CN102305661A - Denoising processing method for inhaul cable vibration signal of cable-stayed bridge - Google Patents

Denoising processing method for inhaul cable vibration signal of cable-stayed bridge Download PDF

Info

Publication number
CN102305661A
CN102305661A CN 201110163366 CN201110163366A CN102305661A CN 102305661 A CN102305661 A CN 102305661A CN 201110163366 CN201110163366 CN 201110163366 CN 201110163366 A CN201110163366 A CN 201110163366A CN 102305661 A CN102305661 A CN 102305661A
Authority
CN
China
Prior art keywords
cable
domain signal
data sequence
vibration time
vibration
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN 201110163366
Other languages
Chinese (zh)
Inventor
叶庆卫
冯志敏
王晓东
武冬星
胡海刚
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ningbo University
Original Assignee
Ningbo University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ningbo University filed Critical Ningbo University
Priority to CN 201110163366 priority Critical patent/CN102305661A/en
Publication of CN102305661A publication Critical patent/CN102305661A/en
Pending legal-status Critical Current

Links

Images

Landscapes

  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)

Abstract

The invention discloses a denoising processing method for an inhaul cable vibration signal of a cable-stayed bridge, comprising the processing steps of: collecting according to a fixed sampling frequency set by an acceleration transducer, and sampling the inhaul cable vibration time domain signals of the cable-stayed bridge, thereby obtaining a sample data sequence of the vibration time domain signals; obtaining a vibration time domain signal discrete sequence by null equalizing the sample data sequence of the vibration time domain signals; obtaining a segmented data sequence set by sampling and segmenting the vibration time domain signal discrete sequence at equal intervals; obtaining a frequency spectrum set by performing fast Fourier transform to the segmented data sequence set; and obtaining an average frequency spectrum by averaging the frequency spectrum set, and then obtaining the denoised time domain signals after performing fast Fourier transform to the average frequency spectrum. The denoising processing method for inhaul cable vibration signal of cable-stayed bridge eliminates the influence brought by various interference signals in actual measurement, effectively denoises, provides strong guarantee for following analysis of engineering, is convenient to realize, and has wide application range in the field of bridge engineering construction and monitoring.

