A large-scale rotary equipment blade tooth height automatic measurement method and system fusing ROI optimization and contour peak detection

Abstract

The application provides a large rotary equipment blade tooth height automatic measurement method and system fusing ROI optimization and contour peak value detection. The method comprises the following steps: step one, adopting principal component analysis on the tenon to extract the section in the blade coordinate system as the reference, and then extracting the pitch line; step two, determining the longitudinal reference position through the pitch line, combining the tooth profile scale experience value to establish the ROI range containing all tooth profile structures, and carrying out the identification of the tooth tip and tooth root on the basis, so as to complete the calculation of the tooth height according to the tooth tip and tooth root data. The method realizes the automatic identification of the tooth tip and tooth root of the tenon on the basis of obtaining the pitch line position, and completes the calculation of the tooth height, thereby providing the basis for the subsequent evaluation of the tenon parameters.

Description

Technical Field

[0001] This invention relates to the field of surface profile measurement technology, and in particular to an automated method and system for measuring the tooth height of blades in large rotating equipment that integrates ROI optimization and profile peak detection. Background Technology

[0002] As the power source of aircraft, aero-engines directly determine the performance of aircraft. Therefore, the quality inspection of engine manufacturing is crucial. Blades are the most numerous and important components of an aero-engine, with complex shapes, numerous characteristic parameters, and significant challenges in measurement and evaluation. Their large number also poses a challenge to the efficiency of measurement and inspection. Therefore, automated measurement and evaluation methods for aero-engine blades are of great importance. However, aero-engines are complex aerodynamic and thermodynamic rotating machines with numerous components, operating in high-temperature and high-pressure environments. Turbine disks and blades, as one of the most important rotating components that decisively influence the shape and size of an aero-engine, must also withstand the effects of high-speed rotation and strong vibration. Under such conditions, a very common and dangerous failure mode in aero-engines is the formation and eventual fracture of cracks at the blade edge, root, and tenon, threatening engine performance and reliability. Therefore, it is necessary to measure the morphology of the blade tenon to ensure machining accuracy and quality.

[0003] Tenon structures come in various types, such as fir tree and dovetail shapes, with the fir tree type widely used in advanced engines due to its excellent stress distribution and high load-bearing capacity. This invention relates to a non-perfectly symmetrical tenon structure approaching the fir tree shape, characterized by a certain curvature variation in its tooth profile boundaries, complex geometric features, and high difficulty in processing and evaluation. In this type of structure, tooth height significantly impacts meshing quality and fit accuracy. The tooth height of a fir tree tenon is defined as the distance from the tooth tip to the tooth root in the direction perpendicular to the pitch line. However, in actual measurement, traditional tooth tip extraction methods based on geometric extrema face numerous challenges due to uneven distribution of point cloud data, blurred edge transitions, significant local interference, and the difficulty in accurately and explicitly determining the pitch line. These methods are extremely sensitive to local noise and boundary truncation, often leading to large fluctuations in tooth height measurement results, making it difficult to meet engineering accuracy requirements. Furthermore, the tooth structure exhibits obvious periodicity, with the tooth tip and root regions showing small curvature or even plateau characteristics in some sections, further exacerbating the ambiguity of tooth tip identification. Therefore, relying solely on local maximum extraction strategies makes it difficult to achieve stable and accurate tooth height recognition.

[0004] To address the aforementioned issues, when evaluating the parameters of aero-engine tenons, considering the definition of tenon tooth height in aerospace industry standards and combining the spatial characteristics of the tenon structure, a region-limiting strategy based on the tenon section extraction method and estimated tooth height is proposed. This strategy extracts the region of interest (ROI) in the tooth area, effectively suppressing the interference of non-tooth point clouds on tooth height calculation. The method determines the longitudinal reference position through the section line and establishes an ROI range encompassing the entire tooth structure by combining empirical values ​​of tooth profile dimensions. Based on this, tooth tip and root identification and tooth height measurement are performed. Summary of the Invention

[0005] The purpose of this invention is to solve the problems existing in the prior art, and to propose an automated measurement method and system for the tooth height of blades of large rotating equipment that integrates ROI optimization and profile peak detection.

[0006] This invention is achieved through the following technical solution: This invention proposes an automated measurement method for the tooth height of blades in large rotating equipment, integrating ROI optimization and profile peak detection. The method includes: Step 1: Using the blade coordinate system as a reference, principal component analysis is used to extract the cross section of the tenon, followed by nodal line extraction; Step 2: Determine the longitudinal reference position through the nodal line, and establish the region of interest (ROI) range that includes the entire tooth structure by combining the empirical values ​​of tooth profile scale. On this basis, tooth tip and tooth root identification is carried out, and the tooth height is calculated based on the tooth tip and tooth root data.

