CN108573224B - Bridge structure damage positioning method for mobile reconstruction of principal components by using single sensor information - Google Patents

Bridge structure damage positioning method for mobile reconstruction of principal components by using single sensor information Download PDF

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CN108573224B
CN108573224B CN201810293223.XA CN201810293223A CN108573224B CN 108573224 B CN108573224 B CN 108573224B CN 201810293223 A CN201810293223 A CN 201810293223A CN 108573224 B CN108573224 B CN 108573224B
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聂振华
陈威
郭恩国
马宏伟
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Abstract

The invention discloses a bridge structure damage positioning method for mobile reconstruction of principal components by using single sensor information, which comprises the following steps: s1, mounting an acceleration sensor at any position of the bridge; s2, measuring the acceleration response of the vehicle when the load passes through the bridge to obtain the acceleration signal of the measuring pointx(t)(ii) a S3, defining a moving time window; s4, time sequence x in windowi(n) reconstructing the matrix A by using a time delay methodi(ii) a S5, pairing matrix AiPerforming principal component analysis and calculation, and taking eigenvalue vector ViThe maximum value of the element of (1) is defined as RMPCA; s6, moving a time window to obtain a reconstructed moving principal component damage index time sequenceRMPCA(i)(ii) a S7 passing damage characteristic quantity indexRMPCAThe curve localizes the lesion. According to the method, only single sensor data are needed, and intact bridge nondestructive data are not needed, the damage position of the bridge can be accurately positioned, the number and the cost of the sensors are reduced, and the problem that damage cannot be detected under the condition that existing bridge nondestructive data are incomplete is solved.

