CN112834193B - Operation bridge vibration and health state abnormity early warning method based on three-dimensional graph - Google Patents
Operation bridge vibration and health state abnormity early warning method based on three-dimensional graph Download PDFInfo
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Abstract
The invention discloses a three-dimensional graph-based operation bridge vibration and health state abnormity early warning method, which comprises the following steps of: testing and collecting an acceleration response signal of an operating bridge; preprocessing acceleration response signals such as drift removal; carrying out windowing function on the acceleration response signal within a preset time length, calculating a power spectrum, and removing interference of excitation frequency; collecting the power spectrums of the acceleration response signals of a plurality of time periods in a three-dimensional graph, and converting the three-dimensional graph into a color plane graph or a gray plane graph; and judging the abnormal vibration and the abnormal health state of the bridge according to the change trend and the color or gray scale change of each-order frequency in the three-dimensional graph, thereby realizing bridge early warning. The method can effectively judge the abnormal vibration and the abnormal health state of the bridge so as to send out early warning, does not need a structural analysis model, is simple and convenient to calculate, has good robustness of environmental factors, and has good engineering applicability.
Description
Technical Field
The invention belongs to the field of structural health monitoring, relates to a structural vibration and state abnormity early warning technology, and particularly relates to an operation bridge vibration and state abnormity early warning method based on a time-frequency energy three-dimensional graph.
Background
The number of bridge structures in the world is increasing, the bridge structures are often huge and complex, after the bridge structures are built and put into use, the performance of the structures is inevitably reduced and even damaged under the combined action of factors such as environmental erosion, material aging and various dynamic and static loads which are aged for a long time, and finally the structures are locally and integrally collapsed, and when an accident happens, not only is huge economic loss caused, but also the life safety of people is damaged, so that the vibration and state monitoring of the bridge structures are very necessary.
In recent decades, bridge structure health monitoring methods based on vibration parameters including frequency, mode shape, frequency response function, modal strain energy, strain response, acceleration response and the like have been proposed. However, the application of these methods in the actual engineering structure is hindered by the following problems, firstly, when the mode identification is performed, problems such as subjective error, power spectrum leakage, dense mode loss, truncation error and the like are inevitably generated; secondly, in the time domain health monitoring method based on the vibration parameters, the problems of system order fixing and modal loss exist; thirdly, modal information with more orders cannot be contained as much as possible, and high-order modes related to damage in response signals are lost, so that the extracted indexes are not sensitive enough to structural state change; fourthly, some methods need manual participation to generate randomness of manual intervention, and are not suitable for automatic online analysis and health monitoring of massive continuous monitoring data; fifth, due to too many uncertainty factors of an actual bridge structure, it is difficult to establish an exact-match structural analysis model, resulting in that a health monitoring method relying on the structural analysis model is difficult to apply to an actual bridge structure.
The vibration monitoring system of the actual operation bridge has the characteristics of massive vibration data, complex excitation environment and the like, and a three-dimensional graph early warning method based on time, frequency and vibration energy is urgently needed to be provided at present.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provide a method for early warning the abnormal vibration and health state of an operating bridge based on a three-dimensional graph, wherein the inherent frequency and the corresponding vibration energy of each order of the structure are associated with time, and the high-order inherent frequency and the vibration energy are sensitive to the change of the structure state, so that the abnormal vibration and health state of the structure can be timely early warned, and the health state of the bridge structure can be effectively monitored.
The purpose of the invention can be achieved by adopting the following technical scheme:
a method for early warning of abnormal vibration and health state of an operating bridge based on a three-dimensional graph comprises the following steps:
s1, arranging an acceleration sensor at a key position on a bridge structure to test an acceleration response signal;
s2, preprocessing an acceleration response signal obtained by testing the acceleration sensor, for example, rejecting data when the sensor is abnormal, and performing processing such as drift removal;
s3, performing a windowing function on the acceleration response signal with the preset time length;
s4, performing power spectrum calculation and power spectrum average calculation on the acceleration response signal of the windowing function so as to eliminate the influence of excitation and environmental factors;
s5, drawing the power spectrums of the acceleration response signals in all time periods into a three-dimensional graph with time as an x axis, frequency as a y axis and vibration energy as a z axis, representing the vibration energy by using colors or gray values, and converting the space three-dimensional graph into a two-dimensional plane graph;
and S6, early warning bridge vibration abnormity and health state abnormity from the change trend and color or gray scale change of each order of frequency in the spatial three-dimensional graph.
