Recursion method for automatic identification of vortex-induced vibration

Abstract

The invention discloses a recursion method for automatically identifying vortex-induced vibration, which comprises the following steps of: firstly, performing phase space reconstruction on a vortex-induced vibration time-history signal, and further performing visual presentation on a reconstructed phase space by adopting a recursion plot; and then performing quantitative analysis on the recursion plot information, and determining an alarm threshold value in the vortex-induced vibration development process, thereby identifying a vortex-induced vibration event according to a recursion quantitative index. According to the method, due to the adoption of the recursion plot and the improved recursion quantitative index, the problems that an existing vortex-induced vibration event recognition result is not visual and the recognition timeliness is low are solved, the recursion plot can graphically display the vortex-induced vibration event, and the improved recursion quantitative index can achieve early warning in the whole development process of the vortex-induced vibration event; the method can be used for real-time monitoring and alarming of the bridge vortex-induced vibration event, and the intuition and recognition timeliness of the recognition result of the bridge vortex-induced vibration event are improved.

Description

Technical Field

[0001] The invention belongs to the field of vortex-induced vibration event recognition and early warning for bridge health monitoring, and relates to an automatic recognition and early warning method for vortex-induced vibration events. Background Art

[0002] Real-time online monitoring and early warning of vortex-induced vibration events are important aspects of bridge health monitoring. Existing research methods mainly include the long-short time window ratio method, Hilbert transform, clustering algorithm, and comprehensive analysis algorithms of various methods. Existing vortex-induced vibration identification methods have problems such as low timeliness or unintuitive identification results. Real-time monitoring and early warning of vortex-induced vibration events are difficult, and it is impossible to make timely decision-making recommendations for bridge maintenance. Summary of the invention

[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a recursive method for automatic identification of vortex-induced vibration, introduce a recursive graph and an improved recursive quantitative index to solve the problems of non-intuitive identification results and low identification timeliness of vortex-induced vibration events, so as to realize graphical display and real-time warning of vortex-induced vibration events.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions to achieve the above problems:

[0005] A recursive method for automatic identification of vortex-induced vibrations comprises the following steps:

[0006] Step 1, reconstructing the phase space of the vortex-induced vibration event time series and determining the parameters required for the phase space reconstruction;

[0007] Step 2, draw a recursive graph of the phase space reconstruction result and determine the parameters required for drawing the recursive graph;

[0008] Step 3, according to the characteristics of the recursion graph, quantitative analysis is performed on the recursion graph to determine the vortex-induced vibration recursion quantitative index;

[0009] Step 4: Provide graded warning for the development process of the vortex-induced vibration event based on the recursive quantitative index and the set warning threshold.

[0010] The present invention also includes the following technical features:

[0011] Specifically, in step 1, the delayed coordinate method is used to reconstruct the phase space of the time series as follows:

[0012] X(i)={x(i),x(i+τ),...,x(i+(m-1)τ)}

[0013] Where: X(i) is the time series matrix after phase space reconstruction, i represents the time; x(i+(m-1)τ) represents the time series at the i+(m-1)τth time, m is the embedding dimension, and τ is the delay time.

[0014] Specifically, in step 1, the process of determining the embedding dimension m of the parameters required for phase space reconstruction includes: using the false neighbor point method to determine the embedding dimension m, defining the time series x(t) as a series of phase point vectors in the m-dimensional phase space, solving its false neighbor points, calculating the ratio of false neighbor points corresponding to different embedding dimensions to determine the optimal embedding dimension, and when the ratio of false neighbor points is less than 5% or the false neighbor points no longer decrease with the increase of m, the corresponding dimension is the optimal embedding dimension.

[0015] Specifically, in step 1, the process of determining the parameter delay time τ required for phase space reconstruction includes: using the mutual information method to determine the delay time τ, defining the time series x(t) and the time series x(t+τ) of the delay time τ as two sets of discrete sequences, and according to the principle of information theory, obtaining the mutual information I(τ) of the two sets of sequences; if I(τ)=0, then x(t+τ) is uncorrelated with x(t); the minimum value of I(τ) indicates that x(t+τ) and x(t) are as uncorrelated as possible; the first minimum point in I(τ) is the optimal delay time sought.

