CN103698071B - The data-driven method of Suo Li course identification is become during drag-line based on monitoring acceleration - Google Patents
The data-driven method of Suo Li course identification is become during drag-line based on monitoring acceleration Download PDFInfo
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- CN103698071B CN103698071B CN201310714346.3A CN201310714346A CN103698071B CN 103698071 B CN103698071 B CN 103698071B CN 201310714346 A CN201310714346 A CN 201310714346A CN 103698071 B CN103698071 B CN 103698071B
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Abstract
The data-driven method of Suo Li course identification is become when the present invention proposes a kind of drag-line based on monitoring acceleration, complexity is used to seek this efficient unsupervised learning algorithm of track, utilize the monitoring information of the hyperchannel acceleration transducer that drag-line is arranged, achieve and real-time identification is carried out to Suo Li time-histories.Complexity seeks the single modal response that the acceleration responsive of drag-line independently can be decomposed into drag-line by track algorithm, and then by the real-time frequency of the acceleration information identification drag-line of very short time, calculates Suo Li time-histories by tensioning string theory.By combining actual measurement Suo Li and the actual measurement sunykatuib analysis of cable-stayed bridge of wind speed and the model test of drag-line, demonstrate proposed complexity seek track algorithm can pair time become Suo Li course and carry out Real time identification accurately.The present invention be a kind of directly effective time become Suo Li course discrimination method, be simple and easy to use, Suo Li identification precision is high, ageing strong and can realize online real-time identification, is particularly useful for the online evaluation of drag-line.
Description
Technical field
The present invention relates to the method for a kind of civil engineering structure response identification, when being specifically related to a kind of drag-line based on monitoring acceleration, become the data-driven method of Suo Li course identification.
Background technology
Cable-stayed bridge, due to features such as its span is large, handsome in appearance, easy constructions, is current most widely used bridge type in the world.Cable-stayed bridge generally can become a regional landmark building, and a regional transport hub often, has vital impact to the political economy of this area.
Suspension cable, as the main supporting member of cable-stayed bridge, is made up of plow-steel tow and PE sheath.Suspension cable is reaching in operating period decades; under the long-term effect of environmental attack, material aging and load, the coupling of the disaster factors such as fatigue effect and mutation effect; to inevitably cause damage accumulation and the degradation resistance of structure and system; thus the ability of opposing disaster, even home effect declines, and will cause catastrophic burst accident under extreme case.For ensureing the normal operation of bridge structure, best solution is exactly carry out regular detection to suspension cable to safeguard, and needs to carry out safety assessment according to testing result to it.But due to the limitation of lossless detection method (detection of Magnetic Flux Leakage Inspecting, X ray, Ultrasonic Detection, detect based on the Suo Li of vibratory drilling method), in the actual operation process of cable-stayed bridge, only have small part suspension cable all to be detected.In view of the ubiquity of suspension cable disease, and the high expense of suspension cable reparation and replacing, for carrying out the needs of corrosion fatigue assessment under reply suspension cable structure operation situation, be urgently familiar with the structure Suo Li course response of suspension cable structure under arms in overall process at present.
The cable tension test device of current existence has: pressure rings, magnetic flux transducer, optical fiber grating intelligent dragline etc., wherein pressure rings and optical fiber grating intelligent dragline directly can test Suo Li course, install widely and are applied on newly building bridge.But these cable force monitoring sensor price general chargeds are expensive, install complicated (can only be used for newly building bridge), more importantly the permanance of sensor is poor, and these inherent defects limit the large-scale application of the sensor.Take time and effort owing to changing cable force monitoring sensor, expensive, thus need that development a kind ofly saves time, laborsaving, economic real-time Suo Li course monitoring method badly.
Summary of the invention
Based on above weak point, the invention provides a kind of data-driven method based on becoming Cable power course during the identification of multi-channel testing acceleration, when solving drag-line, becoming the problem of Suo Li course identification.