Further, the step S1 process is as follows:
and (3) installing an acceleration sensor at a key position on the bridge structure, such as a bridge midspan position with obvious vibration, and continuously recording an acceleration response signal of the bridge every 24 hours by the acceleration sensor. Because the vibration mode of the bridge structure does not need to be identified, and only the frequency and vibration energy of each order are utilized, a large number of acceleration sensors do not need to be arranged, only one acceleration sensor can be installed, but a plurality of sensors can be generally installed on the actual engineering structure, so that the subsequent data analysis results can be mutually proved.
Further, the step S2 process is as follows:
the method comprises the steps of preprocessing an acceleration response signal obtained by an acceleration sensor test, removing abnormal data (such as data distributed in a straight line) generated due to faults of the acceleration sensor, solving an autocorrelation coefficient of the acceleration response signal, judging whether the autocorrelation coefficient of the acceleration response signal is an oscillation attenuation function changing up and down along a time axis, if so, obtaining a drift curve through polynomial fitting, and subtracting the drift curve from the original acceleration response signal to obtain the acceleration response data after drift removal.
Further, the step S3 process is as follows:
the acceleration response signal of the operation bridge has the characteristics of continuous and uninterrupted test and massive data, and on the other hand, the environment uncertain factors are many, the excitation environment is complex, and the excitation input data is unknown. To eliminate these disadvantages, the acceleration response signal in the time domain is fourier transformed into the frequency domain, and a windowing function, such as a rectangular window, a hanning window, etc., is first performed on the acceleration response signal for subsequent fourier transformation.
Dividing the acceleration response signal with a preset time length (for example, within one hour or half hour) into L sections, and then carrying out windowing processing on the segmented data by multiplying a window function, wherein the window function is a rectangular window or a Hanning window, and the acceleration response signal with the length of the j section being n
t 1 ，t 2 ，…，t k ，…，t n Assuming a window function of w (t) for n times of acquiring the acceleration response signal 1 ),w(t 2 )，…,w(t k )，…w(t n ) Windowing to obtain a new signal y j (t k ) K =1,2, \8230;, n, j =1,2, \8230;, L, calculated by the formula,
further, the step S4 process is as follows:
the acceleration response signal of the windowing function is subjected to the following fourier transform:
in the formula (2), ω is a circular frequency variable, i is a complex symbol, then spectrum estimation is performed, the actual operation bridge bears various excitations, such as traffic flow, wind and ground pulsation, and the like, but the external excitations have randomness and uncertainty, the natural frequency of the structure is not influenced by the external environment, the L-section power spectrum of the acceleration response signal with a given time length in advance is averaged, the power of the excitation frequency can be reduced, and the power of the natural frequency of the structure is more prominent and becomes a few extreme points on the power spectrum curve, and the average power spectrum is calculated as follows:
further, the step S5 process is as follows:
the existing structure state monitoring method and technology mainly identify frequency, vibration mode and damping from a power spectrum curve, compare the frequency, vibration mode and damping before and after structure damage to judge the change of the structure state, but because of the problems of uncertain environment factors, resolution ratio when modal parameters are extracted and the like, the identified modal parameters have inevitable errors, and the early warning of the change of the structure state cannot be well realized; some methods directly compare power spectrum curves before and after structural damage, but are difficult to obtain an accurate conclusion due to interference of environmental factors; there are also methods to determine structural state changes by performing statistical analysis on power spectrum curves or identified modal parameters and giving thresholds, but thresholds are difficult to give accurately. In order to avoid errors in modal parameter identification and fully utilize the characteristics of massive data, power spectrum curves of all monitoring periods are collected into one graph, namely, the power spectrums of acceleration response signals of all different periods are drawn into a three-dimensional graph with time on the x axis, frequency on the y axis and vibration energy on the z axis, the vibration energy of the z axis is represented by colors or gray values, and the three-dimensional space graph is converted into a color plane graph or a gray graph. The extreme points of the power spectrum curves of all periods form ridges, the central parts of the ridges have the highest power values, namely vibration energy values, when the structural state changes, the change trend of the ridges changes, namely the ridges deviate from the time axis, the vibration energy values corresponding to the central parts of the ridges also change, and therefore the corresponding gray scales change. The three-dimensional graph has the advantages that all data are associated in one graph, all monitoring data are fully used, periodic change of each order of natural frequency caused by environmental temperature and humidity, periodic change and random change of corresponding vibration energy caused by traffic flow change can be visually displayed, and deterministic change trend and corresponding change trend of vibration energy caused by structural state change of each natural frequency are reserved without error and can be visually observed. The method has the advantages that complex statistical analysis is not needed, new uncertainty caused by the statistical analysis is avoided, threshold values are not needed to be defined, a plurality of natural frequencies of the structure can be contained in the graph, and changes of the natural frequencies due to the fact that the ambient temperature and humidity are within a certain range and changes of a certain range of vibration energy corresponding to each natural frequency can be tolerated.
