CN116522085A - Full-automatic inhaul cable frequency extraction, fixed-order and cable force identification method and application - Google Patents
Full-automatic inhaul cable frequency extraction, fixed-order and cable force identification method and application Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000000605 extraction Methods 0.000 title claims abstract description 22
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 29
- 238000001228 spectrum Methods 0.000 claims abstract description 18
- 238000012216 screening Methods 0.000 claims abstract description 17
- 238000006073 displacement reaction Methods 0.000 claims abstract description 11
- 230000001133 acceleration Effects 0.000 claims abstract description 8
- 238000012896 Statistical algorithm Methods 0.000 claims abstract description 6
- 230000008569 process Effects 0.000 claims abstract description 6
- 239000004973 liquid crystal related substance Substances 0.000 claims description 10
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- 238000013507 mapping Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 claims description 3
- 230000007774 longterm Effects 0.000 claims description 3
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
- G06F18/15—Statistical pre-processing, e.g. techniques for normalisation or restoring missing data
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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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The invention relates to the technical fields of bridge engineering and signal processing, in particular to a full-automatic inhaul cable frequency extraction, order determination and cable force identification method and application. Firstly, acquiring acceleration, displacement and the like time-course signals of inhaul cable vibration by adopting a sensor, and acquiring frequency spectrums of the vibration signals by adopting Fourier transformation and the like; then searching a peak value in the frequency spectrum, namely the modal frequency of the inhaul cable by adopting a memory type peak screening algorithm; hash voting is carried out on all the obtained frequency peaks, the nominal fundamental frequency is obtained, and then the orders corresponding to all the pickup frequencies are determined; after all frequencies and orders are obtained, parameters such as cable force and the like can be obtained by utilizing a cable beam vibration theory. The invention combines the memory type peak screening algorithm and the Hash voting statistical algorithm, realizes full automation and dry-free pre-selection in the cable force identification process, and has strong engineering applicability.
Description
Technical Field
The invention relates to the technical fields of bridge engineering and signal processing, in particular to a full-automatic inhaul cable frequency extraction, order determination and cable force identification method and application.
Background
The inhaul cable is a civil structure member with wide application, and has the advantages of strong bearing capacity, high strength, light weight and the like. In bridge engineering, the inhaul cable is widely applied to cable-supported bridges such as cable-stayed bridges, suspension bridges and suspender arch bridges. The suspender or the pull/sling is used as a main stress member, and the cable force directly influences the internal force distribution of the whole structure, so that the state and the safety of the whole bridge are determined. Therefore, the cable force is an important indicator in bridge inspection and long-term monitoring.
The existing cable force identification method mainly comprises a pressure gauge method, a pressure sensor testing method, a magnetic flux method, a vibration method and the like. The vibration method identifies the vibration mode information of the cable according to the vibration signal of the cable, and then adopts a proper power model to calculate the cable force, and the vibration method has the characteristics of convenient sensor installation, no influence on the cable stress, no need of calibration, good durability and the like, so that the vibration method is most widely applied. For the case of larger cable length, the cable vibration frequency can be obtained by adopting single-point vibration measurement, so that the cable force can be obtained more accurately. The invention relates to a measuring algorithm of a long cable force based on a frequency method.
The key point of identifying the cable force by adopting a vibration method is frequency pickup and accurate determination of the mode order; meanwhile, the number of bridge inhaul cables is numerous, a large amount of data needs to be processed in detection and monitoring, full-automatic data processing and high-efficiency processing without manual intervention are realized, and the method has important practical significance. In addition, due to the influence of environmental noise, the recognition error of part of characteristic frequencies is large, even peak missing phenomenon (especially fundamental frequency) exists, and automatic extraction and accurate order determination of the frequencies are challenging tasks.