Description

Bridge structure damage positioning method for mobile reconstruction of principal components by using single sensor information
Technical Field
The invention relates to the technical field of nondestructive structure detection, in particular to a bridge structure damage positioning method for mobile reconstruction of main components by using single sensor information.
Background
The civil engineering structure plays an important role in the development of national economy and is an infrastructure which has great influence on the people's county. Civil engineering structures can be damaged due to erosion by the environment and harmful substances. Fatigue failure may also result from a structure in continuous overload service during construction and use of a bridge when traffic flow increases rapidly. As the economy of China develops rapidly, a certain part of bridges are seriously overloaded and in service in the using process, so that all parts of bridge structures are damaged and deteriorated to different degrees before the design life is reached. Therefore, structural health monitoring of bridges is essential. In the field of structural health monitoring, various sensors are generally mounted on a bridge for data acquisition in order to obtain bridge structural response, and then corresponding damage characteristics are extracted from different types of data based on different damage recognition theories, so that the purposes of diagnosing and positioning the damage of the bridge are achieved.
The traditional bridge structure health monitoring and damage identification system needs a large amount of sensors and complete initial state data of the bridge. At present, hundreds of sensors are installed in bridge structure health monitoring and damage recognition systems at home and abroad, which not only consumes huge cost, but also causes 'mass garbage data'. In addition, most of the conventional power fingerprint methods need to be based on original data, namely, a structure lossless state or an initial state after construction, and in fact, the original data is difficult to achieve in engineering, and data imperfection is a common engineering problem.
The development of bridge structure health monitoring and damage identification technology is still in the basic exploration stage at present, and has a certain gap from engineering practice, and the main problems are as follows: the number of installed sensors is too many, the cost is huge, and massive data which are difficult to process exist; and data incompletion, many bridges have been built and put into service, and lack lossless data for comparison as baseline status data. Based on the above background, a method for measuring the dynamic response of a bridge, analyzing a response signal, and performing health diagnosis and damage location of the bridge without relying on bridge nondestructive data by installing a few sensors on the bridge is urgently needed to solve the problems of excessive sensors, huge cost, incomplete data and the like.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a bridge structure damage positioning method for reconstructing a main component by using the movement of single-sensor information.
The purpose of the invention can be achieved by adopting the following technical scheme:
a method for beam bridge structure damage localization using mobile reconstructed principal components of single sensor information, the method comprising:
s1, mounting an acceleration sensor a at any position on the beam bridge, wherein the mounting direction is the direction vertical to the bridge deck;
s2, measuring the acceleration response of the vehicle load when passing through the bridge at a constant speed to obtain an acceleration signal x (t) of the acceleration sensor a;
s3, defining a moving time window, intercepting the measured single signal to obtain a time sequence x of the signal in the windowi(n), n is 1,2,.., L is the length of the moving window, i is the step point of the window moving, and the calculation method of L is as follows:
Figure GDA0002975909600000021
wherein f is1Is the fundamental frequency, f, of the bridgesIs the sampling frequency.
S4 time series x of signals in windowi(n) reconstructing the matrix A by using a time delay methodi。
Figure GDA0002975909600000031
In the formula, q is the column number of the reconstruction matrix, and the value is as follows:
Figure GDA0002975909600000032
wherein f is*Is the bandwidth limiting frequency, i.e. greater than f in the spectrogram of signal x (t)*There are no significant power bands in the frequency domain.
S5, performing Principal Component Analysis (PCA) on the matrix in the formula (2) to obtain an eigenvalue matrix S
PCA(Ai)＝[Ui,Si,Vi] (4)
Wherein, UiIs a principal component matrix, SiTo correspond to the matrix UiThe contribution rate vector of (1), ViTo correspond to UiVector of eigenvalues of, ViThe elements in the interior are arranged from large to small. Taking characteristic valuesVector ViMaximum value of, i.e. ViThe first element is the intra-window lesion feature quantity, defined as RMPCA:
RMPCA(i)＝Vi(1) (5)
s6, moving a time window from t-0 on a time axis of the measured signal, where the moving step is Δ t, and Δ t is a sampling time interval of the signal, to obtain a reconstructed moving principal component damage indicator time sequence rmpca (i), where i is 1, 2.
And S7, positioning the damage through the damage characteristic quantity index RMPCA curve.
Further, in step S7, the process of locating the lesion by the lesion feature quantity index RMPCA curve is as follows:
s701, drawing a damage characteristic quantity index RMPCA curve according to the time sequence of the damage characteristic quantity index RMPCA;
s702, when the moving time window moves to the position where the vehicle just passes through the bridge damage position, the damage characteristic quantity index RMPCA curve has a peak value, and the time when the vehicle passes through the damage position is determined according to the maximum peak value position of the damage characteristic quantity index RMPCA curve.
And S703, multiplying the vehicle speed by the time when the vehicle passes through the damage position, namely converting the time axis into a space position axis, thereby determining the damage position.