Description

A kind of denoise processing method of cable-stayed bridge cable vibration signal
Technical field
The present invention relates to a kind of treatment technology of cable-stayed bridge cable vibration signal, especially relate to the denoise processing method in early stage of the cable-stayed bridge cable vibration signal in a kind of science of bridge building construction and the monitoring field.
Background technology
Drag-line is the main bearing carrier in the cable-stayed bridge structure, so cable-stayed bridge cable vibration signals, effective analyzing and processing, is the basic guarantee that science of bridge building construction and monitoring are effectively carried out.Yet because the external environment interference of noise, the vibration signal that acceleration transducer collects often is superimposed with noise signal, makes the vibration signal curve burr occur, has had a strong impact on analysis result.In order to weaken the influence of undesired signal; Need carry out noise reduction process to the vibration signal that collects; In " based on the vibration signal smoothing processing method of MATLAB " literary composition on the periodical " electronic measurement technique " (the 6th phases 30 volume June in 2007) a kind of 5 triple smoothings are disclosed to reduce noise signal; It mainly is to utilize principle of least square method that discrete data is carried out three level and smooth methods of least square polynomial expression sampled signal is carried out noise reduction process; This method obtains level and smooth vibration signal curve and frequency spectrum needs smoothing processing 40 times, causes computation complexity higher.
Summary of the invention
It is fast that technical matters to be solved by this invention provides a kind of processing speed, can effectively improve the denoise processing method based on the cable-stayed bridge cable vibration signal of spectrum averaging of vibration signal signal to noise ratio (S/N ratio).
The present invention solves the problems of the technologies described above the technical scheme that is adopted: a kind of denoise processing method of cable-stayed bridge cable vibration signal mainly comprises following treatment step:
A) acceleration transducer is set on cable-stayed bridge cable, acceleration transducer is gathered the vibration time-domain signal of cable-stayed bridge cable under environmental excitation, and by the fixed sampling frequency of acceleration transducer setting the vibration time-domain signal is sampled; Obtain vibration time-domain signal sampled data sequence x (n), (n=1,2; ..., L), wherein; L is a sampling length, and n is a sequence number;
B) to vibration time-domain signal sampled data sequence x(n); (n=1; 2; ...; L) carry out the zero-mean processing; Detailed process is: according to each sampled value of vibration time-domain signal sampled data sequence and the average sample value of vibration time-domain signal sampled data sequence; Calculate vibration time-domain signal discrete series <img file= " BDA0000069036160000021.GIF " he= " 48 " img-content= " drawing " img-format= " tif " inline= " yes " orientation= " portrait " wi= " 129 " /> <img file= " BDA0000069036160000022.GIF " he= " 48 " img-content= " drawing " img-format= " tif " inline= " yes " orientation= " portrait " wi= " 610 " /> wherein; <img file= " BDA0000069036160000023.GIF " he= " 33 " img-content= " drawing " img-format= " tif " inline= " yes " orientation= " portrait " wi= " 30 " /> is the average sample value of vibration time-domain signal sampled data sequence, < maths num= " 0001 " >! [CDATA[< math > < mrow > < mover > < mi > x </ mi > < mo > &OverBar; </ mo > </ mover > < mo >=</ mo > < mfrac > < mn > 1 </ mn > < mi > L </ mi > </ mfrac > < munderover > < mi > &Sigma; </ mi > < mrow > < mi > n </ mi > < mo >=</ mo > < mn > 1 </ mn > </ mrow > < mi > L </ mi > </ munderover > < mi > x </ mi > < mrow > < mo > (</ mo > < mi > n </ mi > < mo >) </ mo > </ mrow > < mo >; </ mo > </ mrow > </ math >]] > </maths>
C) vibration time-domain signal discrete series
Figure BDA0000069036160000025
is carried out dividing processing by fixed sampling step-length N, obtain the partition data sequence set x j ( i ) , x j ( i ) = x &OverBar; ( N ( i - 1 ) + j ) , ( j = 1,2 , . . . , N ; i = 1,2 , . . . , M ) , Wherein, N ∈ [2; L/2],
Figure BDA0000069036160000027
is for rounding symbol downwards;
D) to partition data sequence set x j(i), (j=1,2 ..., N; I=1,2 ..., M) carry out Fast Fourier Transform (FFT) and handle, obtain spectrum group X j(w), X j(w)=FFT (x j(i)), (j=1,2 ..., N; I=1,2 ..., M), wherein, FFT () is the Fast Fourier Transform (FFT) function, w represents frequency;
E) through spectrum group X j(w), (j=1,2 ..., N) spectrum averaging obtains average frequency spectrum
Figure BDA0000069036160000028
Figure BDA0000069036160000029