The present invention adopts following technical scheme to realize: the data-driven method becoming the identification of Suo Li course during a kind of drag-line based on monitoring acceleration, and step is as follows:
Step 1: to the acceleration transducer laying 2-3 in drag-line same plane to be tested, the acceleration responsive of test suspension cable under environmental excitation, and by the acceleration signal in 10 seconds of a passage, identification drag-line fundamental frequency and the highest frequency that can encourage in this time period, the fundamental frequency f of drag-line
1represent,
can the highest frequency f of drag-line of identification in monitoring acceleration signal
irepresent,
f
i≈ i × f
1, wherein,
represent that drag-line is under Suo Li T effect, the circular frequency of the i-th rank mode, μ is the density of unit rope length, and L is suspension cable length is L;
Step 2: design Hi-pass filter, cutoff frequency (i-1.5) f
1, by the highest two order frequency f occurred in the hyperchannel acceleration signal of drag-line
iand f
i-1extract;
Step 3: the window function selecting 3 seconds, filter preprocessing is carried out to the hyperchannel acceleration signal in window function, pre-service post-acceleration signal is sought the separation of track algorithm by complexity and is obtained single modal response signal, by the frequency of Fast Fourier Transform (FFT) or spectra calculation single modal response signal, utilize tensioning string theory, by the frequency computation part drag-line of identification time become Suo Li;
Step 4: sliding window function, repeats step 3 to the acceleration signal in window function, identification drag-line time become Suo Li course.
The present invention also has following technical characteristic:
1, should by the time varying frequency of hyperchannel acceleration signal identification drag-line, so identification drag-line time become Suo Li course.
2, high-pass filtering pre-service is carried out to hyperchannel acceleration signal, extract the 2 the highest order frequencies that drag-line can be energized.
3, utilize complexity to seek track Processing Algorithm, multi-mode response in sliding window is decomposed into single modal response, the frequency of the frequency resolution accurate recognition single modal response of frequency resolution Δ f≤0.025HZ, when utilizing tensioning string theory to calculate, become Suo Li course.
The present invention is based on ripe acceleration transducer measuring technology, relative to the cable force monitoring sensor of the other types of development at present, the acceleration transducer mature and reliable that the present invention adopts, measuring accuracy is high, low price, sensor is installed and changes all very convenient, thus during the drag-line that the present invention is proposed, change Suo Li course identification system has high reliability and permanance.The present invention monitors acceleration information, calculates Suo Li by identification time varying frequency, and method is simple and easy to use, Suo Li identification precision is high, ageing strong and online real-time identification can be realized, the robustness of the inventive method and reliability strong, be particularly useful for the online evaluation of drag-line.
Accompanying drawing explanation
Fig. 1 is that the smart stay cable of Benchmark bridge makes structure and C8 drag-line location diagram;
Fig. 2 is Benchmark bridge actual measurement wind speed and Suo Li timeamplitude map (test duration is 1:00-2:00AM on January 17th, 2008, the Suo Li to be identified for selecting in red frame);
Fig. 3 is Benchmark bridge cable acceleration-time curve figure (start time is 1:40AM on January 17th, 2008);
Fig. 4 is C8 drag-line eight branch path 10 acceleration second power spectrum chart;
Fig. 5 is C8 drag-line SHG properties figure;
Fig. 6 is C8 drag-line eight branch passage and quartile passage 3 seconds filtered time-history curves of acceleration and power spectrum chart thereof;
Fig. 7 is the single modal response time-history curves and power spectrum chart thereof that complexity seeks that track obtains;
Fig. 8 is the C8 drag-line time varying frequency figure of identification;
Fig. 9 is algorithm identification C8 Cable power of the present invention and actual measurement Suo Li comparison diagram;
Suo Li course identification test unit sketch is become when Figure 10 is drag-line;
Figure 11 is S#1 testing acceleration 10 seconds power spectrum charts;
Figure 12 is test drag-line frequency multiplication graph of a relation
Figure 13 is test drag-line S#1 and S#3 sensor 3 seconds filtered time-history curves of acceleration and power spectrum thereof;
Figure 14 is the single modal response time-history curves and power spectrum chart thereof that complexity seeks that track obtains;
Figure 15 is the test drag-line time varying frequency figure of identification;
Figure 16 is that algorithm identification of the present invention test Cable power contrasts operating mode 1 figure with actual measurement Suo Li;
Figure 17 is that algorithm identification of the present invention test Cable power contrasts operating mode 2 figure with actual measurement Suo Li.
