A real-time monitoring method for fatigue damage of steel structures

Abstract

The present invention relates to the technical field of steel structure vibration testing, and specifically to a real-time monitoring method for fatigue damage of steel structures. The method obtains the vibration signal of the steel structure to be monitored in real time and segments it, obtains the periodic parameters and period of the corresponding vibration segmented signal, merges and updates the adjacent vibration segmented signals, and obtains all the segmented signals to be analyzed; then obtains the abnormality degree of each segmented signal to be analyzed; and performs abnormal warning for the steel structure to be monitored according to the abnormality degree. The present invention evaluates the possibility of each segmented signal to be analyzed being affected by the defect based on the change of local signal characteristics of the vibration signal caused by abnormal defects such as fatigue damage of the steel structure, combined with the noise signal and the normal local signal and the defective local signal and their distinguished fluctuation characteristics, thereby avoiding filtering out the abnormal fluctuation characteristics of the segmented signal to be analyzed caused by the defect damage, performing precise noise reduction, and improving the accuracy of real-time abnormal warning for steel structures.

Description

Technical Field

[0001] The invention relates to the technical field of steel structure vibration testing, and in particular to a real-time monitoring method for fatigue damage of a steel structure. Background Art

[0002] Steel structures are widely used in human production and life. Long-term use and external environmental influences may cause fatigue damage to steel structures, reduce structural strength and stability, and long-term operation with damage may eventually lead to failure of steel structures, posing a threat to personnel and property safety. Therefore, real-time monitoring and regular maintenance of steel structures are essential. The introduction of real-time monitoring technology can provide timely warnings for abnormal conditions such as fatigue damage of steel structures. By collecting real-time vibration data of steel structures, it is possible to analyze and discover cracks, deformations or other abnormalities that may exist in the structure, so as to provide timely warnings and take repair measures to ensure the safe operation of steel structures.

[0003] Traditional real-time monitoring methods for steel structures are often based on the analysis of real-time vibration signals to evaluate whether there are abnormalities in the steel structure. However, due to environmental interference and errors in the sensor itself, noise signals may exist in the real-time monitoring signals, which may affect the accuracy of the real-time vibration signal analysis and evaluation of the steel structure. When traditional filters are used directly for denoising, small defect signals may be smoothed out, affecting the accurate judgment of fatigue damage of the steel structure. Summary of the invention

[0004] In order to solve the technical problem that the noise reduction effect of steel structure vibration signal is poor and thus the judgment accuracy of steel structure fatigue damage is low, the purpose of the present invention is to provide a real-time monitoring method for steel structure fatigue damage. The technical solution adopted is as follows:

[0005] Acquire the vibration signal of the steel structure to be monitored in real time at a preset sampling frequency, segment the vibration signal, and acquire all vibration segment signals;

[0006] According to the autocorrelation of each of the vibration segmented signals, the periodicity parameters and the period of the corresponding vibration segmented signals are obtained; according to the difference of the periodicity parameters and the period between the vibration segmented signals, the adjacent vibration segmented signals are merged and updated to obtain all the updated segmented signals to be analyzed;

[0007] In each of the segmented signals to be analyzed, a first abnormality determination factor of each of the segmented signals to be analyzed is obtained according to the periodic parameters and the period of all the segmented vibration signals; a second abnormality determination factor of each of the segmented signals to be analyzed is obtained according to the fluctuation shape characteristics of each of the segmented signals to be analyzed;

[0008] The abnormality degree of each of the segmented signals to be analyzed is obtained according to the first abnormality determination factor and the second abnormality determination factor; and an abnormality warning is issued to the monitored steel structure according to the abnormality degree.

[0009] Furthermore, the method for obtaining the periodic parameter and the period includes:

[0010] Fit the vibration signal function of the vibration signal; construct the autocorrelation function of each vibration segmented signal in the corresponding time period of each vibration segmented signal, use the time delay corresponding to the maximum autocorrelation function value as the period of the vibration segmented signal, and standardize the maximum autocorrelation function value as the periodic parameter of the vibration segmented signal.

[0011] Furthermore, the method for acquiring the segmented signal to be analyzed includes:

[0012] Acquire a merging degree parameter according to the difference of the periodic parameters and the period between adjacent vibration segment signals;

[0013] Taking the first vibration segmented signal in the vibration signal as the starting point, judging whether the merging degree parameter between the starting point and the second vibration segmented signal is less than a preset merging threshold; if it is less than the preset merging threshold, merging the starting point with the second vibration segmented signal, and updating the vibration segmented signal in the vibration signal and the periodic parameters and period of each vibration segmented signal, and continuously iterating the judgment until the merging degree parameter between the starting point and the next adjacent vibration segmented signal after each update is not less than the preset merging threshold, and taking the previous merging result as a segmented signal to be analyzed;

[0014] Taking the next adjacent vibration segmented signal of the segmented signal to be analyzed as a new starting point, all the segmented signals to be analyzed in the vibration signal are iteratively acquired.