Under the condition that the number of inhaul cables and the number of working conditions are small, peaks (namely, characteristic frequencies of each step) in a frequency spectrum can be screened through manual operation, and the fundamental frequency of the cable is estimated through the difference value between two large-amplitude peaks. However, modern large-scale cable-stayed bridges contain a large number of inhaul cables, and the appearance of non-contact instruments such as microwave radars enables the vibration time course of a large number of inhaul cables in the whole cable surface to be obtained through single measurement, so that the data volume to be processed is huge, manual operation is very time-consuming and labor-consuming, certain subjectivity is achieved, and accuracy cannot be guaranteed. Therefore, an automatic peak screening algorithm and a fundamental frequency automatic extraction algorithm need to be proposed to improve the efficiency and accuracy of the cable force identification.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a full-automatic stay cable frequency extraction, order determination and cable force identification method and application, and aims to solve the problems of the existing stay cable force identification technology and improve the cable force detection efficiency and precision of a large number of stay cables under a large number of working conditions. Firstly, acquiring acceleration, displacement and the like time-course signals of inhaul cable vibration by adopting a sensor, and acquiring frequency spectrums of the vibration signals by adopting Fourier transformation and the like; then searching a peak value in the frequency spectrum, namely the modal frequency of the inhaul cable by adopting a memory type peak screening algorithm; hash voting is carried out on all the obtained frequency peaks, the nominal fundamental frequency is obtained, and then the orders corresponding to all the pickup frequencies are determined; after all frequencies and orders are obtained, parameters such as cable force and the like can be obtained by utilizing a cable beam vibration theory. The invention combines the memory type peak screening algorithm and the Hash voting statistical algorithm, realizes full automation and dry-free pre-selection in the cable force identification process, and has strong engineering applicability.
The aim of the invention can be achieved by the following technical scheme:
the first object of the invention is to provide a full-automatic inhaul cable frequency extraction, grading and cable force identification method, which comprises the following steps:
(S1) collecting vibration time course data of a inhaul cable;
(S2) performing spectrum analysis on the vibration time course data measured in the step (S1) to obtain a displacement spectrum;
(S3) identifying the characteristic frequency in the displacement frequency spectrum obtained in the step (S2) through a memory type peak screening algorithm to obtain the modal frequency of the inhaul cable;
(S4) extracting the nominal fundamental frequency of the inhaul cable from the modal frequency of the inhaul cable in the step (S3) through a voting statistical algorithm of hash mapping;
(S5) determining the order of all the extracted cable modal frequencies in the step (S3) according to the cable nominal fundamental frequency obtained in the step (S4) through a modal frequency scaling algorithm;
and (S6) extracting the cable force according to the cable modal frequency obtained in the step (S3) and the order of the cable modal frequency obtained in the step (S5) by combining the cable beam vibration theory.
In one embodiment of the present invention, in step (S1), the vibration time-course data of the cable is one of cable acceleration data or cable vibration displacement data.
In one embodiment of the present invention, the cable acceleration data is obtained by measurement with a wired or wireless acceleration sensor.
In one embodiment of the invention, the cable vibration displacement data is observed by microwave radar or video technology.
In one embodiment of the present invention, in step (S3), the memory-type peak screening algorithm specifically includes the following steps:
(S301) setting a size S of the screening window;
(S302) defining a Boolean array B with initial values of true;
(S303) traversing each peak in the shift spectrum sequence, and for the current index i, if Bj is false, directly skipping the current value to reach the next value in the sequence;
(S304) for the current value, comparing it with the size of the following S/2 points and updating the B array;
(S305) comparing the current value with the previous S/2 points in size;
(S306) comparing, if the current value is larger than all surrounding S values, the point value is the peak point;
(S307) repeating the processes of steps (S303) to (S306) until the traversal is completed, and outputting all peak points.
In one embodiment of the invention, the size S of the screening window is initialized to 1.5 times the design value of the fundamental frequency of the inhaul cable.
In one embodiment of the present invention, in step (S4), the voting algorithm of the hash map specifically includes the steps of:
(S401) determining a difference sequence of the characteristic frequency sequences in step (S3), i.e., a frequency difference sequence; meanwhile, the amplitude weight corresponding to each frequency difference value is calculated, the value of the amplitude weight is smaller in the frequency spectrum amplitude values corresponding to two adjacent frequencies, and the corresponding formula is as follows:
AW i ＝min(A i ,A i-1 )；
wherein, the liquid crystal display device comprises a liquid crystal display device,
i-index number of characteristic frequency sequence;
a-amplitude of the characteristic frequency;
AW-magnitude weights;
(S402) traversing each element in the sequence of frequency differences, multiplying the element by an integer factor to obtain a hash value; for two integers adjacent to the hash value, the distance weights of the two integers are respectively calculated, and the adopted formula is as follows:
PW floor (val)＝ceil(val)-val；
PW ceil (val)＝val-floor(val)；
wherein, the liquid crystal display device comprises a liquid crystal display device,
PW-distance weights;
floor-rounding down;
ceil-rounding up;
the value of an element in the val-sequence;
(S403) voting to two integers adjacent to the hash value, wherein the vote number is the product of the amplitude weight and the distance weight;
(S404) circularly performing the steps (S402) and (S403) until the hash voting result is counted after the traversal is completed, and finding out the hash value with the most votes;
(S405) traversing each element in the differential sequence again, multiplying the element by the integer factor in step (S402) to obtain a hash value, and comparing the hash value with the most ticket obtained in step (S404);
(S406) if the difference in step (S405) does not exceed 1, the corresponding difference may participate in the weighted average calculation of the fundamental frequency, the weight of which is the product of the amplitude weight and the distance weight;
(S407) looping through steps (S405) and (S406) until the traversal is completed; and meanwhile, the characteristic frequency difference value is weighted and averaged, and the obtained result is the nominal fundamental frequency.