[0016] Specifically, in step 2, the recursive graph is a graph composed of a 0-1 matrix described by different colors and two time axes, and the distance between any two vectors is defined as follows:

[0017]

[0018] Where: R i,j is a two-dimensional square matrix of order N-(m-1)·τ, i,j represents the i,jth moment, N is the state vector , m is the embedding dimension, τ is the delay time, ε is the cutoff threshold, ||·|| represents the Euclidean norm, Θ(·) represents the Heaviside function, Θ(x≤0)=0; Θ(x>0)=1; In the recursive graph, different R i,j Values ​​are represented in two easily distinguishable colors.

[0019] Specifically, the cutoff threshold ε is selected based on 25% of the maximum phase space diameter r m ;

[0020] When ε≥r m When , all points are recursive points, and the recursive graph is pure black; when ε<r m , the signal is divided into recursive points and non-recursive points, and the recursive graph is composed of intersecting black and white lines.

[0021] Specifically, in step 3, the recursive graph is quantitatively analyzed to determine the recursive quantitative index RCS, which is an index for measuring the stability of the periodic signal, and its formula is as follows:

[0022]

[0023] Where: N represents the number of sampling points of the recursive graph, TT represents the average vertical line distance, V max represents the maximum vertical line distance, v represents the length of the vertical line in the recursive graph, and v min It represents the minimum vertical line length, and P(ν) represents the probability distribution of the vertical line v.

[0024] Specifically, in step 3, the TT calculation formula is as follows:

[0025]

[0026] Where N is the number of sampling points in the recursive graph, v is the length of the vertical line in the recursive graph, and v min represents the minimum vertical line length, and P(v) is the distribution probability of a vertical line of length ν in the recursive graph.

[0027] Specifically, in step 3, the maximum vertical line distance V max The calculation formula is as follows:

[0028]

[0029] In the formula, i represents the i-th moment, v represents the length of the vertical line in the recursive graph, and v min represents the minimum vertical line length, and P(v) is the distribution probability of a vertical line of length ν in the recursive graph.

[0030] Specifically, in step 4, based on the recursive quantization index RCS and the vortex-induced vibration warning index, the following warning is performed:

[0031] The RCS index is [ν min / N, 0.05~0.1), non-eddy signal;

[0032] When the RCS index is [0.05~0.1,0.5), the first-level warning is issued and vortex shedding occurs;

[0033] When the RCS index is [0.5, 0.8), the second-level warning is issued, and the amplitude of vortex-induced vibration increases;

[0034] When the RCS index is in [0.8,1), the third-level warning is in effect and the vortex-induced vibration amplitude is stable.

[0035] Compared with the prior art, the present invention has the following technical effects:

[0036] The present invention uses a recursive graph to visualize the phase space reconstruction result to analyze the characteristics of the dynamic system. The recursive graph can more intuitively observe and understand the nonlinear characteristics of the dynamic system. The vortex-induced vibration signal is identified based on the improved recursive quantitative index. The index calculation is simple and the calculation amount is small, and the vortex-induced vibration event identification speed is fast. The recursive graph has strong adaptability to random signal data containing noise and the index is stable. The improved recursive quantitative index can realize the early warning of the whole development process of the vortex-induced vibration event, and the vortex-induced vibration event is identified in real time online without any human intervention.

[0037] The present invention is a vortex-induced vibration signal recognition method based on a recursive graph, which combines an improved recursive quantitative index to provide early warning for the entire development process of vortex-induced vibration events. In the face of massive monitoring data, this method can automatically, quickly and accurately identify vortex-induced vibration signals, meeting the basic requirements of real-time online bridge health monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a principle framework diagram of the method of the present invention.