Then average frequency spectrum is carried out Fast Fourier Transform Inverse (FFTI) and handle, the time-domain signal behind the acquisition noise reduction
Figure BDA00000690361600000210
Figure BDA00000690361600000211
Wherein, IFFT () is the Fast Fourier Transform Inverse (FFTI) function.
The fixed sampling frequency of described step a) setting is 32～256Hz, and sampling length L is greater than 4096; The fixed sampling step-length N of described step c) is 4～16.
The fixed sampling frequency of described step a) setting is 128Hz, and sampling length L is 8192; The fixed sampling step-length N of described step c) is 8.
Compared with prior art; The calculated amount that the invention has the advantages that signal Processing is little; Processing speed is fast; Can effectively improve signal to noise ratio (S/N ratio), thereby the cable-stayed bridge cable vibration signal that overcomes collection is subject to the problem of ambient noise interference, satisfies the noise reduction process requirement in early stage of the cable-stayed bridge cable vibration signal monitoring analysis of complex environment under changing.
Description of drawings
Fig. 1 is a processing flow chart of the present invention;
Fig. 2 is the time-domain signal cut-away view after the zero-meanization of the present invention;
Fig. 3 for time-domain signal behind the noise reduction of the present invention and zero-meanization after the comparison diagram of discrete series data point;
Fig. 4 is a data dividing processing fundamental diagram among Fig. 1;
Fig. 5 is a FFT work of treatment schematic diagram among Fig. 1;
Fig. 6 is the average and IFFT work of treatment schematic diagram of Fig. 1 intermediate frequency spectrum.
Embodiment
Embodiment describes in further detail the present invention below in conjunction with accompanying drawing.
As shown in Figure 1, a kind of denoise processing method of cable-stayed bridge cable vibration signal mainly comprises following treatment step:
A) acceleration transducer is set on cable-stayed bridge cable, acceleration transducer is gathered the vibration time-domain signal of cable-stayed bridge cable under environmental excitation, and by the fixed sampling frequency of acceleration transducer setting the vibration time-domain signal is sampled, and obtains vibration time-domain signal sampled data sequence x (n); (n=1,2 ..., L); Wherein, L is a sampling length, and n is a sequence number, and the fixed sampling frequency of setting is 32～256Hz; Preferred 128Hz, sampling length L is preferably 8192 greater than 4096;
B) to vibration time-domain signal sampled data sequence x(n); (n=1; 2; ...; L) carry out the zero-mean processing; Detailed process is: according to each sampled value of vibration time-domain signal sampled data sequence and the average sample value of vibration time-domain signal sampled data sequence; Calculate vibration time-domain signal discrete series <img file= " BDA0000069036160000031.GIF " he= " 48 " img-content= " drawing " img-format= " tif " inline= " yes " orientation= " portrait " wi= " 129 " /> <img file= " BDA0000069036160000032.GIF " he= " 48 " img-content= " drawing " img-format= " tif " inline= " yes " orientation= " portrait " wi= " 610 " /> wherein; <img file= " BDA0000069036160000033.GIF " he= " 38 " img-content= " drawing " img-format= " tif " inline= " yes " orientation= " portrait " wi= " 46 " /> is the average sample value of vibration time-domain signal sampled data sequence, < maths num= " 0003 " >! [CDATA[< math > < mrow > < mover > < mi > x </ mi > < mo > &OverBar; </ mo > </ mover > < mo >=</ mo > < mfrac > < mn > 1 </ mn > < mi > L </ mi > </ mfrac > < munderover > < mi > &Sigma; </ mi > < mrow > < mi > n </ mi > < mo >=</ mo > < mn > 1 </ mn > </ mrow > < mi > L </ mi > </ munderover > < mi > x </ mi > < mrow > < mo > (</ mo > < mi > n </ mi > < mo >) </ mo > </ mrow > < mo >; </ mo > </ mrow > </ math >]] > </maths>
C) vibration time-domain signal discrete series
Figure BDA0000069036160000035
is carried out dividing processing by fixed sampling step-length N, obtain the partition data sequence set x j ( i ) , x j ( i ) = x &OverBar; ( N ( i - 1 ) + j ) , ( j = 1,2 , . . . , N ; i = 1,2 , . . . , M ) , Wherein, N ∈ [2; L/2];
Figure BDA0000069036160000037
preferred immobilization sampling step-length N is 4～16, and optimal fixed sampling step-length N is 8;
D) to partition data sequence set x j(i), (j=1,2 ..., N; I=1,2 ..., M) carry out Fast Fourier Transform (FFT) and handle, obtain spectrum group X j(w), X j(w)=FFT (x j(i)), (j=1,2 ..., N; I=1,2 ..., M);
E) pass through spectrum group X j(w), (j=1,2 ..., N) spectrum averaging obtains average frequency spectrum
Figure BDA0000069036160000042
Then average frequency spectrum is done Fast Fourier Transform Inverse (FFTI) and handle, the time-domain signal behind the acquisition noise reduction x &CenterDot; ( t ) = IFFT ( X &OverBar; ( w ) ) .

