A method for automatically identifying the precession direction of the axis trajectory of a rotor system

Abstract

The present invention discloses a method for automatically identifying the precession direction of the axis trajectory of a rotor system, which relates to the technical field of identifying the precession direction of the axis trajectory, including obtaining the number of intersection points of the axis trajectory, the discrete index and fractal box dimension of the axis trajectory profile, and calculating the complexity of the axis trajectory profile; dividing the axis trajectory into multiple local arc segments according to different division frequencies, determining the precession direction of the axis trajectory, and recording it as the precession direction of different arc segment division frequencies, combining the analysis time of different arc segment division frequencies, calculating the appropriate coefficients of different arc segment division frequencies, determining the optimal arc segment division frequency for each complexity, determining the arc segment division frequency corresponding to the complexity of the axis trajectory profile to be analyzed according to the division data set, and determining the precession direction of the axis trajectory to be analyzed. The strategy of local arc segment division can be adjusted more flexibly to meet the analysis requirements of different axis trajectories.

Description

Technical Field

[0001] The invention relates to the technical field of shaft center trajectory precession direction recognition, and in particular to a method for automatically recognizing the precession direction of a shaft center trajectory of a rotor system. Background Art

[0002] In the field of rotating machinery, the operation status monitoring and fault diagnosis of the rotor system are of vital importance. As an important feature in the vibration status detection and diagnosis system of rotating machinery, the shape and precession direction of the axis trajectory can intuitively reflect the motion state or fault information of the rotor. Most of the traditional axis trajectory recognition methods are based on the "human-computer" dialogue method, relying on the experience of technicians for subjective judgment, which not only affects the accuracy and intelligence level of fault diagnosis, but also makes it difficult to adapt to the needs of modern industry for efficient and accurate diagnosis. The axis trajectory means that when the shaft rotates, it will vibrate around the center point of the shaft, and the trajectory of the movement is the axis trajectory. Under normal circumstances, the axis trajectory should be a relatively stable ellipse with a small difference between the long and short axes. However, when the rotor fails, such as imbalance, misalignment, oil film vortex, etc., the shape and precession direction of the axis trajectory will change significantly. For example, an unbalanced fault will make the axis trajectory elliptical, and usually positive precession; a misalignment fault will make the axis trajectory banana-shaped or 8-shaped, with positive and negative precession coexisting.

[0003] In the Chinese invention application with application publication number CN103412145A, a method for automatically identifying the precession direction of the axis trajectory of a rotor system is disclosed, including setting a sampling frequency, using a vibration sensor to collect the radial vibration signal of the rotor, and obtaining an amplitude spectrum through fast Fourier transform FFT; secondly, using a phase difference correction method to correct the main frequency components in the rotor vibration signal and reconstruct the signal; finally, using the reconstructed signal to synthesize the rotor to purify the axis trajectory, and the precession direction of the entire axis trajectory is obtained by identifying the precession direction of the local arc segment of the purified axis trajectory. The method of the present invention identifies the precession direction of each local arc segment of the axis trajectory from a local perspective, is not affected by the complexity of the axis trajectory, and has stronger versatility.

[0004] In the above invention application, the precession direction of the entire axial trajectory is obtained by identifying the precession direction of the local arc segment, but the local arc segments of different axial trajectories are divided into the same frequency. Due to the different shapes and characteristics of the axial trajectories, for some simple axial trajectory shapes, using too high a division frequency may lead to unnecessary increase in calculation amount, thereby reducing calculation efficiency, and too high a division frequency will also introduce noise interference, and may not be able to adapt to the changes in the axial trajectory under different working conditions, thereby reducing the accuracy and reliability of identification.

[0005] To this end, the present invention provides a method for automatically identifying the precession direction of the axis trajectory of a rotor system. Summary of the invention

[0006] 1. Technical issues to be resolved

[0007] In view of the shortcomings of the prior art, the present invention provides a method for automatically identifying the precession direction of the axis trajectory of a rotor system. The present invention divides the axis trajectory into multiple local arc segments according to different division frequencies, determines the precession direction of the axis trajectory, and records it as the precession direction of different arc segment division frequencies. , combined with the analysis duration of different arc segment division frequencies , calculate the appropriate coefficients for the frequency of arc division , determine the optimal arc segment division frequency for each complexity, determine the arc segment division frequency corresponding to the complexity Fz of the axis trajectory profile to be analyzed based on the divided data set, and determine the precession direction of the axis trajectory to be analyzed. This can reduce the amount of calculation in subsequent analysis and improve the analysis efficiency. It brings many benefits in improving the accuracy of axis trajectory analysis, optimizing arc segment division strategy, improving analysis efficiency and reliability, and providing strong support for fault diagnosis, thereby solving the technical problems recorded in the background technology.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for automatically identifying the precession direction of the axis trajectory of a rotor system, comprising the following steps:

