Full-automatic inspection device for high-pressure turbine blade of aero-engine

Abstract

The utility model belongs to the field of blade detection, particularly relates to a full-automatic inspection device for a high-pressure turbine blade of an aero-engine, and aims to solve the problems of large manual error, low efficiency, limited visual angle and difficulty in data recording and analysis in the traditional turbine blade inspection method. The device is characterized in that a rotation driving device is in driving connection with a rotating shaft of an engine high-pressure rotor; the first probe is used for acquiring image data of the blade leading edge; the second probe is used for acquiring image data of the blade trailing edge; the rotation driving device, the first probe and the second probe are all electrically connected with the terminal, and the terminal is used for controlling the rotation speed, the rotation direction and the rotation amount of the rotation driving device, obtaining image data of the front edge and image data of the rear edge and obtaining the damage area in combination with an inspection method. According to the utility model, the influence of human factors is reduced in an automatic manner, the accuracy of a detection result and the detection efficiency are improved, and all-directional dead-corner-free detection is realized by using the first probe and the second probe.

Description

Technical Field

[0001] This utility model belongs to the field of blade inspection, and specifically relates to a fully automatic inspection device for high-pressure turbine blades of aero-engines. Background Technology

[0002] As the core power plant of modern aircraft, the performance and safety of turbofan engines directly affect flight safety and economy. In turbofan engines, high-pressure turbine blades play a crucial role, converting the high-temperature, high-pressure gas generated in the combustion chamber into mechanical energy, which then drives the low-pressure turbine and fan, providing thrust to the aircraft. Therefore, the condition of the high-pressure turbine blades directly affects the engine's efficiency and lifespan.

[0003] Due to the extremely harsh working environment—high temperature, high pressure, and high-speed rotation—high-pressure turbine blades are highly susceptible to damage such as corrosion, cracks, and wear. If this damage is not detected and addressed promptly, it can lead to blade breakage or even more serious accidents. Therefore, regular inspection and maintenance of high-pressure turbine blades is a crucial measure to ensure reliable engine operation.

[0004] Traditionally, turbine blade inspection has primarily relied on borescope inspection technology, a method that involves inserting a thin optical instrument into the engine to observe and assess the condition of the turbine blades. However, this method has some limitations:

[0005] Human error: Since the inspection process is highly dependent on the operator's experience and skills, there may be inconsistencies in judgment standards between different operators, which may lead to the same damage being misjudged or missed.

[0006] Inefficient: Traditional borehole inspection is a time-consuming process, especially when conducting a comprehensive inspection of multiple engines on large commercial aircraft, which requires a high amount of time and human resources.

[0007] Limited field of view: Borehole equipment provides a limited field of view and may not be able to fully cover all areas that need to be inspected, especially those hard-to-reach locations.

[0008] Data recording and analysis are difficult: Manually recording inspection results is not only time-consuming, but also detrimental to subsequent data analysis and comparison, affecting the accuracy and efficiency of fault diagnosis.

[0009] Based on this, this utility model proposes a fully automatic inspection device for high-pressure turbine blades of aero-engines. Utility Model Content

[0010] To address the aforementioned problems in existing technologies, namely the large human error, low efficiency, limited field of view, and difficulty in data recording and analysis of traditional turbine blade inspection methods, this utility model provides a fully automatic inspection device for high-pressure turbine blades of aero-engines, including a rotation drive device, a first probe, a second probe, and a terminal.

[0011] The rotation drive device is connected to the rotation shaft of the engine's high-pressure rotor and is used to drive the rotation shaft to rotate.

[0012] The first probe passes through the outer casing and inner casing of the engine and is positioned on one side of the leading edge of the blade to acquire image data of the leading edge of the blade.

[0013] The second probe passes through the outer casing and inner casing of the engine and is positioned on one side of the trailing edge of the blade to acquire image data of the trailing edge of the blade.

[0014] The rotation drive device, the first probe, and the second probe are all electrically connected to the terminal. The terminal is used to control the rotation speed, rotation direction, and rotation amount of the rotation drive device, and to acquire image data of the leading edge and the trailing edge, and to obtain the damaged area in combination with the inspection method.