[0015] Furthermore, the method for obtaining the merging degree parameter includes:

[0016] Obtain the difference in the periods between adjacent vibration segment signals as a period judgment value; obtain the difference in the periodic parameters between adjacent vibration segment signals as a reference weight; normalize the product of the reference weight and the period judgment value and use it as a merging degree parameter between adjacent vibration segment signals.

[0017] Furthermore, the method for updating the vibration segment signal and the periodic parameters and period of each vibration segment signal includes:

[0018] The merged result is taken as the first vibration segmented signal in the vibration signal; in the vibration segmented signal corresponding to each merged result, the average of the periodic parameters of all the vibration segmented signals in the merged result is taken as the periodic parameter of the merged result, and the average of the periods of all the vibration segmented signals in the merged result is taken as the period of the merged result.

[0019] Furthermore, the method for obtaining the first abnormality determination factor includes: obtaining the first abnormality determination factor of each segmented signal to be analyzed according to the periodic parameter and the period of each segmented signal to be analyzed, the periodic parameter is positively correlated with the first abnormality determination factor, and the period is negatively correlated with the first abnormality determination factor.

[0020] Furthermore, the method for obtaining the second abnormality determination factor includes:

[0021] Acquire the information entropy of each of the segmented signals to be analyzed;

[0022] Obtaining an upper envelope and a lower envelope of each of the segmented signals to be analyzed, and obtaining an abnormal reference factor of each of the segmented signals to be analyzed according to the similarity of changes of the upper envelope and the lower envelope;

[0023] A second abnormality determination factor is obtained for each of the segmented signals to be analyzed according to the information entropy and the abnormality reference factor, and both the information entropy and the abnormality reference factor are positively correlated with the second abnormality determination factor.

[0024] Furthermore, the method for obtaining the abnormal reference factor includes:

[0025] At each moment in each of the segmented signals to be analyzed, the slopes of the upper envelope and the lower envelope at the envelope points at the corresponding moments are obtained respectively, the differences of the slopes at all the same moments are squared and averaged, the negative correlation mapping of the averaged results is normalized, and the abnormal reference factor corresponding to the segmented signal to be analyzed is obtained.

[0026] Furthermore, the method for providing abnormal warning to the monitored steel structure according to the abnormality degree includes:

[0027] Denoising the vibration signal according to the abnormality degree;

[0028] The denoised vibration signal is compared with the preset standard vibration signal, and the variance of the amplitude difference between all signal points at the same acquisition time is calculated; when the variance is greater than or equal to the preset discrimination threshold, it is considered that the steel structure to be tested has damage defects and an abnormal warning is issued.

[0029] Furthermore, the method for reducing noise on the vibration signal includes:

[0030] When the abnormality degree of the segmented signal to be analyzed is greater than a preset degree threshold, the segmented signal to be analyzed is used as a signal segment to be retained;

[0031] In the EMD decomposition process of the vibration signal, the component segment of the time period corresponding to the signal segment to be retained in the first-order component of the vibration signal is replaced with the signal segment to be retained to obtain an updated first-order component, all components and residual signal components of the vibration signal are obtained, the residual signal component is used as the noise signal in the vibration signal, all components of the vibration signal except the noise signal are reconstructed to obtain the vibration signal after noise reduction.

[0032] The present invention has the following beneficial effects:

[0033] The present invention obtains the vibration signal of the steel structure to be monitored in real time at a preset sampling frequency, segments the vibration signal, and obtains all vibration segmented signals; according to the autocorrelation of each vibration segmented signal, obtains the periodic parameter and period of the corresponding vibration segmented signal, so as to subsequently evaluate the first abnormality determination factor of each segmented signal to be analyzed in combination with the periodic impact of the defect on the vibration segmented signal; according to the difference in periodic parameters and period between the vibration segmented signals, merges and updates the adjacent vibration segmented signals to obtain all updated segmented signals to be analyzed, so as to simplify the vibration signal analysis process; in each segmented signal to be analyzed, according to the periodic parameters and period of all vibration segmented signals, obtains the first abnormality determination factor of each segmented signal to be analyzed; according to the fluctuation shape characteristics of each segmented signal to be analyzed, obtains the second abnormality determination factor of each segmented signal to be analyzed; according to the first abnormality determination factor and the second abnormality determination factor, obtains the abnormality degree of each segmented signal to be analyzed, and the abnormality degree reflects whether it is affected by defects such as fatigue damage of the steel structure; according to the abnormality degree, an abnormal warning is given to the steel structure to be monitored. The present invention evaluates the first abnormality determination factor according to the change of local signal characteristics of vibration signals caused by abnormal defects such as fatigue damage of steel structures, combined with the different fluctuation characteristics of noise signals, normal local signals, and defective local signals, based on the change of local signal characteristics with shortened period, and evaluates the second abnormality determination factor according to the sudden change of local amplitude but still having a certain fluctuation law. The two are combined to evaluate the possibility that each segmented signal to be analyzed is affected by the defect, thereby avoiding filtering out the abnormal fluctuation characteristics of the segmented signal to be analyzed caused by the defect, performing precise noise reduction, and improving the accuracy of real-time abnormal warning of steel structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 The present invention provides a flowchart of a method for real-time monitoring of fatigue damage of a steel structure according to an embodiment of the present invention.