Claims (3)

1. the denoise processing method of a cable-stayed bridge cable vibration signal is characterized in that comprising following treatment step:
A) acceleration transducer is set on cable-stayed bridge cable, acceleration transducer is gathered the vibration time-domain signal of cable-stayed bridge cable under environmental excitation, and by the fixed sampling frequency of acceleration transducer setting the vibration time-domain signal is sampled; Obtain vibration time-domain signal sampled data sequence x (n), (n=1,2; ..., L), wherein; L is a sampling length, and n is a sequence number;
B) to vibration time-domain signal sampled data sequence x(n); (n=1; 2; ...; L) carry out the zero-mean processing; Detailed process is: according to each sampled value of vibration time-domain signal sampled data sequence and the average sample value of vibration time-domain signal sampled data sequence; Calculate vibration time-domain signal discrete series <img file= " FDA0000069036150000011.GIF " he= " 48 " id= " ifm0001 " img-content= " drawing " img-format= " tif " inline= " yes " orientation= " portrait " wi= " 129 " /> <img file= " FDA0000069036150000012.GIF " he= " 48 " id= " ifm0002 " img-content= " drawing " img-format= " tif " inline= " yes " orientation= " portrait " wi= " 610 " /> wherein; <img file= " FDA0000069036150000013.GIF " he= " 33 " id= " ifm0003 " img-content= " drawing " img-format= " tif " inline= " yes " orientation= " portrait " wi= " 30 " /> is the average sample value of vibration time-domain signal sampled data sequence, < maths num= " 0001 " >! [CDATA[< math > < mrow > < mover > < mi > x </ mi > < mo > &OverBar; </ mo > </ mover > < mo >=</ mo > < mfrac > < mn > 1 </ mn > < mi > L </ mi > </ mfrac > < munderover > < mi > &Sigma; </ mi > < mrow > < mi > n </ mi > < mo >=</ mo > < mn > 1 </ mn > </ mrow > < mi > L </ mi > </ munderover > < mi > x </ mi > < mrow > < mo > (</ mo > < mi > n </ mi > < mo >) </ mo > </ mrow > < mo >; </ mo > </ mrow > </ math >]] > </maths>
C) to vibration time-domain signal discrete series
Figure FDA0000069036150000015
(n=1,2 ..., L) carry out dividing processing by fixed sampling step-length N, obtain partition data sequence set x j(i),
Figure FDA0000069036150000016
(j=1,2 ..., N; I=1,2 ..., M), wherein, N ∈ [2, L/2],
Figure FDA0000069036150000017
For rounding symbol downwards;
D) to partition data sequence set x j(i), (j=1,2 ..., N; I=1,2 ..., M) carry out Fast Fourier Transform (FFT) and handle, obtain spectrum group X j(w), X j(w)=FFT (x j(i)), (j=1,2 ..., N; I=1,2 ..., M), wherein, FFT () is the Fast Fourier Transform (FFT) function, w represents frequency;
E) pass through spectrum group X j(w), (j=1,2 ..., N) spectrum averaging obtains average frequency spectrum
Figure FDA0000069036150000018
Figure FDA0000069036150000019
Then average frequency spectrum is carried out Fast Fourier Transform Inverse (FFTI) and handle, the time-domain signal behind the acquisition noise reduction
Figure FDA00000690361500000111
Wherein, IFFT () is the Fast Fourier Transform Inverse (FFTI) function.
2. the denoise processing method of a kind of cable-stayed bridge cable vibration signal according to claim 1, the fixed sampling frequency that it is characterized in that described step a) setting is 32～256Hz, sampling length L is greater than 4096; The fixed sampling step-length N of described step c) is 4～16.
3. the denoise processing method of a kind of cable-stayed bridge cable vibration signal according to claim 2, the fixed sampling frequency that it is characterized in that described step a) setting is 128Hz, sampling length L is 8192; The fixed sampling step-length N of described step c) is 8.
CN 201110163366 2011-06-17 2011-06-17 Denoising processing method for inhaul cable vibration signal of cable-stayed bridge Pending CN102305661A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN 201110163366 CN102305661A (en) 2011-06-17 2011-06-17 Denoising processing method for inhaul cable vibration signal of cable-stayed bridge

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN 201110163366 CN102305661A (en) 2011-06-17 2011-06-17 Denoising processing method for inhaul cable vibration signal of cable-stayed bridge

Publications (1)

Publication Number Publication Date
CN102305661A true CN102305661A (en) 2012-01-04

Family

ID=45379544

Family Applications (1)

Application Number Title Priority Date Filing Date
CN 201110163366 Pending CN102305661A (en) 2011-06-17 2011-06-17 Denoising processing method for inhaul cable vibration signal of cable-stayed bridge

Country Status (1)

Country Link
CN (1) CN102305661A (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103196407A (en) * 2012-01-09 2013-07-10 中联重科股份有限公司 Method, device and system for measuring vibration displacement of pump truck arm support and engineering mechanical equipment
CN104155044A (en) * 2014-07-14 2014-11-19 华南理工大学 Measuring device and measuring method for cable force of cable-stayed bridge based on mobile terminal
CN104200002A (en) * 2014-07-24 2014-12-10 宁波大学 Method for extracting modal parameter from viscous damping vibration signals
CN106340303A (en) * 2016-09-20 2017-01-18 南京朗逸锐科电子科技有限公司 Speech denoising method based on time frequency domain
CN109813809A (en) * 2019-04-01 2019-05-28 石家庄铁道大学 Non-fragment orbit defect non-contact non-destructive testing method, terminal device and system
CN116609402A (en) * 2023-07-19 2023-08-18 中国地质大学（北京） Method and system for identifying dielectric constant temperature effect of rock-soil body