[0039] Figure 2 The false neighbor method is used to determine the embedding phase space dimension diagram.

[0040] Figure 3 The delay time map is determined using the mutual information method.

[0041] Figure 4 is the recurrence graph of a random signal.

[0042] Figure 5 It is the recursion diagram of the vortex shedding stage.

[0043] Figure 6 It is the recursion diagram of the stable stage of vortex-induced vibration.

[0044] Figure 7 It is a comparison chart of acceleration amplitude and vortex vibration alarm index.

[0045] Figure 8 It is a time chart of vortex-induced vibration RCS warning indicators.

[0046] Fig. 9 This is a comparison chart of the recognition results of the three methods. DETAILED DESCRIPTION

[0047] The present invention provides a recursive method for automatic identification of vortex-induced vibration. First, the phase space of the time series of vortex-induced vibration events is reconstructed, and the parameters required for the phase space reconstruction are determined; then, a recursive graph is drawn for the phase space reconstruction result, and the parameters required for the recursive graph drawing are determined; then, the recursive quantitative index of vortex-induced vibration is determined according to the characteristics of the recursive graph, and the size of the warning threshold is determined by comparing the recursive quantitative index with the time history curve of the vortex-induced vibration signal; finally, the development process of the vortex-induced vibration event is graded and warned according to the set warning threshold. Among them, the recursive graph describes the recursive phenomenon of the orbit in the system state space at different times. Its essence is the two-dimensional presentation of the one-dimensional time series, which can reveal the periodicity, chaos or other nonlinear characteristics of the dynamic system. Recursive quantitative analysis quantifies the recursive phenomenon through measurement values. When a vortex-induced vibration event occurs, the periodic characteristics of the recursive graph are obvious, and obvious black and white alternating stripes appear in the recursive graph. In contrast, under random environmental excitation, there is no obvious regularity in the black and white stripes in the recursive graph.

[0048] like Figure 1 , the present invention specifically comprises the following steps:

[0049] Step 1: Phase space reconstruction

[0050] The delayed coordinate method is used to reconstruct the phase space of the chaotic time series and extract more information from the time series. The following formula is the reconstruction of the time series:

[0051] X(i)={x(i),x(i+τ),...,x(i+(m-1)τ)}

[0052] Where: X(i) is the time series matrix after phase space reconstruction, i represents the time; x(i+(m-1)τ) represents the time series at the i+(m-1)τth time, m is the embedding dimension, and τ is the delay time.

[0053] From the coordinate delay method formula, we can see that phase space reconstruction requires two parameters: one is the delay time τ, and the other is the embedding dimension m. The embedding dimension and time delay must be selected according to the actual situation, otherwise it will affect the quality of phase space reconstruction. The specific selection method is:

[0054] The embedding dimension m is determined by the false neighbor method. In this embodiment, the optimal embedding dimension m=2; the time series x(t) is defined as a series of phase point vectors in the m-dimensional phase space, and its false neighbor points are solved. The false neighbor method is to calculate the ratio of false neighbor points corresponding to different embedding dimensions to determine the optimal embedding dimension. It can be considered that when the ratio of false neighbor points is less than 5% or the false neighbor points no longer decrease with the increase of m, the corresponding dimension is the optimal embedding dimension, see Appendix Figure 2 .

[0055] The mutual information method is used to determine the delay time τ. In this embodiment, the optimal delay time τ=4. For a complex system like a bridge structure, its response evolution must be a nonlinear process, and the mutual information method is used to judge the correlation of nonlinear systems. Define the time series x(t) and the time series x(t+τ) of the delay time τ as two sets of discrete sequences. According to the principle of information theory, the mutual information I(τ) of the two sets of sequences is obtained. Under the premise that x(t) is known, if the size of I(τ) is also known, the size of the certainty of x(t+τ) can be calculated. If I(τ)=0, then x(t+τ) is uncorrelated with x(t); the minimum value of I(τ) indicates that x(t+τ) and x(t) are as uncorrelated as possible; the first minimum point in I(τ) is the optimal delay time required, see the attached figure. Figure 3 .