[0036] Figure 2 A segmented schematic diagram of a vibration signal provided by an embodiment of the present invention.

[0037] Figure 3 A schematic diagram of corresponding envelope points on an upper envelope and a lower envelope at the same time in a segmented signal to be analyzed provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, characteristics and effects of a real-time monitoring method for fatigue damage of steel structure proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0039] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0040] The specific scheme of the real-time monitoring method for fatigue damage of steel structure provided by the present invention is described in detail below with reference to the accompanying drawings.

[0041] See also Figure 1 , which shows a flow chart of a method for real-time monitoring of fatigue damage of a steel structure provided by an embodiment of the present invention, specifically comprising:

[0042] Step S1, obtaining the vibration signal of the steel structure to be monitored in real time at a preset sampling frequency, segmenting the vibration signal, and obtaining all vibration segment signals.

[0043] In one embodiment of the present invention, an external force is applied to a steel structure to make the steel structure vibrate, and vibration sensors are evenly arranged on the surface of the steel structure to be measured in an embedded manner to collect vibration signals of the steel structure; in order to perform real-time monitoring of the steel structure to be measured, the embodiment of the present invention applies an external force to the steel structure at a preset sampling frequency, and collects the vibration signals of the steel structure in real time through the vibration sensors, wherein the preset sampling frequency is once every 6 hours, that is, the vibration signals of the steel structure are collected once every 6 hours, and the fatigue damage of the steel structure is evaluated by analyzing the vibration signals.

[0044] It should be noted that the implementer can set the layout position of the vibration sensor according to the needs of the specific implementation scenario, apply external force at the connection point of the steel structure, or apply external force at the stress concentration location through finite element analysis; the external force applied should not be too large, and a mechanical vibration device can be used to apply external excitation to the steel structure to ensure that the external force in different application processes is consistent, while avoiding unnecessary external damage to the steel structure; obtaining vibration signals according to specific implementation scenarios is a common technical means used by technical personnel in this field and will not be elaborated here.

[0045] It should be noted that the analysis and processing method of each vibration signal on the steel structure is consistent, and only the analysis and processing of one vibration signal is described here.

[0046] To facilitate the subsequent analysis of the local fluctuation characteristics and periodicity of the vibration signal, after obtaining the vibration signal, the embodiment of the present invention further segments the vibration signal to obtain all vibration segmented signals. In one embodiment of the present invention, the vibration signal is evenly divided into a preset length to obtain all vibration segmented signals, and the preset length is 5000; specifically, the first vibration signal point is taken as the starting point, and every 5000 vibration signal points are taken as a vibration segmented signal, and all vibration segmented signals are divided in sequence to obtain.

[0047] See also Figure 2 , which shows a segmented schematic diagram of a vibration signal provided by an embodiment of the present invention, Figure 2 In the figure, the length between adjacent vertical lines is the preset length, which includes 5000 vibration signal points. The vibration signal is divided into multiple vibration segment signals by vertical lines. It should be noted that the last vibration segment signal may be less than 5000 vibration signal points. When its vibration signal points are less than 2000, it can be merged into the adjacent previous vibration segment. The length of the vibration segment signal is related to the sampling frequency of the vibration sensor. The implementer can set the preset length by himself.

[0048] Step S2, according to the autocorrelation of each vibration segmented signal, obtain the periodic parameters and period of the corresponding vibration segmented signal; according to the differences in periodic parameters and periods between the vibration segmented signals, merge and update the adjacent vibration segmented signals to obtain all updated segmented signals to be analyzed.

[0049] In general, the vibration signal of a normal steel structure usually shows periodic fluctuations in the time domain, with clear periodic characteristics; however, if the steel structure is fatigue damaged, the vibration signal will cause sudden amplitude changes and shortened signal periodicity in local segments due to the nonlinear characteristics of the steel structure or local damage; and the noise signal usually shows irregular random fluctuations, and the corresponding local segments lack obvious periodicity and regularity. The embodiment of the present invention evaluates the possibility that the fluctuation characteristics of each vibration segment signal are affected by defects such as fatigue damage of the steel structure based on the fluctuation characteristics of the signals under three vibration conditions, and then performs real-time monitoring of the fatigue damage of the steel structure and abnormal warning.

[0050] Since there are obvious periodic differences between the local segments corresponding to fatigue damage and normal conditions and the local segments corresponding to noise; and since the autocorrelation can measure the similarity of vibration signals under different time delays, and the periodic signal has a repetitive pattern under time delay, the periodicity of the function can be analyzed through the autocorrelation analysis; therefore, the embodiment of the present invention obtains the periodic parameters and period of the corresponding vibration segmented signal according to the autocorrelation of each vibration segmented signal.