[0007] Furthermore, in step one, the blade coordinate system is determined as follows: the Z-axis of the coordinate system is perpendicular to the engine axis, but not necessarily intersecting it; the X-axis is parallel to the average chord line, with the direction from the leading edge to the trailing edge taken as the positive direction; and the Y-axis direction is determined by the right-hand rule.

[0008] Furthermore, principal component analysis (PCA) was used to determine the evaluation criteria for the measurement data, specifically: (1) Calculate the position of the centroid of the point cloud Let the leaf point cloud data be Then the center of mass of the cloud. It is given by the following formula:

[0009] (2) Calculate the covariance matrix Covariance of two coordinate axes The correlation of point cloud data across these two axes is represented by the following formula:

[0010] The formula for calculating the covariance matrix C is as follows:

[0011] (3) Calculate the PCA transformation matrix Let the eigenvalues ​​of the covariance matrix C be... ,and The corresponding feature vectors are respectively Then the homogeneous coordinate transformation matrix T PCA The formula is as follows:

[0012] The transformed point cloud coordinates are .

[0013] Furthermore, spatial constraints are extracted based on the aligned point cloud to ensure that the extraction range includes the tooth region from the tooth root to the tooth tip.

[0014] Furthermore, the nodal line extraction specifically involves: firstly, extracting the working and non-working surfaces in the tenon structure; then, based on the feature lines of the extracted working and non-working surfaces, obtaining the midpoint sequence through the set of line intersections; then, fitting the midpoint sequence using the least squares method to obtain the initial position of the nodal line; and finally, obtaining the final position of the nodal line through an optimization method.

[0015] Furthermore, during the identification of tooth tip and tooth root, the pitch line is used as the projection reference benchmark to extract its slope and intercept parameters, and a unified normal projection coordinate system is established accordingly.

[0016] Furthermore, a signed normal projection distance metric mechanism is introduced into the tooth tip and tooth root identification method; specifically, each point in the tooth shape point cloud is projected onto the nodal line along the normal direction, and the positive and negative signs of the projection direction are retained, thereby constructing a distance function with directional information.

[0017] Furthermore, in this distance function, the tooth tip region represents a local maximum point in the positive direction, while the tooth root corresponds to a local maximum point in the negative direction. During the tooth tip and tooth root identification process, a peak detection method based on prominence is introduced into the normal projection distance function, which is a sliding window filtering and saliency constraint method, to ensure that the extracted extreme values ​​have sufficient structural independence and geometric representativeness. In the neighborhood of each detected significant peak, a local refinement strategy is adopted, and the maximum distance point is used as the final tooth tip position to achieve precise tooth tip localization. The tooth root localization is similar.

[0018] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the automated measurement method for blade tooth height of large rotating equipment that integrates ROI optimization and profile peak detection.

[0019] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the automated measurement method for blade tooth height of large rotating equipment that integrates ROI optimization and profile peak detection.

[0020] The beneficial effects of this invention are: This invention addresses the technical problem of tenon evaluation for aero-engine blades by proposing an automated measurement method and system for tooth height of large rotating equipment blades that integrates ROI optimization and profile peak detection. Based on obtaining the pitch line position, it realizes the automatic identification of the tenon tooth tip and tooth root, and completes the calculation of tooth height, providing a basis for subsequent evaluation of tenon parameters. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the dot matrix for the tenon.

[0023] Figure 2 This is a schematic diagram of the two-dimensional point cloud of the tenon cross-section.

[0024] Figure 3 This is a schematic diagram for extracting the tenon joint line.

[0025] Figure 4 This is a schematic diagram of the tooth-shaped region ROI.

[0026] Figure 5 This is a schematic diagram of the tooth tip extraction method.

[0027] Figure 6 This is a schematic diagram of tooth tip and root extraction and tooth height calculation. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Combination Figures 1-6 This invention proposes an automated measurement method for the tooth height of blades in large-scale rotating equipment, integrating ROI optimization and profile peak detection. The method includes: Step 1: Using the blade coordinate system as a reference, principal component analysis is used to extract the cross section of the tenon, followed by nodal line extraction; In step one, the blade coordinate system is determined as follows: the Z-axis of the coordinate system is perpendicular to the engine axis, but not necessarily intersecting it; the X-axis is parallel to the average chord line, with the direction from the leading edge to the trailing edge taken as the positive direction; and the Y-axis direction is determined by the right-hand rule.

