CN110231117A - A kind of drag-line fundamental frequency feature identification method based on S-transformation - Google Patents
A kind of drag-line fundamental frequency feature identification method based on S-transformation Download PDFInfo
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- CN110231117A CN110231117A CN201910494529.6A CN201910494529A CN110231117A CN 110231117 A CN110231117 A CN 110231117A CN 201910494529 A CN201910494529 A CN 201910494529A CN 110231117 A CN110231117 A CN 110231117A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L5/00—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes
- G01L5/04—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes for measuring tension in flexible members, e.g. ropes, cables, wires, threads, belts or bands
- G01L5/042—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes for measuring tension in flexible members, e.g. ropes, cables, wires, threads, belts or bands by measuring vibrational characteristics of the flexible member
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Abstract
The drag-line fundamental frequency feature identification method based on S-transformation that the invention discloses a kind of, comprising the following steps: collected cable-stayed bridge cable acceleration signal is expressed using S-transformation in time-frequency domain, the noise of different periods and different frequency range is found；Noise is filtered out by acting on Time frequency Filter, obtains the time-frequency domain signal of filtering environmental noise；Time domain is converted into using S inverse transformation to the signal after filtering environmental noise, Fast Fourier Transform (FFT) is carried out to the signal after filtering environmental noise, time-domain signal is converted into frequency-region signal, it is final to recognize fundamental frequency feature.The present invention can filter out the ambient noise interference near inhaul cable vibration signal fundamental frequency, thus the feature of prominent drag-line fundamental frequency, and then obtain accurately and reliably fundamental frequency characteristic information.
Description
Technical field
The present invention relates to bridge health monitoring field, especially a kind of drag-line fundamental frequency feature identification side based on S-transformation
Method.
Background technique
Cable-stayed bridge is because the advantages that its stress performance is good, span is big, handsome in appearance, is widely adopted in bridge construction, drag-line
It is the main bearing member of cable-stayed bridge, the safe operation of cable-stayed bridge is extremely closed in the health monitoring of the drag-line especially detection of Suo Li
It is important.
Cord force of cable-stayed bridge test in frequency method the most directly, but inhaul cable vibration frequency vulnerable to sensor, wind load and
The influence of the environmental factors such as temperature is especially affected by temperature obvious.Importantly, since inhaul cable vibration signal fundamental frequency includes
Energy it is weak, big by ambient noise interference, the factors such as vehicle driving, wind can all form huge interference to actual signal, such as
What is the need except influence of the ambient noise to drag-line fundamental frequency is current urgent problem to be solved, and existing method either time domain noise reduction is also
It is that frequency domain noise reduction can only all filter in some dimension, treatment effect is unobvious, and useful information is easily lost.
Summary of the invention
The drag-line fundamental frequency feature identification method based on S-transformation that the purpose of the present invention is to provide a kind of.
The present invention uses following technical scheme to solve above-mentioned technical problem: a kind of drag-line fundamental frequency feature based on S-transformation
Discrimination method, comprising the following steps:
Step 1: inputting collected cable-stayed bridge cable vibration time-domain signal；
Step 2: doing S-transformation to the vibration signal of collected cable-stayed bridge cable, it is expressed in time-frequency domain, finds ring
Time-frequency band where the noise of border；
Step 3: Time frequency Filter is acted on time-frequency domain, filtering environmental noise；
Step 4: S inverse transformation is done to filtered signal, by vibration signal from time-frequency domain conversation to time domain；
Step 5: Fast Fourier Transform (FFT) is done to the inhaul cable vibration time-domain signal after filtering environmental noise, by time-domain signal
In frequency domain presentation, the fundamental frequency feature of drag-line is identified.
Compared with prior art, the present invention its remarkable advantage are as follows: (1) be directed to drag-line fundamental frequency identification difficulty problem, establish base
In the feature identification method of time frequency analysis, cable-stayed bridge cable vibratory response is carried out using the adaptive time frequency resolution of S-transformation
It effectively decomposes, accurately finds noise position and noise is filtered out by Time frequency Filter, false make an uproar can be effectively removed
Sound ingredient achievees the effect that promotion signal signal-to-noise ratio, obtains fundamental frequency characteristic information to prominent true frequecy characteristic；(2) gram
The shortcomings that conventional Fourier transform is without time resolution feature is taken；(3) the Gauss function window width and frequency dependence used,
Improve Short Time Fourier Transform window function fix and Wavelet transformation in the fixed defect of wavelet basis function；(4) of the invention
Noise reduction process have that time alternative and frequency are alternative, will not be to useful signal on the basis of filtering environmental noise
It causes to damage, the drag-line fundamental frequency feature picked out is accurate and reliable.
