CN110598265A - Bridge health data abnormity correction method and system based on wavelet analysis - Google Patents
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Abstract
The invention discloses a bridge health data abnormity correction method and system based on wavelet analysis, wherein the correction method comprises the following steps: acquiring original data of bridge health; decomposing original data into low-frequency data and high-frequency data by utilizing wavelet transformation, and reconstructing all the low-frequency data to obtain long-period trend data; calculating the difference value between the original data and the long-period trend data; based on the difference value, establishing a normal distribution curve of the difference value; obtaining a confidence interval of the normal distribution curve according to a preset guarantee rate, judging that the part, outside the confidence interval, in the difference value is abnormal data, and obtaining the sampling time of the abnormal data; and selecting data with the same sampling time as the abnormal data from the long-period trend data, and replacing the data with the same sampling time as the abnormal data in the original data. The abnormal data can be automatically identified and corrected, the identification precision is high, and the correction effect is good.
Description
Technical Field
The invention relates to the technical field of bridge health monitoring, in particular to a bridge health data abnormity correction method and system based on wavelet analysis.
Background
At present, the state recommends the implementation of a health monitoring system for the management and maintenance of large bridges to guarantee and evaluate the operation safety of bridge structures. Along with the development of economy, a plurality of bridges in China are provided with health monitoring systems, meanwhile, the systems accumulate a large amount of data such as structures, environments and the like, and due to the complexity of the monitoring systems and various interferences of all links forming the systems, abnormal data generally exist. The processing of the abnormal data usually consumes a large amount of manpower and material resources, and the quality of the abnormal data processing is directly related to the accuracy and scientificity of subsequent data analysis and mining work, and the extraction of bridge characteristic indexes in operation and subsequent structural safety evaluation work are also influenced.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a bridge health data abnormity correction method and system based on wavelet analysis, which can automatically identify and correct abnormity data, and has high identification precision and good correction effect.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
a bridge health data abnormity correction method based on wavelet analysis comprises the following steps:
acquiring original data of bridge health;
decomposing the original data into low-frequency data and high-frequency data by utilizing wavelet transformation, and reconstructing all the low-frequency data to obtain long-period trend data;
calculating the difference value between the original data and the long-period trend data;
establishing a normal distribution curve of the difference value based on the difference value;
obtaining a confidence interval of the normal distribution curve according to a preset guarantee rate, judging that the part, outside the confidence interval, in the difference value is abnormal data, and obtaining the sampling time of the abnormal data;
and selecting data with the same sampling time as the abnormal data from the long-period trend data, and replacing the data with the same sampling time as the abnormal data in the original data.
On the basis of the technical scheme, the original data are decomposed into low-frequency data and high-frequency data by utilizing wavelet transformation, and the method specifically comprises the following steps:
decomposing the original data into a plurality of decomposed data with different frequencies;
and screening out low-frequency data meeting preset conditions from all the decomposed data, wherein the rest decomposed data are high-frequency data.
On the basis of the technical scheme, the preset conditions are as follows: the frequency is less than 0.0017Hz, and the period is more than 10 min.
On the basis of the technical scheme, the confidence interval of the normal distribution curve is obtained according to the preset guarantee rate, and the method specifically comprises the following steps:
obtaining the mean value mu and the standard deviation sigma of the difference value according to the normal distribution curve;
based on μ and σ, a confidence interval satisfying a preset assurance rate is obtained.
On the basis of the technical scheme, the preset guarantee rate is 99.7%, and the confidence interval is [ mu-3 sigma, mu +3 sigma ].
On the basis of the technical scheme, the method for judging the part, which is positioned outside the confidence interval, of the difference value to be abnormal data specifically comprises the following steps:
and when the difference is larger than mu +3 sigma or smaller than mu-3 sigma, judging the difference as abnormal data.
On the basis of the technical scheme, the preset guarantee rate is 95%, and the confidence interval is [ mu-2 sigma, mu +2 sigma ].
On the basis of the technical scheme, the method for judging the part, which is positioned outside the confidence interval, of the difference value to be abnormal data specifically comprises the following steps:
and when the difference is larger than mu +2 sigma or smaller than mu-2 sigma, judging the difference as abnormal data.