[0051] Preferably, in one embodiment of the present invention, considering that it is impossible to accurately determine the period of each vibration segmented signal, the time delay is iteratively changed to determine whether the vibration signal has periodicity, and when the time delay in the autocorrelation function is 0 or takes a periodic value, the autocorrelation function takes a maximum value, and then the period of the vibration segmented signal can be iteratively obtained; based on this, the method for obtaining the periodicity parameter and the period includes:

[0052] A vibration signal function is fitted to the vibration signal; within the time period corresponding to each vibration segmented signal, an autocorrelation function of each vibration segmented signal is constructed, the time delay corresponding to the maximum autocorrelation function value is used as the period of the vibration segmented signal, and the maximum autocorrelation function value is standardized and used as the periodic parameter of the vibration segmented signal.

[0053] The calculation formula of the periodic parameter is:

[0054] ;in, is the sequence number of the vibration segment signal; For the The periodic parameters of the vibration segmented signal; for From 1 to During the iteration process, the function takes the maximum value sign; is the serial number of the vibration signal point in the vibration segment signal; is the total number of vibration signal points in the vibration segment signal, that is, the length of the vibration segment signal; is the vibration signal function; is the time delay function of the vibration signal function; is the delay amount; The value of the maximum autocorrelation function.

[0055] It should be noted that the method for fitting the vibration signal function is the least squares method. The implementer can also use polynomial interpolation method or nonlinear model for fitting. It and the autocorrelation function are existing technologies well known to those skilled in the art and will not be elaborated here. The iterative change unit of the time delay is consistent with the unit scale of the horizontal axis coordinate of the vibration signal. The time delay is greater than zero and does not exceed the duration of the vibration segmented signal. The specific standardization method in the calculation formula of the periodic parameter is: when the time delay is 0, the autocorrelation function value of the vibration segmented signal is obtained. , and use it as the denominator for standard normalization.

[0056] In another embodiment of the present invention, each vibration signal function value may be standardized first. Specifically, the amplitude standard deviation of all vibration signal points in each vibration segment signal may be used as the denominator for standard normalization. Then, the calculation formula of the periodic function of each vibration segment signal is:

[0057] ;in, is the sequence number of the vibration segment signal; For the The periodic parameters of the vibration segmented signal; is the maximum value symbol; for From 1 to During the iteration process, the function takes the maximum value sign; is the serial number of the vibration signal point in the vibration segment signal; is the total number of vibration signal points in the vibration segment signal, that is, the length of the vibration segment signal; is the vibration signal function; is the time delay function of the vibration signal function; is the delay amount; For the The standard deviation of the amplitude of all vibration signal points in a vibration segment signal.

[0058] The larger the periodicity parameter of the vibration segment signal, the greater the possibility that the vibration segment signal has periodicity, and the greater the possibility that it corresponds to a normal vibration signal or an abnormal vibration signal caused by fatigue damage defects.

[0059] In order to simplify the vibration signal analysis process, reduce the computational complexity and improve the analysis efficiency, the vibration segmented signals with similar periodic characteristics are merged; considering that the vibration segment with periodicity and smaller period difference is more likely to be a similar vibration segmented signal, and considering that the periodic parameter obtained when the time delay is iterated to a certain specific value may be the maximum autocorrelation iterative function value, but it may not have periodic characteristics, so although the periods of some vibration segmented signals are close, their periodic characteristics may not be strong; therefore, according to the difference in periodic parameters and periods between vibration segmented signals, the embodiment of the present invention merges and updates adjacent vibration segmented signals, merges adjacent vibration segmented signals with similar periodic fluctuations, and obtains all updated segmented signals to be analyzed, so as to better extract signal features and perform subsequent analysis.

[0060] Preferably, in one embodiment of the present invention, the method for acquiring the segmented signal to be analyzed includes:

[0061] According to the difference of periodic parameters and periods between adjacent vibration segment signals, a merging degree parameter is obtained;

[0062] Taking the first vibration segmented signal in the vibration signal as the starting point, judging whether the merging degree parameter between the starting point and the second vibration segmented signal is less than a preset merging threshold; if it is less than the preset merging threshold, merging the starting point and the second vibration segmented signal, and updating the vibration segmented signal in the vibration signal and the periodic parameters and period of each vibration segmented signal, and continuously iterating the judgment until the merging degree parameter between the starting point and the next adjacent vibration segmented signal after each update is not less than the preset merging threshold, and taking the previous merging result as a segmented signal to be analyzed;

[0063] Taking the next adjacent vibration segmented signal of the segmented signal to be analyzed as a new starting point, all the segmented signals to be analyzed in the vibration signal are iteratively obtained.

[0064] It should be noted that the preset merging threshold is 0.4, which can be set by the implementer.

[0065] The method for obtaining the merging degree parameter specifically includes: obtaining the difference in the period between adjacent vibration segmented signals as a period determination value; obtaining the difference in the periodic parameters between adjacent vibration segmented signals as a reference weight; normalizing the product of the reference weight and the period determination value, and using it as the merging degree parameter between adjacent vibration segmented signals. In one embodiment of the present invention, specifically between adjacent vibration segmented signals, the absolute value of the period difference is multiplied by the absolute value of the periodic parameter difference, and the product is normalized by an inverse hyperbolic tangent function. In other embodiments of the present invention, the implementer may also adopt other normalization methods, which will not be described here.

