A rotor shaft intelligent detection method and system

Abstract

The present invention relates to the field of rotor shaft image processing, and in particular to a rotor shaft intelligent detection method and system. The method comprises: collecting images of multiple continuous moments during the cutting process of the rotor shaft; the edge of the rotor shaft in contact with the cutting knife in the image is the cutting edge, and the edge of the cutting edge symmetrical about the central axis of the rotor shaft is the detection edge; the target image is screened out from the images of multiple continuous moments, and when the target image meets at least one condition, the rotor shaft is unqualified; wherein condition one is: the smoothness index of the detection edge is not within the preset smoothness index range; condition two is: the variance of the coordinates of each pixel point of the detection edge and the preset coordinates of each pixel point is not within the preset variance range; condition three is: the coordinates of the pixel point of the detection edge are not within the preset pixel point coordinate range and the number of pixel points exceeds the preset number. The present invention effectively solves the problem of being unable to timely and accurately screen unqualified rotor shafts.

Description

Technical Field

[0001] The present invention relates to the field of rotor shaft image processing, and more specifically, to a rotor shaft intelligent detection method and system. Background Art

[0002] In the cutting process of the rotor shaft, it is crucial to ensure the machining accuracy, but a variety of factors may lead to inaccurate cutting. With the increase of use time, the cutting edge of the tool will gradually become blunt, resulting in increased cutting force, increased surface roughness, and even dimensional deviation. The lack of rigidity of the machine tool will also cause slight deformation of the machine tool when cutting at high speed or processing hard materials, which may affect the machining accuracy. Unstable tool clamping may cause offset or vibration during cutting, while loose clamping of the workpiece may cause displacement or deformation of the workpiece during processing. Improper setting of cutting parameters will lead to inaccurate cutting, and may also cause tool breakage, deterioration of workpiece surface quality or dimensional error. Improper tool selection, such as tool material, geometric angles and processing materials do not match, will also affect the cutting effect and lead to reduced machining accuracy. In addition, improper use of cutting fluid should not be ignored. Improper cutting fluid may cause excessive temperature in the cutting area, affect the thermal stability of the tool and workpiece, and then affect the machining accuracy of the rotor shaft. The reduced accuracy of the machine tool, such as the wear of key components such as guide rails, lead screws, and bearings, will lead to reduced positioning accuracy and repeat positioning accuracy of the machine tool, thereby affecting the cutting accuracy of the rotor shaft.

[0003] At present, the patent application document with publication number CN117697536A and name “A combined processing device for a rotor shaft of a gas generator set” discloses a combined processing device for a rotor shaft of a gas generator set. The device scans and detects the surface of the rotor shaft body through a laser scanner, detects the processing accuracy of the rotor shaft body, transmits the detection data to the controller, and the controller automatically adjusts the No. 1 servo frame, the No. 2 servo frame and the No. 3 servo frame. The processing host re-processes the positions with unqualified accuracy, so that the processing accuracy of the rotor shaft body meets the requirements, avoids the need to remove the rotor shaft body for detection and re-positioning processing, and improves the accuracy and efficiency of the rotor shaft body processing.

[0004] Since the rotor shaft rotates at an extremely high speed during processing, the laser scanning method cannot process a large amount of data in a timely manner, and therefore it is impossible to accurately screen out unqualified rotor shafts in a timely manner. Summary of the invention

[0005] In order to solve the above-mentioned problem of being unable to timely and accurately screen out unqualified rotor shafts, the present invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides an intelligent detection method for a rotor shaft, comprising: collecting images of a plurality of consecutive moments during the cutting process of the rotor shaft; the edge of the rotor shaft in contact with the cutting tool in the image is the cutting edge, and the edge of the cutting edge that is symmetrical about the central axis of the rotor shaft is the detection edge; a target image is screened out from the images of the plurality of consecutive moments, and when the target image satisfies at least one condition, the rotor shaft is unqualified; wherein condition one is: the smoothness index of the detection edge is not within a preset smoothness index range; condition two is: the variance between the coordinates of each pixel point of the detection edge and the preset coordinates of each pixel point is not within a preset variance range; condition three is: the coordinates of the pixel point of the detection edge are not within a preset pixel point coordinate range and the number of pixel points exceeds a preset number; the target image is an image corresponding to the minimum image recognition coefficient; the image recognition coefficient ,in, The ordinate of the pixel point where the cutting edge of the first image among the images at the plurality of consecutive moments contacts the cutting blade tip is represented, represents the ordinate of the rotor axis in the mth image, Indicates the edge detection of the mth image The corresponding pixel points have the same vertical coordinate as the horizontal coordinate of the pixel point.