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101586997A (en) * 2009-06-26 2009-11-25 贵州师范大学 Method for calculating guy cable vibrating base frequency

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101586997A (en) * 2009-06-26 2009-11-25 贵州师范大学 Method for calculating guy cable vibrating base frequency

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
《中国优秀硕士学位论文全文数据库信息科技辑》 20071115 于徐红 "红枫湖大桥健康监测与评估***数据处理的研究与发现" 第I140-538页 , 第5期 *
《中国科技论文在线》 20080128 洪晨曦等 "基于移动平均的加窗DFT算法" , *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103196407A (en) * 2012-01-09 2013-07-10 中联重科股份有限公司 Method, device and system for measuring vibration displacement of pump truck arm support and engineering mechanical equipment
CN103196407B (en) * 2012-01-09 2015-06-17 中联重科股份有限公司 Method, device and system for measuring vibration displacement of pump truck arm support and engineering mechanical equipment
CN104155044A (en) * 2014-07-14 2014-11-19 华南理工大学 Measuring device and measuring method for cable force of cable-stayed bridge based on mobile terminal
CN104200002A (en) * 2014-07-24 2014-12-10 宁波大学 Method for extracting modal parameter from viscous damping vibration signals
CN104200002B (en) * 2014-07-24 2017-05-24 宁波大学 Method for extracting modal parameter from viscous damping vibration signals
CN106340303A (en) * 2016-09-20 2017-01-18 南京朗逸锐科电子科技有限公司 Speech denoising method based on time frequency domain
CN109813809A (en) * 2019-04-01 2019-05-28 石家庄铁道大学 Non-fragment orbit defect non-contact non-destructive testing method, terminal device and system
CN116609402A (en) * 2023-07-19 2023-08-18 中国地质大学（北京） Method and system for identifying dielectric constant temperature effect of rock-soil body
CN116609402B (en) * 2023-07-19 2023-09-15 中国地质大学（北京） Method and system for identifying dielectric constant temperature effect of rock-soil body

Similar Documents

Publication Publication Date Title
Li et al. Application of EEMD and improved frequency band entropy in bearing fault feature extraction
CN102305661A (en) Denoising processing method for inhaul cable vibration signal of cable-stayed bridge
CN101221066B (en) Engineering nonlinear vibration detecting method
CN101561314B (en) Method for testing stochastic resonance-chaotic weak signal
Zhang et al. Energy operator demodulating of optimal resonance components for the compound faults diagnosis of gearboxes
CN102141403A (en) Real-time mixed denoising method based on wavelet threshold denoising, median filtering and mean filtering
CN101251446B (en) Method for denoising bump-scrape acoustic emission signal based on discrete fraction cosine transform
CN113343928B (en) Method and device for detecting rail wave mill of speed change section high-speed railway and computer equipment
CN101586997A (en) Method for calculating guy cable vibrating base frequency
Sheng et al. Applications in bearing fault diagnosis of an improved Kurtogram algorithm based on flexible frequency slice wavelet transform filter bank
CN105510032A (en) Deconvolution method based on harmonic to noise ratio guidance
CN111505716A (en) Seismic time-frequency analysis method for extracting generalized Chirplet transform based on time synchronization
CN111769810B (en) Fluid mechanical modulation frequency extraction method based on energy kurtosis spectrum
Feng et al. Efficient implementation of envelope analysis on resources limited wireless sensor nodes for accurate bearing fault diagnosis
RU2014133726A (en) ROTARY ENGINE VIBRATION SIGNAL SYSTEM
CN104089699A (en) Substation equipment sound reconstruction algorithm
CN102269803B (en) Method for correcting low-frequency components in discrete spectrum based on time delay
CN111487318B (en) Time-varying structure instantaneous frequency extraction method
CN105699082A (en) Sparse maximum signal-to-noise ratio deconvolution method
CN106202977A (en) A kind of low frequency oscillation mode based on blind source separation algorithm analyzes method
Zhang et al. Improved local cepstrum and its applications for gearbox and rolling bearing fault detection
CN104503432A (en) Autonomous underwater robot fault identification method based on wavelet energy
Ding et al. Multiple instantaneous frequency ridge based integration strategy for bearing fault diagnosis under variable speed operations
CN102890750A (en) Data analysis method for transportation safety recorder
Yu et al. Adaptive multiple second-order synchrosqueezing wavelet transform and its application in wind turbine gearbox fault diagnosis

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C02 Deemed withdrawal of patent application after publication (patent law 2001)
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20120104