The invention also provides a bridge health data abnormity correction system based on wavelet analysis, which comprises:
a first module for obtaining raw data of bridge health;
the second module is used for decomposing the original data into low-frequency data and high-frequency data by utilizing wavelet transformation, and reconstructing all the low-frequency data to obtain long-period trend data;
a third module for calculating a difference between the raw data and the long-period trend data;
a fourth module for establishing a normal distribution curve for the difference based on the difference;
a fifth module, configured to obtain a confidence interval of the normal distribution curve according to a preset guarantee rate, determine that a part of the difference value outside the confidence interval is abnormal data, and obtain sampling time of the abnormal data;
and the sixth module is used for selecting data with the same sampling time as the abnormal data from the long-period trend data and replacing the data with the same sampling time as the abnormal data in the original data.
On the basis of the above technical solution, the second module includes:
a decomposition module for decomposing the raw data into a plurality of decomposed data of different frequencies;
and the screening module is used for screening out low-frequency data meeting preset conditions from all the decomposed data, and the rest decomposed data are high-frequency data.
Compared with the prior art, the invention has the advantages that:
according to the bridge health data abnormality correction method based on wavelet analysis, original data are decomposed and reconstructed through wavelet transformation, long-period trend data are obtained, interference of low-frequency data is removed through the difference value of the original data and the long-period trend data, a normal distribution curve is established for the difference value, the normal distribution curve is used as a judgment standard to conduct difference value data distribution rule analysis, abnormal changes in the difference value data are found quickly, sampling time of the abnormal data is judged, data with the same sampling time as the abnormal data in the long-period trend data are used for replacing the data with the same sampling time as the abnormal data in the original data, the long-period trend data can well predict the change trend of the data, and the original data are restored to the maximum extent.
Drawings
FIG. 1 is a flowchart of a bridge health data anomaly correction method based on wavelet analysis according to an embodiment of the present invention;
FIG. 2 is a graph of the displacement time course of the original data according to the embodiment of the present invention;
FIG. 3 is a graph of displacement time-courses of long-period trend data in an embodiment of the present invention;
FIG. 4 is a graph of the displacement time course of the difference in the embodiment of the present invention;
FIG. 5 is a time-course graph of the corrected data displacement according to the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Referring to fig. 1, an embodiment of the present invention provides a bridge health data anomaly correction method based on wavelet analysis, which includes the following steps:
s1, acquiring original data of bridge healthoriginOriginal data of bridge healthoriginMeasured by a health monitoring system installed on the bridge, and the original dataoriginRecording the original data of the ith sampling time as data according to the sequence of the health monitoring system for a group of data collected at a certain frequency in a periodi,originWhere i is 1, 2 … … n, and n represents the original dataoriginThe total number of (c);
s2, using wavelet transform to convert the original dataoriginDecomposing the data into low-frequency data and high-frequency data, and reconstructing all the low-frequency data to obtain long-period trend datatrendDecomposed low frequency data and high frequency data and original dataoriginThe wavelengths of the data are equal, the total number of the data is equal, and the data correspond to each other one by one according to the acquisition order. The original dataoriginLong period trend data obtained after medium and low frequency data reconstructiontrendDecomposed data also corresponding to the original dataoriginThe wavelength of the data is equal, the total number of the data is equal, the data are in one-to-one correspondence according to the acquisition sequence, and the long period trend data corresponding to the original data of the ith sampling time is recorded as datai,trendLong period trend datatrendThe change trend of the data can be well predicted, and the original data can be restored to the maximum extentoriginInformation;
s3, calculating all original dataoriginWith all the long period trend datatrendIs calculated from the difference datadiffRecording the ith sampling time of the original datai,originLong period trend data corresponding to ith sampling timei,trendIs datai,diff＝datai,origin-datai,trendThe total number of the difference data is also n, the obtained difference datadiffThe interference of low-frequency data is eliminated, abnormal data can be identified more easily, and the found abnormal data is more accurate;
s4, data based on difference valuediffEstablishing the difference datadiffA normal distribution curve of (a);
s5, obtaining a confidence interval of the normal distribution curve according to a preset guarantee rate, and judging the difference value datadiffThe part of the data which is positioned outside the confidence interval is abnormal data, and the sampling time of the abnormal data is obtained to obtain the difference datadiffThe normal distribution curve can obtain an upper and lower quantile value meeting the preset guarantee rate through table lookup according to the preset guarantee rate, the range in the upper and lower quantile value is a confidence interval, and the difference value data is explaineddiffIn the middle positionIf the data in the signal interval is normal data, the part outside the confidence interval is abnormal data, and sampling time of the abnormal data can be obtained because each data corresponds to sampling time;
s6, according to the sampling time of the abnormal data, the long period trend datatrendThe data with the same sampling time as the abnormal data is selected and used for replacing the original dataoriginThe same data as the sampling time of the abnormal data.
