A multi-point response modal identification signal processing method and system

Abstract

The present invention relates to the technical field of subway tunnel structure damage, and discloses a multi-point response modal identification signal processing method, which specifically includes processing trend items and isolated data of the time-domain dynamic response data of each measuring point of the tunnel structure under a harsh environment; introducing the autocorrelation function of the vibration response and designing a Chebyshev low-pass filter; forming a Hankel matrix from the filtered autocorrelation data and performing modal order analysis; performing ARMA time-domain modal parameter identification and stability diagram analysis on each measuring point to determine the modal parameters of each order of the single measuring point response; and performing K-Means clustering fusion analysis of the modal parameters identified by ARMAs of multiple measuring points. The modal parameters of the tunnel structure are identified by stability identification of each measuring point through ARMA; and clustering fusion analysis of the identification results of ARMAs of multiple measuring points through K-Means secondary scattering is then performed to eliminate false modal data and improve the accuracy of modal parameter identification of the dynamic response of the tunnel structure.

Description

Technical Field

[0001] The present invention relates to the technical field of subway tunnel structure damage, and in particular to a multi-measurement point response mode identification signal processing method and a system using the method. Background Art

[0002] As subway tunnels age, they are subject to fatigue effects from low-frequency, cyclical dynamic loads from rail transit, the migration of corrosive substances from the environment, and operational disturbances from existing rail transit and buildings. Tunnels gradually develop varying degrees of defects, which accumulate and lead to structural damage. Segment damage not only creates leakage paths, exacerbating tunnel problems like water seepage and deformation, and impacting structural bearing capacity, but can also directly affect the normal operation and safety of the tunnel, resulting in significant economic losses and negative social impacts.

[0003] The random vibration signals in subway tunnel environments are complex, and the dynamic characteristics of tunnel structures are not very sensitive to structural damage, exhibiting characteristics such as medium and low frequencies and multi-modal coupling. Existing structural damage identification methods based on dynamic monitoring suffer from low signal-to-noise ratio and low modal identification accuracy. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a multi-measurement point response mode identification signal processing method.

[0005] The multi-point response mode identification signal processing method according to the first embodiment of the present invention includes:

[0006] Step S1. Perform dynamic response monitoring at multiple measuring points within the subway tunnel section and collect dynamic response data of each measuring point under environmental excitation;

[0007] Step S2. Processing the dynamic response data of each measuring point to eliminate trend items and isolated data;

[0008] Step S3. Perform autocorrelation function analysis on the response data of each measuring point after eliminating trend items and isolated data;

[0009] Step S4. Filtering the autocorrelation time domain data of each measuring point;

[0010] Step S5. Constructing a Hankel matrix from the filtered autocorrelation time domain data and performing modal order determination;

[0011] Step S6. Perform ARMA (Auto-Regressive and Moving Average, ARMA for short) modal parameter identification on a single measuring point to obtain the modal frequency and mode coefficient;

[0012] Step S7. Identify the ARMA modal parameters of each measuring point, perform frequency response decoupling and normalization to obtain the modal parameters of multiple response points ARMAs, and form a modal stability diagram with the modal parameter scatter data identified by the ARMAs of each measuring point;

[0013] Step S8. The secondary scattering of the stable data points of the stability diagram of each measuring point constitutes the near-average category of each modal order frequency. For the n-order structural mode, there are n clusters, and the n near-average Mean data sets constitute the cluster vector. K-means cluster analysis is performed on the modal parameters of multiple response points;

[0014] Step S9: Complete multi-measurement point response modal identification signal processing.

[0015] According to the multi-point response modal identification signal processing method of the present invention, ARMA is used to identify the stability of each measurement point and determine the modal parameters of the tunnel structure. K-Means secondary scattering is then used to perform clustering and fusion analysis on the multi-point ARMA identification results, eliminating false modal data and improving modal identification accuracy. This method is suitable for modal identification and fusion analysis of the structural response of different subway tunnels under environmental excitation at multiple measurement points. It overcomes the shortcomings of traditional single-point tunnel modal identification, which often suffers from low computational accuracy and a high number of spurious modes, and improves the accuracy of modal parameter identification of the dynamic response of tunnel structures.