[0007] By calculating the image recognition coefficient of each image and screening out the target image, the most suitable image can be selected for further analysis based on the minimum value of the image recognition coefficient, which can effectively improve the accuracy of image recognition. Based on the image smoothness index, the variance of the pixel coordinates and the pixel coordinate range, it is possible to accurately detect whether the rotor shaft meets the predetermined standards during the processing. This multi-dimensional detection based on images and angular velocity can fully reflect the machining accuracy and quality of the rotor shaft. In summary, this method can intelligently, accurately and quickly detect and screen out unqualified rotor shafts.

[0008] Preferably, the smoothness index of the detected edge is specifically: ,in, is the smoothness index of the detected edge, The number of pixels for edge detection in the target image, Indicates the target image edge detection The coordinates of the pixels and The distance between the coordinates of the pixel points, Indicates the target image edge detection The coordinates of the pixels and The distance between the coordinates of the pixel points.

[0009] By calculating the distance between each pixel and considering the distribution of the pixels, the smoothness of the detected edge can be described more accurately. The smoothness of the edge directly affects the quality of rotor shaft processing, and the quantification of the smoothness index helps to accurately determine whether there are machining defects or irregularities in the rotor shaft.

[0010] Preferably, the variance between the coordinates of each pixel point of the detected edge and the coordinates of each preset pixel point is specifically: , where p is the variance between the coordinates of each pixel point of the detected edge and the coordinates of each preset pixel point, The number of pixels for edge detection in the target image, Represents the horizontal coordinate of the ath pixel point on the edge of the target image detection. Indicates the ordinate of the ath pixel point on the edge of the target image detection. Indicates the horizontal coordinate of the ath pixel of the preset detected edge. Indicates the ordinate of the ath pixel of the preset detected edge.

[0011] By calculating the variance between the pixel coordinates of the target image detection edge and the preset detection edge coordinates, the accuracy and consistency of the detected edge can be effectively quantified. The smaller the variance, the better the match between the target image and the preset edge. Conversely, it may indicate deviation or irregular cutting during the processing. The variance calculation is based on the coordinate information of each pixel point and can reflect the changes in edge morphology from multiple angles. This multi-dimensional detection can more comprehensively identify potential problems of the rotor shaft during the processing process and avoid missed judgments and misjudgments.

[0012] Preferably, the preset smoothness index range includes: collecting the smoothness indexes of the target image detection edges corresponding to multiple qualified rotor shafts cut at the time corresponding to the first image in history, and determining the preset smoothness index range according to the maximum and minimum values ​​therein.

[0013] By analyzing the smoothness index of the target image detection edge corresponding to multiple qualified rotor shafts cut at the time corresponding to the first image, the smoothness index range of the rotor shaft that meets the quality standard in the actual processing process can be obtained. This setting method based on historical data is more in line with the actual production situation, avoids theoretical inaccuracies, and is more practical.

[0014] Preferably, the preset variance range includes: collecting the variances of the coordinates of each pixel point on the edge of the target image corresponding to multiple qualified rotor shafts cut at the time corresponding to the first image and the preset coordinates of each pixel point, and determining the preset variance range according to the maximum and minimum values ​​therein.

[0015] Preferably, the preset pixel point coordinate range includes: collecting the coordinates of each pixel point on the edge of the target image corresponding to a plurality of qualified rotor shafts in history after being cut at the time corresponding to the first image, and determining the preset pixel point coordinate range according to the maximum and minimum values ​​therein.