According to the bridge health data abnormality correction method based on wavelet analysis, original data are decomposed and reconstructed through wavelet transformation, long-period trend data are obtained, interference of low-frequency data is removed through the difference value of the original data and the long-period trend data, a normal distribution curve is established for the difference value, the normal distribution curve is used as a judgment standard to conduct difference value data distribution rule analysis, abnormal changes in the difference value data are found quickly, sampling time of the abnormal data is judged, data with the same sampling time as the abnormal data in the long-period trend data are used for replacing the data with the same sampling time as the abnormal data in the original data, the long-period trend data can well predict the change trend of the data, and the original data are restored to the maximum extent.
Further, in step S2, decomposing the original data into low frequency data and high frequency data by using wavelet transform specifically includes the following steps:
s2-1, decomposing the original data into a plurality of decomposed data with different frequencies, wherein the obtained original data are measured under the influence of conditions such as temperature, wind speed and vehicle load, and the decomposed data under the influence of conditions such as temperature, wind speed and vehicle load can be obtained by decomposing the original data;
s2-2, screening out low-frequency data meeting preset conditions from all the decomposed data, taking the rest decomposed data as high-frequency data, and deleting the high-frequency data.
Further, the preset conditions are as follows: the frequency is less than 0.0017Hz, and the period is more than 10 min. And finding data meeting the preset condition from the decomposed data according to the preset condition, defining the data as low-frequency data, and reconstructing all the found low-frequency data to obtain long-period trend data.
Further, the step S5 of obtaining the confidence interval of the normal distribution curve according to the preset guarantee rate specifically includes the following steps:
s5-1, obtaining a mean value mu and a standard deviation sigma of the difference value according to the normal distribution curve;
s5-2, obtaining a confidence interval meeting the preset guarantee rate through edge checking based on mu and sigma, wherein when the preset guarantee rate is 99.7%, the confidence interval obtained through table checking is [ mu-3 sigma, mu +3 sigma ], the greater the guarantee rate is, the greater the confidence interval is, the smaller the probability that normal data is judged to be abnormal data is, and the more accurate the result is.
In step S5, determining that the part of the difference value outside the confidence interval is abnormal data specifically includes the following steps: when the difference is greater than μ +3 σ or less than μ -3 σ, it is determined that the difference is abnormal data.
The confidence interval was [ mu-2. sigma.,. mu + 2. sigma. ] when the preset assurance rate was 95% as obtained by table lookup.
In step S5, determining that the part of the difference value outside the confidence interval is abnormal data specifically includes the following steps: when the difference is greater than μ +2 σ or less than μ -2 σ, the difference is determined to be abnormal data.
The embodiment of the invention also provides a bridge health data abnormity correction system based on wavelet analysis, which comprises:
a first module for obtaining raw data of bridge health;
the second module is used for decomposing original data into low-frequency data and high-frequency data by utilizing wavelet transformation, and reconstructing all the low-frequency data to obtain long-period trend data;
a third module for calculating a difference between the raw data and the long-period trend data;
a fourth module for establishing a normal distribution curve for the difference based on the difference;
a fifth module, configured to obtain a confidence interval of the normal distribution curve according to a preset guarantee rate, determine that a part of the difference value outside the confidence interval is anomalous data, and obtain sampling time of the anomalous data;
and the sixth module is used for selecting data with the same sampling time as the abnormal data from the long-period trend data and replacing the data with the same sampling time as the abnormal data in the original data.
The invention relates to a bridge health data abnormity correction system based on wavelet analysis, a second module decomposes and reconstructs original data by utilizing wavelet transformation to obtain long-period trend data, a third module removes interference of low-frequency data by a difference value of the original data and the long-period trend data, a fourth module establishes a normal distribution curve for the difference value, a fifth module takes the normal distribution curve as a judgment standard to analyze a difference value data distribution rule, abnormal change in the difference value data is rapidly found and the sampling time of the abnormal data is judged, a sixth module replaces data in the original data, which is the same as the sampling time of the abnormal data, by using data in the long-period trend data, which is the same as the sampling time of the abnormal data, the long-period trend data can well predict the change trend of the data, the original data is restored to the maximum extent, and the abnormal data can be automatically identified and corrected, the recognition precision is high, and the correction effect is good.
Preferably, the second module comprises a decomposition module and a screening module, wherein the decomposition module is used for decomposing the original data into a plurality of decomposed data with different frequencies, and the obtained original data are measured under the influence of conditions such as temperature, wind speed and vehicle load, so that the decomposed data under the influence of conditions such as temperature, wind speed and vehicle load can be respectively obtained by decomposing the original data; the screening module is used for screening out low-frequency data meeting preset conditions from all the decomposed data, the rest decomposed data are high-frequency data, and all the high-frequency data are removed.