[0010] Two eddy current displacement sensors are installed in the directions perpendicular to each other on the rotating shaft bearing seat to collect vibration signals to obtain the axis trajectory, and the axis trajectory is purified to obtain the axis trajectory map and extract the edge contour of the axis trajectory;

[0011] Obtain the number of axis track intersections Su, the discrete index e and the fractal box dimension D of the axis track profile, and calculate the complexity Fz of the axis track profile;

[0012] The axis trajectory is divided into multiple local arc segments according to different division frequencies, and the precession direction of the axis trajectory is determined, which is recorded as the precession direction of different arc segment division frequencies , combined with the analysis duration of different arc segment division frequencies , calculate the appropriate coefficients for the frequency of arc division , determine the optimal arc segment division frequency for each complexity, determine the arc segment division frequency corresponding to the complexity Fz of the axis trajectory profile to be analyzed based on the divided data set, and determine the precession direction of the axis trajectory to be analyzed.

[0013] Furthermore, two eddy current displacement sensors are installed in directions perpendicular to the shaft bearing seat, ensuring that they are at a 90-degree angle. The rotor system is started to rotate the shaft. At the same time, the two sensors synchronously collect vibration signals to measure the vibration displacement of the shaft in two perpendicular directions.

[0014] Furthermore, the vibration signals output by the two eddy current vibration sensors are connected to the x-axis and y-axis input terminals of the oscilloscope respectively to obtain the axis trajectory.

[0015] The following are some common axis trajectory types:

[0016] Ellipse: This is the typical axis trajectory of a rotating machine during normal operation. The ellipse indicates that the rotor maintains a relatively stable motion state during rotation. When the unbalance of the rotor system is small, the axis trajectory will be close to a circle.

[0017] Crescent (banana) shape: This trajectory usually indicates rotor misalignment or other combined fault factors. When the crescent becomes more curved and close to a banana shape, it indicates that the rotor misalignment may be quite serious.

[0018] Figure 8: This includes inner and outer figures. The inner figure 8 may be related to faults such as oil film vortex, while the outer figure 8 usually indicates that the rotor has a serious misalignment problem.

[0019] Inner ring: This track usually appears when the oil film is swirling, indicating that the rotor is affected by unstable oil film force during rotation.

[0020] Irregular shape: When there is severe friction between the shaft and the bearing, excessive vibration, or other complex faults, the axis trajectory may become chaotic and form an irregular shape.

[0021] Furthermore, the axis trajectory is purified to obtain an axis trajectory map, and an edge detection algorithm (such as Canny operator, Sobel operator, etc.) is introduced to detect the edge of the axis trajectory. Based on the edge detection, a contour extraction algorithm (such as Hough transform, contour tracking, etc.) is used to extract the edge contour of the axis trajectory.

[0022] The methods for purifying the axis trajectory include wavelet transform and wavelet packet transform, harmonic wavelet transform, mathematical morphology filtering, EEMD (ensemble empirical mode decomposition), Vold-Kalman filtering, methods based on harmonic windows, and methods based on signal sparse representation.

[0023] Furthermore, an intersection detection algorithm is used to find the intersection of the axis track edge contour, and the intersection detection results are traversed and counted to obtain the number of axis track intersections Su.

[0024] Furthermore, the perimeter L and area S of the axis trajectory edge contour are extracted, and the discrete index e of the axis trajectory contour is calculated:

[0025]

[0026] Among them, the larger the e value is, the larger the perimeter of the unit area is, the more discrete the area is, and the more complex the shape is; conversely, the smaller the e value is, the smaller the perimeter of the unit area is, the closer the area is to a circle, and the simpler the shape is.

[0027] Further, the edge contour of the axis trajectory is obtained using a side length of The box (or grid) covers the figure formed by the edge contour of the axis track. Let N( ) is the minimum number of boxes required to cover S, and the fractal box dimension D of the axis trajectory contour is calculated:

[0028]

[0029] Box-counting Dimension, also known as box dimension or Minkowski dimension, is an important dimension measurement method in fractal geometry. If the fractal box dimension is large, it means that the axis trajectory contour is relatively complex and irregular. On the contrary, if the fractal box dimension is small, it means that the axis trajectory contour is relatively simple and regular.