[0030] By specifying in the design that the blade profile section is perpendicular to the Z-axis of the blade coordinate system, and considering that the relationship between the measurement model and the engine axis during installation cannot be known in actual measurements, Principal Component Analysis (PCA) is used to determine the evaluation criteria for the measurement data. Specifically: (1) Calculate the position of the centroid of the point cloud Let the leaf point cloud data be Then the center of mass of the cloud. It is given by the following formula:

[0031] (2) Calculate the covariance matrix Covariance of two coordinate axes The correlation of point cloud data across these two axes is represented by the following formula:

[0032] The formula for calculating the covariance matrix C is as follows:

[0033] (3) Calculate the PCA transformation matrix Let the eigenvalues ​​of the covariance matrix C be... ,and The corresponding feature vectors are respectively Then the homogeneous coordinate transformation matrix T PCA The formula is as follows:

[0034] The transformed point cloud coordinates are The geometric features of tenons exhibit a periodic distribution in space, and their key contours (such as tooth tip, tooth root, working surface, and non-working surface) are significantly analyzable within a specific cross-sectional area. To accurately extract tenon features, spatial constraint extraction based on the aligned point cloud is required to ensure that the extraction range includes the tooth region from tooth root to tooth tip.

[0035] The nodal line extraction process is as follows: First, the working surface and non-working surface of the tenon structure are extracted. Then, based on the feature lines of the extracted working surface and non-working surface, the midpoint sequence is obtained through the set of intersection points of the lines. The midpoint sequence is then fitted using the least squares method to obtain the initial position of the nodal line. Finally, the final position of the nodal line is obtained through an optimization method.

[0036] Step 2: Combining the spatial characteristics of the tenon structure, a region-limiting strategy based on the pitch line and estimated tooth height is designed to extract the region of interest (ROI) in the tooth area, thereby effectively suppressing the interference of non-tooth point cloud on tooth height calculation. This method determines the longitudinal reference position through the pitch line and establishes the ROI range encompassing the entire tooth structure by combining empirical values ​​of tooth profile dimensions. Based on this, tooth tip and tooth root identification is performed, thus completing the tooth height calculation based on the tooth tip and tooth root data.

[0037] To address the issues of noise interference and geometric ambiguity in tooth tip and root identification, a detection and tooth height extraction method based on signed normal projection distance analysis is proposed. This method uses the pitch line as a projection reference, extracts its slope and intercept parameters, and establishes a unified normal projection coordinate system accordingly. Within this framework, to address the problem of misjudgment easily caused by the traditional curvature extremum method when dealing with boundary transition zones or local interference points, the method of this invention introduces a signed normal projection distance measurement mechanism; specifically, each point in the tooth profile point cloud is projected onto the pitch line along the normal direction, retaining the positive or negative sign of the projection direction, thereby constructing a distance function with directional information.

[0038] In this distance function, the tooth tip region represents a local maximum point in the positive direction, while the tooth root corresponds to a local maximum point in the negative direction. Compared with traditional methods that rely solely on the absolute value of the distance, the symbolic projection strategy more fully preserves the geometric distribution characteristics of the tooth structure along the normal direction of the pitch line, effectively improving the spatial separation and recognition saliency of the tooth tip. During the tooth tip and tooth root identification process, to further improve robustness to abnormal fluctuations, a peak detection method based on prominence is introduced into the normal projection distance function, employing a sliding window filtering and saliency constraint to ensure that the extracted extreme values ​​have sufficient structural independence and geometric representativeness. Within the neighborhood of each detected significant peak, a local refinement strategy is used, with the maximum distance point as the final tooth tip position, achieving precise tooth tip localization; the tooth root localization follows the same principle.

[0039] The method described in this invention will be explained in detail with reference to the accompanying drawings, and the method obtained is described below. Figure 1 After obtaining the tenon point cloud, the PCA algorithm is used to extract the two-dimensional point cloud of the tenon cross-section. The extracted cross-section is shown below. Figure 2 As shown, nodal lines are extracted from the obtained two-dimensional point cloud of the tenon cross-section, and the resulting nodal lines are as follows. Figure 3As shown. Based on the longitudinal reference position of the extracted pitch line and the selected empirical values ​​of the tooth profile, the region of interest in the tooth area was determined, as follows. Figure 4 As shown. A unified normal vector projection coordinate system is constructed within the region of interest based on the nodal line. In this coordinate system, the tooth tip region represents the maximum point in the positive direction, and the tooth root region represents the maximum point in the negative direction. Then, sliding window filtering and extreme value detection with saliency constraints are performed to finally determine the maximum distance point as the final tooth tip position, as shown. Figure 5 As shown. The tooth height is calculated by taking the median value from the obtained data, as shown. Figure 6 As shown.