[0016] According to some embodiments of the present invention, in step S4, a Chebyshev II type low-pass filter LPF (Low Pass Filter, LPF for short) suitable for a subway shield tunnel structure is designed, and the pass-band frequency is set to 0-200 Hz, and the pass-band boundary frequency coefficient ω p =0.2, stop band cutoff frequency ω s =0.4, adding a Hamming window can effectively reduce spectrum energy leakage and improve the data accuracy of multi-point responses in the medium and low frequency domains.

[0017] According to some embodiments of the present invention, in step S5, Block-SVD singular decomposition is used to perform modal order determination.

[0018] According to some embodiments of the present invention, in step S6, in the modal stability diagram analysis, if the allowable error of the modal frequency between two adjacent modal orders is less than 2%, it is considered stable.

[0019] According to some embodiments of the present invention, step S7 includes the following:

[0020] (1) The secondary scattering of the stable data points of the response of each measuring point constitutes the near-average category of each modal order frequency. For the n-order structural mode, there are n clusters and n near-average Means cluster vectors, which form the cluster frequency and mode coefficient of each measuring point: the modal frequency between two adjacent modal orders in the secondary scattering is considered stable if the allowable error is less than 2%;

[0021] (2) Each cluster vector can be regarded as a p-dimensional vector in space, forming a Means cluster K;

[0022] (3) K-means cluster analysis is performed on the modal frequencies and mode coefficients of the response points of multiple measurement points. The modal eigenvalues and eigenvectors of the tunnel structure at any order from low to high order can be analyzed to achieve data fusion analysis of a limited number of response points.

[0023] A multi-measurement point response modal identification signal processing system according to an embodiment of the second aspect of the present invention includes using the above-mentioned method to perform multi-measurement point response modal identification signal processing.

[0024] Compared with the prior art, the present invention has the following advantages:

[0025] 1. This invention is applicable to shield tunnel structures in various types of stratum conditions and can be used to analyze the vibration response of multiple measuring points in a shield tunnel within a certain range.

[0026] 2. The present invention can perform time-domain AMRM modal parameter identification analysis on a limited number of structural dynamic response measurement points;

[0027] 3. The multimodal identification signal analysis using K-Means clustering fusion in the present invention can analyze the modal eigenvalues and eigenvectors of tunnel structures of any order from low to high order;

[0028] 4. The present invention can perform multi-measurement point fusion, greatly improving the accuracy of modal parameter identification of tunnel structure dynamic response.

[0029] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. 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 any creative work.

[0031] Figure 1This is a flow chart of a method for processing multi-measurement point response modal identification signals under subway tunnel environmental excitation according to an embodiment of the present invention;

[0032] Figure 2 is a time history curve diagram of dynamic response data of each measuring point according to an embodiment of the present invention;

[0033] Figure 3 is a time history curve diagram of the autocorrelation function data of each measuring point according to an embodiment of the present invention;

[0034] Figure 4 is a frequency response characteristic curve diagram of a low-pass filter of time domain data at each measuring point according to an embodiment of the present invention;

[0035] Figure 5 is a Hankel matrix modal order diagram of each measuring point according to an embodiment of the present invention;

[0036] Figure 6 is a single-measurement-point ARMAs modal frequency identification stability diagram according to an embodiment of the present invention;

[0037] Figure 7 is a multi-point K-Means clustering fusion multi-point modal fusion stabilization graph according to an embodiment of the present invention;

[0038] Figure 8 3 is a graph of the multi-point K-Means clustering fusion mode coefficients according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The embodiments of the present invention are described in detail below. The embodiments described with reference to the accompanying drawings are exemplary. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0040] In the specification, claims, and drawings of this application, the terms "first," "second," "third," and the like are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include," "comprise," "have," and any variations thereof are intended to cover non-exclusive inclusions. For example, a series of steps or units are included, or optionally, steps or units not listed are included, or optionally, other steps or units inherent to these processes, methods, products, or devices are included. (Computer template)

[0041] Only portions, not all, of the content relevant to the present application are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations can be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc. (Computer template)

[0042] As used in this specification, the terms "component," "module," "system," "unit," and the like are used to refer to computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a unit can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or distributed between two or more computers. In addition, these units can be executed from various computer-readable media having various data structures stored thereon. Units can communicate, for example, through local and / or remote processes based on signals having one or more data packets (e.g., data from a second unit interacting with another unit in a local system, a distributed system, and / or a network. For example, the Internet interacts with other systems via signals).