[0030] Furthermore, the number of axis trajectory intersections Su, the discrete index e and the fractal box dimension D of the axis trajectory profile are obtained, and the complexity Fz of the axis trajectory profile is calculated:

[0031]

[0032] in, Indicates the maximum number of intersections in the historical axis trajectory samples, Indicates the minimum number of intersections in the historical axis trajectory samples, Indicates the maximum value of the discrete index in the historical axis trajectory sample, Indicates the minimum value of the discrete index in the historical axis trajectory sample, Indicates the maximum value of the fractal box dimension in the historical axis trajectory samples, Indicates the minimum value of the fractal box dimension in the historical axis trajectory samples.

[0033] Furthermore, the axis trajectory is divided into multiple local arc segments according to different division frequencies. For each local arc segment, the velocity vector analysis method is used to identify its precession direction. The precession directions of all local arc segments are counted to determine the precession direction of the axis trajectory, which is recorded as the precession direction of different arc segment division frequencies. , where positive precession is recorded as 1 and negative precession is recorded as 0.

[0034] Furthermore, a timer is used to time the analysis duration of the precession direction under different arc segment division frequencies, which is recorded as the analysis duration of different arc segment division frequencies. .

[0035] Furthermore, the precession direction of the frequency division of different arc segments is obtained , calculate the mean value of the precession direction , combined with the analysis duration of different arc segment division frequencies , calculate the appropriate coefficients for the frequency of arc division :

[0036]

[0037] in, i The number indicating the frequency of division of different arc segments.

[0038] Furthermore, obtain the appropriate coefficients for the frequency of different arc segment divisions The arc segment division frequency corresponding to the minimum value is used as the optimal arc segment division frequency of the axis trajectory, and the axis trajectory samples are collected. The corresponding complexity and optimal arc segment division frequency are analyzed, and the optimal arc segment division frequency of each complexity is determined, and a division data set is constructed;

[0039] Among them, the optimal arc segment division frequency of each complexity is the average of the optimal arc segment division frequencies of the axis trajectory of the same complexity.

[0040] Furthermore, the arc segment division frequency corresponding to the complexity Fz of the axis trajectory profile to be analyzed is determined based on the divided data set, and the trajectory is divided into multiple local arc segments. For each local arc segment, the velocity vector analysis method is used to identify its precession direction, and the precession directions of all local arc segments are counted to determine the precession direction of the axis trajectory to be analyzed.

[0041] (III) Beneficial effects

[0042] The present invention provides a method for automatically identifying the precession direction of the axis trajectory of a rotor system, which has the following beneficial effects:

[0043] 1. Install two eddy current displacement sensors in the directions perpendicular to each other on the rotating shaft bearing seat, collect vibration signals to obtain the axis trajectory, purify the axis trajectory, obtain the axis trajectory diagram, and extract the edge contour of the axis trajectory. By purifying the axis trajectory and extracting the edge contour, the operating status of the rotating machinery can be judged more accurately, potential faults can be discovered in time, and the accuracy of fault diagnosis can be improved.

[0044] 2. Obtaining the number of axis trajectory intersections Su, the discrete index e and fractal box dimension D of the axis trajectory contour, and calculating the complexity Fz of the axis trajectory contour can comprehensively evaluate the complexity and irregularity of the axis trajectory, thereby formulating a more reasonable local arc segment division strategy. The local arc segment division strategy can be adjusted more flexibly to meet the analysis needs of different axis trajectories.

[0045] 3. Divide the axis trajectory into multiple local arc segments according to different division frequencies, determine the precession direction of the axis trajectory, and record it as the precession direction of different arc segment division frequencies , combined with the analysis duration of different arc segment division frequencies , calculate the appropriate coefficients for the frequency of arc division , determine the optimal arc segment division frequency for each complexity, determine the arc segment division frequency corresponding to the complexity Fz of the axis trajectory profile to be analyzed based on the divided data set, and determine the precession direction of the axis trajectory to be analyzed. This can reduce the amount of calculation in subsequent analysis and improve the analysis efficiency. It brings many benefits in improving the accuracy of axis trajectory analysis, optimizing the arc segment division strategy, improving analysis efficiency and reliability, and providing strong support for fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The present invention is a flow chart of a method for automatically identifying the precession direction of the axis trajectory of a rotor system. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] See also Figure 1 The present invention provides a method for automatically identifying the precession direction of the axis trajectory of a rotor system, comprising the following steps:

[0049] Step 1: Install two eddy current displacement sensors in directions perpendicular to the shaft bearing seat, collect vibration signals to obtain the axis trajectory, purify the axis trajectory, obtain the axis trajectory map, and extract the edge contour of the axis trajectory.

