CN109827697A - Suspension cable time-varying Suo Li recognition methods based on local mean value mode decomposition - Google Patents
Suspension cable time-varying Suo Li recognition methods based on local mean value mode decomposition Download PDFInfo
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Abstract
The suspension cable time-varying Suo Li recognition methods based on local mean value mode decomposition that the invention discloses a kind of, includes the following steps: to obtain Inclined Cable Vibration Acceleration time course data, carries out signal denoising using signal processing method；Using the fundamental frequency of Fourier transformation (FFT) estimation drag-line, some further is designed according to fundamental frequency and carries out preliminary treatment；Using the vibration signal after local mean value mode decomposition method resolution process, each rank mode of oscillation of drag-line is obtained；Signal is handled using Hilbert-Huang transform (HHT), the instantaneous frequency time history data of each rank mode of oscillation is obtained, using Suo Li-fundamental frequency formula, instantaneous Suo Li time course data can be obtained.The present invention can obtain the time-varying Suo Li of suspension cable, and the defect of average Suo Li can only be measured by overcoming existing cable force measurement method, and measurement accuracy is high, and at low cost, application range is wider.
Description
Technical field
The present invention relates to civil engineerings and signal processing interaction technique field, especially a kind of based on local mean value mode point
The suspension cable time-varying Suo Li recognition methods of solution.
Background technique
Important primary structure member of the suspension cable as cable-stayed bridge, it not only by the tension of itself by the weight of bridge span structure and
Mobile load is most or all of on bridge passes to Sarasota, while by the displacement transfer of Sarasota to girder.Cable-stayed bridge in operation,
Under the long term of itself dead load and extraneous mobile load, the force-bearing situation of each component is constantly changing.Extraneous load it is each
It is secondary to change the Suo Li corresponding change that all cause suspension cable, new Suo Li distribution is formed, structure is made to reach new equilibrium state.It can
See in stayed-cable bridge structure system, suspension cable not only plays a part of to connect girder Sarasota, transmission internal force and deformation, it is often more important that
It is adjusted using mutual cable force adjustment come the global symmetry to structure, stability, whole resistance to deformation, structure is kept to begin
It is in balanced steady state eventually.Therefore the size of inclined guy cable stretching power and situation of change directly decide king-tower and overall structure
Stress, deformation state；The Suo Li variation of research suspension cable can also study the fatigue effect of suspension cable, be the maintenance of suspension cable
Scientific basis is provided.
The method of existing measurement Suo Li mainly has fundamental vibration frequency method, magnetic flux transducer method, static method.Fundamental vibration frequency method is logical
The fundamental frequency for crossing measurement a period of time inhaul goes to calculate Suo Li, but is only capable of calculating average Suo Li, and non-time-varying Suo Li；Magnetic flux passes
Sensor method measures Suo Li by measuring the relationship of Suo Liyu magnetic flux, different drag-lines is needed to repeat to demarcate, higher cost；
Static method, which refers to, is inserted into static(al) sensor in drag-line and anchored end, is not suitable for into bridge.Three kinds of methods in the prior art are not
It is capable of measuring time-varying Suo Li, the fatigue effect of suspension cable can not be studied, and cost is higher, therefore proposes a kind of measurement drag-line time-varying
The method of Suo Li has biggish practical application meaning.
Summary of the invention
Goal of the invention: the object of the present invention is to provide a kind of suspension cable time-varying Suo Lishi based on local mean value mode decomposition
Other method, to overcome the problems, such as the average Suo Li measured in the prior art.
Technical solution: in order to achieve the above object, the suspension cable time-varying of the present invention based on local mean value mode decomposition
Suo Li recognition methods, includes the following steps:
S1: the vibration acceleration time course data signal of suspension cable is measured；
S2: denoising is carried out to measured vibration acceleration TIME HISTORY SIGNAL；
S3: the signal after denoising is smoothed；
S4: it utilizes power spectrum method (PSD), calculates the fundamental frequency estimation value f of signal0；
S5: the signal after smoothing processing obtained in S3 step is filtered；
S6: the S5 signal walked after obtained filtering processing is divided using local mean value mode decomposition algorithm (LMD)
Solution, obtains each rank modal vibration signal, and take first step mode signal；
S7: calculating first step mode signal obtained in S6 using Hilbert-Huang transform (HHT), obtains
The instantaneous fundamental frequency time course data of first-order modal signal；
S8: the instantaneous fundamental frequency time course data according to obtained in S7 utilizes fundamental frequency-Suo Li formula: T=4mL2(fn/n)2, obtain
To the time-varying rope force data T of suspension cable, m represents the quality of drag-line unit length in formula, and L represents the length of drag-line, fnRepresent n-th
Order frequency, n indicate rank number of mode.