The present invention will be described in detail below with reference to 1 example.
The calculation process is illustrated by taking the displacement of the support of the great bridge of the Changjiang river of the turnip lake in one day as an example. The turnip lake Yangtze river bridge is a nine-five key traffic construction project in China, and is also a first double-layer steel truss girder cable-stayed bridge for public and iron use built on the Yangtze river in China. The bridge comprises a highway and railway shared part, a railway approach bridge and a highway approach bridge. The highway-railway shared part comprises a non-shore highway-railway approach bridge shared part, a main bridge steel truss beam part and a turnip lake shore highway-railway approach bridge shared part. In the highway and railway shared part of the bridge, the highway is on the upper side and the railway is on the lower side. Wherein the total length of the highway bridge is 5681.25m, the main bridge is 2193.7m, no bank approach bridge is 1449.25m, and no turnip lake bank approach bridge is 2038.3 m; the total length of the railway bridge is 10520.966m, the main bridge is 2193.7m, no shore approach bridge is 6098.816m, and the turnip lake shore approach bridge is 2228.45 m. The structural health monitoring system of the turnip lake Yangtze river bridge operates at the end of 2017, and is divided into an automatic data acquisition monitoring subsystem, a traffic monitoring subsystem, a data management subsystem, a structural safety early warning and comprehensive evaluation subsystem and a user interface subsystem according to functional layers.
S1, acquiring original data of one-day support displacement acquisition of the bridge health monitoring system on the great bridge of the Yangtze river of the Weuwang lakeoriginRecording the ith sampling time as datai,originIn the embodiment of the present invention, a magnetostrictive displacement meter is used for the displacement of the support, the sampling frequency is 1Hz, the precision is 0.01mm, the time corresponding to the 1 st sampling is 0:00, and the time corresponding to the 3600 th sampling is 1: 00. The displacement time course of the raw data is shown in fig. 2.
S2, using wavelet transform to convert the original dataoriginDecomposing the data into various decomposed data with different frequencies, screening out low-frequency data with the frequency less than 0.0017Hz and the period more than 10min from the decomposed data, deleting the high-frequency data from the rest decomposed data, reconstructing all the low-frequency data, and obtaining long-period trend data under the influence of temperaturetrendRecording long period trend data corresponding to the original data of the ith sampling time as datai,trendThe displacement time course of the long-period trend data is shown in fig. 3.
S3, calculating all original dataoriginWith all the long period trend datatrendIs calculated from the difference datadiffRecording the ith sampling time of the original datai,originLong period trend data corresponding to ith sampling timei,trendIs datai,diff＝datai,origin-datai,trendThe displacement time course of the difference is shown in fig. 4.
S4, data based on difference valuediffEstablishing the difference datadiffA normal distribution curve of (a);
s5, obtaining the average value u of normal distribution 0, standard deviation σ 0.8495, taking the guarantee rate a 99.7%, calculating the upper quantile value yu + u +3 × σ 0+3 × 0.8495 2.5485, the lower quantile value yd-u-3 × σ 0-3 × 0.8495-2.5485, and obtaining the confidence interval of 99.7% [ -2.548,2.5485]. Finding the difference value in the confidence interval [ -2.548,2.5485 [ -2.548]The data is obtained by recording the data as abnormal datai,diffSample time for data > yu 2.5485 and is denoted as Ij,uJ is 1, 2 … … m1, and m1 is the number of data with difference value larger than the upper quantile value; and datai,diffSample time for data < yd-2.5485 and noted as Ij,dJ is 1, 2 … … m2, and m2 is the number of data having a difference smaller than the lower quantile.
S6, according to the sampling time of the abnormal data, the long period trend datatrendThe data with the same sampling time as the abnormal data is selected and used for replacing the original dataoriginThe data correction is completed by the data with the same sampling time as the abnormal data, the displacement time-course chart of the corrected data is shown in fig. 5, and the information of the corrected data is shown in the following table:
table 1 data correction information Table
The present invention is not limited to the above-described embodiments, and it will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements are also considered to be within the scope of the present invention. Those not described in detail in this specification are within the skill of the art.