In one embodiment of the present invention, the hash value is obtained by multiplying 200 by the difference, and the ticket number is the result of the amplitude weight and the distance weight, and is generally not an integer.
In one embodiment of the present invention, in step (S5), the mode frequency scaling algorithm specifically includes the following steps:
(S501) traversing the characteristic frequency sequence obtained in step (S3) with a front pointer;
(S502) dividing the current frequency by the nominal fundamental frequency, and if about an integer, obtaining a first effective modal order; otherwise, returning to the step (S501) to continue traversing;
(S503) traversing the remaining frequency sequence with a back pointer;
(S504) subtracting frequencies corresponding to the front pointer and the rear pointer, if the obtained difference is divided by the nominal fundamental frequency to be about an integer, obtaining an effective modal order, and updating the front pointer by the rear pointer;
(S505) steps (S503) and (S504) are circularly carried out until the traversal is completed, and all the identified mode orders are output.
The second object of the invention is to provide an application of a full-automatic inhaul cable frequency extraction, grading and cable force identification method in cable force identification of a cable stayed bridge, an arch bridge and a suspension bridge load test, periodic detection and long-term monitoring of a mid-pull sling.
Compared with the prior art, the invention has the following beneficial effects:
(1) The memory type peak screening algorithm provided by the invention has small complexity, better efficiency compared with the existing algorithm, and is suitable for simultaneously processing the vibration response of a plurality of inhaul cables;
(2) The hash mapping voting algorithm provided by the invention realizes stable estimation of nominal fundamental frequency, is subject to partial modal frequency deletion and small environmental noise interference, and greatly improves the robustness of the algorithm;
(3) The invention combines the memory type peak screening algorithm and the Hash voting statistical algorithm, realizes full automation and dry-free pre-selection in the cable force identification process, and has strong engineering applicability.
Drawings
FIG. 1 is a flowchart of a fully automatic cable frequency extraction, scaling and cable force identification method of embodiment 1;
FIG. 2 is a spectrum of a missing mode for a certain identified characteristic frequency of each order in example 1;
FIG. 3 is a detailed flowchart of the memory type peak screening algorithm in example 1;
FIG. 4 is a detailed flow chart of the voting statistical algorithm of the hash map in example 1;
FIG. 5 is a detailed flowchart of the mode frequency scaling algorithm in embodiment 1;
fig. 6 is a schematic diagram of an order-frequency linear fit.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples.
In the following examples, unless otherwise specified, the detection means and methods used are conventional in the art.
Example 1
The embodiment provides a full-automatic inhaul cable frequency extraction, order determination and cable force identification method.
Fig. 1 is a general flow chart of the method, and the following further describes a full-automatic cable frequency extraction, step determination and cable force identification method according to the present invention, and the main operation is in step (S4), specifically as follows:
step (S1), measuring the vibration displacement time course of a certain inhaul cable by utilizing a microwave radar;
step (S2), performing Fast Fourier Transform (FFT) on the original data obtained in the step (S1) to obtain a frequency spectrum shown in figure 2;
step (S3), automatically identifying peaks (characteristic frequencies) in the frequency spectrum by using a memory type peak screening algorithm, wherein the specific flow is shown in figure 3;
step (S4), extracting the nominal fundamental frequency of the inhaul cable by utilizing a voting algorithm of hash mapping, wherein the specific flow is shown in figure 4; the detailed process is illustrated by taking the frequency spectrum shown in fig. 2 as an example, and it is noted that the frequency spectrum has identified the 6 th order cable vibration frequency through step (S3), but lacks the 5 th order mode.
(S401) calculating the frequency difference of the peak sequence obtained in step (S3), and calculating the amplitude weight. As shown in table 1, columns 1 and 2 are the frequencies and magnitudes of fig. 1, respectively, and columns 3 and 4 are the differential and magnitude weights.