[0050] The step 1 includes the following contents:

[0051] Step 101, install two eddy current displacement sensors in directions perpendicular to the shaft bearing seat, ensure that they are at a 90-degree angle, start the rotor system to rotate the shaft, and at the same time, the two sensors synchronously collect vibration signals to measure the vibration displacement of the shaft in two perpendicular directions.

[0052] Step 102: Connect the vibration signals output by the two eddy current vibration sensors to the x-axis and y-axis input terminals of the oscilloscope respectively to obtain the axis trajectory.

[0053] The following are some common axis trajectory types:

[0054] Ellipse: This is the typical axis trajectory of a rotating machine during normal operation. The ellipse indicates that the rotor maintains a relatively stable motion state during rotation. When the unbalance of the rotor system is small, the axis trajectory will be close to a circle.

[0055] Crescent (banana) shape: This trajectory usually indicates rotor misalignment or other combined fault factors. When the crescent becomes more curved and close to a banana shape, it indicates that the rotor misalignment may be quite serious.

[0056] Figure 8: This includes inner and outer figures. The inner figure 8 may be related to faults such as oil film vortex, while the outer figure 8 usually indicates that the rotor has a serious misalignment problem.

[0057] Inner ring: This track usually appears when the oil film is swirling, indicating that the rotor is affected by unstable oil film force during rotation.

[0058] Irregular shape: When there is severe friction between the shaft and the bearing, excessive vibration, or other complex faults, the axis trajectory may become chaotic and form an irregular shape.

[0059] Step 103, purify the axis trajectory to obtain an axis trajectory map, import an edge detection algorithm (such as Canny operator, Sobel operator, etc.) to detect the edge of the axis trajectory, and based on the edge detection, use a contour extraction algorithm (such as Hough transform, contour tracking, etc.) to extract the edge contour of the axis trajectory.

[0060] The methods for purifying the axis trajectory include wavelet transform and wavelet packet transform, harmonic wavelet transform, mathematical morphology filtering, EEMD (ensemble empirical mode decomposition), Vold-Kalman filtering, methods based on harmonic windows, and methods based on signal sparse representation.

[0061] When using, combine the contents in steps 101 to 103:

[0062] Two eddy current displacement sensors are installed in directions perpendicular to each other on the rotating shaft bearing seat to collect vibration signals to obtain the axis trajectory, which is then purified to obtain an axis trajectory diagram and extract the edge contour of the axis trajectory. By purifying the axis trajectory and extracting the edge contour, the operating status of the rotating machinery can be judged more accurately, potential faults can be discovered in time, and the accuracy of fault diagnosis can be improved.

[0063] Step 2: Obtain the number of axis trajectory intersection points Su, the discrete index e and the fractal box dimension D of the axis trajectory profile, and calculate the complexity Fz of the axis trajectory profile.

[0064] The step 2 includes the following contents:

[0065] Step 201: Use an intersection detection algorithm to find the intersection of the axis track edge contour, traverse the intersection detection results and count to obtain the number of axis track intersections Su.

[0066] Step 202: extract the perimeter L and area S of the axis track edge contour, and calculate the discrete index e of the axis track contour:

[0067]

[0068] Among them, the larger the e value is, the larger the perimeter of the unit area is, the more discrete the area is, and the more complex the shape is; conversely, the smaller the e value is, the smaller the perimeter of the unit area is, the closer the area is to a circle, and the simpler the shape is.

[0069] Step 203: Get the edge contour of the axis trajectory, using a side length of The box (or grid) covers the figure formed by the edge contour of the axis track. Let N( ) is the minimum number of boxes required to cover S, and the fractal box dimension D of the axis trajectory contour is calculated:

[0070]

[0071] Box-counting Dimension, also known as box dimension or Minkowski dimension, is an important dimension measurement method in fractal geometry. If the fractal box dimension is large, it means that the axis trajectory contour is relatively complex and irregular. On the contrary, if the fractal box dimension is small, it means that the axis trajectory contour is relatively simple and regular.