[0015] In some preferred embodiments, the first probe includes a first probe tube and a lens portion at the end of the first probe;

[0016] The first probe tube body and the lens portion at the end of the first probe are connected by a rotating mechanism, which is used to drive the lens portion at the end of the first probe to rotate to an angle parallel to the length direction of the HPT1 level guide.

[0017] The first probe tube and the lens portion at the end of the first probe enter the engine outer casing through the probe port of the HPT1 level guide.

[0018] In some preferred embodiments, the second probe includes a second probe tube body and a lens portion at the end of the second probe;

[0019] The second probe tube and the lens portion at the end of the second probe enter the engine outer casing through the probe port of the HPT2 level guide. The length direction of the second probe tube is perpendicular to the length direction of the engine outer casing. The second probe tube and the lens portion at the end of the second probe are fixed and are arranged parallel to the length direction of the second probe tube and the length direction of the lens portion at the end of the second probe.

[0020] In some preferred embodiments, the lens portion at the end of the first probe and the lens portion at the end of the second probe each include multiple lenses and multiple light sources.

[0021] In some preferred embodiments, the rotation drive device includes a drive end and a connector;

[0022] The drive end is coaxially connected to the input shaft of the connector, and the output shaft of the connector is coaxially connected to the rotating shaft. The drive end is used to drive the rotating shaft to rotate.

[0023] In some preferred embodiments, the driving end is a stepper motor, which is connected to a power source to supply power to the stepper motor. A wireless transmission module is installed on the stepper motor, and the stepper motor is connected to a terminal through the wireless transmission module.

[0024] In some preferred embodiments, the motor shaft is detachably fixed to the handle.

[0025] In some preferred embodiments, both the first probe tube and the second probe tube are equipped with limiting devices, which are located outside the engine outer casing and overlap with the probe opening on the engine outer casing.

[0026] In some preferred embodiments, the light source is an LED light source.

[0027] In some preferred embodiments, multiple lenses are used to acquire image data of the leading edge, trailing edge, leaf tip, and leaf root of the leaf.

[0028] The beneficial effects of this utility model are:

[0029] Improved detection accuracy: Automation reduces the impact of human factors and ensures consistent standards for each inspection, thereby improving the accuracy of test results. High-resolution lenses and light sources can capture minute damage, helping to detect potential problems at an early stage.

[0030] Improving inspection efficiency: Automated inspection equipment can quickly complete the entire inspection process, significantly reducing the time required for a single inspection, especially when inspecting multiple engines, where the efficiency improvement is particularly noticeable. This not only saves time and manpower but also speeds up aircraft maintenance and repair, improving the operational efficiency of airlines.

[0031] Expanded inspection range: The design of the first and second probes allows the device to cover multiple key areas of the blade, including the leading edge, trailing edge, tip, and root, achieving comprehensive, blind-spot-free inspection. This design is particularly suitable for areas that are difficult to reach using traditional methods, ensuring thorough inspection.

[0032] Optimized data management and analysis: This device can automatically record all image data acquired during the inspection process and perform efficient data processing and analysis through the terminal. This not only facilitates the preservation and review of historical data but also supports the comparison of inspection results at different time periods through algorithms, helping engineers better understand the damage development process of the blades and thus formulate more scientific and reasonable maintenance strategies.

[0033] Enhanced safety: By identifying and addressing potential damage early, this device helps prevent major safety incidents caused by blade damage, ensuring flight safety. Simultaneously, automated inspection reduces the number of times personnel are directly exposed to high-temperature, high-pressure environments, lowering occupational health risks during the inspection process.

[0034] Reduced maintenance costs: In the long run, improving inspection efficiency and accuracy can effectively extend engine lifespan, reduce unnecessary repair and replacement costs, and bring economic benefits to airlines. Attached Figure Description

[0035] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0036] Figure 1 This is a front view of a fully automatic inspection device for high-pressure turbine blades of an aero-engine according to this utility model;

[0037] Figure 2 This is a side view of a fully automatic inspection device for high-pressure turbine blades of an aero-engine according to this utility model;

[0038] Figure 3 This is a schematic diagram of the probe structure in a fully automatic inspection device for high-pressure turbine blades of an aero-engine according to this utility model.

[0039] Figure 4 This is a schematic diagram of the terminal in a fully automatic inspection device for high-pressure turbine blades of an aero-engine according to this utility model.