[0043] Example 1

[0044] like Figure 1 As shown, the present invention provides a multi-point response modal identification signal processing method, which is particularly suitable for K-Means clustering fusion modal identification signal processing of multi-point responses under subway tunnel environmental excitation. It solves the problems of complex random vibration signals, low signal-to-noise ratio, and low modal identification accuracy in subway tunnel environments. Specifically, it includes:

[0045] Step S1. Perform dynamic response monitoring at multiple measuring points within a certain section of a subway tunnel, and collect dynamic response data of each measuring point under environmental excitation, such as Figure 2 As shown;

[0046] Step S2. Processing the dynamic response data of each measuring point to eliminate trend items and isolated data;

[0047] Step S3. Perform autocorrelation function analysis on the response data of each measuring point after eliminating trend items and isolated data, as shown in Figure 3 As shown;

[0048] Step S4. Filter the autocorrelation time domain data of each measuring point, specifically designing a Chebyshev II low-pass filter LPF (Low Pass Filter, LPF for short) suitable for subway shield tunnel structure, setting the pass-band frequency to 0-200Hz, and the passband boundary frequency coefficient ω p =0.2, stop band cutoff frequency ω s = 0.4, add a Hamming window, and filter the autocorrelation time domain data of each measuring point, such as Figure 4 As shown, it can effectively reduce spectrum energy leakage and improve the data accuracy of multi-measurement point responses in the medium and low frequency domains;

[0049] Step S5. The filtered autocorrelation time domain data is constructed into a Hankel matrix and the modal order is determined. The filtered autocorrelation data is constructed into a Hankel matrix. The Hankel matrix Block-SVD singular decomposition is performed based on the time series of the system correlation data of each measuring point using the system identification theory to determine the modal order, as shown in the following example: Figure 5 As shown;

[0050] Step S6. Perform ARMA modal parameter identification on a single measurement point to obtain modal frequency and vibration mode coefficient, such as Figure 6 As shown;

[0051] In modal stability analysis, the permissible error of the frequency value between two adjacent modal orders is less than 2%, that is, it satisfies formula (1) and is considered stable.

[0052]

[0053] Where: f (p) The p-order modal frequency of the ARMA-identified mode, f (p+1) Identify the p+1th modal frequency of the mode for ARMA.

[0054] Step S7. Identify the ARMA modal parameters of each measuring point, perform frequency response decoupling and normalization to obtain the modal parameters of multiple response points ARMAs, and form a modal stability diagram with the modal parameter scatter data identified by the ARMAs of each measuring point, as shown in the following example: Figure 7 As shown, it mainly includes;

[0055] 1. The stable data point scattering of each response point constitutes the near-average category of each modal order frequency. For an n-order mode, there are n clusters. The Mean cluster vector and the modal frequency cluster vector of each measurement point are as follows:

[0056] f={f 1 , f 2 ,...,f p} (2)

[0057] Where, f is the modal frequency cluster vector of each measuring point, f 1 is the first-order modal frequency vector set of all measurement points, f 2 is the set of second-order modal frequency vectors of all measurement points, f p is the set of p-order modal frequency vectors of all measuring points.

[0058] 2. Each clustered data set can be regarded as a p-dimensional vector in space, and the Means cluster K is:

[0059]

[0060] Where f is the modal frequency cluster vector of each measuring point, μ p is the near mean value of the p-order modal frequency, S i is the modal frequency domain of all measurement points.

[0061] Step S8. Perform K-means cluster analysis on the modal parameters of multiple response points, which can analyze the modal eigenvalues and eigenvectors of any order of tunnel structure from low order to high order, and realize data fusion analysis of a limited number of response points. Figure 8 As shown in the figure, the vibration mode coefficients of the structure at each modal frequency can be identified;

[0062] Step S9: Complete multi-measurement point response modal identification signal processing.