Further, the step S6 process is as follows:
by observing the variation trend of each frequency in the graph, the time period when the variation trend of some frequencies changes significantly or the time period corresponding to the color abnormal area in a certain frequency range is the time when the bridge structure vibrates and the state is abnormal. Therefore, the abnormal vibration and the abnormal health state of the bridge can be judged through the change trend of each frequency and the color or gray scale change of the vibration energy corresponding to each frequency in the three-dimensional graph, and the bridge early warning is realized.
Compared with the prior art, the invention has the following advantages and effects:
1) The acceleration response signal used by the invention is easy to measure and obtain, and the signal energy is large;
2) The method uses the three-dimensional graph to carry out health monitoring on the engineering structure, is simple and convenient to calculate, does not need complex calculation and feature extraction, and can effectively achieve the purpose of rapidly processing mass data;
3) The three-dimensional graph used by the invention is theoretically limited only by the data sampling frequency, can contain modal information with higher orders as much as possible, avoids losing the high-order modes related to the damage in the acceleration response signal, and ensures that the established three-dimensional graph is more sensitive to the damage;
4) When the time, frequency and vibration energy three-dimensional graph is used for bridge monitoring, a structural analysis model is not needed, manual participation is not needed, the method is suitable for on-line continuous analysis, is robust to noise, and is more suitable for data analysis of an actual engineering structure health monitoring system.
Drawings
FIG. 1 is a flow chart of a method for warning abnormal vibration and health status of an operating bridge based on a time-frequency energy three-dimensional graph, disclosed by the invention;
FIG. 2 is a schematic view of the distribution of the measuring points of a vibration monitoring system of a certain bridge disclosed in the first embodiment of the present invention;
FIG. 3 is a schematic diagram of the acceleration response signal recorded by the 11 th acceleration sensor (one quarter across the downstream vertical direction) in the first embodiment of the present invention;
FIG. 4 is a schematic diagram of an acceleration response signal with de-drift pre-processing according to an embodiment of the present invention;
FIG. 5 is a graph illustrating an average power spectrum of a calculated acceleration response signal according to one embodiment of the present invention;
FIG. 6 is a three-dimensional graph obtained from the front 632 hours acceleration response signal of the bridge according to one embodiment of the present invention;
FIG. 7 is a three-dimensional graph of acceleration response signals obtained from the 633 th hour to the 812 th hour of the bridge in the first embodiment of the present invention;
FIG. 8 is a schematic diagram of a simply supported beam model under the action of a moving load according to a second embodiment of the present invention;
FIG. 9 is a three-dimensional graph of acceleration response signals of 40 hours before and after the damage of the simply supported beam according to the second embodiment of the present invention;
FIG. 10 is an enlarged view of the 5 th ridgeline in FIG. 9;
fig. 11 is an enlarged view of the 6 th ridgeline in fig. 9.
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.