[0066] It should be noted that between adjacent vibration segmented signals, one of the vibration segmented signals may be the result of the previous merger, then the previous merger result needs to be regarded as a new vibration segmented signal; the periodic parameter of the new vibration segmented signal is the mean of the periodic parameters of all vibration segmented signals in the merger result, and the period of the new vibration segmented signal is the mean of the periods of all vibration segmented signals in the merger result.

[0067] As an example, to help understand the merging and updating process of vibration segmented signals: take the first vibration segmented signal I1 in the vibration signal as the starting point, evaluate the merging degree parameter between it and the next adjacent vibration segmented signal I2, if it is less than the preset merging threshold value 0.4, then splice the tail of I1 and the head of I2 to merge into a new vibration segmented signal I, and obtain the periodic parameters and period of the new vibration segmented signal I; evaluate the merging degree parameter between the vibration segmented signal I and the next adjacent vibration segmented signal I3, if it is greater than or equal to the preset merging threshold value 0.4, then take the vibration segmented signal I as a segmented signal to be analyzed, take the vibration segmented signal I3 as the new starting point, calculate the merging degree parameter between it and the next adjacent vibration segmented signal I4 to determine the merger, and iterate continuously until all segmented signals to be analyzed are obtained.

[0068] Step S3, in each segmented signal to be analyzed, according to the periodic parameters and periods of all vibration segmented signals, obtain the first abnormality determination factor of each segmented signal to be analyzed; according to the fluctuation shape characteristics of each segmented signal to be analyzed, obtain the second abnormality determination factor of each segmented signal to be analyzed.

[0069] Taking into account that defects may cause the periodicity to shorten in local segments of the vibration signal, noise has no periodicity, and the periodicity of the normal vibration signal is larger than the periodicity of the signal segment corresponding to the defect; therefore, in the local segment of the vibration signal corresponding to the defect, its periodicity has obvious distinguishing characteristics; therefore, in each segmented signal to be analyzed, the embodiment of the present invention obtains the first abnormality determination factor of each segmented signal to be analyzed according to the periodic parameters and periods of all vibration segmented signals.

[0070] Preferably, in one embodiment of the present invention, considering that the stronger the periodicity of the segmented signal to be analyzed but the shorter the period, the more likely it is that the segmented signal to be analyzed is a defect causing the corresponding periodic feature; based on this, the method for obtaining the first abnormality determination factor includes:

[0071] The first abnormality determination factor of each segmented signal to be analyzed is obtained according to the periodicity parameter and period of each segmented signal to be analyzed, the periodicity parameter is positively correlated with the first abnormality determination factor, and the period is negatively correlated with the first abnormality determination factor.

[0072] It should be noted that, in the process of merging and updating the vibration segmented signals, the periodic parameters and period acquisition methods of the vibration segmented signals corresponding to the merging results have been described. The segmented signal to be analyzed is the final merging result, and the corresponding periodic parameters and period acquisition methods are the same as above, that is, in each segmented signal to be analyzed, the mean of the periodic parameters of all vibration segmented signals is used as the periodic parameter of the corresponding segmented signal to be analyzed, and the mean of the periods of all vibration segmented signals is used as the period of the corresponding segmented signal to be analyzed.

[0073] The calculation formula of the first abnormality determination factor is:

[0074] ;in, is the sequence number of the segmented signal to be analyzed; For the A first abnormality determination factor of a segmented signal to be analyzed; For the The periodicity parameters of the segmented signal to be analyzed; For the The period of the segmented signal to be analyzed; The natural constant The exponential function of base .

[0075] In the calculation formula of the first abnormality determination factor, the periodic negative correlation is specifically mapped to an exponential function so that it is negatively correlated with the first abnormality determination factor, and then the periodic parameter is multiplied and combined with the mapping result; in other embodiments of the present invention, the implementer may adopt other basic mathematical operations and related mapping means, which are all existing technical means and will not be repeated here.

[0076] Considering that although it is possible to distinguish whether each segmented signal to be analyzed contains noise based only on periodic characteristics, it may be difficult to distinguish the signal characteristics corresponding to normal conditions and abnormal defect conditions, resulting in low analysis accuracy of the possibility of each segmented signal to be analyzed being a local signal segment corresponding to a defect anomaly; considering that the amplitude of the noise signal is randomly disordered, its upper and lower envelope trends also change disorderly and have no correlation, and although the amplitude of the abnormal defect signal may have a sudden change in amplitude compared to the normal situation, its upper and lower envelope trends still have a similar change trend, and then combined with the first abnormality determination factor, the possibility of each segmented signal to be analyzed being a local signal segment corresponding to a defect anomaly can be accurately evaluated; therefore, the embodiment of the present invention obtains the second abnormality determination factor for each segmented signal to be analyzed according to the fluctuation shape characteristics of each segmented signal to be analyzed.