[0040] Figure 5 This is a multi-angle schematic diagram of two probes in a fully automatic inspection device for high-pressure turbine blades of an aero-engine, according to this utility model. Detailed Implementation

[0041] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0042] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0043] like Figures 1-5 As shown, see Figure 1 This utility model provides a fully automatic inspection device for high-pressure turbine blades of aero-engines, including a rotation drive device, a first probe 1, a second probe 2 and a terminal 3;

[0044] The rotation drive device is connected to the rotation shaft of the engine's high-pressure rotor and is used to drive the rotation shaft to rotate.

[0045] The first probe 1 passes through the outer casing 4 and the inner casing 5 of the engine and is positioned on one side of the leading edge of the blade 6 to acquire image data of the leading edge of the blade 6.

[0046] The second probe 2 passes through the outer casing 4 and the inner casing 5 of the engine and is positioned on one side of the trailing edge of the blade 6 to acquire image data of the trailing edge of the blade 6.

[0047] The rotation drive device, the first probe 1, and the second probe 2 are all electrically connected to the terminal 3. The terminal 3 is used to control the rotation speed, rotation direction, and rotation amount of the rotation drive device, and to acquire image data of the leading edge and the trailing edge, and to obtain the damaged area in combination with the inspection method.

[0048] Among them, such as Figure 4 As shown, terminal 3 includes a computing unit and an interactive PAD 31. Terminal 3 can control the rotation speed, direction, and amount of rotation of the rotation drive device; and receive information on the rotation speed, direction, and amount of rotation of the rotating tool, enabling precise positioning of each blade 6; the terminal receives images from the first probe 1 and the second probe 2, and through calculation, the rotating tool has image recognition and size calculation functions. When blade 6 passes the middle position of the probe, an image of blade 6 is captured, and the blade 6 in the image is located using data such as the amount of rotation; damage on the engine blade is identified through a damage recognition model, and by analyzing the pixel size of the damage in the image and comparing the ratio of the actual size of the blade edge to the pixel size, the actual size and specific location of the damage are calculated. Simultaneously, for damaged blade leading edge areas with overlapping areas in the observed images, the comparison relationship of the two cameras at a unified position can be used to form a binocular measurement condition, thereby providing more accurate measurements. Furthermore, this part can also generate engine reports, call standards in the AMM manual, and perform automatic comparisons to determine the usability of the damage; and provide damage comparisons between two inspections.

[0049] in addition, Figure 4It also includes a second-stage high-pressure turbine shroud ring 13 and a second-stage high-pressure turbine rotor blade 14, which are existing technologies and will not be described in detail here.

[0050] For further explanation of this utility model, see [link to relevant documentation]. Figure 1 and Figure 2 The first probe 1 includes a first probe tube body 11 and a lens portion 12 at the end of the first probe;

[0051] The first probe tube body 11 and the lens portion 12 at the end of the first probe are connected by a rotating mechanism. The rotating mechanism is used to drive the lens portion 12 at the end of the first probe to rotate to an angle parallel to the length direction of the HPT1 level guide 7.

[0052] The first probe tube body 11 and the lens portion 12 at the end of the first probe enter the engine outer casing 4 through the probe port of the HPT1 level guide 7.

[0053] Specifically, in the initial state, the rotating mechanism does not drive the lens portion 12 at the end of the first probe to rotate. At this time, there is no angle between the first probe tube body 11 and the lens portion 12 at the end of the first probe. The lens portion 12 at the end of the first probe is then inserted into the outer casing 4 of the engine through the probe port of the HPT1 stage guide 7. After passing through the inner casing 5 of the engine, the rotating device is activated, driving the lens portion 12 at the end of the first probe to deflect backward by 40 degrees to approach the leading edge of the blade 6 and obtain a better field of view. During inspection, the leading edge of the HPT1 stage rotor blade 6 is observed sequentially from front to back.

[0054] For further explanation of this utility model, see [link to relevant documentation]. Figure 1 and Figure 2 The second probe 2 includes a second probe tube body 21 and a lens portion 22 at the end of the second probe;

[0055] The second probe tube body 21 and the lens portion 22 at the end of the second probe enter the engine outer casing 4 through the probe port of the HPT2 level guide 8. The length direction of the second probe tube body 21 is perpendicular to the length direction of the engine outer casing 4. The second probe tube body 21 and the lens portion 22 at the end of the second probe are fixed. The length direction of the second probe tube body 21 and the length direction of the lens portion 22 at the end of the second probe are parallel.