[0072] Step 204, obtain the number of axis track intersection points Su, the discrete index e and the fractal box dimension D of the axis track profile, and calculate the complexity Fz of the axis track profile:

[0073]

[0074] in, Indicates the maximum number of intersections in the historical axis trajectory samples, Indicates the minimum number of intersections in the historical axis trajectory samples, Indicates the maximum value of the discrete index in the historical axis trajectory sample, Represents the minimum value of the discrete index in the historical axis trajectory sample, Indicates the maximum value of the fractal box dimension in the historical axis trajectory samples, Indicates the minimum value of the fractal box dimension in the historical axis trajectory samples.

[0075] When using, combine the contents in steps 201 to 204:

[0076] By obtaining the number of axis trajectory intersections Su, the discrete index e and fractal box dimension D of the axis trajectory contour, and calculating the complexity Fz of the axis trajectory contour, the complexity and irregularity of the axis trajectory can be comprehensively evaluated, so as to formulate a more reasonable local arc segment division strategy. The local arc segment division strategy can be more flexibly adjusted to meet the analysis requirements of different axis trajectories.

[0077] Step 3: Divide the axis trajectory into multiple local arc segments according to different division frequencies, determine the precession direction of the axis trajectory, and record it as the precession direction of different arc segment division frequencies , combined with the analysis duration of different arc segment division frequencies , calculate the appropriate coefficients for the frequency of arc division , determine the optimal arc segment division frequency for each complexity, determine the arc segment division frequency corresponding to the complexity Fz of the axis trajectory profile to be analyzed based on the divided data set, and determine the precession direction of the axis trajectory to be analyzed.

[0078] The step three includes the following contents:

[0079] Step 301: divide the axis trajectory into multiple local arc segments according to different division frequencies. For each local arc segment, use the velocity vector analysis method to identify its precession direction, count the precession directions of all local arc segments, determine the precession direction of the axis trajectory, and record it as the precession direction of different arc segment division frequencies. , where positive precession is recorded as 1 and negative precession is recorded as 0.

[0080] Step 302: Use a timer to count the analysis duration of the precession direction under different arc segment division frequencies, and record it as the analysis duration of different arc segment division frequencies. .

[0081] Step 303: Obtain the precession direction of different arc segment division frequencies , calculate the mean value of the precession direction , combined with the analysis duration of different arc segment division frequencies , calculate the appropriate coefficients for the frequency of arc division :

[0082]

[0083] in, i The number indicating the frequency of division of different arc segments.

[0084] Step 304: Obtain appropriate coefficients for dividing frequencies of different arc segments The arc segment division frequency corresponding to the minimum value is taken as the optimal arc segment division frequency of the axis trajectory. Multiple axis trajectory samples are collected, and their corresponding complexity and optimal arc segment division frequency are analyzed. The optimal arc segment division frequency for each complexity is determined, and a division data set is built.

[0085] Among them, the optimal arc segment division frequency of each complexity is the average of the optimal arc segment division frequencies of the axis trajectory of the same complexity.

[0086] Step 305: Determine the arc segment division frequency corresponding to the complexity Fz of the axis trajectory profile to be analyzed based on the divided data set, divide the trajectory into multiple local arc segments, and use the velocity vector analysis method to identify the precession direction of each local arc segment. Count the precession directions of all local arc segments to determine the precession direction of the axis trajectory to be analyzed.

[0087] When using, combine the contents in steps 301 to 305:

[0088] The axis trajectory is divided into multiple local arc segments according to different division frequencies, and the precession direction of the axis trajectory is determined, which is recorded as the precession direction of different arc segment division frequencies , combined with the analysis duration of different arc segment division frequencies , calculate the appropriate coefficients for the frequency of arc division , determine the optimal arc segment division frequency for each complexity, determine the arc segment division frequency corresponding to the complexity Fz of the axis trajectory profile to be analyzed based on the divided data set, and determine the precession direction of the axis trajectory to be analyzed. This can reduce the amount of calculation in subsequent analysis and improve the analysis efficiency. It brings many benefits in improving the accuracy of axis trajectory analysis, optimizing the arc segment division strategy, improving analysis efficiency and reliability, and providing strong support for fault diagnosis.