[0016] Preferably, the step of selecting a target image from images at multiple consecutive moments includes: presetting an image selection time interval as ,from Filter out the target image from the corresponding image. and The calculation method is as follows: the rotor shaft angular velocity is collected at the time corresponding to the first image as the starting time, and the collection period is T. , , s represents the number of times the angular velocity is collected when the sum of the product of the angular velocity and the collection period is greater than or equal to π for the first time.

[0017] During the machining process of the rotor shaft, the angular velocity is constantly changing. This method can minimize the time interval containing the target image, thereby significantly reducing the amount of calculation.

[0018] In a second aspect, the present invention further provides a rotor shaft intelligent detection system, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-mentioned rotor shaft intelligent detection method.

[0019] The beneficial effects of the present invention are: by calculating the image recognition coefficient of each image and screening out the target image, the most suitable image can be selected for further analysis based on the minimum value of the image recognition coefficient, which can effectively improve the accuracy of image recognition. Based on the smoothness index of the image, the variance of the pixel coordinates and the pixel coordinate range, it is possible to accurately detect whether the rotor shaft meets the predetermined standards during the processing. This multi-dimensional detection based on images and angular velocity can comprehensively reflect the processing accuracy and quality of the rotor shaft. In summary, this method can intelligently, accurately and quickly detect and screen out unqualified rotor shafts. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0021] Figure 1 is a flow chart of a rotor shaft intelligent detection method provided by an embodiment of the present invention;

[0022] Figure 2This is a block diagram of a rotor shaft intelligent detection system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0025] Figure 1 This is a flow chart of a rotor shaft intelligent detection method provided by an embodiment of the present invention, comprising the following steps:

[0026] S101, collecting images of the rotor shaft at multiple consecutive moments during the cutting process; the edge of the rotor shaft in the image that contacts the cutting tool is the cutting edge, and the edge of the cutting edge that is symmetrical about the central axis of the rotor shaft is the detection edge.

[0027] The cutting edge of the rotor shaft is cut by the cutting knife in the first image. Since the cutting edge is in contact with the cutting knife, the cutting edge in the image captured at this time cannot reflect the actual situation of the rotor shaft after cutting. It is necessary to analyze the actual situation of the rotor shaft by detecting the edge of the rotor shaft in the image. The cutting edge of the rotor shaft is generally the upper edge. Therefore, an image acquisition device can be installed at a suitable position on the side of the rotor shaft and at the same horizontal line as the central axis of the rotor shaft to acquire the image of the rotor shaft. The acquired image must be able to include the entire rotor shaft.

[0028] S102. Filter out a target image from images at multiple consecutive moments. When the target image meets at least one condition, the rotor shaft is unqualified; wherein condition one is: a smoothness index of a detected edge is not within a preset smoothness index range; condition two is: a variance between coordinates of each pixel point of the detected edge and coordinates of each preset pixel point is not within a preset variance range; condition three is: coordinates of pixel points of the detected edge are not within a preset pixel point coordinate range and the number of pixels exceeds a preset number.

[0029] During the machining of the rotor shaft, the cutting tool cuts the cutting edge of the rotor shaft in the first image. To detect whether the rotor shaft is qualified after this cutting, it is necessary to collect the image of the detected edge after the rotor shaft rotates 180° for analysis. The image collected at this time is the target image. Therefore, a method is needed to select the target image from the multiple collected images. In some embodiments, the target image is the image corresponding to the minimum image recognition coefficient. The image recognition coefficient is ,in, The ordinate of the pixel point where the cutting edge of the first image among the images at the plurality of consecutive moments contacts the cutting blade tip is represented, represents the ordinate of the rotor axis in the mth image, Indicates the edge detection of the mth image The corresponding pixel points have the same vertical coordinate as the horizontal coordinate of the pixel point. It is reflected in the mth image. and The degree of symmetry about the rotor shaft centerline, The smaller it is, the higher the degree of symmetry is, and the mth image is more likely to be the image of the rotor shaft rotated 180° after the cutting edge is cut by the cutting knife in the first image.