Further, the fundamental frequency f of the beam bridge1With said bandwidth limiting frequency f*Obtained by FFT analysis of the measured acceleration signal x (t).
Compared with the prior art, the invention has the following advantages and effects:
1) according to the invention, the damage position of the bridge can be positioned only by using data of a single sensor, so that the number and the cost of the sensors are reduced.
2) The method can be used for carrying out damage positioning on the bridge without taking the data of the nondestructive state of the structure as a reference for comparison, and overcomes the problem that the structure can be damaged only by detecting according to the nondestructive data.
3) The method provided by the invention is simple to operate, rapid in calculation and obvious in bridge structure damage positioning effect.
Drawings
FIG. 1 is a flow chart of a method for locating damage to a beam bridge structure using a single sensor as disclosed in the present invention;
FIG. 2 is a schematic view of a beam bridge model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an acceleration signal and a moving window measured according to an embodiment of the present invention;
FIG. 4 is a frequency spectrum of a measured acceleration signal in an embodiment of the present invention;
FIG. 5 is a graph of the RMPCA index for 10% bridge damage in an embodiment of the present invention;
FIG. 6 is a graph of the RMPCA index for a bridge damaged 30% according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
Fig. 1 is a flow chart of a single sensor method for locating damage to a bridge, as shown in fig. 1, and a schematic diagram of a steel bridge model used in the embodiment is shown in fig. 2. The length of the model beam l was 20m, the sampling frequency was 200Hz, and the damage was 2/5 points of the beam length. The specific implementation process is as follows:
and S1, mounting the acceleration sensor a at any position on the bridge, wherein the mounting direction is the direction vertical to the bridge surface, and in the embodiment shown in FIG. 2, the mounting position of the sensor is 1/4 of the length of the bridge.
S2, measuring the acceleration response of the vehicle when the load passes through the bridge to obtain a measuring point acceleration signal x (t) of the acceleration sensor a, wherein the signal measured by the sensor is shown in FIG. 3, and the signal length N is 4000.
S3, defining a moving time window and intercepting the measured listA signal xi(n), n ═ 1, 2., L, where L is the moving window length, and L is calculated as in formula (1):
Figure GDA0002975909600000051
wherein f is1Is the fundamental frequency, f, of the bridgesIs the sampling frequency.
Firstly, FFT analysis is carried out on the signal measured in S2 to obtain the fundamental frequency f of the beam bridge1. FIG. 4 shows a spectrum diagram after FFT analysis, from which a fundamental frequency f is obtained1Is 1.123 Hz. Then according to the sampling frequency fsThe window length L is calculated as 200/1.123 178.09, rounded to 178.
S4 time series x of signals in windowi(n) reconstructing the matrix A by using a time delay methodi。
Figure GDA0002975909600000061
In the formula, q is the column number of the reconstruction matrix, and the value is as follows:
Figure GDA0002975909600000062
wherein f is*Is the bandwidth limiting frequency, i.e. greater than f in the spectrogram of signal x (t)*There are no significant power bands in the frequency domain. Bandwidth limiting frequency f*10.25Hz, and according to the sampling frequency fsThe number of matrix columns q is calculated to be 200/10.25 or 19.51, and the integer is 19.
S5, performing Principal Component Analysis (PCA) on the matrix in the formula (2) to obtain an eigenvalue matrix S
PCA(Ai)＝[Ui,Si,Vi] (4)
Wherein, UiIs a principal component matrix, SiTo correspond to the matrix UiThe contribution rate vector of (1), ViTo correspond to UiVector of eigenvalues of, ViThe elements in the interior are arranged from large to small. Vector V of eigenvalues is takeniThe maximum value of (a), i.e. the first element is the intra-window damage feature quantity, is defined as RMPCA:
RMPCA(i)＝Vi(1) (5)
s6, moving the time window from t-0 on the time axis of the measured signal, as shown in fig. 3, where the moving step is Δ t, and Δ t is the sampling time interval of the signal, to obtain a reconstructed moving principal component damage indicator time sequence rmpca (i), where i is 1, 2.
And S7, positioning the damage through the damage characteristic quantity index RMPCA curve.
The specific process of the step is as follows:
s701, drawing a damage characteristic quantity index RMPCA curve according to the time sequence of the damage characteristic quantity index RMPCA;
s702, when the moving time window moves to the position where the vehicle just passes through the bridge damage position, the damage characteristic quantity index RMPCA curve has a peak value, and the time when the vehicle passes through the damage position is determined according to the maximum peak value position of the damage characteristic quantity index RMPCA curve.
And S703, multiplying the vehicle speed by the time when the vehicle passes through the damage position, namely converting the time axis into a space position axis, thereby determining the damage position.
If fig. 5 is the RMPCA curve of 10% of the damage of the bridge and fig. 6 is the RMPCA curve of 30% of the damage of the bridge, it is determined from the peak values of the curves of fig. 5 and 6 that the damage position of the bridge is 0.4, i.e. 2/5 of the length of the beam, and the damage of the bridge is accurately located.
In summary, the method for positioning damage to a beam bridge structure disclosed in this embodiment only uses a single sensor mounted on a beam bridge under the action of a moving vehicle load, and can diagnose and position damage to the bridge by analyzing reconstructed moving principal components of acceleration signals measured by the single sensor without comparing data of a lossless state.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (3)