Further, in step S6, each rank modal vibration signal is calculated using local mean value mode decomposition algorithm (LMD) and is had
Body the following steps are included:
Local Extremum n S6.1 all using the signal x (t) that peak extraction method calculating step S5 is obtainedi(i=1,2,
3...), the average value of all adjacent Local Extremums is found out:
All adjacent mean points are connected with straight line, is then smoothed, is obtained with moving average method
Local mean value function m11(t)；
S6.2 calculates envelope estimated value ai:
All two neighboring envelope estimated values are connected with straight line, is smoothed, is obtained using moving average method
Envelope estimation function；
The local mean value function m that S6.3 obtains step S6.111(t) it is isolated from the signal x (t) that step S5 is obtained
Come, obtain:
h11(t)=x (t)-m11(t)；
S6.4 h11(t) divided by envelope estimation function a11(t), to h11(t) it is demodulated, the modulation after being demodulated
Signal S11(t),
s11(t)=h11(t)/a11(t)
To S11(t) it repeats the above steps, i.e., x (t) is replaced with into S in step S6.111(t), S6.2 is repeated, S6.3's
Calculating process obtains S11(t) envelope estimation function a12(t)；Above-mentioned iterative process n times are repeated, until obtained modulation is believed
Number S1n(t) it is a pure FM signal:
In formula,
The condition of iteration ends is
S6.5 is multiplied all envelope estimation functions generated in iterative process to obtain envelope signal, i.e. instantaneous amplitude letter
Number:
Envelope signal is multiplied to obtain first PF component of original signal with pure FM signal by S6.6:
PF1(t)=a1(t)s1n(t)
Its instantaneous amplitude is envelope signal, instantaneous frequency f1(t) it can then be found out by pure FM signal:
S6.7 is by first PF component from original signal PF1(t) it is separated in, obtains new signal u1(t), by u1(t)
Above step S6.1-S6.7 is repeated as initial data, is recycled k times, until ukUntil for a monotonic function:
Original signal x (t) can be by all PF components and ukReconstruct, i.e.,
Further, in step S1, the vibration acceleration time course data signal of acceleration transducer measurement suspension cable is utilized.
Further, in step S2, using Wavelet noise-eliminating method to measured vibration acceleration time course data signal into
Row denoising.
Further, in step S3, using the Kalman filtering algorithm in data process of fitting treatment to denoising after
Signal is smoothed.
Further, iteration ends value is 1F-6.
Further, it in step S5, according to fundamental frequency estimation value, is filtered using bandpass filtering method, filter range
It is taken as [0.8f0, 1.2f0]。
Method And Principle: local mean value mode decomposition is a kind of signal decomposition method, and signal adaptive can be decomposed by it
Signal adaptive, can be decomposed into each rank mode signals by each rank natural mode of vibration letter (IMF), be calculated using local mean value mode decomposition
Method handles Inclined Cable Vibration signal, available each rank mode of oscillation of suspension cable；Hilbert-Huang transform (HHT) formula is to signal
A kind of demodulation process, the instantaneous frequency of signal can be extracted, handled using first rank mode of oscillation of the HHT to drag-line,
The first rank instantaneous frequency is obtained, then the time-varying Suo Li of available suspension cable.
The utility model has the advantages that compared with prior art, the present invention has following remarkable advantage: method of the present invention can be quasi-
Really identification suspension cable time-varying Suo Li, local mean value mode decomposition can overcome end effect present in traditional EMD method,
The effects such as modal overlap, this method can decomposite the radio-frequency component in signal, can be used in the damage check of drag-line.This method benefit
The fatigue effect that drag-line can be calculated with obtained time-varying Suo Li, simultaneously can be used for the non-destructive tests of suspension cable, can be used for tiltedly
In the long term monitoring and monitoring of drag-line, the limitation of average Suo Li can only be detected by overcoming existing method.In addition, the method is letter
Number Processing Algorithm, it can also be used in the Underwater Acoustic channels of sound emission concrete flaw detection, can be used in sound emission non-destructive tests, therefore
This method has biggish application value, is averaged Suo Li recognition methods with apparent advantage compared to traditional suspension cable.
Detailed description of the invention
Fig. 1 is the method flow diagram of suspension cable time-varying Suo Li recognition methods of the present invention.
Specific embodiment
Technical solution of the present invention is described further with reference to the accompanying drawing.