[0030] In the above method, screening the target image requires calculating the image recognition coefficients of a large number of collected images, which requires a large amount of calculation and is slow. Therefore, in some embodiments, the image screening time interval can be preset as ,from Filter out the target image from the corresponding image. and The calculation method is as follows: the rotor shaft angular velocity is collected at the time corresponding to the first image as the starting time, and the collection period is T. , , s represents the number of times the angular velocity is collected when the sum of the product of the angular velocity and the collection period is greater than or equal to π for the first time. For example, to detect whether the rotor shaft is qualified after the cutting edge is cut by the cutting knife in the first image, the angular velocity of the rotor shaft is collected starting at the time corresponding to the first image, and the collection period is T. The angular velocities collected in chronological order are , , , , , ,therefore , .

[0031] After obtaining the target image, it is necessary to analyze the detected edge in the target image to determine whether the rotor shaft is qualified. In some embodiments, whether the rotor shaft is qualified can be determined by calculating whether the smoothness index of the detected edge of the rotor shaft in the target image is within a preset smoothness index range. The smoothness index of the detected edge of the rotor shaft in the target image is specifically: ,in, The smoothness index of the edge detected by the rotor axis of the target image, The number of pixels for edge detection in the target image, Indicates the target image edge detection The coordinates of the pixels and The distance between the coordinates of the pixel points, Indicates the target image edge detection The coordinates of the pixels and The preset smoothness index range is generally determined by collecting the smoothness index of the target image detection edge corresponding to multiple qualified rotor shafts cut at the time corresponding to the first image in history, and the preset smoothness index range is determined according to the maximum and minimum values ​​therein.

[0032] In addition, whether the rotor shaft is qualified can also be determined by calculating whether the variance between the coordinates of each pixel point of the rotor shaft detection edge of the target image and the preset coordinates of each pixel point is within a preset variance range, specifically: , The variance between the coordinates of each pixel point of the detected edge and the coordinates of each preset pixel point, wherein: The number of pixels for edge detection in the target image, Represents the horizontal coordinate of the ath pixel point on the edge of the target image detection. Indicates the ordinate of the ath pixel point on the edge of the target image detection. Indicates the horizontal coordinate of the ath pixel of the preset detected edge. Represents the ordinate of the ath pixel of the preset detection edge. The preset variance range is generally the variance of the coordinates of each pixel of the target image detection edge corresponding to the time when multiple qualified rotor shafts were cut at the first image, and the preset variance range is determined according to the maximum and minimum values. By calculating the variance between the pixel coordinates of the target image detection edge and the preset detection edge coordinates, the accuracy and consistency of the detection edge can be effectively quantified. The smaller the variance, the better the match between the target image and the preset edge. Otherwise, it may indicate deviation or irregular cutting during the processing.

[0033] In addition, whether the rotor shaft is qualified can be determined by calculating whether the number of pixel points whose coordinates of the target image edge pixel points are not within the preset pixel point coordinate range exceeds a preset number. The preset pixel point coordinate range is generally the coordinates of each pixel point of the target image edge detection edge corresponding to multiple qualified rotor shafts in history after being cut at the time corresponding to the first image. The preset pixel point coordinate range is determined based on the maximum and minimum values ​​therein.

[0034] The present invention calculates the image recognition coefficient of each image and screens out the target image, and based on the image smoothness index, the variance of the pixel coordinates and the pixel coordinate range, accurately detects whether the rotor shaft meets the predetermined standards during the processing. This multi-dimensional detection based on image and angular velocity can fully reflect the processing accuracy and quality of the rotor shaft. In summary, this method can intelligently, accurately and quickly detect and screen out unqualified rotor shafts, and screening out unqualified rotor shafts during the processing process can stop the processing of the rotor shaft in time, reducing energy waste and machine wear.

