CN109783939A - A kind of data processing method of combination Grubbs method and 3 σ methods - Google Patents
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Abstract
The invention proposes the data processing methods of a kind of combination Grubbs method and 3 σ methods, it is by carrying out low-and high-frequency classification to bridge timing monitoring data, 3 σ methods are carried out to the monitoring data more than high frequency samples respectively and carry out error rejecting, the monitoring data few to low frequency samples are handled using Grubbs method, this method treatment effect with higher and accuracy rate；To rejecting can not letter data, by lagrange-interpolation carry out compensation data so that compensated monitoring data are more reasonable；Monitoring data after compensated can effectively restore the monitoring data of the monitoring time, so that monitoring data are whole more credible, be conducive to science decision and further analysis.
Description
Technical field
The present invention relates to information analysis field more particularly to the data processing sides of a kind of combination Grubbs method and 3 σ methods
Method.
Background technique
China is bridge big country, and highway bridge sum is more than 800,000, influence bridge quality factor include human factor,
Overload of vehicle factor, material factor, maintenance management not in time etc., if failing the damage and deterioration that find bridge in time, in time
Be monitored and overhaul, may will affect traffic safety and shorten bridge service life, even result in bridge it is unexpected destruction with
Collapse.Therefore it is necessary for being monitored to the health status of bridge.Bridge health condition monitoring generally comprises stress and answers
Become monitoring, the monitoring of drag-line cable force monitoring, vibration acceleration, deformation displacement monitoring, settlement monitoring, Crack Monitoring, vehicular load prison
Survey, the projects such as air monitoring and temperature-humidity monitoring, these monitoring projects can generate a large amount of monitoring data, monitoring data need into
The reliable processing of row can just obtain reliable data for further assessment and decision.
Summary of the invention
In order to solve the above technical problems, the invention proposes one kind to carry out difference to high-frequency data and low-frequency data respectively
Gross error analysis, gross error analysis is reliable, combination Grubbs method of accurate reproduction measurement moment truthful data and 3 sides σ
The data processing method of method.
The technical scheme of the present invention is realized as follows: the data processing method of a kind of combination Grubbs method and 3 σ methods,
Include the following steps:
S1: according to the monitoring project of bridge, pass through the continuous monitoring of monitoring device within a certain period of time, obtain bridge
Monitoring data；
S2: according to the frequency of sampling, the monitoring data of bridge is divided into high frequency monitoring data by different type and low frequency is supervised
Measured data；
S3: high frequency monitoring data are handled using 3 σ methods, the part that ± 3 σ are exceeded in high-frequency data is rejected；
S4: low frequency monitoring data are handled using Grubbs method, the part beyond critical value is rejected；
S5: the data after data processing are rejected to S3, S4 step respectively and carry out lagrange-interpolation to the number of rejecting
According to numerical compensation is carried out, the period is obtained treated normal data.
On the basis of above technical scheme, it is preferred that the monitoring device includes ess-strain monitoring device, Suo Lijian
Measurement equipment, vibration acceleration monitoring device, deformation displacement monitoring device, settlement monitoring equipment, Crack Monitoring equipment, vehicular load
Monitoring device, air monitoring equipment and temperature-humidity monitoring equipment；Ess-strain monitoring device and vehicular load monitoring device are used for
Monitor the stress data of bridge；Cable force monitoring equipment is used to monitor the stress data of drag-line；Vibration acceleration monitoring device is used for
Monitor the vibration data of bridge；Deformation displacement monitoring device, settlement monitoring equipment and Crack Monitoring equipment are for monitoring bridge box beam
Amount of deflection, bridge foundation sedimentation and bridge expanssion joint data；Air monitoring equipment and temperature-humidity monitoring equipment can monitor wind-force
Wind direction, temperature and humidity data.
On the basis of above technical scheme, it is preferred that the frequency that the sampling is distinguished is 1Hz, is greater than 1Hz using frequency
Bridge monitoring data be high frequency monitoring data；The monitoring data of bridge of the sample frequency less than or equal to 1Hz are low frequency monitoring
Data.
On the basis of above technical scheme, it is preferred that it is described that high frequency monitoring data are handled using 3 σ methods, it is selection
M high frequency monitoring data X of same type monitoring device acquisition, high frequency monitoring data are numbered in order X1、X2、X3...Xm,
(wherein m >=1), high frequency monitoring data X Normal Distribution X~N (μ, σ2), wherein μ is the mean value of X, σ2For the standard variance of X,
The range for calculating (+3 σ of μ -3 σ, μ), the data that will be distributed in (+3 σ of μ -3 σ, μ) range are rejected.