[0056] A rotating mechanism can be installed between the second probe tube body 21 and the lens part 22 at the end of the second probe, or they can be directly fixed as a whole.

[0057] Among them, such as Figure 5 As shown, Figure 5In the diagram, 'a' represents the first observation angle of the leading edge, 'b' represents the second observation angle of the leading edge, 'c' represents the first observation angle of the trailing edge, and 'd' represents the second observation angle of the trailing edge.

[0058] Figure 5 The first probe 1 and the second probe 2 in the diagram are both top-view schematic diagrams.

[0059] For further explanation of this utility model, see [link to relevant documentation]. Figure 3 The lens portion 12 at the end of the first probe and the lens portion 22 at the end of the second probe each include multiple lenses and multiple light sources.

[0060] The rotating mechanism is wirelessly connected to the terminal.

[0061] The lenses and light sources in the lens portion 12 at the end of the first probe and the lens portion 22 at the end of the second probe are connected to the terminal via a wireless transmission module, which is embedded inside the lens portion 12 at the end of the first probe and the lens portion 22 at the end of the second probe.

[0062] The wireless transmission module in this utility model can also be replaced with a line connection according to actual needs. This utility model does not specifically limit this content, and any method that can transmit data is within the protection scope of this utility model.

[0063] In this embodiment, each probe is equipped with two high-definition cameras, each with a diameter of no more than 8 mm and a resolution of no less than 5 megapixels, and two LED light sources. These cameras can inspect the leading edge, leaf basin area, and leaf underside area of ​​the same leaf 6 separately, providing comprehensive high-definition images of the leaf 6. Compared with the examination by an endoscopic operator, this avoids missed damage caused by poor viewing angles.

[0064] The two lenses are a first lens 121 and a second lens 122, and the light source includes a first light source 123 and a second light source 124.

[0065] The first light source 123 is disposed between the first lens 121 and the second lens 122, and the second light source 124 is disposed below the second lens 122.

[0066] The rotation drive device includes a drive end and a connector;

[0067] The drive end is coaxially connected to the input shaft of the connector, and the output shaft of the connector is coaxially connected to the rotating shaft. The drive end is used to drive the rotating shaft to rotate.

[0068] In this embodiment, the driver can be controlled by a computing terminal or manually:

[0069] When the drive end is electric, it can be a stepper motor. The stepper motor is connected to a power source, which supplies power to the stepper motor. A wireless transmission module is installed on the stepper motor, and the stepper motor is connected to terminal 3 through the wireless transmission module.

[0070] This part receives signals from the calculation and control section to control the speed and amount of rotation of the stepper motor, thereby precisely controlling the rotation of the engine's high-pressure turbine shaft.

[0071] When the drive end is manual, it can be selected as a handle to switch to manual control. The handle also inputs electrical signals to control the motor to rotate, rather than manually turning the engine.

[0072] The motor shaft and handle are detachably fixed.

[0073] The drive unit has two signal input sources: a computing terminal that can automatically control the drive unit to precisely control the rotation of the blade 6 to be inspected. However, the drive unit can also be controlled by its built-in control handle, which can control the rotation direction, speed, and amount of rotation. The control handle has higher priority than the computing terminal. The actual rotation amount, speed, and direction information are collected by sensors near the motor shaft and fed back to the computing terminal.

[0074] See Figure 1 Both the first probe tube body 11 and the second probe tube body 21 are equipped with limiting devices 9. The limiting devices 9 are located outside the engine outer casing 4 and overlap with the probe opening on the engine outer casing 4.

[0075] The limiting device 9 in this utility model can be selected as a limiting ring, and the diameter of the limiting ring is larger than the diameter of the probe.

[0076] The above steps ensure that the probe is securely attached to the engine, preventing it from falling off.

[0077] As a further explanation of this utility model, multiple lenses are used to acquire image data of the leading edge, trailing edge, tip, and root of the blade 6.