[0077] Preferably, in one embodiment of the present invention, considering that information entropy can reflect the fluctuation randomness and disorder degree of a signal, evaluating the information entropy of the segmented signal to be analyzed can distinguish the signal characteristics corresponding to the normal situation and the abnormal defect situation; considering that the more similar the change trend of the upper and lower envelopes of each segmented signal to be analyzed is, the greater the possibility of the local signal segment corresponding to the abnormal defect situation is; based on this, the method for obtaining the second abnormality determination factor includes:

[0078] Obtaining the information entropy of each segmented signal to be analyzed;

[0079] Obtaining the upper envelope and the lower envelope of each segmented signal to be analyzed, and obtaining the abnormal reference factor of each segmented signal to be analyzed according to the similarity of the changes of the upper envelope and the lower envelope;

[0080] The second abnormality determination factor of each segmented signal to be analyzed is obtained according to the information entropy and the abnormality reference factor, and both the information entropy and the abnormality reference factor are positively correlated with the second abnormality determination factor.

[0081] It should be noted that information entropy is an existing technology well known to those skilled in the art and will not be described in detail here. In the embodiment of the present invention, the information entropy is specifically multiplied and combined with the abnormal reference factor to obtain the second abnormal determination factor. In other embodiments, the implementer may select other basic mathematical operations and related mapping methods to combine the two.

[0082] Preferably, in one embodiment of the present invention, if the upper and lower envelopes at the same time have similar instantaneous change trends, it means that their change trends are similar, and the slope can reflect the change trend; based on this, the method for obtaining the abnormal reference factor includes:

[0083] At each moment in each segmented signal to be analyzed, the slopes of the upper envelope and the lower envelope at the envelope points at the corresponding moments are obtained respectively, the differences of all the lower slopes at the same moment are squared and averaged, the negative correlation mapping of the averaged result is normalized to obtain the abnormal reference factor of the corresponding segmented signal to be analyzed.

[0084] To understand the envelope points at the same time, see Figure 3 , which shows a schematic diagram of corresponding envelope points on an upper envelope and a lower envelope at the same time in a segmented signal to be analyzed provided by an embodiment of the present invention, Figure 3 The dashed line in the middle corresponds to the first The intersection of the dotted line and the upper envelope is the moment when the first vibration signal point on the upper envelope. Similarly, the intersection of the dotted line and the lower envelope line is the first envelope point on the lower envelope line. Envelope points.

[0085] The calculation formula of abnormal reference factor is:

[0086] ;in, For the The abnormal reference factor of the segmented signal to be analyzed; The natural constant The exponential function with base ; is the sequence number of the vibration signal point in the segmented signal to be analyzed, also the The serial number of the envelope point on the upper and lower envelope lines at the time corresponding to each vibration signal point; For the The total number of vibration signal points in the segmented signal to be analyzed; is the code for the upper envelope; is the code for the lower envelope; is the first The slope at each envelope point; is the first The slope at each envelope point.

[0087] It should be noted that in the calculation formula of the abnormal reference factor, the slope at the envelope point can be obtained by calculating the coordinates of the adjacent envelope points through the slope formula, or by fitting the curve function of the upper and lower envelope lines and then using the derivative method; the similarity of the slopes between the corresponding envelope points of the upper and lower envelope lines at all the same times is evaluated in the form of variance. The smaller it is, the greater the similarity of the fluctuation changes of the upper and lower envelope lines, and the larger the abnormal reference factor is. Therefore, the logic is adjusted and normalized by mapping the negative correlation to the exponential function; in other embodiments of the present invention, the implementer may also adopt other negative correlation normalization means, which and the acquisition of the slope of the envelope line and envelope point are all existing technologies well known to technical personnel in this field and will not be repeated here.

[0088] In another embodiment of the present invention, the envelope point sequence of the upper and lower envelope lines can be fitted, and the DTW distance between the two envelope point sequences can be obtained by a dynamic time warping (DTW) algorithm, and the DTW distance negative correlation mapping can be normalized as an abnormal reference factor; in other embodiments of the present invention, the implementer can also evaluate the change similarity between the upper and lower envelope lines by similarity metrics such as mean square error and correlation coefficient to obtain the abnormal reference factor, which are all existing technologies well known to those skilled in the art and will not be elaborated here.

[0089] Step S4, obtaining the abnormality degree of each segmented signal to be analyzed according to the first abnormality determination factor and the second abnormality determination factor; and issuing an abnormality warning to the monitored steel structure according to the abnormality degree.

[0090] Considering that the first abnormality determination factor reflects whether each segmented signal to be analyzed has a strong periodicity and whether the corresponding period is relatively short, it can be preliminarily determined whether it is affected by defects such as fatigue damage of the steel structure; the second abnormality determination factor reflects the degree of fluctuation disorder of each segmented signal to be analyzed and the similarity of the change of its envelope line. Combining the two can also determine whether it is affected by defects such as fatigue damage of the steel structure; the first abnormality determination factor and the second abnormality determination factor can be used to comprehensively evaluate the abnormality degree of each segmented signal to be analyzed; the abnormality degree reflects the possibility that the fluctuation characteristics of each segmented signal to be analyzed are caused by defects such as fatigue damage of the steel structure, and then the abnormality degree can be used to evaluate whether there are defect abnormalities in the vibration signal, so as to give an abnormal warning to the monitored steel structure.