[0089] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0090] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0091] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for automatically identifying the precession direction of the axis trajectory of a rotor system, characterized by: The steps include: Two eddy current displacement sensors are installed in the directions perpendicular to each other on the rotating shaft bearing seat to collect vibration signals to obtain the axis trajectory, and the axis trajectory is purified to obtain the axis trajectory map and extract the edge contour of the axis trajectory; Obtain the number of axis track intersections Su, the discrete index e and the fractal box dimension D of the axis track profile, and calculate the complexity Fz of the axis track profile; The axis trajectory is divided into multiple local arc segments according to different division frequencies, and the precession direction of the axis trajectory is determined, which is recorded as the precession direction of different arc segment division frequencies , combined with the analysis duration of different arc segment division frequencies , calculate the appropriate coefficients for the frequency of arc division , determine the optimal arc segment division frequency for each complexity, determine the arc segment division frequency corresponding to the complexity Fz of the axis trajectory profile to be analyzed based on the divided data set, and determine the precession direction of the axis trajectory to be analyzed.

2. The method for automatically identifying the precession direction of the axis trajectory of a rotor system according to claim 1, characterized in that: Use the intersection detection algorithm to find the intersection of the axis track edge contour, traverse the intersection detection results and count to obtain the number of axis track intersections Su.

3. The method for automatically identifying the precession direction of the axis trajectory of a rotor system according to claim 1, characterized in that: Extract the perimeter L and area S of the axis trajectory edge contour, and calculate the discrete index e of the axis trajectory contour: Among them, the larger the e value is, the larger the perimeter of the unit area is, the more discrete the area is, and the more complex the shape is; conversely, the smaller the e value is, the smaller the perimeter of the unit area is, the closer the area is to a circle, and the simpler the shape is.

4. The method for automatically identifying the precession direction of the axis trajectory of a rotor system according to claim 1, characterized in that: Get the edge contour of the axis trajectory, using a side length of The box covers the figure formed by the edge contour of the axis track, let N( ) is the minimum number of boxes required to cover S, and the fractal box dimension D of the axis trajectory contour is calculated: The calculation formula of the fractal box dimension D of the corresponding axis trajectory contour is as above.

5. The method for automatically identifying the precession direction of the axis trajectory of a rotor system according to claim 4, characterized in that: Obtain the number of axis track intersections Su, the discrete index e of the axis track profile, and the fractal box dimension D, and calculate the complexity Fz of the axis track profile: in, Indicates the maximum number of intersections in the historical axis trajectory samples, Indicates the minimum number of intersections in the historical axis trajectory samples, Indicates the maximum value of the discrete index in the historical axis trajectory sample, Represents the minimum value of the discrete index in the historical axis trajectory sample, Indicates the maximum value of the fractal box dimension in the historical axis trajectory samples, Indicates the minimum value of the fractal box dimension in the historical axis trajectory samples.

6. The method for automatically identifying the precession direction of the axis trajectory of a rotor system according to claim 1, characterized in that: The axis trajectory is divided into multiple local arc segments according to different division frequencies. For each local arc segment, the velocity vector analysis method is used to identify its precession direction. The precession directions of all local arc segments are counted to determine the precession direction of the axis trajectory, which is recorded as the precession direction of different arc segment division frequencies. , where positive precession is recorded as 1 and negative precession is recorded as 0.

7. The method for automatically identifying the precession direction of the axis trajectory of a rotor system according to claim 6, characterized in that: Use a timer to count the analysis duration of the precession direction at different arc segment division frequencies, and record it as the analysis duration of different arc segment division frequencies .

8. The method for automatically identifying the precession direction of the axis trajectory of a rotor system according to claim 7, characterized in that: Get the precession direction of different arc segment division frequencies , calculate the mean value of the precession direction , combined with the analysis duration of different arc segment division frequencies , calculate the appropriate coefficients for the frequency of arc division : in, i The number indicating the frequency of division of different arc segments.

9. The method for automatically identifying the precession direction of the axis trajectory of a rotor system according to claim 8, characterized in that: Get the appropriate coefficients for different arc segment division frequencies The arc segment division frequency corresponding to the minimum value is used as the optimal arc segment division frequency of the axis trajectory, and the axis trajectory samples are collected. The corresponding complexity and optimal arc segment division frequency are analyzed, and the optimal arc segment division frequency of each complexity is determined, and a division data set is constructed; Among them, the optimal arc segment division frequency of each complexity is the average of the optimal arc segment division frequencies of the axis trajectory of the same complexity.

10. The method for automatically identifying the precession direction of the axis trajectory of a rotor system according to claim 9, characterized in that: According to the divided data set, the arc segment division frequency corresponding to the complexity Fz of the axis trajectory profile to be analyzed is determined, and the trajectory is divided into multiple local arc segments. For each local arc segment, the velocity vector analysis method is used to identify its precession direction. The precession directions of all local arc segments are counted to determine the precession direction of the axis trajectory to be analyzed.

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