Detailed description of the invention
Fig. 1 is the drag-line fundamental frequency feature identification method flow diagram the present invention is based on S-transformation.
Fig. 2 is cable-stayed bridge cable Measurement of Vibration time-domain signal schematic diagram in the embodiment of the present invention.
Fig. 3 is that cable-stayed bridge cable vibrates frequency-region signal schematic diagram in the embodiment of the present invention.
Fig. 4 is that cable-stayed bridge cable vibrates frequency-region signal fundamental frequency partial schematic diagram in the embodiment of the present invention.
Fig. 5 is cable-stayed bridge cable vibration signal fundamental frequency identification result schematic diagram in the embodiment of the present invention.
Specific embodiment
As shown in Figure 1, a kind of drag-line fundamental frequency feature identification method based on S-transformation, comprising the following steps:
Step 1: inputting collected cable-stayed bridge cable vibration time-domain signal；
Step 2: doing S-transformation to the vibration signal of collected cable-stayed bridge cable, it is expressed in time-frequency domain, finds ring
Time-frequency band where the noise of border；
Step 3: acting on Time frequency Filter on time-frequency domain, the ambient noise near the true fundamental frequency of drag-line is filtered out；
Step 4: S inverse transformation is done to filtered signal, by vibration signal from time-frequency domain conversation to time domain；
Step 5: Fast Fourier Transform (FFT) is done to the inhaul cable vibration time-domain signal after filtering environmental noise, by time-domain signal
In frequency domain presentation, the fundamental frequency feature of drag-line is identified.
Further, the specific calculation of step 2 is as follows:
Wherein h (t) is given continuous signal；W (t- τ, f) is Gauss function；τ is time shift method；F is signal frequency；i
For imaginary unit.
For the form of expression of discrete series signal are as follows:
Wherein T is time sampling interval, and N is sampling number,For collected discrete series signal, m is time shift
The discrete serial number of the factor, n are the discrete serial number of frequency values；J, m, n=0,1 ..., N-1.
Further, the specific calculation of step 3 is as follows:
Sc(τ, f)=S (τ, f) H (τ, f)
Wherein H (τ, f) is Time frequency Filter；S (τ, f) is time-frequency domain signal；Sc(τ, f) is filtered time-frequency domain letter
Number；For noise reduction coefficient,R is noise region.
Further, the specific calculation of step 4 is as follows:
dτFor time diffusion；
For the form of expression of discrete series signal are as follows:
Further, the specific calculation of step 5 is as follows:
Wherein x (n) is finite length sequence, and X (k) is the obtained frequency domain sequence of Fast Fourier Transform (FFT), and N is sampling number, 2
π k/N is the corresponding frequency values of k-th of sampled point.
Below with reference to embodiment and attached drawing, the present invention is described in detail.
Embodiment
A kind of drag-line fundamental frequency feature identification method based on S-transformation, comprising the following steps:
Step 1 utilizes the collected cable-stayed bridge cable vibration monitoring of acceleration transducer in the input of MATLAB software platform
Time-domain signal.
Step 2, the S-transformation using discrete form do S-transformation to the time-domain signal of input, it is expressed in time-frequency domain,
Period and frequency range where finding noise on time-frequency domain map.
Wherein T is time sampling interval；N is sampling number；M is the discrete serial number of time shift method；N is that frequency values obtain discrete sequence
Number；J, m, n=0,1 ..., N-1.
Step 3 acts on Time frequency Filter, filtering environmental noise on the time-frequency domain of inhaul cable vibration signal.
Sc(τ, f)=S (τ, f) H (τ, f) (2)
Wherein H (τ, f) is Time frequency Filter；S (τ, f) is time-frequency domain signal；Sc(τ, f) is filtered time-frequency domain letter
Number；For noise reduction coefficient,R is noise region.
Step 4 does S inverse transformation to filtered signal, by signal from time-frequency domain conversation to time domain.
Step 5 does Fast Fourier Transform (FFT) to the time-domain signal after filtering environmental noise, by time-domain signal in frequency domain table
It reaches, identifies fundamental frequency feature.
Wherein x (n) is finite length sequence；N is sampling number；2 π k/N are the corresponding frequency values of k-th of sampled point.
By taking certain bridge practical frequency signal as an example, the production of Jiangsu Dong Hua measuring technology limited liability company is arranged on drag-line
1A002E model acceleration transducer, acquire vibration signal of the drag-line under environmental excitation, measured signal sample frequency 20Hz,
Data length 72000, as shown in Figure 2.After carrying out conventional Fourier transform to signal sequence, cable-stayed bridge cable vibration frequency is obtained
Domain signal.As shown in figure 3, the high frequency section feature of frequency-region signal is obvious, it is readily discernible, but low frequency part energy is faint and special
Levy unobvious, especially fundamental frequency nearby multiple peak values occurs, as shown in figure 4, conventional Fourier transform treatment effect is undesirable, draws
The feature identification degree of rope fundamental frequency is low.
