and normal blades in the cascade channel respectively, and ensure that they are fixed in the appropriate position, configure the test section to provide stable and controllable airflow, install pressure sensors, temperature sensors, flow meters and other measuring equipment at the outlet of the cascade, and finally set the airflow velocity, pressure, temperature and other related parameters and start the experiment, so that the airflow passes through the cascade, record and analyze the data of all measuring equipment, calculate the surface pressure, outlet velocity field, etc., and finally obtain the relevant thermodynamic parameters of the blade.

[0057] By using the thermodynamic parameters obtained for different blades and under different working conditions, the correlation law between the normal vector angles of key points obtained by extracting and calculating the blade geometric features is established. Specifically, a mathematical model is established to identify and process the complex relationship between the parameters, so as to accurately reflect the changes in thermodynamic parameters caused by changes in blade geometric characteristics.

[0058] Collect a series of life-long blade data under different failure modes, including the damage type (such as cracks, wear, corrosion, etc.) and severity of the blade, annotate the fault type for the collected data set, and associate the measured normal vector angle with the fault type to determine which faults are caused by changes in blade geometric characteristics.

[0059] The above principles are applied to the condition monitoring of actual gas turbines. When the gas turbine is running, the thermodynamic parameters of the blades are monitored online to determine whether the normal vector angle of the key points of the blades has changed. If there is a slight change, the diagnostic model is immediately used to identify whether the blades have failed, as well as the type and degree of the failure.

[0060] In the embodiment, a domestic 30MW gas turbine was used to conduct a 4000-hour reliability experiment. Different indicators were selected: the classic degradation indicator and the new indicator - the key point normal vector angle, and the gas path performance was evaluated respectively. The results were compared, and some diagnostic time results are shown in the figure below. The evaluation results of the classic degradation indicators efficiency degradation and flow degradation are very volatile, and are very close to the life threshold at some times, which may cause the risk of false alarms. After adopting the new indicator, the gas path performance evaluation results are relatively stable, and the normal vector angle changes greatly when the blade fails, exceeding the life threshold, and the fault can be accurately reported.

[0061] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating gas path performance based on the change of normal vector angles of key points on the blade surface, characterized in that: The following steps are involved: Step 1: Obtain a series of serviceable blades and normal blades with different fault information, and obtain the three-dimensional model of the blade through high-precision scanning equipment; Step 2: Identify key points on the three-dimensional model of the blade, and calculate the angle between the legal vectors of the neighborhood point cloud at the key points at the same position of the normal blade and the blade at the end of its service life; Step 3: Reconstruct the three-dimensional model obtained in step 1 into the service life blade and the normal blade, and conduct cascade experiments to obtain thermodynamic parameters. The experimental data are used to establish a relationship model between thermodynamic parameters and blade geometric characteristics; Step 4: Analyze the correlation between the normal vector angles of key points of the service life blade and the normal blade and the thermodynamic parameters, and explore the blade failure mechanism caused by the change of thermodynamic parameters caused by the change of blade geometric characteristics; Step 5: Establish a fault diagnosis model based on the mapping relationship between the blade normal vector angle change and the fault mode; Step 6: Monitor the thermodynamic parameters of the gas turbine in real time during operation, input them into the diagnostic model in step 5 to determine the change in the normal vector angle of the key points of the blade, and diagnose the type and degree of fault according to the change in the geometric characteristics of the blade.

2. The method for evaluating gas path performance based on the change of normal vector angle of key points on the blade surface according to claim 1, characterized in that: In step 1: the fault information includes blade surface wear, blade leading edge cracks, blade deformation, and blade corrosion.

3. The gas path performance evaluation method based on the change of the normal vector angle of key points on the blade surface according to claim 1 is characterized in that: In step 1: The specific scanning process is as follows: fix the blade samples on the scanning table to ensure that they remain stable during the scanning process, and use a laser scanner with a resolution of 0.01 mm to perform a 3D scan of the blade to ensure accurate capture of the blade surface details.

4. The method for evaluating gas path performance based on the change of normal vector angle of key points on the blade surface according to claim 1, characterized in that: The key points in step 2 come from the characteristic points on the blade surface, including the leading edge, the trailing edge, the maximum thickness point, the minimum thickness point, the blade turning point, and the significant characteristic points on the blade surface.

5. The method for evaluating gas path performance based on the change of normal vector angle of key points on the blade surface according to claim 1, characterized in that: Step 4 is as follows: first, extract the normal vector angle features of the key points of the blade from the 3D scanning data, then extract the thermodynamic parameter features of the blade surface from the experimental data, and finally create a feature vector to combine the normal vector angle with the thermodynamic parameters; use a machine learning algorithm to learn the relationship between the feature vector and the blade performance, and use historical data sets to train the model to learn the correlation between the normal vector angle and the thermodynamic parameters.

6. The method for evaluating gas path performance based on the change of normal vector angle of key points on the blade surface according to claim 1, characterized in that: In step 5: based on the collected data of a series of blades under different failure modes, including the damage type and severity of the blades, the collected data set is labeled with the fault type, location and severity, and the normal vector angle measured in step 2 is associated with the fault type to determine which faults are caused by the changes in the blade geometric characteristics; Specifically, a data set is created, which includes the geometric features of the blade, the normal vector angle and the corresponding failure mode. The data set is divided into a training set and a test set for model training and verification. Specific principles of the model: Li is the change in blade geometric characteristics, that is, the normal vector angle between the faulty blade and the normal blade at a certain key point; thermodynamic parameters are represented by corresponding symbols, such as flow, pressure, and temperature are represented by G, P, and T respectively; the monitored thermodynamic parameter is parameter1, and the normal thermodynamic parameter parameter2 is used as the input of the model. The output of the model is the change in the normal vector angle at a specific key point, that is, the change in the normal vector of the blade relative to the normal blade, corresponding to the specific fault type; the location of the blade fault can be determined by the change in the normal vector at a certain key point; the type and severity of the blade fault can be determined by the size and range of the change in the normal vector angle; F(parameter1,parameter2)=L i 。

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