Example one
The implementation flow of the method for early warning the abnormal vibration and health state of the bridge structure is shown in figure 1, and the method for early warning the abnormal vibration and health state of the operating bridge based on the three-dimensional graph of time, frequency and energy comprises the following specific steps:
s1, arranging one or more acceleration sensors on a bridge structure to test acceleration response signals;
s2, preprocessing an acceleration response signal obtained by testing the acceleration sensor, for example, rejecting data when the sensor is abnormal, and performing processing such as drift removal;
s3, performing a windowing function on the acceleration response signal with the preset time length;
s4, performing power spectrum calculation and power spectrum average calculation on the acceleration response signal of the windowing function;
s5, drawing the power spectrums of the acceleration response signals in all time periods into a three-dimensional graph with time as an x axis, frequency as a y axis and vibration energy as a z axis, representing the vibration energy by using colors or gray values, and converting the space three-dimensional graph into a two-dimensional plane graph;
and S6, early warning the bridge vibration abnormity and health state abnormity from the frequency change trend and color or gray scale change of each order in the three-dimensional graph.
The method is characterized in that actually measured data of a certain real bridge are taken as a research object to describe the implementation process of the abnormal early warning of the vibration and the health state of the bridge in actual operation.
The bridge vibration monitoring system is built in 2005 and comprises a data acquisition system, a data transmission system, a power supply system and an alarm system, wherein the first three systems are installed on a main body structure of a bridge suspension bridge, and the alarm system is installed in a bridge management center. The system comprises 17 acceleration sensor measuring points, 36 channels in total, which are respectively arranged at the positions of a bridge site foundation, a bridge tower, a main beam and the like. The bridge vibration monitoring system adopts a continuous recording mode, and continuous acceleration response signals of all channels are permanently stored in a database. The system station channel number, position and direction are shown in FIG. 2. The bridge vibration monitoring system is stable in operation since being built, and accumulates mass real-time continuous acceleration response signals of key parts of the bridge. The acceleration response signals of the 11 th acceleration sensor on the bridge structure, which were continuously recorded for 812 hours, were now analyzed. After 632 hours, vibration suppression measures are applied to the bridge, and some wind shielding members on the bridge are dismantled.
The specific implementation steps of the structural vibration and health state abnormity early warning are as follows:
t1, an acceleration response signal of 6 seconds recorded by the 11 th acceleration sensor on the bridge box girder recorded by the bridge vibration monitoring system is shown in figure 3. This is the acceleration response signal of the bridge under the conditions of traffic flow, wind and ground pulsation, the sampling frequency is 200Hz, and the acceleration response signal can be seen to deviate from the time axis obviously, i.e. have obvious drift.
T2, carrying out pretreatment
The recorded acceleration response signal is preprocessed by de-drifting and the like, so that the acceleration response signal shown in fig. 4 can be obtained, and the acceleration response signal can be seen as an up-down oscillation function around a time axis.
T3, calculating the power spectrum
The preprocessed 1-hour acceleration response signal is divided into 8 equal-length sections to be subjected to windowing function and power spectrum calculation, a power spectrum curve with the x axis as frequency and the y axis as power can be obtained, peak points can appear on the whole power spectrum curve, the frequency corresponding to some peak points is the excitation frequency or the frequency caused by environmental factors, the frequency corresponding to some peak points is the structure inherent frequency, and the power corresponding to the peak points is the vibration energy corresponding to the frequency. The frequencies caused by the excitation frequency and the environmental factors have randomness and uncertainty, so that a plurality of power spectrum curves in a plurality of time intervals are averaged, peak points corresponding to the frequencies caused by the excitation frequency and the environmental factors disappear, and peak points of the natural frequency of the structure are reserved and enhanced, so that an average power spectrum curve which can reflect the state of the structure more can be obtained. Therefore, the power spectrum curves shown in fig. 5 are obtained by averaging 8 power spectrum curves, most peak points in the curves are structural natural frequency peak points, and some peak points are still peak points caused by excitation and environmental factors, which may cause the failure of the existing method and technology when the bridge structural vibration and health state are abnormal in early warning, but these false frequency peak points are only sporadic due to lack of consistency of the excitation and environmental factors, and are eliminated by using the power spectrum curves of all monitoring time periods.