[0056] Step 2: Draw a recursion diagram

[0057] Phase space reconstruction is a prerequisite for drawing a recursion graph, and the recursion graph is a visual presentation of the reconstructed phase space. After step 1, the optimal embedding dimension and the optimal delay time are determined, and the phase space is reconstructed. The recursion graph is drawn below. The recursion graph is a graph composed of a 0-1 matrix described by different colors and two time axes. The distance between any two vectors is defined as follows:

[0058]

[0059] Where: R i,j is a two-dimensional square matrix of order N-(m-1)·τ, i,j represents the i,jth moment, N is the state vector , m is the embedding dimension, τ is the delay time, ε is the cutoff threshold, ||·|| represents the Euclidean norm, Θ(·) represents the Heaviside function, Θ(x≤0)=0; Θ(x>0)=1.

[0060] In the recurrence diagram, different R i,j Values ​​are represented in two easily distinguishable colors.

[0061] The cutoff threshold ε is an empirical value. Different values ​​will have a great impact on the drawing and quantitative analysis of the recursive graph. The recursive threshold selection method for vortex-induced vibration signal recognition is the dynamic threshold method. When the threshold is too small, there are too many recursive points, and the diagonal lines will be discontinuous, resulting in increased sensitivity of the quantitative index, which is not conducive to vortex-induced vibration recognition; when the threshold is too high, black lines perpendicular to the diagonal lines will appear, affecting the description of the diagonal spacing, thereby interfering with the periodic characteristics of the vortex-induced vibration recursive graph. The selection standard is 25% of the maximum phase space diameter r m .

[0062] The maximum phase space diameter r mIt describes the maximum distance between orbital i and orbital j in a system, that is:

[0063] r m =max|x i -x j |

[0064] When ε≥r m When , all points are recursive points and the recursive graph is pure black. m , the signal will be divided into recursive points and non-recursive points, and the recursive graph is composed of black and white lines crossing each other.

[0065] Step 3: Determine the recursive quantitative index

[0066] The recurrence diagram is a tool for qualitative analysis of signals. Quantitative analysis of the recurrence diagram can quantify the vortex-induced vibration signal, laying the foundation for the automatic identification of vortex-induced vibration events. The average vertical line length (Trapping time, TT) is selected, combined with the maximum vertical line length (Length of longest vertical line, Vmax), and an index RCS (Recurrence cycle smoothness) is proposed to measure the smoothness of periodic signals, that is, the ratio of the average vertical line distance to the maximum vertical line distance. The formula is as follows:

[0067]

[0068] Where: N represents the number of sampling points of the recursive graph, TT represents the average vertical line distance, V max represents the maximum vertical line distance, v represents the length of the vertical line in the recursive graph, and v min It represents the minimum vertical line length, which is 2. P(ν) represents the probability distribution of the vertical line v. R i,j is the recursive value, and k is a natural number.

[0069] The average vertical line (TT) in the recursive graph indicates that some states of this signal change slowly or do not change at some moments, that is, stagnation. This indicator is calculated by calculating the minimum length ν min The average length of the vertical line is used to estimate the average dead time of the signal in some states. The calculation formula is as follows:

[0070]

[0071] Where N represents the number of sampling points in the recursive graph, v represents the length of the vertical line in the recursive graph, and v min It represents the minimum vertical line length, which is 2. P(v) is the distribution probability of a vertical line of length ν in the recursive graph.

[0072] Maximum vertical line length (V max ) by finding all the vertical lines that reach the minimum threshold value ν min The maximum value in the vertical line can also measure the speed of signal change. The calculation formula is as follows:

[0073]

[0074] In the formula, i represents the i-th moment, v represents the length of the vertical line in the recursive graph, and v min It represents the minimum vertical line length, which is 2. P(v) is the distribution probability of a vertical line with a length of ν in the recursive graph.

[0075] R i,j is the recursive value, and k is a natural number.