[0078] The second embodiment of this utility model provides a fully automated inspection method for high-pressure turbine blades of an aero-engine, the method comprising:

[0079] Image data of the leaf to be detected is acquired and used as input data to a trained leaf damage recognition model to obtain a prediction result. The prediction result includes the damage type and the bounding box coordinates of the damage location. The image data is obtained by acquiring images of different positions of the leaf using the first probe 1 and the second probe 2 and aligning the images.

[0080] Key feature points of the blade edge in the image data are obtained, and the pixel distance of the key feature points in the image data is calculated according to the pre-constructed blade curvature model. The conversion ratio between the pixel distance and the actual size is calculated based on the pixel distance.

[0081] The size of the bounding box is calculated based on the bounding box coordinates of the damage location, and then the actual size of the damage to the blade to be detected is obtained by combining the transformation ratio.

[0082] A repair plan is generated based on the actual size of the damage to the blade to be inspected.

[0083] To more clearly illustrate the fully automated inspection method for high-pressure turbine blades of aero-engines according to this utility model, the steps in the embodiments of this utility model are described in detail below:

[0084] Image data of the leaf to be detected is acquired and used as input data to a trained leaf damage recognition model to obtain a prediction result. The prediction result includes the damage type and the bounding box coordinates of the damage location. The image data is obtained by acquiring images of different positions of the leaf using different probes and aligning the images.

[0085] The image data described in this invention is acquired based on two probes set at the leading and trailing edges of HPT1 grade blades. The two probes are used to collect images from multiple perspectives of the leading edge, trailing edge, leaf tip, and leaf root of the blade. After image alignment, 360-degree image information of each blade is obtained as image data.

[0086] The image alignment method is as follows:

[0087] The rotation speed and rotation amount signals transmitted from the engine rotation tool are acquired, the actual movement of the HPT rotor blade per unit time is calculated, the time it takes for the blade to pass through two probes is obtained, and images of the blade taken by different probes are acquired. Multiple images are correlated to the same blade to obtain image data of the same blade from multiple angles.

[0088] Specifically, after calculating the actual movement of the HPT rotor blades per unit time, the time it takes for blade 1 to pass through the first probe 1 and the time it takes for blade 1 to pass through the second probe 2 can be obtained, and images captured by blade 1 in different lenses can be acquired. These images are then associated with blade 1 to obtain image information of blade 1 at various angles. The methods for acquiring image data of other blades are as described above and will not be repeated here.

[0089] In this invention, the damage recognition model comprises a basic feature extraction network, a multi-scale feature map generation network, and a prediction network connected in sequence.

[0090] In this embodiment, the damage recognition model is built based on YOLOv5, and its architecture includes three parts: Backbone, Neck, and Head.

[0091] Backbone: Used to extract basic image features, typically using a convolutional neural network (CNN) structure, such as CSPDarknet.

[0092] Neck: Used to generate multi-scale feature maps, typically employing the FPNFeature Pyramid Network or PANetPath Aggregation Network structure.

[0093] Head: Used to predict the category, location, and confidence level of each target object.

[0094] The training method for the damage recognition model described in this invention includes the following steps:

[0095] Step S1: Input the dataset into the damage recognition model to obtain the prediction results;

[0096] Step S2: Calculate the total loss of the prediction results and the actual annotations based on the loss function;

[0097] Step S3: Calculate the gradient using the backpropagation algorithm and update the weights of the damage recognition model using the optimizer to gradually reduce the loss function value;

[0098] Step S4: Jump to step S1 and execute steps S1-S3 until the loss function converges or the predetermined number of training rounds is reached, then stop jumping.

[0099] The dataset is a large collection of images with HPT blade damage labels, including coating peeling, ablation, cracks, burn-through, and material loss, collected through routine engine borescope inspections. The labels include the category and bounding box of the target object.

[0100] The images and labeled data in the dataset are formatted, such as by resizing images and normalizing pixel values. Data augmentation operations are also performed, such as random cropping, rotation, scaling, and color adjustment, to increase data diversity and prevent overfitting.

[0101] The optimizer can be either Adam or SGD.

[0102] The loss function in this embodiment includes classification loss, localization loss, and confidence loss.

[0103] Classification loss: measures the difference between the predicted target class and the actual class, and is usually achieved using cross-entropy loss.

[0104] Localization loss: measures the difference between the predicted bounding box and the actual bounding box, usually using IoU Intersection over Union or GIoU Generalized IoU loss.