On the basis of above technical scheme, it is preferred that the Grubbs method carries out processing low-frequency data, is that will select together
I low frequency monitoring data K of one type monitoring device acquisition is arranged according to the numerical value of low frequency monitoring data by sequence from small to large
Arrange K(1)、K(2)、K(3)...K(i), wherein (3≤i≤100), calculate separately statistic G on GrubbsiWith statistic under Grubbs
G′i:
WhereinFor the average value of low frequency monitoring data K, s is the standard deviation of low frequency monitoring data K；Determine Grubbs method
Detect horizontal α and the horizontal α of rejecting*, and α*< α；
Critical value G is A.2 found according to the subordinate list of national standard GB/T4883-20081-α/2(i)；
Work as Gi> G 'iAnd Gi> G1-α/2(i), determine K(i)For outlier；
As G 'i> GiAnd G 'i> G1-α/2(i), determine K(1)For outlier；
For the outlier K of detection(i)Or K(1), determine and reject horizontal α*, critical value is A.2 found in subordinate listWhenWhen determine K(1)To count outlier；WhenWhen, determine K(i)For
Outlier is counted, after respectively rejecting outlier and statistics outlier, next round inspection is carried out to low frequency monitoring data K again.
It is further preferred that the horizontal α of detection is 0.1 or 0.5；Reject horizontal α*It is 0.05 or 0.1.
On the basis of above technical scheme, it is preferred that the data after the rejecting data processing carry out Lagrange and insert
Value method is after proposing the part for exceeding ± 3 σ in high-frequency data after the duplicate numerical value of removal, and will be in low-frequency data
Outlier and statistics outlier are utilized respectively former and later two data for rejecting data to the number being removed after removing duplicate numerical value
According to progress linear numerical compensation.
The present invention provides the data processing methods of a kind of combination Grubbs method and 3 σ methods, compared with prior art, this hair
It is bright to have the advantages that
(1) present invention carries out low-and high-frequency classification for bridge timing monitoring data, to the monitoring data more than high frequency samples into
3 σ method of row carries out error rejecting, and the monitoring data few to low frequency samples are handled using Grubbs method, place with higher
Manage effect and accuracy rate；
(2) to rejecting can not letter data, by lagrange-interpolation carry out compensation data so that compensated prison
Measured data is more reasonable；
(3) monitoring data after compensated can effectively restore the monitoring data of the monitoring time, so that monitoring data are whole
It is more credible, be conducive to science decision and further analysis.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is the flow chart of the data processing method of a kind of combination Grubbs method of the present invention and 3 σ methods.
Specific embodiment
Below in conjunction with embodiment of the present invention, the technical solution in embodiment of the present invention is carried out clearly and completely
Description, it is clear that described embodiment is only some embodiments of the invention, rather than whole embodiments.Base
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts all
Other embodiments shall fall within the protection scope of the present invention.
The present invention provides the data processing method of a kind of combination Grubbs method and 3 σ methods, this method includes following step
It is rapid:
S1: according to the monitoring project of bridge, pass through the continuous monitoring of monitoring device within a certain period of time, obtain bridge
Monitoring data；
S2: according to the frequency of sampling, the monitoring data of bridge is divided into high frequency monitoring data by different type and low frequency is supervised
Measured data；
S3: high frequency monitoring data are handled using 3 σ methods, the part that ± 3 σ are exceeded in high-frequency data is rejected；
S4: low frequency monitoring data are handled using Grubbs method, the part beyond critical value is rejected；
S5: the data after data processing are rejected to S3, S4 step respectively and carry out lagrange-interpolation to the number of rejecting
According to numerical compensation is carried out, the period is obtained treated normal data.
In the present invention, monitoring device include ess-strain monitoring device, cable force monitoring equipment, vibration acceleration monitoring set
Standby, deformation displacement monitoring device, settlement monitoring equipment, Crack Monitoring equipment, vehicular load monitoring device, air monitoring equipment and
Temperature-humidity monitoring equipment；Ess-strain monitoring device and vehicular load monitoring device are used to monitor the stress data of bridge；Suo Li
Monitoring device is used to monitor the stress data of drag-line；Vibration acceleration monitoring device is used to monitor the vibration data of bridge；Deformation
Displacement monitoring equipment, settlement monitoring equipment and Crack Monitoring equipment are used to monitor the amount of deflection of bridge box beam, bridge foundation sedimentation and bridge
Beam expansion joint data；Air monitoring equipment and temperature-humidity monitoring equipment can monitor wind direction, temperature and humidity data.Bridge
Malformation include Dun Ta, beam body and arch ring etc. deformation and foundation settlement, the stabilization of bridge structure is to ensure that bridge security
The premise of operation, the foundation settlement of bridge, box beam deflection are supervised by deformation displacement monitoring device, settlement monitoring equipment and crack
Survey is monitored；Bridge floor carries vehicle load, is directly affected by load, can be carried out by vehicular load monitoring device
Load monitoring；The cable force monitoring of cable bridge beam is the monitoring object of cable force monitoring equipment；The dynamic parameters of bridge and vibration
Level is the standard of bridge general safety, and the variation of bridge quality can cause the change of vibration characteristics, this part is vibration
The monitoring object of acceleration monitoring equipment；Air monitoring equipment and temperature-humidity monitoring equipment can be bridge working environment.