When the structural state changes, the peak point changes, i.e. the order frequency and the corresponding vibration energy changes.
T4, drawing a three-dimensional graph
Since the operation bridge data is massive and the excitation environment is complex, and when only a few groups of power spectral curves are compared, changes caused by structural states, environmental noises and the like can be confused, the power spectral curves of all monitoring time periods are collected, a plane gray scale graph is drawn, the average power spectral curve of the last 632 hours is drawn into a gray scale graph shown in fig. 6, the x axis is time, the y axis is frequency, the gray scale value of the graph corresponds to different vibration energies, a straight line parallel to the frequency axis corresponding to a given time period is a power spectral curve shown in fig. 5, peak points at the same frequency of all the power spectral curves form a ridge line parallel to the time axis, so that the gray scale at the ridge line corresponds to a higher vibration energy, 8 ridge lines parallel to the time axis are arranged in the graph, 8 natural frequencies are arranged in the graph, and the ridge lines also periodically change due to the periodic changes of traffic flow and environmental temperature of the bridge in 24 hours, namely, the straight line parallel to the frequency axis appears in the graph. When the bridge structure is excited normally and abnormally, the gray scale change of each ridge line is the same periodic change, but when the structure vibrates abnormally, some ridge lines have points or areas with different gray scales, such as a plurality of high bright spots on the ridge line near 0.2Hz in the figure, the gray scale values of the areas correspond to high vibration energy, the high vibration energy points appear sporadically, no periodic characteristics exist, the time points are obviously different from other time points of the ridge lines, the abnormal vibration time period of the bridge is a bridge abnormal vibration time period, the resonance with the frequency of 0.2258Hz actually occurs for the bridge, the result is also consistent with the displacement data analysis result monitored by the bridge GPS, the bridge management party suppresses vibration of the bridge during the 620 hour to 632 hour, removes part of wind shielding components, and continuously analyzes the acceleration response signals recorded by the 11 th acceleration sensor from the 633 th hour to 812 th hour to obtain a three-dimensional graph as shown in fig. 7, the ridge line distribution in the figure is the same as that of fig. 6, but the high bright spots appearing in fig. 6 do not exist in fig. 7, and the abnormal vibration is indicated. The effectiveness of the early warning method is proved through analysis of the acceleration response signals within 812 hours.
Example two
The numerical simulation study is performed on the simply supported beam under the action of the moving load as shown in fig. 8, so as to further demonstrate the abnormal early warning method for the vibration and the health state provided by the invention.
The length of the uniform-section simply supported beam is 60 meters, the section is rectangular, the Young modulus is 206GPa, and the linear density is 90000.6kg/m 2 The two ends of the beam are simply supported, the upper surface of the simply supported beam uses moving loads moving from the left side to the right side at a constant speed and from the right side to the left side at a constant speed to simulate the vehicle-mounted condition, the number, the weight and the speed of the moving loads are generated by random numbers to simulate the traffic flow condition of an actual bridge, and a vertical acceleration sensor is mounted on the simply supported beam and is positioned three eighths of the distance from a left end support.
The specific implementation steps of the early warning method for the abnormal vibration and health state of the operating bridge based on the three-dimensional graph of time, frequency and energy are as follows:
(1) Firstly, establishing a finite element model for the simply supported beam, wherein the model is 15 euler beam units with equal length, each beam unit has two nodes, 16 nodes are counted, each node has 2 degrees of freedom, and 32 degrees of freedom, the acceleration response signal of the simply supported beam at the installation position of the acceleration sensor in the graph 8 is calculated by a gradual integration method to simulate a test signal, the sampling frequency is 200Hz, firstly, when the simply supported beam is not damaged, the acceleration response is calculated for 20 hours under the action of bidirectional random moving load, at the time of 21 hour, the rigidity of the 12 th unit is reduced by 15% to introduce simulated damage to the simply supported beam, then, the acceleration response is continuously calculated for 20 hours, and 15% of white noise is added to the calculated acceleration response to simulate test noise.