Processing method according to the invention, the collected cable-stayed bridge cable under the excitation of MATLAB software platform input environment
Time-domain signal is vibrated, S-transformation is done to time-domain signal using formula (1), the time-frequency domain that signal is converted is operated, in time-frequency domain
Period where upper determining ambient noise and distribution frequency range, act on Time frequency Filter filtering environmental noise using formula (2), to filter
Except the signal after ambient noise using formula (3) carry out S inverse transformation transform to time domain, finally to this method filtering environmental noise after
Time-domain signal using Fast Fourier Transform (FFT) (4), to obtain spectrogram as shown in Figure 5, it can be seen that invention removes environment
Noise, highlights peak value of the frequency at 0.6Hz, i.e., true fundamental frequency is bright to false ambient noise interference ingredient removal effect
It is aobvious, it is seen that the present invention has a significant effect for the fundamental frequency part processing of inhaul cable vibration signal.The present invention can be handled simultaneously
The time of signal and frequency information, filtering environmental noise while, will not interfere actual signal, compensate for the deficiency of existing method.
Claims (5)
1. a kind of drag-line fundamental frequency feature identification method based on S-transformation, which comprises the following steps:
Step 1: inputting collected cable-stayed bridge cable vibration time-domain signal；
Step 2: doing S-transformation to the vibration signal of collected cable-stayed bridge cable, it is expressed in time-frequency domain, environment is found and makes an uproar
Time-frequency band where sound；
Step 3: Time frequency Filter is acted on time-frequency domain, filtering environmental noise；
Step 4: S inverse transformation is done to filtered signal, by vibration signal from time-frequency domain conversation to time domain；
Step 5: Fast Fourier Transform (FFT) is done to the inhaul cable vibration time-domain signal after filtering environmental noise, by time-domain signal in frequency
Domain expression, identifies the fundamental frequency feature of drag-line.
2. the drag-line fundamental frequency feature identification method according to claim 1 based on S-transformation, which is characterized in that step 2
Specific calculation is as follows:
Wherein, h (t) is given continuous signal, and w (t- τ, f) is Gauss function, and τ is time shift method, and f is signal frequency, and i is
Imaginary unit；
For the form of expression of discrete series signal are as follows:
Wherein, T is time sampling interval, and N is sampling number,For collected discrete series signal, m is time shift method
Discrete serial number, n are the discrete serial number of frequency values；J, m, n=0,1 ..., N-1.
3. the drag-line fundamental frequency feature identification method according to claim 1 based on S-transformation, which is characterized in that step 3
Specific calculation is as follows:
Sc(τ, f)=S (τ, f) H (τ, f)
Wherein H (τ, f) is Time frequency Filter；S (τ, f) is time-frequency domain signal；Sc(τ, f) is filtered time-frequency domain signal；For
Noise reduction coefficient,R is noise region.
4. the drag-line fundamental frequency feature identification method according to claim 1 based on S-transformation, which is characterized in that step 4
Specific calculation is as follows:
For the form of expression of discrete series signal are as follows:
5. the drag-line fundamental frequency feature identification method according to claim 1 based on S-transformation, which is characterized in that step 5
Specific calculation is as follows:
Wherein x (n) is finite length sequence, and X (k) is the frequency domain sequence that Fast Fourier Transform (FFT) obtains, and 2 π k/N are k-th of sampling
The corresponding frequency values of point.
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Cited By (4)
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| CN112816056A (en) * | 2021-01-08 | 2021-05-18 | 天津职业技术师范大学(中国职业培训指导教师进修中心) | Method and device for identifying relative position of earth surface excavation operation and underground optical cable |
| CN113280963A (en) * | 2021-05-26 | 2021-08-20 | 南通河海大学海洋与近海工程研究院 | Real-time cable force identification method based on improved S transformation |
| CN114090950A (en) * | 2021-11-25 | 2022-02-25 | 成都飞机工业（集团）有限责任公司 | Method and device for compressing broadband noise equal phase of steady vibration signal |
| CN114595733A (en) * | 2022-05-10 | 2022-06-07 | 山东大学 | Bridge inhaul cable broken wire signal identification method and system based on long-term and short-term memory network |
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| CN114595733A (en) * | 2022-05-10 | 2022-06-07 | 山东大学 | Bridge inhaul cable broken wire signal identification method and system based on long-term and short-term memory network |
| CN114595733B (en) * | 2022-05-10 | 2024-04-02 | 山东大学 | Bridge inhaul cable broken wire signal identification method and system based on long-short-term memory network |
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