The present invention is to distinguish high frequency monitoring data and low frequency monitoring data by sample frequency；Sampling the frequency distinguished is
1Hz uses the monitoring data of bridge of the frequency greater than 1Hz for high frequency monitoring data；Bridge of the sample frequency less than or equal to 1Hz
Monitoring data are low frequency monitoring data.
In the present invention, high frequency monitoring data are handled using 3 σ methods, is m for selecting the acquisition of same type monitoring device
High frequency monitoring data X numbers in order high frequency monitoring data X1、X2、X3...Xm, (wherein m >=1), high frequency monitoring data X clothes
From normal distribution X~N (μ, σ2), wherein μ is the mean value of X, σ2For the standard variance of X, the range of (+3 σ of μ -3 σ, μ) is calculated, it will
The data not being distributed in (+3 σ of μ -3 σ, μ) range are rejected.
In the present invention, sampling Grubbs method carries out processing low-frequency data, is the i that same type monitoring device will be selected to acquire
A low frequency monitoring data K arranges K by sequence from small to large according to the numerical value of low frequency monitoring data(1)、K(2)、K(3)...K(i),
Wherein (3≤i≤100) calculate separately statistic G on GrubbsiWith statistic G ' under Grubbsi:
WhereinFor the average value of low frequency monitoring data K, s is the standard deviation of low frequency monitoring data K；Determine Grubbs method
Detect horizontal α and the horizontal α of rejecting*, and α*< α；
Critical value G is A.2 found according to the subordinate list of national standard GB/T4883-20081-α/2(i)；
Work as Gi> G 'iAnd Gi> G1-α/2(i), determine K(i)For outlier；
As G 'i> GiAnd G 'i> G1-α/2(i), determine K(1)For outlier；
For the outlier K of detection(i)Or K(1), determine and reject horizontal α*, critical value is A.2 found in subordinate listWhenWhen determine K(1)To count outlier；WhenWhen, determine K(i)For
Outlier is counted, after respectively rejecting outlier and statistics outlier, next round inspection is carried out to low frequency monitoring data K again.
In above formula, detecting horizontal α is 0.1 or 0.5；Reject horizontal α*It is 0.05 or 0.1.That is when α is 0.1, α*For
0.05；When α is 0.5, α*It is 0.1.
In the present invention, reject data processing after data carry out lagrange-interpolation be will in high-frequency data beyond ±
After the part of 3 σ proposes the duplicate numerical value of removal later, and by the outlier in low-frequency data and count outlier removal repetition
Numerical value after be utilized respectively and reject former and later two data of data linear numerical compensation is carried out to the data that are removed.
For more real functions, it is known to k+1 given data point (a0,b0),(a1,b1),...(ak,bk), ajIt represents
The position of independent variable, bjThe value of representative function in the position, and any two ajIt is all different, using Lagrange's interpolation
The lagrange polynomial that formula obtains are as follows:
Each ljIt (a) is Lagrangian fundamental polynomials,
Lagrangian fundamental polynomials lj(a) the characteristics of is in ajUpper value 1, in other points ai, i ≠ j value is 0.
The formula of interpolation (a, b) is solved by taking unitary three-point shape formula as an example are as follows:
It is previous by the utilization rejecting point high frequency of front two or low-frequency data, two high frequencies in back or low-frequency data and one
Latter two high frequency or low-frequency data are solved three times respectively, and the average value solved three times is filled into and is removed the original of data
Position, so that monitoring data are more smooth credible.