(2) Because the second embodiment is numerical simulation, the obtained acceleration response does not drift, so preprocessing such as drift removal is not needed, the acceleration response signal of each hour is divided into 8 sections to be subjected to windowing function, fourier transform and power spectrum calculation, in order to eliminate the influence of noise and random moving load, 8 power spectrum curves are averaged to obtain an average power spectrum curve, and 40 average power spectrum curves can be obtained in total.
(3) The method includes the steps of drawing 40 average power spectrum curves into a three-dimensional graph with time on an x axis, frequency on a y axis and vibration energy on a z axis, and representing energy corresponding to each frequency by gray values, wherein as shown in fig. 9, 9 ridge lines are visible in fig. 9 and 9 natural frequencies corresponding to a simply supported beam, and the ridge lines do not periodically change due to no simulation of changes in ambient temperature and humidity, but at 21 hour, the ridge lines parallel to a time axis suddenly change, as shown in fig. 10, the ridge line with 22.72Hz frequency in fig. 9 is enlarged, as shown in fig. 21, the ridge line suddenly changes to 22.35Hz at 21 hour, as shown in fig. 11, the ridge line with 29.9Hz frequency in fig. 9 is also enlarged, as shown in fig. 11, the ridge line suddenly changes to 29.55Hz at 21 hour, which means that at 21 hour, a plurality of natural frequencies change, and therefore, the structural state can be judged to be consistent with the simulation time of damage of the simply supported beam, and an early warning of abnormal structural state is realized. Due to the damage, the 5 th frequency is reduced by 1.6% from 22.72Hz to 22.35Hz, and the 6 th frequency is reduced by 1.1% from 29.9Hz to 29.55Hz, which is better shown in the three-dimensional diagram as shown in fig. 9, but if the existing methods and technologies are used, the change of the natural frequency in the lower order is easily covered by the recognition error and the error generated by the statistical method, and the early warning of the structural state change cannot be realized.
In summary, the above embodiments provide a three-dimensional graph early warning method based on time frequency vibration energy, which makes full use of the inherent characteristic of a structure of which the frequency parameter is irrelevant to environmental factors, and the vibration energy associated with each order of frequency is sensitive to the structural state, and does not need to separate each order of frequency, thereby avoiding the error generated when each order of frequency is extracted, and the early warning method can timely early warn the abnormal state of the structure by including more frequencies of higher orders in the three-dimensional graph as much as possible. The early warning method is simple and convenient to calculate, good in noise robustness, sensitive to structural state change, free of a structural analysis model and suitable for health monitoring of an actual bridge structure.
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 (7)
1. A three-dimensional graph-based operation bridge vibration and health state abnormity early warning method is characterized by comprising the following steps:
s1, arranging an acceleration sensor on a bridge structure to test an acceleration response signal;
s2, preprocessing an acceleration response signal obtained by an acceleration sensor test;
s3, performing a windowing function on the acceleration response signal with the preset time length;
s4, performing power spectrum calculation and averaging on the acceleration response signal of the windowing function;
s5, drawing the power spectrums of the acceleration response signals in all time periods into a three-dimensional graph with time as an x axis, frequency as a y axis and vibration energy as a z axis, representing the vibration energy by using colors or gray values, and converting the space three-dimensional graph into a two-dimensional plane graph;
and S6, early warning bridge vibration abnormity and health state abnormity from the change trend of each order frequency and color or gray scale change in the three-dimensional graph.
2. The method for early warning of vibration and health state abnormity of an operational bridge based on a three-dimensional map as claimed in claim 1, wherein an acceleration sensor is installed at a key position of the bridge on the bridge structure in step S1, and an acceleration response signal of the bridge every 24 hours is continuously recorded.
3. The three-dimensional graph-based operational bridge vibration and health state abnormity early warning method according to claim 1, wherein the step S2 process is as follows:
rejecting abnormal data generated due to faults of the acceleration sensor in the acceleration response signal, calculating an autocorrelation coefficient of the acceleration response signal, judging whether the autocorrelation coefficient of the acceleration response signal is an oscillation attenuation function which changes up and down along a time axis, and if so, performing drift removal preprocessing on the acceleration response signal; if not, obtaining a drift curve through polynomial fitting, and subtracting the drift curve from the original acceleration response signal to obtain the acceleration response data after drift removal.