[0035] The present invention also provides a rotor shaft intelligent detection system. Figure 2 As shown, the system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a rotor shaft intelligent detection method described in the present invention is implemented.

[0036] The system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface, whose configuration and functions are known in the art and thus will not be described in detail here.

[0037] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0038] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0039] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A rotor shaft intelligent detection method, characterized in that: include: Collect images of the rotor shaft at multiple consecutive moments during the cutting process; the edge of the rotor shaft in contact with the cutting blade in the image is the cutting edge, and the edge of the cutting edge symmetrical to the central axis of the rotor shaft is the detection edge; Screening out a target image from images at multiple consecutive moments, when the target image satisfies at least one condition, the rotor shaft is unqualified; The first condition is that the smoothness index of the detected edge is not within the preset smoothness index range; the second condition is that the variance between the coordinates of each pixel point of the detected edge and the preset coordinates of each pixel point is not within the preset variance range; the third condition is that the coordinates of the pixel point of the detected edge are not within the preset pixel point coordinate range and the number of pixels exceeds the preset number; The target image is the image corresponding to the minimum image recognition coefficient; The image recognition coefficient is ,in, The ordinate of the pixel point where the cutting edge of the first image among the images at the plurality of consecutive moments contacts the cutting blade tip is represented, represents the ordinate of the rotor axis in the mth image, Indicates the edge detection of the mth image The corresponding pixel points have the same vertical coordinate as the horizontal coordinate of the pixel point.

2. The rotor shaft intelligent detection method according to claim 1, characterized in that: The smoothness index of the detected edge is specifically: , where q is the smoothness index of the detected edge, The number of pixels for edge detection in the target image, Indicates the target image edge detection The coordinates of the pixels and The distance between the coordinates of the pixel points, Indicates the target image edge detection The coordinates of the pixels and The distance between the coordinates of the pixel points.

3. The rotor shaft intelligent detection method according to claim 1, characterized in that: The variance of the coordinates of each pixel point of the detected edge and the coordinates of each preset pixel point is specifically: ,in, is the variance between the coordinates of each pixel point of the detected edge and the coordinates of each preset pixel point, The number of pixels for edge detection in the target image, Represents the horizontal coordinate of the ath pixel point on the edge of the target image detection. Indicates the ordinate of the ath pixel point on the edge of the target image detection. Indicates the horizontal coordinate of the ath pixel of the preset detected edge. Indicates the ordinate of the ath pixel of the preset detected edge.

4. The intelligent detection method for rotor shaft according to claim 1, characterized in that: The preset smoothing index range includes: The smoothness index of the target image detection edge corresponding to a plurality of qualified rotor shafts in history after being cut at the time corresponding to the first image is collected, and a preset smoothness index range is determined according to the maximum and minimum values ​​thereof.

5. The rotor shaft intelligent detection method according to claim 1, characterized in that: The preset variance range includes: The variances of the coordinates of each pixel point on the edge of the target image corresponding to multiple qualified rotor shafts cut at the time corresponding to the first image and the preset coordinates of each pixel point are collected, and the preset variance range is determined according to the maximum and minimum values ​​therein.

6. The intelligent detection method for rotor shaft according to claim 1, characterized in that: The preset pixel point coordinate range includes: The coordinates of each pixel point on the edge of the target image corresponding to the cutting of multiple qualified rotor shafts at the time corresponding to the first image are collected, and the preset pixel point coordinate range is determined according to the maximum and minimum values ​​therein.

7. The rotor shaft intelligent detection method according to claim 1, characterized in that: The method of selecting a target image from images at multiple consecutive moments includes: presetting an image selection time interval as ,from Filter out the target image from the corresponding image. and The calculation method is as follows: the rotor shaft angular velocity is collected at the time corresponding to the first image as the starting time, and the collection period is T. , , s represents the number of times the angular velocity is collected when the sum of the product of the angular velocity and the collection period is greater than or equal to π for the first time.

8. A rotor shaft intelligent detection system, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the rotor shaft intelligent detection method according to any one of claims 1 to 7.

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