1. A bridge structure damage positioning method for reconstructing a principal component by using movement of single sensor information is characterized by comprising the following steps of:
s1, mounting an acceleration sensor a at any position on the beam bridge, wherein the mounting direction is the direction vertical to the bridge deck;
s2, measuring the acceleration response of the vehicle load when passing through the bridge at a constant speed to obtain an acceleration signal x (t) of the acceleration sensor a;
s3, defining a moving time window, intercepting the measured single signal to obtain a time sequence x of the signal in the windowi(n), n is 1, 2., L, where L is the length of the moving window, i is the step point of the window movement, and the calculation method of the length L of the moving window is as follows:
Figure FDA0002975909590000011
wherein f is1Is the fundamental frequency, f, of the bridgesIs the sampling frequency;
s4 time series x of signals in windowi(n) reconstructing the matrix A by using a time delay methodi：
Figure FDA0002975909590000012
In the formula, q is the column number of the reconstruction matrix, and the value is as follows:
Figure FDA0002975909590000013
wherein f is*Is the bandwidth limiting frequency, i.e. greater than f in the spectrogram of signal x (t)*Frequency bands with significant power do not exist in the frequency domain;
s5, performing principal component analysis on the matrix in the formula (2) to obtain a characteristic value matrix S
PCA(Ai)＝[Ui,Si,Vi] (4)
Wherein, UiIs a principal component matrix, SiTo correspond to a principal component matrix UiThe contribution rate vector of (1), ViTo correspond to a principal component matrix UiVector of eigenvalues of, ViThe medium elements are arranged from large to small, and a characteristic value vector V is takeniMaximum value of, i.e. ViThe first element is the intra-window lesion feature quantity, defined as RMPCA:
RMPCA(i)＝Vi(1) (5)；
s6, moving a time window from t-0 on a time axis of the measured signal, where the moving step is Δ t, and Δ t is a sampling time interval of the signal, to obtain a reconstructed moving principal component damage indicator time sequence rmpca (i), where i is 1, 2.
And S7, positioning the damage through the damage characteristic quantity index RMPCA curve.
2. The method for locating the damage of the bridge structure with the principal component reconstructed by the movement of the single-sensor information as claimed in claim 1, wherein the step S7 of locating the damage through the damage characteristic quantity index RMPCA curve includes the following steps:
s701, drawing a damage characteristic quantity index RMPCA curve according to the time sequence of the damage characteristic quantity index RMPCA;
s702, when the moving time window moves to a position where the vehicle just passes through the damage position of the bridge, the damage characteristic quantity index RMPCA curve has a peak value, and the time when the vehicle passes through the damage position is determined according to the maximum peak value position of the damage characteristic quantity index RMPCA curve;
and S703, multiplying the vehicle speed by the time when the vehicle passes through the damage position, namely converting the time axis into a space position axis, thereby determining the damage position.
3. The method for locating damage to a bridge structure using moving reconstructed principal components of a single sensor according to claim 1,
the fundamental frequency f of the beam bridge1With said bandwidth limiting frequency f*Obtained by FFT analysis of the measured acceleration signal x (t).
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CN109443672A (en) * 2018-11-19 2019-03-08 暨南大学 A kind of beam bridge structure damage positioning method of the mobile the First Eigenvalue curvature using single-sensor information
CN109406075A (en) * 2018-11-19 2019-03-01 暨南大学 A kind of beam bridge structure damage positioning method of the mobile first principal component using single-sensor information
CN109406076A (en) * 2018-11-19 2019-03-01 暨南大学 A method of beam bridge structure damage reason location is carried out using the mobile principal component of displacement sensor array output
CN109684970B (en) * 2018-12-18 2020-08-07 暨南大学 Window length determination method for moving principal component analysis of structural dynamic response
CN109839440B (en) * 2019-03-20 2021-03-30 合肥工业大学 Bridge damage positioning method based on static vehicle test
CN110657882B (en) * 2019-09-23 2021-07-27 暨南大学 Bridge real-time safety state monitoring method utilizing single-measuring-point response
CN110954154A (en) * 2019-11-29 2020-04-03 暨南大学 Bridge damage positioning method based on mobile sensing and filtering integrated system
CN111723427B (en) * 2020-06-24 2022-03-25 暨南大学 Bridge structure damage positioning method based on recursive feature decomposition

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103971018A (en) * 2014-05-23 2014-08-06 福州大学 Method for node rigidity prediction based on vibration measurement
CN104331595A (en) * 2014-09-04 2015-02-04 天津大学 Moving principal component correlation analysis for early warning of damage of bridge

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10497109B2 (en) * 2015-10-07 2019-12-03 Fuchs Consulting, Inc. Time-lapse infrared thermography system and method for damage detection in large-scale objects

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103971018A (en) * 2014-05-23 2014-08-06 福州大学 Method for node rigidity prediction based on vibration measurement
CN104331595A (en) * 2014-09-04 2015-02-04 天津大学 Moving principal component correlation analysis for early warning of damage of bridge

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