Claims (10)
1. A bridge health data abnormity correction method based on wavelet analysis is characterized by comprising the following steps:
acquiring original data of bridge health;
decomposing the original data into low-frequency data and high-frequency data by utilizing wavelet transformation, and reconstructing all the low-frequency data to obtain long-period trend data;
calculating the difference value between the original data and the long-period trend data;
establishing a normal distribution curve of the difference value based on the difference value;
obtaining a confidence interval of the normal distribution curve according to a preset guarantee rate, judging that the part, outside the confidence interval, in the difference value is abnormal data, and obtaining the sampling time of the abnormal data;
and selecting data with the same sampling time as the abnormal data from the long-period trend data, and replacing the data with the same sampling time as the abnormal data in the original data.
2. The bridge health data anomaly correction method based on wavelet analysis as claimed in claim 1, wherein said original data is decomposed into low frequency data and high frequency data by using wavelet transform, specifically comprising the steps of:
decomposing the original data into a plurality of decomposed data with different frequencies;
and screening out low-frequency data meeting preset conditions from all the decomposed data, wherein the rest decomposed data are high-frequency data.
3. The wavelet analysis-based bridge health data anomaly correction method according to claim 2, wherein the preset conditions are: the frequency is less than 0.0017Hz, and the period is more than 10 min.
4. The bridge health data abnormality correction method based on wavelet analysis as claimed in claim 1, wherein a confidence interval of the normal distribution curve is obtained according to a preset assurance rate, specifically comprising the steps of:
obtaining the mean value mu and the standard deviation sigma of the difference value according to the normal distribution curve;
based on μ and σ, a confidence interval satisfying a preset assurance rate is obtained.
5. The wavelet analysis-based bridge health data anomaly correction method according to claim 4, wherein said preset assurance rate is 99.7% and said confidence interval is [ μ -3 σ, μ +3 σ ].
6. The bridge health data abnormality correction method based on wavelet analysis according to claim 5, wherein the step of determining that the part of the difference value outside the confidence interval is abnormal data specifically comprises the steps of:
and when the difference is larger than mu +3 sigma or smaller than mu-3 sigma, judging the difference as abnormal data.
7. The wavelet analysis-based bridge health data anomaly correction method according to claim 4, wherein said preset assurance rate is 95%, and said confidence interval is [ μ -2 σ, μ +2 σ ].
8. The bridge health data abnormality correction method based on wavelet analysis according to claim 7, wherein the step of determining that the part of the difference value outside the confidence interval is abnormal data specifically comprises the steps of:
and when the difference is larger than mu +2 sigma or smaller than mu-2 sigma, judging the difference as abnormal data.
9. A bridge health data abnormity correction system based on wavelet analysis is characterized by comprising:
a first module for obtaining raw data of bridge health;
the second module is used for decomposing the original data into low-frequency data and high-frequency data by utilizing wavelet transformation, and reconstructing all the low-frequency data to obtain long-period trend data;
a third module for calculating a difference between the raw data and the long-period trend data;
a fourth module for establishing a normal distribution curve for the difference based on the difference;
a fifth module, configured to obtain a confidence interval of the normal distribution curve according to a preset guarantee rate, determine that a part of the difference value outside the confidence interval is abnormal data, and obtain sampling time of the abnormal data;
and the sixth module is used for selecting data with the same sampling time as the abnormal data from the long-period trend data and replacing the data with the same sampling time as the abnormal data in the original data.
10. The wavelet analysis-based bridge health data anomaly correction system of claim 9, wherein said second module comprises:
a decomposition module for decomposing the raw data into a plurality of decomposed data of different frequencies;
and the screening module is used for screening out low-frequency data meeting preset conditions from all the decomposed data, and the rest decomposed data are high-frequency data.
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| CN111274544B (en) * | 2020-01-19 | 2024-02-20 | 中铁桥隧技术有限公司 | Early warning threshold setting method based on mobile interval relevance trend |
| CN113240358A (en) * | 2021-07-13 | 2021-08-10 | 中铁大桥科学研究院有限公司 | Automatic recording method for creep data of ultra-large span cable-stayed bridge construction |
| CN113938219A (en) * | 2021-10-08 | 2022-01-14 | 天津津航计算技术研究所 | Channel calibration method |
| CN113938219B (en) * | 2021-10-08 | 2024-10-01 | 天津津航计算技术研究所 | Channel calibration method |
| CN114491383A (en) * | 2022-04-15 | 2022-05-13 | 江西飞尚科技有限公司 | Abnormal data processing method and system for bridge monitoring |
| CN114491383B (en) * | 2022-04-15 | 2022-09-16 | 江西飞尚科技有限公司 | Abnormal data processing method and system for bridge monitoring |
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Application publication date: 20191220 |