As shown in Figure 1, the suspension cable time-varying Suo Li recognition methods based on local mean value mode decomposition includes the following steps:
S1: the vibration acceleration time course data of acceleration transducer measurement suspension cable is utilized；
S2: using the WAVELET PACKET DECOMPOSITION method in signal processing, denoising is carried out to signal；
S3: being smoothed data using the Kalman filtering algorithm in data process of fitting treatment, and iteration ends value is
1E-6:
S4: it utilizes power spectrum method (PSD), calculates the fundamental frequency estimation value f of signal0；
S5: it according to fundamental frequency estimation value, is filtered using signal obtained in bandpass filtering method S3 step, filter range
It is taken as [0.8f0, 1.2f0]；
S6: obtained signal is walked to S5 using local mean value mode decomposition algorithm (LMD) and is decomposed, each rank mould is obtained
State vibration signal takes first step mode signal；Steps are as follows for the specific calculating of each rank modal vibration signal:
S6.1 calculates signal x ((t) all Local Extremum n that step S5 is obtained using peak extraction methodi(i=1,2,
3...), the average value of all adjacent Local Extremums is found out:
All adjacent mean points are connected with straight line, is then smoothed, is obtained with moving average method
Local mean value function m11(t)；
S6.2 finds out envelope estimated value
All two neighboring envelope estimated values are connected with straight line, are then smoothed using moving average method,
Obtain envelope estimation function；
The local mean value function m that S6.3 obtains step S6.111(t) signal x (t) punching obtained from step S5 is isolated
Come, obtains
h11(t)=x (t)-m11(t)
S6.4 h11(t) divided by envelope estimation function a11(t) to h11(t) it is demodulated, the modulation letter after being demodulated
Number S11(t),
s11(t)=h11(t)/a11(t)
To S11(t) it repeats the above steps, i.e., x (t) is replaced with into S in step S6.111(t), S6.2 is repeated, S6.3's
Calculating process just can obtain S11(t) envelope estimation function a12(t), if a12(t) it is not equal to 1, illustrates S11It (t) is not one
Pure FM signal needs to repeat above-mentioned iterative process n times, until being a pure FM signal namely S1n(t) envelope estimates letter
Number a1(n+1)(t)=1, so having
In formula,
The condition of iteration ends is
In practical application, under the premise of not influencing discomposing effect, in order to reduce the number of iterations, operation time is reduced, it can
With with
a1n(t)≈1
Condition as iteration ends；
All envelope estimation functions multiplication generated in iterative process can be obtained envelope signal (instantaneous amplitude by S6.5
Function)
Envelope signal is multiplied by S6.6 with pure FM signal can obtain first PF component of original signal
PF1(t)=a1(t)s1n(t)
It contains highest frequency content in original signal, is the AM/FM amplitude modulation/frequency modulation signal an of simple component, instantaneous width
Value is exactly envelope signal, instantaneous frequency f1(t) it can then be found out by pure FM signal, i.e.,
S6.7 is by first PF component from original signal PF1(t) it is separated in, obtains a new signal u1(t), will
u1(t) above step S6.1-S6.7 is repeated as initial data, recycled k times, until ukUntil for a monotonic function:
Original signal x (t) can be by all PF components and ukReconstruct, i.e.,
S7: calculating signal obtained in S6 using Hilbert-Huang transform (HHT), obtains the instantaneous base of signal
Frequency time course data；
S8: fundamental frequency-Suo Li formula: T=4mL is utilized2(fn/n)2, obtain the time-varying rope force data of suspension cable；M is represented in formula
The quality of drag-line unit length, L represent the length of drag-line, fnN-th order frequency is represented, n indicates rank number of mode.
The laboratory condition of test of heuristics: BeanAir has linear acceleration transducer 8,167.85m long, meter Chong 28.0kg,
Diameter is LZM7-85 suspension cable one of 87mm.
Claims (7)
1. a kind of suspension cable time-varying Suo Li recognition methods based on local mean value mode decomposition, it is characterised in that: including walking as follows
It is rapid:
S1: the vibration acceleration time course data signal of suspension cable is measured；
S2: denoising is carried out to measured vibration acceleration TIME HISTORY SIGNAL；
S3: the signal after denoising is smoothed；
S4: it utilizes power spectrum method (PSD), calculates the fundamental frequency estimation value f of signal0；
S5: the signal after smoothing processing obtained in S3 step is filtered；
S6: the S5 signal walked after obtained filtering processing is decomposed using local mean value mode decomposition algorithm (LMD), is obtained
To each rank modal vibration signal, and take first step mode signal；
S7: first step mode signal obtained in S6 is calculated using Hilbert-Huang transform (HHT), obtains the first rank
The instantaneous fundamental frequency time course data of mode signals；
S8: the instantaneous fundamental frequency time course data according to obtained in S7 utilizes fundamental frequency-Suo Li formula: T=4mL2(fn/n)2, obtain tiltedly
The time-varying rope force data T of drag-line, m represents the quality of drag-line unit length in formula, and L represents the length of drag-line, fnRepresent n-th order frequency
Rate, n indicate rank number of mode.