[0105] Confidence loss: measures the difference between the predicted probability of a target existing and the actual probability.

[0106] The damage recognition model of this invention undergoes model evaluation and testing after training. The method is as follows:

[0107] Validation set evaluation: The model performance is evaluated using an independent validation dataset. Commonly used metrics include mAP (mean Average Precision).

[0108] Hyperparameter tuning: Based on the evaluation results of the validation set, adjust model hyperparameters such as learning rate and batch size to further optimize model performance.

[0109] Test set evaluation: Use the test dataset for final evaluation to ensure that the model performs well on unseen data.

[0110] Model export: Export the trained model to a deployable format, such as ONNX, TorchScript, etc.

[0111] Reasoning: In practical applications, models are used for real-time or batch target detection.

[0112] The HPT leaf damage recognition model was trained using the above steps.

[0113] Key feature points of the blade edge in the image data are obtained, and the pixel distance of the key feature points in the image data is calculated according to the pre-constructed blade curvature model. The conversion ratio between the pixel distance and the actual size is calculated based on the pixel distance.

[0114] In this invention, a trained YOLOv5 model is used to detect damage in HPT blade images, and the bounding box coordinates of each damage are obtained. The coordinates of key feature points on the blade edge are detected by manual annotation.

[0115] To accurately calculate the actual size of the damage, a blade curvature model needs to be established. This can be achieved through the following methods:

[0116] Calibration Image: Using a calibration image of known size, obtain the image pixel distance of the blade at different curvature positions.

[0117] Geometric modeling: Based on the design parameters and geometry of the blade, a mathematical model of the blade surface is established.

[0118] The method based on calibrated images calculates pixel distances. Specifically, it calculates the pixel distances of key feature points in the image data based on a pre-constructed blade curvature model. The method is as follows:

[0119] Acquire a standard object of known size, and use the same camera settings and environmental conditions as when photographing the leaf to capture images of the standard object from multiple angles and distances;

[0120] Extract the radian position of a standard object and calculate the image pixel distance at different radian positions;

[0121] Establish a conversion relationship between pixel size and actual size based on the actual size of a standard object and its pixel distance in the image;

[0122] The pixel distance between key feature points in the image data is calculated based on the transformation relationship.

[0123] The specific method based on geometric modeling is as follows:

[0124] Collect blade design parameters: Obtain blade design drawings and technical parameters from the manufacturer, including but not limited to blade length, width, thickness, radius of curvature, etc.

[0125] Create a 3D model: Use CAD software such as SolidWorks or AutoCAD to create a 3D geometric model of the blade based on the design parameters. The model should reflect the actual shape and size of the blade as accurately as possible, especially the curved parts of the blade.

[0126] Model validation: The accuracy of the model is verified by actually measuring the key dimensions of the blade and comparing them with the 3D model. This step is crucial for ensuring the accuracy of subsequent dimensional calculations.

[0127] Model application: Import the established 3D model into image processing software, and use the geometric information provided by the model to help calculate the actual distance between any two points on the blade, especially the distance calculation on curved surfaces.

[0128] In this invention, the method for calculating the conversion ratio between pixel distance and actual size is as follows:

[0129] Obtain the actual size of key feature points on the actual blade, and use the ratio of the actual size to the pixel distance as the conversion ratio.

[0130] In this invention, by calculating the image pixel distance of the arc position of a standard object, the projection of the arc position on the plane can be obtained, and by calculating the transformation relationship, the true arc length or area can be obtained.

[0131] The size of the bounding box is calculated based on the bounding box coordinates of the damage location, and then the actual size of the damage to the blade to be detected is obtained by combining the transformation ratio.

[0132] A repair plan is generated based on the actual size of the damage to the blade to be inspected.

[0133] In this invention, the actual size of the damage is calculated using the aforementioned ratio based on the detected damage bounding box pixel size. Furthermore, the influence of the curvature at the damage location on the size calculation is considered, thereby obtaining an accurate damage size.

[0134] Although the steps in the above embodiments are described in the above order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not need to be executed in such order. They can be executed simultaneously (in parallel) or in reverse order. These simple changes are all within the protection scope of this utility model.