[0063] The multi-point response modal identification signal processing method of the present invention uses ARMA to identify the stability of each measurement point and determine the modal parameters of the tunnel structure. K-Means secondary scattering is then used to perform clustering and fusion analysis on the ARMA identification results of multiple measurement points, eliminating false modal data and improving modal identification accuracy. This method is suitable for modal identification and fusion analysis of the structural response of different subway tunnels under environmental excitation at multiple measurement points. It overcomes the shortcomings of traditional single-point tunnel modal identification, which suffers from low computational accuracy and a high number of spurious modes, and improves the accuracy of modal parameter identification of the dynamic response of tunnel structures.

[0064] The present invention also provides another embodiment, which is a multi-measurement point response modal identification signal processing system, including the above-mentioned K-Means clustering fusion modal identification signal processing method applicable to multi-measurement point responses under subway tunnel environmental excitation.

[0065] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "example," "specific example," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example.

[0066] Obviously, the described embodiments are only some of the embodiments of the present application, rather than all of the embodiments. Mentioning "embodiment" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present embodiment application. The appearance of this phrase in various positions in the specification does not necessarily mean that they are all the same embodiments, nor are they independent or alternative embodiments that are mutually exclusive with other embodiments. It can be understood explicitly and implicitly by those skilled in the art that the embodiments described herein can be combined with other embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of this application.

[0067] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A multi-point response mode identification signal processing method, characterized in that: include: Step S1. Perform dynamic response monitoring at multiple measuring points within the subway tunnel section and collect dynamic response data of each measuring point under environmental excitation; Step S2. Processing the dynamic response data of each measuring point to eliminate trend items and isolated data; Step S3. Perform autocorrelation function analysis on the response data of each measuring point after eliminating trend items and isolated data; Step S4. Filtering the autocorrelation time domain data of each measuring point; Step S5. Constructing a Hankel matrix from the filtered autocorrelation time domain data and performing modal order determination; Step S6. Perform ARMA modal parameter identification on a single measuring point to obtain modal frequency and mode coefficient; Step S7. Identify the ARMA modal parameters of each measuring point, perform frequency response decoupling and normalization to obtain the modal parameters of multiple response points ARMAs, and form a modal stability diagram with the modal parameter scatter data identified by the ARMAs of each measuring point; Step S8. Perform K-means cluster analysis on the modal parameters of multiple response points: the secondary scattering of the stable data points of the stability diagram of each measuring point constitutes the near-average category of each modal order frequency. For the n-order structural mode, there are n clusters, and the n near-average Mean data sets constitute the cluster vector. Perform K-means cluster analysis on the modal parameters of multiple response points; Step S9: Complete multi-measurement point response modal identification signal processing.

2. The multi-point response mode identification signal processing method according to claim 1, characterized in that: In step S4, a Chebyshev II low-pass filter LPF suitable for a subway shield tunnel structure is designed, and the pass-band frequency is set to 0-200 Hz, and the pass-band boundary frequency coefficient ω p =0.2, stop band cutoff frequency ω s =0.4, add a Hamming window.

3. The multi-point response mode identification signal processing method according to claim 1, characterized in that: In step S5, Block-SVD singular decomposition is used to determine the modal order.

4. The multi-point response mode identification signal processing method according to claim 1, characterized in that: The step S6 includes: in the modal stability diagram analysis, if the allowable error of the modal frequency between two adjacent modal orders is less than 2%, it is considered to be stable.

5. The multi-point response mode identification signal processing method according to claim 1, characterized in that: The step S7 includes the following contents: The secondary scattering of the stable data points of the response of each measuring point constitutes the near-average category of each modal order frequency. For the n-order structural mode, there are n clusters and n near-average Means cluster vectors, which form the cluster frequency and mode coefficient of each measuring point: the modal frequency between two adjacent modal orders in the secondary scattering is considered stable if the allowable error is less than 2%; Each cluster vector can be regarded as a p-dimensional vector in space, forming a K-Means cluster; K-means clustering analysis is performed on the modal frequencies and vibration mode coefficients of multiple measurement response points to achieve data fusion analysis of a limited number of response points.

6. A multi-point response modal identification signal processing system, comprising the method according to any one of claims 1 to 5.

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