2. the suspension cable time-varying Suo Li recognition methods according to claim 1 based on local mean value mode decomposition, feature
It is: in step S6, calculates each rank modal vibration signal using local mean value mode decomposition algorithm (LMD) and specifically include following step
It is rapid:
Local Extremum n S6.1 all using the signal x (t) that peak extraction method calculating step S5 is obtainedi(i=1,2,3...),
Find out the average value of all adjacent Local Extremums:
All adjacent mean points are connected with straight line, is then smoothed with moving average method, obtains part
Mean function m11(t)；
S6.2 calculates envelope estimated value ai:
All two neighboring envelope estimated values are connected with straight line, is smoothed using moving average method, obtains envelope
Estimation function；
The local mean value function m that S6.3 obtains step S6.111(t) it separates, obtains from the signal x (t) that step S5 is obtained
It arrives:
h11(t)=x (t)-m11(t)；
S6.4 h11(t) divided by envelope estimation function a11(t), to h11(t) it is demodulated, the modulated signal after being demodulated
S11(t),
s11(t)=h11(t)/a11(t)
To S11(t) it repeats the above steps, i.e., x (t) is replaced with into S in step S6.111(t), S6.2, the calculating of S6.3 are repeated
Journey obtains S11(t) envelope estimation function a12(t)；Above-mentioned iterative process n times are repeated, until obtained modulated signal S1n
(t) it is a pure FM signal:
In formula,
The condition of iteration ends is
S6.5 is multiplied all envelope estimation functions generated in iterative process to obtain envelope signal, i.e. instantaneous amplitude function:
Envelope signal is multiplied to obtain first PF component of original signal with pure FM signal by S6.6:
PF1(t)=a1(t)s1n(t)
Its instantaneous amplitude is envelope signal, instantaneous frequency f1(t) it can then be found out by pure FM signal:
S6.7 is by first PF component from original signal PF1(t) it is separated in, obtains new signal u1(t), by u1(t) conduct
Initial data repeats above step S6.1-S6.7, recycles k times, until ukUntil for a monotonic function:
Original signal x (t) can be by all PF components and ukReconstruct, i.e.,
3. the suspension cable time-varying Suo Li recognition methods according to claim 1 based on local mean value mode decomposition, feature
It is: in step S1, utilizes the vibration acceleration time course data signal of acceleration transducer measurement suspension cable.
4. the suspension cable time-varying Suo Li recognition methods according to claim 1 based on local mean value mode decomposition, feature
It is: in step S2, denoising is carried out to measured vibration acceleration time course data signal using Wavelet noise-eliminating method.
5. the suspension cable time-varying Suo Li recognition methods according to claim 1 based on local mean value mode decomposition, feature
It is: in step S3, the signal after denoising is carried out using the Kalman filtering algorithm in data process of fitting treatment smooth
Processing.
6. the suspension cable time-varying Suo Li recognition methods according to claim 5 based on local mean value mode decomposition, feature
Be: iteration ends value is 1E-6.
7. the suspension cable time-varying Suo Li recognition methods according to claim 1 based on local mean value mode decomposition, feature
It is: in step S5, according to fundamental frequency estimation value, is filtered using bandpass filtering method, filter range is taken as [0.8f0,
1.2f0]。
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| CN112099100A (en) * | 2020-08-25 | 2020-12-18 | 吉林大学 | Magnetic resonance sounding signal envelope extraction method based on adaptive local iterative filtering |
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| CN112985672A (en) * | 2021-02-23 | 2021-06-18 | 中冶建筑研究总院有限公司 | Prestressed cable force analysis method based on non-contact space vibration test |
| CN113358245A (en) * | 2021-04-27 | 2021-09-07 | 明阳智慧能源集团股份公司 | Draught fan inhaul cable tension measuring method and system, storage medium and computing equipment |
| CN113971421A (en) * | 2021-11-19 | 2022-01-25 | 福州大学 | Track slab deformation identification method based on track side vibration acceleration |
| CN115014617A (en) * | 2022-06-21 | 2022-09-06 | 福州大学 | Cable force synchronous monitoring method for cable-stayed bridge inhaul cable based on ground-based radar |
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| CN113358245A (en) * | 2021-04-27 | 2021-09-07 | 明阳智慧能源集团股份公司 | Draught fan inhaul cable tension measuring method and system, storage medium and computing equipment |
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