[0135] In the description of this utility model, terms such as "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," which indicate direction or positional relationships, are based on the direction or positional relationships shown in the accompanying drawings. These are used merely for ease of description and do not indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation; therefore, they should not be construed as limitations on this utility model. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0136] Furthermore, it should be noted that, in the description of this utility model, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this utility model according to the specific circumstances.

[0137] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0138] The technical solution of this utility model has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the protection scope of this utility model is obviously not limited to these specific embodiments. Without departing from the principle of this utility model, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of this utility model.

Claims

1. A fully automatic inspection device for high-pressure turbine blades of aero-engines, characterized in that, It includes a rotation drive device, a first probe (1), a second probe (2), and a terminal (3); The rotation drive device is connected to the rotation shaft of the engine's high-pressure rotor and is used to drive the rotation shaft to rotate. The first probe (1) passes through the outer casing (4) and the inner casing (5) of the engine and is set on one side of the leading edge of the blade (6) to acquire image data of the leading edge of the blade (6); The second probe (2) passes through the outer casing (4) and the inner casing (5) of the engine and is set on one side of the trailing edge of the blade (6) to acquire image data of the trailing edge of the blade (6); The rotation drive device, the first probe (1) and the second probe (2) are all electrically connected to the terminal (3). The terminal (3) is used to control the rotation speed, rotation direction and rotation amount of the rotation drive device, and to acquire the image data of the leading edge and the image data of the trailing edge, and to obtain the damage area in combination with the inspection method.

2. The fully automatic inspection device for high-pressure turbine blades of aero-engines according to claim 1, characterized in that, The first probe (1) includes a first probe tube body (11) and a lens portion (12) at the end of the first probe. The first probe tube body (11) and the lens part (12) at the end of the first probe are connected by a rotating mechanism. The rotating mechanism is used to drive the lens part (12) at the end of the first probe to rotate to an angle parallel to the length direction of the HPT1 level guide (7). The first probe tube body (11) and the lens portion (12) at the end of the first probe enter the engine outer casing (4) through the probe port of the HPT1 level guide (7).

3. The fully automatic inspection device for high-pressure turbine blades of aero-engines according to claim 2, characterized in that, The second probe (2) includes a second probe tube body (21) and a lens portion (22) at the end of the second probe. The second probe tube (21) and the lens portion (22) at the end of the second probe enter the engine outer casing (4) through the probe port of the HPT2 level guide (8). The length direction of the second probe tube (21) is perpendicular to the length direction of the engine outer casing (4). The second probe tube (21) and the lens portion (22) at the end of the second probe are fixed. The length direction of the second probe tube (21) and the length direction of the lens portion (22) at the end of the second probe are set parallel to each other.

4. The fully automatic inspection device for high-pressure turbine blades of aero-engines according to claim 3, characterized in that, The lens portion (12) at the end of the first probe and the lens portion (22) at the end of the second probe each include multiple lenses and multiple light sources.

5. The fully automatic inspection device for high-pressure turbine blades of aero-engines according to claim 1, characterized in that, The rotation drive device includes a drive end and a connector; The drive end is coaxially connected to the input shaft of the connector, and the output shaft of the connector is coaxially connected to the rotating shaft. The drive end is used to drive the rotating shaft to rotate.

6. The fully automatic inspection device for high-pressure turbine blades of an aero-engine according to claim 5, characterized in that, The driving end is a stepper motor, which is connected to a power supply. The power supply is used to power the stepper motor. A wireless transmission module is installed on the stepper motor, and the stepper motor is connected to the terminal (3) through the wireless transmission module.

7. The fully automatic inspection device for high-pressure turbine blades of an aero-engine according to claim 6, characterized in that, The motor shaft and handle are detachably fixed.

8. The fully automatic inspection device for high-pressure turbine blades of an aero-engine according to claim 3, characterized in that, Both the first probe tube body (11) and the second probe tube body (21) are equipped with limiting devices (9). The limiting devices (9) are located outside the engine outer casing (4) and overlap with the probe opening on the engine outer casing (4).

9. The fully automatic inspection device for high-pressure turbine blades of an aero-engine according to claim 4, characterized in that, The light source is an LED light source.

10. The fully automatic inspection device for high-pressure turbine blades of an aero-engine according to claim 4, characterized in that, Multiple lenses were used to acquire image data of the leading edge, trailing edge, leaf tip, and leaf root of the leaf (6).

Images