Embodiment
Specific embodiment of the invention scheme, is described by the sunykatuib analysis of cable-stayed bridge and the model test of drag-line combining actual measurement Suo Li and actual measurement wind speed.
For the suspension cable (length is L, and area of section is A, and Young modulus is E) of little sag, the horizontal vibrating movement equation of its out-of-plane i-th rank mode can be expressed as
In formula, m
ithe weight that=μ L/2 representation unit rope is long, the density that μ representation unit rope is long; ζ
iit is the i-th rank damping ratios; α
i=i
2π
2t/2L, β
ik=EA π
4i
2k
2/ 8L
3; F
it () is dynamic excitation;
represent that drag-line is under Suo Li T effect, the circular frequency of the i-th rank mode, ω
iunit be rad/s.
computing formula show to there is frequency multiplication relation between drag-line each rank circular frequency and fundamental frequency, i.e. ω
i≈ i × ω
1; Each rank mode circular frequency ω of drag-line simultaneously
iall and between Cable power T there are direct mapping relations.According to tensioning string theory, Cable power T can be expressed as
The core becoming the data-driven method of Suo Li course identification during the drag-line based on monitoring acceleration of the present invention is time-varying modal circular frequency ω
iidentification algorithm.Theoretical according to modal superposition, motion vector x (t)=[x of environmental excitation downhaul
1(t) ..., x
n(t)]
tcan be expressed as
In formula,
represent Mode Shape matrix, q (t)=[q
1(t) ..., q
n(t)]
tfor modal response vector, the i-th first order mode (the i-th row of Φ matrix) and modal response are expressed as φ
iand q
i(t).The decomposition computation of above-mentioned motion vector is realized by blind element separation algorithm, order
represent modal response vector estimated value,
represent Mode Shape vector estimated value, y
it the complexity of () signal is defined as
In formula,
with
represent the long-term forecasting index of n × n and the variance matrix of index for short term prediction,
with
matrix element be expressed as
Long-term forecasting value
with short-term forecasting value
computing formula is
In the present invention,
H
l> > h
s,
with
matrix can be calculated by fast convolution algorithm and obtain.If signal y
it () is desirable single modal response signal, signal complexity functional value F (y
i) maximum, therefore only need signal complexity function F (y
i) ask extreme value, optimal response signal y
it () is with regard to the reconstruction value of energy accurate description modal response vector.
Complexity function F (y
i) ask the process of extreme value can be realized by classical gradient optimal method, complexity function F (y
i) to w
idifferential can be expressed as
When
during F=0, complexity function F (y
i) can optimum point be reached, above formula can be rewritten as
Above formula is deformed into a generalized eigenvalue problem, so w
ibe
proper vector, eigenwert is γ
i=V
i/ U
i.Obtain w
iafter can have formula (3) obtain reconstruct single modal response vector
above by optimization signal complexity function F (y
i), obtained as single modal response q (t)=[q by displacement monitoring response vector x (t)
1(t) ..., q
n(t)]
tprocess be called that complexity seeks track algorithm.Acceleration responsive and speed responsive can be calculated by same mode.
For single modal response signal q (t)=[q
1(t) ..., q
n(t)]
t, be dominant, so only need isolated q (t)=[q owing to only having the frequency content controlling mode in each modal response
1(t) ..., q
n(t)]
tcarry out fast Fourier (FFT) conversion or power spectrumanalysis, the model frequency of signal can be tried to achieve, by relation formula (2) Suo Shi between Cable power and drag-line frequency, try to achieve Cable power.
