CN114935450A - Failure monitoring and alarming method for large-span cable-stayed bridge inhaul cable damper - Google Patents
Failure monitoring and alarming method for large-span cable-stayed bridge inhaul cable damper Download PDFInfo
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- CN114935450A CN114935450A CN202210406354.0A CN202210406354A CN114935450A CN 114935450 A CN114935450 A CN 114935450A CN 202210406354 A CN202210406354 A CN 202210406354A CN 114935450 A CN114935450 A CN 114935450A
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Abstract
The invention discloses a failure monitoring and alarming method for a large-span cable-stayed bridge inhaul cable damper, belonging to the technical field of bridge structure performance monitoring, and comprising the following steps of: (1) collecting acceleration response at the section of the distributed measuring points; (2) calculating acceleration power spectrums of different sections; (3) performing Gaussian clustering analysis to obtain a Gaussian mixture model; (4) obtaining a maximum power spectrum value to form a maximum power spectrum value time-course curve; (5) establishing a linear regression model of the maximum power spectral value and the ambient temperature of the bridge and a temperature normalized maximum power spectral value time-course curve; (6) carrying out extreme value analysis to obtain a maximum value of a maximum power spectrum value with 99% of guarantee rate; (7) and monitoring the sudden change of the pulse, and giving an alarm of the failure of the stay rope damper. According to the invention, the early warning of the failure of the damper is realized by utilizing the relevance between the acceleration response of the main beam and the acceleration response of the inhaul cable, and the maintenance personnel can sense and check the failure in time conveniently.
Description
Technical Field
The invention relates to the field of bridge structure performance monitoring, in particular to a state sensing method for a stay rope damper based on a main beam acceleration power spectrum Gaussian mixture model.
Background
The stay cable of the cable-stayed bridge is a key force transmission path, and the working state of the stay cable has great influence on the performance of the cable-stayed bridge, so a damper is often additionally arranged on the stay cable, and the mechanical property of the cable-stayed bridge is improved by improving the mechanical characteristics of the stay cable.
The damper is in an open environment, is directly acted by wind, rain and sunshine, and simultaneously bears the alternating load of the vehicle. Therefore, the inhaul cable damper is easy to cause diseases and fatigue damage, and even is directly and thoroughly damaged under the action of strong wind. In order to sense the working performance of the damper, an acceleration sensor is usually mounted on the damper, and when the dynamic performance parameters of the damper are changed, the damage condition of the damper can be judged. However, for a large-span cable-stayed bridge, the number of cable components is large, and the number of the cable components can reach hundreds or even hundreds of cables in the large-span cable-stayed bridge. Only by installing sensors on the guy cables, the cost is unacceptable, and therefore a new technology for sensing the guy cable state by using bridge multi-source response data must be explored.
Disclosure of Invention
The invention aims to provide a failure monitoring and alarming method for a large-span cable-stayed bridge stay damper based on a main beam acceleration power spectrum Gaussian mixed model.
The technical scheme of the invention is as follows: a failure monitoring and alarming method for a large-span cable-stayed bridge inhaul cable damper is characterized by comprising the following steps:
step 1: arranging a plurality of measuring points on a main beam of the cable-stayed bridge, wherein the measuring points have the function of expressing the potential energy of the main beam of the cable-stayed bridge, and acquiring the acceleration response of the section position of each measuring point by an acceleration sensor;
step 2: calculating the acceleration power spectrum of the cross section of each distribution measuring point under the same time step;
and step 3: carrying out Gaussian cluster analysis on the acceleration power spectrums of all the distributed measuring point sections to obtain a Gaussian mixture model of the acceleration power spectrums with the time step as a time interval;
and 4, step 4: obtaining a maximum power spectrum value of the Gaussian mixture model to form a maximum power spectrum value time-course curve;
and 5: establishing a linear regression model of the maximum power spectrum value and the bridge environment temperature, further eliminating the influence of the temperature, and establishing a maximum power spectrum value time-course curve of temperature normalization;
step 6: carrying out extreme value analysis on the temperature normalized maximum power spectrum value time curve to obtain the maximum value of the maximum power spectrum value with 99% of guarantee rate;
and 7: and (6) when the monitored maximum power spectrum value time-course curve generates a pulse mutation and exceeds the maximum value obtained in the step (6), alarming the failure of the inhaul cable damper.
According to the further technical scheme, acceleration responses of a main girder main span, a main span 1/4 span, a main span 1/8 span and a side span of the cable-stayed bridge are collected in the step 1.
In a further technical scheme, the time step in the step 2 is 1 min.
In a further technical scheme, the data acceleration power spectrum needs to be calculated in the step 2, and the calculation method is PSD (position sensitive Detector) or LPSD (Low Power digital Signal) or DPSD (double data Signal) algorithm.
According to the further technical scheme, the mode of acquiring the regression model in the step 5 is a least square or regression technology such as machine learning and deep learning.