[0076] The recursive graph has an "edge effect" when describing periodic characteristics. Since it is difficult for the analysis data to meet the number of integer multiples of the period, the periodicity of the data around the recursive graph will be damaged, thereby interfering with the quantitative indicators. Therefore, before calculating the quantitative indicators, the data in the "edge interference area" needs to be recursively unified. The white dots before the first black dot and after the last black dot in each column of the recursive graph need to be turned into black dots.

[0077] For non-eddy signals, the vertical lines in the recursive graph have a wide distribution range, but are mainly concentrated in the shorter vertical line lengths, and the average vertical line length tends to ν min =2, and the maximum vertical line length tends to the number of sampling points N, and the difference between the two is large. At this time, the RCS quantitative index tends to ν min / N, see attached Figure 4 ;

[0078] For the vortex shedding signal and vortex-induced vibration stability signal, the vertical line distribution range in the recursive diagram becomes narrower, the concentration range begins to approach the longer vertical line distance, and the average vertical line length tends to ν max , and the maximum vertical line length tends to the vortex-induced vibration period length, the two are infinitely close, at this time the RCS quantitative index tends to 1, see attached Figure 5 and attached Figure 6 .

[0079] Step 4: Judge and warn

[0080] Based on the above recursive quantitative index RCS, a three-level vortex-induced vibration warning index is proposed: level one warning (vortex shedding occurs), level two warning (vortex-induced vibration amplitude increases), and level three warning (vortex-induced vibration amplitude is stable).

[0081] Table 1 RCS vortex-induced vibration warning index classification table

[0082]

[0083] It should be noted that the classification of vortex-induced vibration warning indicators is intended to quantify the stability of the initial stage of vortex-induced vibration. For the first-level warning, the initial formation conditions of vortex-induced vibration are not stable enough, and it takes a long time to reach the stable stage of vortex-induced vibration, or even no vortex-induced vibration is formed in the end; while for the second and third-level warnings, the initial conditions of vortex-induced vibration are very stable, and it takes about five minutes to reach the stable stage with the maximum amplitude. Therefore, the main function of this indicator is to quantify the stability of the conditions for the formation of vortex-induced vibration. Users can appropriately (take 0.05~0.1) increase the lower limit of the first-level warning to reduce the warning sensitivity.

[0084] Specific embodiments of the present invention are given below. It should be noted that the present invention is not limited to the following specific embodiments, and all equivalent changes made on the basis of the technical solution of this application fall within the protection scope of the present invention.

[0085] Example:

[0086] The suspension bridge in this embodiment is a single-span double-hinged suspension bridge, the span arrangement is (302+888+348.5)=1538.5m, and the stiffening beam structure is a flat closed streamlined steel box beam.

[0087] The vortex-induced vibration identification method proposed in this invention is used to identify and analyze the acceleration data of channel 14 in the main beam span of a suspension bridge from May 1 to 9. Each analysis takes 1 minute of acceleration data and moves forward with a 1 minute analysis step. Figure 7 It is a comparison chart between the acceleration amplitude and the vortex-induced vibration warning index RCS obtained through analysis. Figure 8 It shows the detailed time course of the size distribution of RCS warning indicators in this vortex vibration event.

[0088] Mainly aiming at the recognition results of the above engineering examples, a comparative analysis is conducted between the early warning indicator RCS of the present invention and the indicators energy similarity ratio SRE and amplitude similarity ratio SRA of two existing methods. Fig. 9 In order to conduct a comparative analysis on the identification results on May 5, the above indicators are compared from three aspects: the timeliness of the warning indicators and the sensitivity to changes in frequency and amplitude. First, the start and end times of the 8 vortex vibration identification results on May 5 are extracted, as shown in the following table.