For single modal response signal q
it (), frequency resolution Δ f is the Δ f=1/ τ reciprocal of the window function time τ selected, and the Suo Li computing formula that there is error term is
In formula,
approximate the fundamental frequency of drag-line
, error item size depends on Δ f/i.As can be seen from formula (9), for the window function that time span is shorter, as long as the identification frequency exponent number (generally getting i>10) of drag-line is higher, also very high frequency resolution can be reached.
Embodiment 1:
Benchmark cable-stayed bridge (the C8 suspension cable on Fig. 1), is made up of 139 5mm steel wires, the long L=100.95m of rope, area of section A=2.73 × 10
-3m
2, the density of unit length is μ=21.43kg/m.2006, C8 suspension cable, in cable replacement engineering, was replaced by smart stay cable, can Real-Time Monitoring Suo Li course.
Step 1: algorithm of the present invention needs the acceleration transducer information identification Cable power utilizing 2 or maximum 3 passages, by the acceleration responsive of numerical simulation calculation C8 suspension cable L/8 and L/4 position under monitoring wind speed (Fig. 2 (a)) and monitoring Suo Li (Fig. 2 (b)) effect.To C8 drag-line L/8 passage 10 seconds Acceleration time course (Fig. 3) carry out power spectrumanalysis (Fig. 4), the most high order of frequency that the excitation of C8 drag-line is got up is the 30th rank, can find out to there is obvious frequency multiplication relation (Fig. 5) between drag-line frequency simultaneously.
Step 2: design Hi-pass filter, cutoff frequency approximates (30-1.5) f
129th rank and 30 order frequency compositions are filtered out by ≈ 38Hz;
Step 3: the window function selecting 3 seconds, carry out filtering to the wave filter that the accelerating curve applying step 2 in window function designs, acceleration-time curve and the power spectrum thereof of filtered L/8 and L/4 passage are shown in Fig. 6.Complexity is carried out to the acceleration-time curve of filtered L/8 and L/4 passage and seeks track algorithm process, isolate single modal response signal, and calculate its power spectrum (Fig. 7), the frequency accounting for dominant mode of identification single mode signal, and tensioning string theory is utilized to calculate Suo Li.
Step 4: the time window that pointwise is slided 3 seconds, repeat step 3, to acceleration signal filtering in sliding window, be separated single modal response signal, by Fourier transform or power spectrumanalysis identification frequency, thus obtain the time dependent curve of frequency (Fig. 8), and then obtain Suo Li time-history curves (Fig. 9).
Together with monitoring Suo Li time-history curves and identification Suo Li time-history curves are well identical, demonstrate the present invention put forward the accuracy of algorithm.
Embodiment 2:
The long 14.02m of test drag-line, diameter is 1.5cm, and linear mass is 1.33kg/m.Test drag-line one section is fixed, and the other end adopts threaded rod to regulate the tensile elongation of rope, and then regulates the size of Suo Li, arranges dynamometer at test stay cable end simultaneously, and the change of monitoring Suo Li time-histories, test drag-line adopts 2 blower fans to produce vibration as dynamic excitation.
Step 1: lay acceleration transducer S#1-S#3 test plane at L/4, L/2 and 3/4L respectively and respond outward, sample frequency is 200Hz, and test unit, sensor are arranged with physical dimension as shown in Figure 8.Power spectrumanalysis (Figure 11) is carried out to 10 seconds Acceleration time course of test drag-line S#1 sensor, the most high order of frequency that the excitation of test drag-line is got up is the 14th rank, can find out to there is obvious frequency multiplication relation (Figure 12) between test drag-line frequency simultaneously;
Step 2: design Hi-pass filter, cutoff frequency approximates (14-1.5) f
113rd rank and 14 order frequency compositions can be filtered out by ≈ 28Hz;
Step 3: the window function selecting 3 seconds, carry out filtering to the wave filter that the accelerating curve applying step 2 in window function designs, acceleration-time curve and the power spectrum thereof of filtered S#1 and S#3 sensor are shown in Figure 13.Complexity is carried out to the acceleration-time curve of filtered S#1 and S#3 sensor and seeks track algorithm process, isolate single modal response signal, and calculate its power spectrum (Figure 14), the frequency accounting for dominant mode of identification single mode signal, and tensioning string theory is utilized to calculate Suo Li.