According to the further technical scheme, the maximum value in the step 6 is set as an early warning threshold value, and the deviation range of the early warning threshold value is adjusted according to the characteristics of the bridge.
The invention has the beneficial effects that:
as a structure for directly connecting the stay cables, the acceleration response of the main beam of the cable-stayed bridge has strong relevance with the acceleration response of the stay cables, and when the working state of the stay cables is suddenly failed, the abnormal vibration of the stay cables is caused certainly, so that the dynamic parameters of the main beam of the cable-stayed bridge are reflected. Therefore, the failure of the stay rope damper can be identified by using the abnormal motion of the power parameters of the main girder of the cable-stayed bridge, and the management and maintenance of the bridge are realized.
According to the method, the abnormal failure of the stay cable damper is identified by utilizing the state of the main beam of the cable-stayed bridge, the abnormal vibration information of the bridge acceleration caused by the damper is amplified, the influence of the ambient temperature on the natural frequency of the bridge member is eliminated, the performance characteristic value of the joint information is extracted, the early warning threshold value for representing the abnormal failure of the damper performance is finally obtained, and the damage event alarm of the bridge member is realized.
Compared with the prior art, the invention has the following beneficial effects:
(1) information multiplication: carrying out Gaussian mixed clustering, jointly using information of all main beam acceleration sensors, multiplying a small signal peak value of the main beam transmitted by abnormal vibration of the stay cable, and inverting basic information assurance of abnormal events of the stay cable damper based on the main beam vibration information;
(2) the feature data has high precision: the acceleration information of the main beam is converted into a power spectrum value, so that the uncertainty caused by the discrete type of acceleration data can be effectively reduced; removing drift errors caused by a temperature effect by establishing a correlation model of the power spectrum characteristic value and the temperature information; and the threshold is determined by adopting the 99% guarantee rate, so that the alarm system has high reliability.
Drawings
Figure 1 is an algorithmic flow chart of the method of the present invention,
FIG. 2 is a power spectrum of data of the acceleration of the main beam in the interval of 1min calculated by the method of the present invention,
FIG. 3 is a Gaussian mixture clustering model of the power spectrum joint information of each main beam acceleration sensor obtained by the method of the invention,
FIG. 4 is a time curve of the peak value of the Gaussian clustering model obtained by the method of the present invention,
figure 5 is a regression model using gaussian clustering of peak time course data and temperature data,
figure 6 is a graph of the power spectrum peak data after the regression model is used to output regression values to remove the temperature effect,
figure 7 is a probabilistic extremum analysis performed using normalized peak time course data,
FIG. 8 is a graph of a failure alarm using normalized peak time history data and thresholds obtained from extremum analysis.
Detailed Description
The invention will be further illustrated and understood by the following non-limiting examples.
Fig. 1 shows a flow chart of the method, which specifically includes the following steps:
step 1: acceleration sensors arranged by a bridge monitoring system are used for acquiring acceleration responses of a main girder main span midspan, a main span 1/4 midspan, a main span 1/8 midspan and a side span midspan of the cable-stayed bridge;
step 2: segmenting the acquired data of each main beam acceleration sensor by taking 1min as a time interval, and then performing power spectrum calculation on each segment of data by adopting a PSD algorithm to obtain 1min interval power spectrum data of all sensors;
and step 3: combining the acceleration power spectrums of all the sections, and then performing Gaussian cluster analysis to obtain a vibration power spectrum Gaussian cluster model of the main beam with 1min as a time interval;
and 4, step 4: taking the maximum power spectrum value of the Gaussian mixture model per minute to form a maximum power spectrum value time-course curve;
and 5: segmenting the environmental temperature of the bridge in the same time length of 1min, taking the average value of each segment of data, obtaining environmental temperature time course data at intervals of 1min, and performing linear regression analysis on the environmental temperature time course data and the maximum power spectrum time course data obtained in the step (4) to obtain a linear regression model expressing correlation between the environmental temperature time course data and the maximum power spectrum time course data; subtracting a regression value obtained by the linear regression model from the power spectrum maximum value time course data obtained in the step (4) to obtain a normalized power spectrum maximum value time course curve, and eliminating the ambient temperature effect in the power spectrum maximum value;
step 6: carrying out extreme value analysis on the temperature normalized maximum power spectrum value time-course curve to obtain a maximum value of the maximum power spectrum value with 99% of guarantee rate, and taking the maximum value as an early warning threshold value;
and 7: when the time curve of the monitored maximum power spectrum value generates a pulse mutation and exceeds the maximum value, the alarm of the failure of the stay rope damper is given.
Example 1:
the concrete implementation process of the invention is explained on the basis of the temperature monitoring data of the Huanggang and Highway and railway dual-purpose cable-stayed bridge and the monitoring data of the deflection of the main beam.