[0091] In one embodiment of the present invention, the first abnormality determination factor and the second abnormality determination factor are both positively correlated with the abnormality degree. Specifically, the abnormality degree is obtained by multiplying the two. In other embodiments of the present invention, the implementer may also combine the two by basic mathematical operations such as addition or weighted combination, which are all existing technical means and are not described in detail here.

[0092] Preferably, in one embodiment of the present invention, the method for providing abnormal warning to the monitored steel structure according to the abnormality degree includes:

[0093] De-noise the vibration signal according to the degree of abnormality;

[0094] The denoised vibration signal is compared with the preset standard vibration signal, and the variance of the amplitude difference between all signal points at the same acquisition time is calculated; when the variance is greater than or equal to the preset discrimination threshold, it is considered that the steel structure to be tested has damage defects and an abnormal warning is issued.

[0095] It should be noted that the preset standard vibration signal is a reference signal of a normal steel structure when an external force is applied. The implementer can combine the factory test indicators, conduct real-time monitoring and analysis of the normal vibration signals of the steel structure under different historical monitoring scenarios, and select any normal vibration signal as the preset standard vibration signal. There is no limitation here. The preset discrimination threshold is 0.3, and the implementer can set other values ​​at will.

[0096] In one embodiment of the present invention, considering that Empirical Mode Decomposition (EMD) extracts intrinsic modal components with different frequencies from the original signal, and the residual term is obtained by subtracting the signal from the decomposed component, and contains information that cannot be decomposed in the original signal, the residual term can be regarded as a noise component; based on this, the method for denoising the vibration signal includes:

[0097] When the abnormality degree of the segmented signal to be analyzed is greater than a preset degree threshold, the segmented signal to be analyzed is used as a signal segment to be retained;

[0098] In the EMD decomposition process of the vibration signal, the component segmentation of the first-order component of the vibration signal corresponding to the time period of the signal segment to be retained is replaced with the signal segment to be retained to obtain the updated first-order component, and all components and residual signal components of the vibration signal are obtained, and the residual signal component is used as the noise signal in the vibration signal; the noise signal is eliminated, and all components of the vibration signal except the noise signal are reconstructed, and the reconstructed vibration signal is used as the denoised vibration signal.

[0099] It should be noted that the preset degree threshold is 0.5, and the implementer can set other values ​​by himself; the process of obtaining component signals by EMD decomposition is a prior art and will not be described in detail here.

[0100] In another embodiment of the present invention, a method for reducing noise of a vibration signal includes: when the abnormality of the segmented signal to be analyzed is greater than a preset threshold, the segmented signal to be analyzed is used as a segmented signal to be retained; when filtering, only the non-retained signal segments in the vibration signal are locally filtered to obtain a reduced noise vibration signal. Filtering is a prior art well known to those skilled in the art and will not be described in detail here.

[0101] In other embodiments of the present invention, when the degree of abnormality of any segmented signal to be analyzed in any vibration signal is greater than a preset degree threshold, it is considered that the segmented signal to be analyzed is affected by defects such as fatigue damage, and a real-time abnormality warning is directly performed to prompt relevant maintenance personnel to perform maintenance.

[0102] In summary, the embodiment of the present invention obtains the vibration signal of the steel structure to be monitored in real time, and after segmenting it, obtains the periodic parameters and period of the corresponding vibration segmented signal, and then merges and updates the adjacent vibration segmented signals to obtain all segmented signals to be analyzed; further obtains the first abnormality determination factor of each segmented signal to be analyzed; further obtains the second abnormality determination factor of each segmented signal to be analyzed; thereby obtains the abnormality degree of each segmented signal to be analyzed; and performs abnormal warning for the steel structure to be monitored according to the abnormality degree. The present invention evaluates the possibility of each segmented signal to be analyzed being affected by the defect based on the change of local signal characteristics of the vibration signal caused by defects such as fatigue damage of the steel structure, combined with the noise signal and the normal local signal and the defective local signal and their distinguishing fluctuation characteristics, thereby avoiding filtering out the abnormal fluctuation characteristics of the segmented signal to be analyzed caused by the defect, performing precise noise reduction, and improving the accuracy of real-time abnormal warning for steel structures.