Step 4: the time window that pointwise is slided 3 seconds, repeats step 3, obtains the time dependent curve of frequency (Figure 15), and then obtain Suo Li time-history curves (Figure 16-17).Together with the monitoring Suo Li time-history curves of test drag-line 2 operating modes well coincide with identification Suo Li time-history curves, from test angle demonstrate the present invention put forward the accuracy of algorithm.
Claims (4)
1. become a data-driven method for Suo Li course identification during drag-line based on monitoring acceleration, it is characterized in that, step is as follows:
Step 1: lay 2-3 acceleration transducer in drag-line same plane to be tested, the acceleration responsive of test suspension cable under environmental excitation, and by the acceleration signal in 10 seconds of a passage, identification drag-line fundamental frequency and the highest frequency that can encourage in this time period, the fundamental frequency f of drag-line
1represent,
can the highest frequency f of drag-line of identification in monitoring acceleration signal
irepresent,
f
i≈ i × f
1, wherein,
represent that drag-line is under Suo Li T effect, the circular frequency of the i-th rank mode, μ is the density of unit rope length, and L is suspension cable length;
Step 2: design Hi-pass filter, cutoff frequency (i-1.5) f
1, by the highest two order frequency f occurred in the hyperchannel acceleration signal of drag-line
iand f
i-1extract;
Step 3: the window function selecting 3 seconds, filter preprocessing is carried out to the hyperchannel acceleration signal in window function, pre-service post-acceleration signal is sought the separation of track algorithm by complexity and is obtained single modal response signal, by the frequency of Fast Fourier Transform (FFT) or spectra calculation single modal response signal, utilize tensioning string theory, by the frequency computation part drag-line of identification time become Suo Li;
Step 4: sliding window function, repeats step 3 to the acceleration signal in window function, identification drag-line time become Suo Li course.
2. become the data-driven method of Suo Li course identification during the drag-line based on monitoring acceleration according to claim 1, it is characterized in that: should by the time varying frequency of hyperchannel acceleration signal identification drag-line, so identification drag-line time become Suo Li course.
3. become the data-driven method of Suo Li course identification during the drag-line based on monitoring acceleration according to claim 1, it is characterized in that: high-pass filtering pre-service is carried out to hyperchannel acceleration signal, extract the 2 the highest order frequencies that drag-line can be energized.
4. during the drag-line based on monitoring acceleration according to claim 1, become the data-driven method of Suo Li course identification, it is characterized in that: utilize complexity to seek track Processing Algorithm, multi-mode response in sliding window is decomposed into single modal response, the frequency of the frequency resolution accurate recognition single modal response of frequency resolution Δ f≤0.025HZ, becomes Suo Li course when utilizing tensioning string theory to calculate.
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CN104713673A (en) * | 2015-03-11 | 2015-06-17 | 哈尔滨工业大学 | Inhaul cable time varying cable force course recognizing method based on extended kalman filter |
CN108613763B (en) * | 2018-05-11 | 2023-11-03 | 上海市建筑科学研究院 | Fiber bragg grating type inhaul cable force monitoring sensor and sensing method based on frequency method |
CN111242058A (en) * | 2020-01-16 | 2020-06-05 | 江苏东印智慧工程技术研究院有限公司 | Android and HHT-based time-varying cable force identification method |
CN118332761A (en) * | 2024-03-06 | 2024-07-12 | 西南交通大学 | Inclined cable bridge monitoring method and system based on cable force and bridge deck acceleration |
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