(1) Building a bridge structure health monitoring system additionally installed on a Huanggang and highway-railway dual-purpose cable-stayed bridge, and collecting main beam accelerations of sections of a main span midspan, a main span 1/4 midspan, a main span 1/8 midspan and a side span midspan, wherein the acceleration data are the basis of data driving;
(2) taking 1min as a time interval, carrying out segmented processing on all acquired acceleration time-course data, carrying out primary power spectrum analysis and calculation on each segment of data, and obtaining a power spectrum schematic diagram of four sensors calculated within a certain 1min by using the method, wherein the power spectrum schematic diagram is shown in FIG. 2;
(3) combining the power spectrum analysis data of the four section acceleration sensors, and then performing Gaussian cluster analysis on the combined data, wherein a cluster model is shown in FIG. 3;
(4) and (3) taking an extreme value of the clustering model shown in the figure 3 to obtain a power spectrum peak value of the joint information in each 1min time interval, so as to obtain a peak value time course curve of Gaussian clustering analysis of the main beam acceleration, wherein the peak value time course curve in a period of time is shown in the figure 4.
(5) Carrying out 1min interval segmentation processing on the bridge environment temperature obtained by the monitoring system, taking the average value per minute as an environment temperature load representative value, obtaining 1min interval time sequence data of the environment temperature, establishing a linear regression model by using the maximum power spectrum value data and the bridge environment temperature shown in the figure 4, further eliminating the influence of the temperature, and establishing a temperature normalized maximum power spectrum value time-course curve shown in the figure 5;
(6) as shown in fig. 6, performing extremum analysis on the maximum power spectrum value time-course curve of the temperature normalization to obtain a maximum value of the maximum power spectrum value with a guarantee rate of 99%, and using the maximum value as an early warning threshold;
(7) when the monitored maximum power spectrum value time-course curve generates a pulse mutation, which represents that the stay rope damper suddenly fails, the stay rope is abnormally transmitted to the main beam, so that the Gaussian cluster extreme value of the vibration power spectrum of the main beam exceeds the extreme value obtained in the figure 7, and the alarm of the stay rope damper failure can be realized; fig. 8 is a diagram showing an example of a primary damper failure alarm.
The above embodiments are intended to be illustrative only and are not intended to be limiting, and various equivalent modifications and alterations of this invention will occur to those skilled in the art upon reading the above embodiments and fall within the scope of the invention as defined in the appended claims.
Claims (6)
1. A failure monitoring and alarming method for a large-span cable-stayed bridge inhaul cable damper is characterized by comprising the following steps:
step 1: arranging a plurality of layout measuring points on a main beam of the cable-stayed bridge, wherein the layout measuring points have the function of expressing the potential energy of the main beam of the cable-stayed bridge, and acquiring the acceleration response of the section position of each layout measuring point through an acceleration sensor;
step 2: calculating the acceleration power spectrum of the cross section of each distribution measuring point under the same time step;
and step 3: carrying out Gaussian cluster analysis on the acceleration power spectrums of all the distributed measuring point sections to obtain a Gaussian mixture model of the acceleration power spectrums with the time step as a time interval;
and 4, step 4: obtaining a maximum power spectrum value of the Gaussian mixture model to form a maximum power spectrum value time-course curve;
and 5: establishing a linear regression model of the maximum power spectrum value and the bridge environment temperature, further eliminating the influence of the temperature, and establishing a maximum power spectrum value time-course curve of temperature normalization;
and 6: carrying out extreme value analysis on the temperature normalized maximum power spectrum value time curve to obtain the maximum value of the maximum power spectrum value with 99% of guarantee rate;
and 7: and (4) when the time curve of the monitored maximum power spectrum value generates a pulse mutation and exceeds the maximum value obtained in the step (6), giving an alarm that the stay rope damper is invalid.
2. The failure monitoring and alarming method for the cable damper of the long-span cable-stayed bridge according to claim 1, characterized in that acceleration responses in a main span midspan, a main span 1/4 midspan, a main span 1/8 midspan and a side span midspan of a main beam of the cable-stayed bridge are collected in step 1.
3. The failure monitoring and alarming method for the large-span cable-stayed bridge stay rope damper according to claim 1, wherein the time step in the step 2 is 1 min.
4. The failure monitoring and alarming method of the large-span cable-stayed bridge cable damper according to claim 1, wherein the data acceleration power spectrum is required to be calculated in the step 2, and the calculation method is PSD, LPSD or DPSD algorithm and the like.
5. The failure monitoring and alarming method for the large-span cable-stayed bridge inhaul cable damper according to claim 1, wherein the mode for obtaining the regression model in the step 5 is a least square or regression technology such as machine learning and deep learning.
6. The failure monitoring and alarming method for the stay cable damper of the long-span cable-stayed bridge according to claim 1, wherein the maximum value in the step 6 is set as an early warning threshold value, and the deviation range of the early warning threshold value is adjusted according to the characteristics of the bridge.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115144069A (en) * | 2022-09-01 | 2022-10-04 | 山东百顿减震科技有限公司 | Early warning system and method based on damper |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115144069A (en) * | 2022-09-01 | 2022-10-04 | 山东百顿减震科技有限公司 | Early warning system and method based on damper |
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