The foregoing is merely better embodiments of the invention, are not intended to limit the invention, all of the invention
Within spirit and principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (7)
1. the data processing method of a kind of combination Grubbs method and 3 σ methods, includes the following steps:
S1: according to the monitoring project of bridge, pass through the continuous monitoring of monitoring device within a certain period of time, obtain the monitoring of bridge
Data；
S2: according to the frequency of sampling, the monitoring data of bridge is divided into high frequency monitoring data by different type and low frequency monitors number
According to；
S3: high frequency monitoring data are handled using 3 σ methods, the part that ± 3 σ are exceeded in high-frequency data is rejected；
S4: low frequency monitoring data are handled using Grubbs method, the part beyond critical value is rejected；
S5: respectively to S3, S4 step reject the data after data processing carry out lagrange-interpolation to the data of rejecting into
Row numerical compensation obtains the period treated normal data.
2. a kind of data processing method of combination Grubbs method and 3 σ methods as described in claim 1, it is characterised in that: described
Monitoring device include ess-strain monitoring device, cable force monitoring equipment, vibration acceleration monitoring device, deformation displacement monitoring set
Standby, settlement monitoring equipment, Crack Monitoring equipment, vehicular load monitoring device, air monitoring equipment and temperature-humidity monitoring equipment；It answers
Stress-strain monitoring device and vehicular load monitoring device are used to monitor the stress data of bridge；Cable force monitoring equipment is drawn for monitoring
The stress data of rope；Vibration acceleration monitoring device is used to monitor the vibration data of bridge；Deformation displacement monitoring device, sedimentation prison
Measurement equipment and Crack Monitoring equipment are used to monitor the amount of deflection of bridge box beam, bridge foundation sedimentation and bridge expanssion joint data；Wind speed prison
Measurement equipment and temperature-humidity monitoring equipment can monitor wind direction, temperature and humidity data.
3. a kind of data processing method of combination Grubbs method and 3 σ methods as described in claim 1, it is characterised in that: described
The frequency that sampling is distinguished is 1Hz, uses the monitoring data of bridge of the frequency greater than 1Hz for high frequency monitoring data；Sample frequency is small
In equal to 1Hz bridge monitoring data be low frequency monitoring data.
4. a kind of data processing method of combination Grubbs method and 3 σ methods as described in claim 1, it is characterised in that: described
High frequency monitoring data are handled using 3 σ methods, are m high frequency monitoring data X for selecting the acquisition of same type monitoring device, it will
High frequency monitoring data number in order X1、X2、X3...Xm, (wherein m >=1), high frequency monitoring data X Normal Distribution X~N
(μ, σ2), wherein μ is the mean value of X, σ2For the standard variance of X, the range of (+3 σ of μ -3 σ, μ) is calculated, will not be distributed in (μ -3 σ,
+ 3 σ of μ) data in range are rejected.
5. a kind of data processing method of combination Grubbs method and 3 σ methods as described in claim 1, it is characterised in that: described
Grubbs method carries out processing low-frequency data, is the i low frequency monitoring data K that same type monitoring device will be selected to acquire, according to
The numerical value of low frequency monitoring data arranges K by sequence from small to large(1)、K(2)、K(3)...K(i), wherein (3≤i≤100), respectively
Calculate statistic G on GrubbsiWith statistic G ' under Grubbsi:
WhereinFor the average value of low frequency monitoring data K, s is the standard deviation of low frequency monitoring data K；Determine the detection of Grubbs method
The horizontal α and horizontal α of rejecting*, and α*< α；
Critical value G is A.2 found according to the subordinate list of national standard GB/T4883-20081-α/2(i)；
Work as Gi> G 'iAnd Gi> G1-α/2(i), determine K(i)For outlier；
As G 'i> GiAnd G 'i> G1-α/2(i), determine K(1)For outlier；
For the outlier K of detection(i)Or K(1), determine and reject horizontal α*, critical value is A.2 found in subordinate listWhenWhen determine K(1)To count outlier；WhenWhen, determine K(i)To count outlier, point
After not rejecting outlier and statistics outlier, next round inspection is carried out to low frequency monitoring data K again.
6. a kind of data processing method of combination Grubbs method and 3 σ methods as claimed in claim 5, it is characterised in that: described
Detecting horizontal α is 0.1 or 0.5；Reject horizontal α*It is 0.05 or 0.1.
7. a kind of data processing method of combination Grubbs method and 3 σ methods as described in claim 1, it is characterised in that: described
It is after proposing the part for exceeding ± 3 σ in high-frequency data that data after rejecting data processing, which carry out lagrange-interpolation,
After removing duplicate numerical value, and it will be utilized respectively after the outlier and the duplicate numerical value of statistics outlier removal in low-frequency data
Former and later two data for rejecting data carry out numerical compensation to the data being removed.
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Application publication date: 20190521 |