[0089] Table 2 Start and end timetable of different indicators of 8 vortex vibrations on May 5, 2020

[0090]

[0091]

[0092] Note 1: "—" indicates that a vortex vibration has not completely subsided and there is no end time; the next vortex vibration has no start time;

[0093] Note 2: The start and end times of SRA and SRE are selected with 0.05 and 0.95 as the threshold lines respectively. If the time is greater than the threshold line, vortex vibration alarm will be triggered, and if the time is less than the threshold line, vortex vibration will end.

[0094] comprehensive Fig. 9 As can be seen from the table above: the identification start time of the RCS indicator is always earlier than that of SRA (within 10 minutes) and close to that of SRE; while the end time is later than that of SRA and SRE (about 10 minutes). The RCS identification time coverage length for the entire vortex-induced vibration is longer than that of the other two indicators. The main reason for this phenomenon is that the indicator is more sensitive to vortex shedding signals (the beginning and end stages of vortex vibration) and can capture vortex shedding signals faster at the beginning of vortex vibration, which means that its effectiveness is better than that of the other two indicators.

[0095] from Fig. 9 From the perspective of region 1, when identifying non-vortex signals, the index SRE is most sensitive to frequency, while the RCS value is always higher than SRA in the overall identification results. Therefore, RCS is second to frequency sensitivity, and SRA is poor. From the perspective of region 2, when identifying vortex vibration signals, the index SRA is most sensitive to amplitude, followed by RCS, and SRE is the worst.

[0096] Overall, the three indicators have distinct characteristics, as shown in the table below.

[0097] Table 3 Comprehensive comparison of three indicators

[0098] Identify indicators Timeliness Frequency sensitivity Amplitude sensitivity RCS Optimal good good SRA good generally Optimal SRE good Optimal Worst

[0099] SRE has low frequency picking accuracy and defects in frequency domain analysis, which causes the fluctuation of the indicator to be too large, and it is difficult to reasonably classify the warning level. The advantage of SRA is that it combines frequency and amplitude characteristics, but this makes it too sensitive to amplitude fluctuations, and not sensitive enough to frequency in the early stage of vortex vibration, resulting in poor warning timeliness. RCS performs well in frequency sensitivity, and is not affected by noise for random signals. The indicator is very stable and it is easy to distinguish vortex vibration signals and random signals and classify them into levels. In addition, it has the best timeliness and can be identified in the early stage of vortex shedding signals, making it very suitable as a warning indicator.

[0100] The present invention combines recursive graphs and recursive quantitative indicators for automatic identification of vortex-induced vibrations, uses the false neighbor method to determine the optimal embedding dimension m, and uses the mutual information method to determine the delay time τ. For the identification of vortex-induced vibration signals, an index RCS for measuring the stability of periodic signals is proposed by combining the average vertical line length and the maximum vertical line length. The vortex-induced vibration data of a suspension bridge for a total of 9 days from May 1 to 9 are measured to verify the timeliness, accuracy and rationality of the early warning indicators.

[0101] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0102] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0103] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A recursive method for automatic identification of vortex-induced vibration, characterized in that: The following steps are involved: Step 1, reconstructing the phase space of the vortex-induced vibration event time series and determining the parameters required for the phase space reconstruction; Step 2, draw a recursive graph of the phase space reconstruction result and determine the parameters required for drawing the recursive graph; Step 3, according to the characteristics of the recursion graph, quantitative analysis is performed on the recursion graph to determine the vortex-induced vibration recursion quantitative index; Step 4: Provide graded warning for the development process of the vortex-induced vibration event based on the recursive quantitative index and the set warning threshold.

2. The recursive method for automatic identification of vortex-induced vibration according to claim 1, characterized in that: In step 1, the delayed coordinate method is used to reconstruct the phase space of the time series as follows: X(i)={x(i),x(i+τ),...,x(i+(m-1)τ)} Where: X(i) is the time series matrix after phase space reconstruction, i represents the time; x(i+(m-1)τ) represents the time series at the i+(m-1)τth time, m is the embedding dimension, and τ is the delay time.