4. The three-dimensional graph-based operational bridge vibration and health state abnormity early warning method according to claim 1, wherein the step S3 process comprises the following steps:
dividing the acceleration response signal with the preset time length into L sections, and then carrying out windowing processing on the segmented data by multiplying a window function, wherein the window function is a rectangular window or a Hanning window, and the acceleration response signal with the length of the jth section being n
t 1 ,t 2 ,…,t k ,…,t n Assuming a window function of w (t) for n times of acquiring the acceleration response signal 1 ),w(t 2 )，…,w(t k )，…,w(t n ) Windowing to obtain a new signal y j (t k ) K =1,2, \8230;, n, j =1,2, \8230;, L, calculated by the formula,
5. the three-dimensional graph-based operational bridge vibration and health state abnormity early warning method according to claim 4, wherein the step S4 comprises the following processes:
the acceleration response signal of the windowing function is subjected to the following fourier transform:
ω in equation (2) is the circular frequency variable, i is the complex symbol, and then spectrum estimation is performed to obtain the average power spectrum as follows:
6. the three-dimensional graph-based operational bridge vibration and health state abnormity early warning method according to claim 5, wherein the step S5 comprises the following processes:
and (3) drawing the acceleration response power spectrums of all different time periods into a three-dimensional graph with the x-axis as time, the y-axis as frequency and the z-axis as vibration energy, and expressing the vibration energy of the z-axis by using colors or gray values to convert the three-dimensional graph into a color plane graph or a gray scale graph.
7. The three-dimensional graph-based operational bridge vibration and health state abnormity early warning method according to claim 5, wherein the step S6 comprises the following processes:
by observing the change trend of each frequency in the graph, the time period when the frequency change trend changes remarkably or the time period corresponding to the color abnormal area in a certain frequency range is used as the moment when the bridge structure vibrates and the state is abnormal, so that the bridge early warning is realized.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014126459A (en) * | 2012-12-26 | 2014-07-07 | National Fisheries Univ | State monitoring system of reciprocating mechanical device, and method and program thereof |
| CN106872985A (en) * | 2017-01-21 | 2017-06-20 | 南京理工大学 | Based on the rocket projectile muzzle velocity measuring method for improving Short Time Fourier Transform |
| CN108318129A (en) * | 2018-02-01 | 2018-07-24 | 石家庄铁道大学 | The true and false discriminating method of bridge structure modal parameter and terminal device |
| CN110320041A (en) * | 2019-07-25 | 2019-10-11 | 北京卫星环境工程研究所 | Large vibration table multiple bearing embedded-type condition monitoring system |
| CN111922095A (en) * | 2020-07-14 | 2020-11-13 | 上海数深智能科技有限公司 | Vibration diagnosis method for abnormal torsional vibration fault of roller of cold rolling mill |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8401820B2 (en) * | 2008-10-03 | 2013-03-19 | United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | In situ health monitoring of piezoelectric sensors |
-
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- 2021-01-11 CN CN202110029605.3A patent/CN112834193B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014126459A (en) * | 2012-12-26 | 2014-07-07 | National Fisheries Univ | State monitoring system of reciprocating mechanical device, and method and program thereof |
| CN106872985A (en) * | 2017-01-21 | 2017-06-20 | 南京理工大学 | Based on the rocket projectile muzzle velocity measuring method for improving Short Time Fourier Transform |
| CN108318129A (en) * | 2018-02-01 | 2018-07-24 | 石家庄铁道大学 | The true and false discriminating method of bridge structure modal parameter and terminal device |
| CN110320041A (en) * | 2019-07-25 | 2019-10-11 | 北京卫星环境工程研究所 | Large vibration table multiple bearing embedded-type condition monitoring system |
| CN111922095A (en) * | 2020-07-14 | 2020-11-13 | 上海数深智能科技有限公司 | Vibration diagnosis method for abnormal torsional vibration fault of roller of cold rolling mill |
Non-Patent Citations (1)
| Title |
|---|
| 大型离心机组运行状态监测与故障诊断应用研究;董芳玺;《中国优秀硕士学位论文全文数据库工程科技Ⅰ辑》;20201015(第10期);正文第23-24页 * |
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