[0103] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0104] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A real-time monitoring method for fatigue damage of steel structure, characterized in that: The method comprises: Acquire the vibration signal of the steel structure to be monitored in real time at a preset sampling frequency, segment the vibration signal, and obtain all vibration segment signals; Obtaining the periodicity parameter and period of the corresponding vibration segmented signal based on the autocorrelation of each vibration segmented signal; merging and updating adjacent vibration segmented signals based on the differences in the periodicity parameter and the period between the vibration segmented signals to obtain all updated segmented signals to be analyzed; In each of the segmented signals to be analyzed, a first abnormality determination factor of each of the segmented signals to be analyzed is obtained based on the periodic parameters and the periods of all the segmented vibration signals; and a second abnormality determination factor of each of the segmented signals to be analyzed is obtained based on the fluctuation morphological characteristics of each of the segmented signals to be analyzed; Obtaining the abnormality degree of each of the segmented signals to be analyzed based on the first abnormality determination factor and the second abnormality determination factor; and issuing an abnormality warning for the monitored steel structure based on the abnormality degree; The method for obtaining the periodic parameter and the period includes: Fitting a vibration signal function of the vibration signal; constructing an autocorrelation function of each vibration segmented signal within a time period corresponding to each vibration segmented signal, taking the time delay corresponding to the maximum autocorrelation function value as the period of the vibration segmented signal, and normalizing the maximum autocorrelation function value as the periodic parameter of the vibration segmented signal; The method for acquiring the segmented signal to be analyzed includes: Obtaining a merging degree parameter according to the difference in the periodic parameters and the difference in the periods between adjacent vibration segment signals; Taking the first vibration segmented signal in the vibration signal as a starting point, determining whether the merging degree parameter between the starting point and the second vibration segmented signal is less than a preset merging threshold; if it is less than the preset merging threshold, merging the starting point with the second vibration segmented signal, and updating the vibration segmented signals and the periodic parameters and periods of each vibration segmented signal in the vibration signal, and continuously iterating the determination until the merging degree parameter between the starting point and the next adjacent vibration segmented signal after each update is not less than the preset merging threshold, and using the previous merging result as a segmented signal to be analyzed; Taking the next adjacent vibration segmented signal of the segmented signal to be analyzed as a new starting point, iteratively acquiring all segmented signals to be analyzed in the vibration signal; The method for obtaining the merging degree parameter includes: Obtaining the difference in the period between adjacent vibration segment signals as a period determination value; obtaining the difference in the periodic parameter between adjacent vibration segment signals as a reference weight; normalizing the product of the reference weight and the period determination value as a merging degree parameter between adjacent vibration segment signals; and updating the periodic parameter and period of the vibration segment signal and each vibration segment signal, including: The merged result is used as the first vibration segmented signal in the vibration signal; in each vibration segmented signal corresponding to the merged result, the mean value of the periodic parameters of all the vibration segmented signals in the merged result is used as the periodic parameter of the merged result, and the mean value of the period of all the vibration segmented signals in the merged result is used as the period of the merged result; the method for obtaining the first abnormality determination factor includes: obtaining the first abnormality determination factor of each segmented signal to be analyzed according to the periodic parameter and the period of each segmented signal to be analyzed, the periodic parameter is positively correlated with the first abnormality determination factor, and the period is negatively correlated with the first abnormality determination factor; the method for obtaining the second abnormality determination factor includes: Obtaining the information entropy of each of the segmented signals to be analyzed; Obtaining an upper envelope and a lower envelope of each of the segmented signals to be analyzed, and obtaining an abnormal reference factor of each of the segmented signals to be analyzed based on similarity in changes between the upper envelope and the lower envelope; A second abnormality determination factor is obtained for each of the segmented signals to be analyzed according to the information entropy and the abnormality reference factor, wherein both the information entropy and the abnormality reference factor are positively correlated with the second abnormality determination factor.

2. A method for real-time monitoring of fatigue damage of steel structure according to claim 1, characterized in that: The method for obtaining the abnormal reference factor includes: At each moment in each of the segmented signals to be analyzed, the slopes of the upper envelope and the lower envelope at the envelope points at the corresponding moments are obtained respectively, the differences of the slopes at all the same moments are squared and averaged, the negative correlation mapping of the averaged results is normalized to obtain the abnormal reference factor corresponding to the segmented signal to be analyzed.

3. A method for real-time monitoring of fatigue damage of steel structure according to claim 1, characterized in that: The method for providing an abnormality warning to the monitored steel structure according to the abnormality degree includes: performing noise reduction on the vibration signal according to the abnormality degree; The noise-reduced vibration signal is compared with the preset standard vibration signal, and the variance of the amplitude difference between all signal points at the same acquisition time is calculated; when the variance is greater than or equal to the preset discrimination threshold, it is considered that the steel structure to be tested has damage defects and an abnormality warning is issued.

4. A method for real-time monitoring of fatigue damage of steel structures according to claim 3, characterized in that: The method for reducing noise on the vibration signal comprises: When the abnormality degree of the segmented signal to be analyzed is greater than a preset degree threshold, the segmented signal to be analyzed is used as a signal segment to be retained; During the EMD decomposition process of the vibration signal, the component segments of the first-order components of the vibration signal corresponding to the time period of the signal segment to be retained are replaced with the signal segment to be retained to obtain updated first-order components, all components and residual signal components of the vibration signal are obtained, the residual signal components are used as the noise signal in the vibration signal, and all components of the vibration signal except the noise signal are reconstructed to obtain a vibration signal after noise reduction.

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