3. The recursive method for automatic identification of vortex-induced vibration according to claim 2, characterized in that: In step 1, the process of determining the embedding dimension m of the parameters required for phase space reconstruction includes: using the false neighbor point method to determine the embedding dimension m, defining the time series x(t) as a series of phase point vectors in the m-dimensional phase space, solving its false neighbor points, calculating the ratio of false neighbor points corresponding to different embedding dimensions to determine the optimal embedding dimension, and when the ratio of false neighbor points is less than 5% or the false neighbor points no longer decrease with the increase of m, the corresponding dimension is the optimal embedding dimension.

4. The recursive method for automatic identification of vortex-induced vibration according to claim 2, characterized in that: In step 1, the process of determining the parameter delay time τ required for phase space reconstruction includes: using the mutual information method to determine the delay time τ, defining the time series x(t) and the time series x(t+τ) of the delay time τ as two sets of discrete sequences, and according to the principle of information theory, obtaining the mutual information I(τ) of the two sets of sequences; if I(τ)=0, then x(t+τ) is uncorrelated with x(t); the minimum value of I(τ) indicates that x(t+τ) and x(t) are as uncorrelated as possible; the first minimum point in I(τ) is the optimal delay time sought.

5. The recursive method for automatic identification of vortex-induced vibration according to claim 1, characterized in that: In step 2, the recursive graph is a graph composed of a 0-1 matrix described by different colors and two time axes. The distance between any two vectors is defined as follows: Where: R i,j is a two-dimensional square matrix of order N-(m-1)·τ, i,j represents the i,jth moment, N is the state vector , m is the embedding dimension, τ is the delay time, ε is the cutoff threshold, ||·|| represents the Euclidean norm, Θ(·) represents the Heaviside function, Θ(x≤0)=0; Θ(x>0)=1; In the recurrence diagram, different R i,j Values ​​are represented in two easily distinguishable colors.

6. The recursive method for automatic identification of vortex-induced vibration according to claim 5, characterized in that: The cut-off threshold ε is selected based on 25% of the maximum phase space diameter r m ; When ε≥r m When , all points are recursive points, and the recursive graph is pure black; when ε<r m , the signal is divided into recursive points and non-recursive points, and the recursive graph is composed of intersecting black and white lines.

7. The recursive method for automatic identification of vortex-induced vibration according to claim 1, characterized in that: In step 3, the recursive graph is quantitatively analyzed to determine the recursive quantitative index RCS, which is an index for measuring the stability of the periodic signal, and its formula is as follows: Where: N represents the number of sampling points of the recursive graph, TT represents the average vertical line distance, V max represents the maximum vertical line distance, v represents the length of the vertical line in the recursive graph, and v min It represents the minimum vertical line length, and P(ν) represents the probability distribution of the vertical line v.

8. The recursive method for automatic identification of vortex-induced vibration according to claim 7, characterized in that: In step 3, the TT calculation formula is as follows: Where N is the number of sampling points in the recursive graph, v is the length of the vertical line in the recursive graph, and v min represents the minimum vertical line length, and P(v) is the distribution probability of a vertical line of length ν in the recursive graph.

9. The recursive method for automatic identification of vortex-induced vibration according to claim 7, characterized in that: In step 3, the maximum vertical line distance V max The calculation formula is as follows: In the formula, i represents the i-th moment, v represents the length of the vertical line in the recursive graph, and v min represents the minimum vertical line length, and P(v) is the distribution probability of a vertical line of length ν in the recursive graph.

10. The recursive method for automatic identification of vortex-induced vibration according to claim 7, characterized in that: In step 4, based on the recursive quantization index RCS and the vortex-induced vibration warning index, the following warning is performed: The RCS index is [ν min / N, 0.05~0.1), non-eddy signal; When the RCS index is [0.05~0.1,0.5), the first-level warning is issued and vortex shedding occurs; When the RCS index is [0.5, 0.8), the second-level warning is issued, and the amplitude of vortex-induced vibration increases; When the RCS index is in [0.8,1), the third-level warning is in effect and the vortex-induced vibration amplitude is stable.

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