[0040] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the automated measurement method for blade tooth height of large rotating equipment that integrates ROI optimization and profile peak detection.

[0041] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the automated measurement method for blade tooth height of large rotating equipment that integrates ROI optimization and profile peak detection.

[0042] The memory in this application embodiment can be volatile memory or non-volatile memory, or it can include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the methods described in this invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0043] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0044] In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software. The steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware processor, or by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are omitted here.

[0045] It should be noted that the processor in the embodiments of this application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiments can be completed by the integrated logic circuitry in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied as execution by a hardware decoding processor, or as a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0046] The above provides a detailed description of the automated measurement method and system for the tooth height of large rotating equipment blades, which integrates ROI optimization and profile peak detection. Specific examples have been used to illustrate the principles and implementation methods of the invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the invention. Therefore, the content of this specification should not be construed as a limitation of the invention.

Claims

1. An automated method for measuring the tooth height of blades in large rotating equipment, integrating ROI optimization and profile peak detection, characterized in that... The method includes: Step 1: Using the blade coordinate system as a reference, principal component analysis is used to extract the cross section of the tenon, followed by nodal line extraction; Step 2: Determine the longitudinal reference position through the nodal line, and establish the region of interest (ROI) range that includes the entire tooth structure by combining the empirical values ​​of tooth profile scale. On this basis, tooth tip and tooth root identification is carried out, and the tooth height is calculated based on the tooth tip and tooth root data. The nodal line extraction is specifically as follows: First, the working surface and non-working surface in the tenon structure are extracted. Then, based on the feature lines of the extracted working surface and non-working surface, the midpoint sequence is obtained through the intersection point set of the lines. The midpoint sequence is then fitted using the least squares method to obtain the initial position of the nodal line. Finally, the final position of the nodal line is obtained through an optimization method. During the identification of tooth tip and tooth root, the pitch line is used as the projection reference benchmark to extract its slope and intercept parameters, and a unified normal projection coordinate system is established accordingly. In the tooth tip and tooth root identification method, a signed normal projection distance measurement mechanism is introduced; specifically, each point in the tooth shape point cloud is projected onto the nodal line along the normal direction, and the positive and negative signs of the projection direction are retained, thereby constructing a distance function with directional information. In this distance function, the tooth tip region represents a local maximum point in the positive direction, while the tooth root corresponds to a local maximum point in the negative direction. During the tooth tip and tooth root identification process, a peak detection method based on prominence is introduced into the normal projection distance function, which is a sliding window filtering and saliency constraint method, to ensure that the extracted extreme values ​​have sufficient structural independence and geometric representativeness. In the neighborhood of each detected significant peak, a local refinement strategy is adopted, and the maximum distance point is used as the final tooth tip position to achieve precise tooth tip localization. The tooth root localization is similar.

2. The method according to claim 1, characterized in that, In step one, the blade coordinate system is determined as follows: the Z-axis of the coordinate system is perpendicular to the engine axis, but not necessarily intersecting it; the X-axis is parallel to the average chord line, with the direction from the leading edge to the trailing edge taken as the positive direction; and the Y-axis direction is determined by the right-hand rule.

3. The method according to claim 2, characterized in that, Principal component analysis (PCA) was used to determine the evaluation criteria for the measurement data, specifically: (1) Calculate the position of the centroid of the point cloud Let the leaf point cloud data be Then the center of mass of the cloud. It is given by the following formula: (2) Calculate the covariance matrix Covariance of two coordinate axes The correlation of point cloud data across these two axes is represented by the following formula: The formula for calculating the covariance matrix C is as follows: (3) Calculate the PCA transformation matrix Let the eigenvalues ​​of the covariance matrix C be... ,and ; The corresponding feature vectors are respectively Then the homogeneous coordinate transformation matrix T PCA The formula is as follows: The transformed point cloud coordinates are .

4. The method according to claim 3, characterized in that, Spatial constraints are extracted based on the aligned point cloud to ensure that the extraction range includes the tooth region from the tooth root to the tooth tip.

5. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-4.

6. A computer-readable storage medium for storing computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-4.

Images