Table 1 voting algorithm of hash map each data summary table
And (S402) - (S403) traversing each element in the differential sequence, multiplying by 200 to obtain a hash value of the 5 th column, wherein the 6 th column and the 7 th column are respectively the results of rounding down and up the hash value. The respective distance weights of two adjacent integers are calculated, the results being shown in columns 8 and 9. And finally voting to two adjacent integers, wherein the vote number is the product of the amplitude weight and the distance weight.
(S404) counting the result of the hash vote, as shown in table 2, which indicates that the most voted integer is 66.
Table 2 hash vote result summary table
| Ranking | Candidate integers | Number of votes obtained |
| 1 | 66 | 0.5742 |
| 2 | 67 | 0.3242 |
| 3 | 133 | 0.0576 |
| 4 | 134 | 0.0271 |
(S405) - (S407) decoding the result of the hash vote. Traversing the difference sequence again, selecting the difference of the most integer (66) given by all votes, taking the product of the amplitude weight and the distance weight as the total weight for weighted average, and finally obtaining the following results:
this is the nominal fundamental frequency.
Step (S5), determining the mode order of the characteristic frequency identified in step (S3) through a mode frequency scaling algorithm, wherein the specific flow is shown in FIG. 5. The results obtained are shown in Table 3, in which the 5 th order mode was deleted.
TABLE 3 Modal frequency scaling Algorithm results summary table
| Characteristic frequency | Mode order |
| 0.328 | 1 |
| 0.660 | 2 |
| 0.992 | 3 |
| 1.323 | 4 |
| 1.990 | 6 |
| 2.321 | 7 |
And 6, performing linear fitting on the characteristic frequency with the fixed order and the corresponding modal order, wherein the obtained result is shown in fig. 6. The slope of the straight line obtained by fitting is 0.3308 (Hz), which is the fundamental frequency of the inhaul cable. In the embodiment, the cable length is larger, and the cable force is calculated by adopting the tensioning string theory of cable vibration:
T＝4m-L 2 f 2 ＝5443kN，
wherein, the liquid crystal display device comprises a liquid crystal display device,
-
m＝125.32kg/m；
L＝314.988m
f＝0.3308Hz。
on the other hand, the cable force measured by the traditional accelerometer method is 5468kN, which is very similar to the result obtained by the method in the embodiment, and the feasibility of implementing the method is proved.
The previous description of the embodiments is provided to facilitate a person of ordinary skill in the art in order to make and use the present invention. It will be apparent to those skilled in the art that various modifications can be readily made to these embodiments and the generic principles described herein may be applied to other embodiments without the use of the inventive faculty. Therefore, the present invention is not limited to the above-described embodiments, and those skilled in the art, based on the explanation of the present invention, should make improvements and modifications without departing from the scope of the present invention.
Claims (10)
1. A full-automatic inhaul cable frequency extraction, order determination and cable force identification method is characterized by comprising the following steps of:
(S1) collecting vibration time course data of a inhaul cable;
(S2) performing spectrum analysis on the vibration time course data measured in the step (S1) to obtain a displacement spectrum;
(S3) identifying the characteristic frequency in the displacement frequency spectrum obtained in the step (S2) through a memory type peak screening algorithm to obtain the modal frequency of the inhaul cable;
(S4) extracting the nominal fundamental frequency of the inhaul cable from the modal frequency of the inhaul cable in the step (S3) through a voting statistical algorithm of hash mapping;
(S5) determining the order of all the extracted cable modal frequencies in the step (S3) according to the cable nominal fundamental frequency obtained in the step (S4) through a modal frequency scaling algorithm;
and (S6) extracting the cable force according to the cable modal frequency obtained in the step (S3) and the order of the cable modal frequency obtained in the step (S5) by combining the cable beam vibration theory.
2. The method for fully automatic cable frequency extraction, calibration and cable force identification according to claim 1, wherein in step (S1), the cable vibration time-course data is one of cable acceleration data and cable vibration displacement data.
3. The fully automatic cable frequency extraction, scaling and cable force identification method of claim 2, wherein cable acceleration data is obtained by wired or wireless acceleration sensor measurements.
4. The fully automatic cable frequency extraction, scaling and cable force identification method of claim 2, wherein cable vibration displacement data is observed by microwave radar or video technology.
5. The method for fully automatic cable frequency extraction, scaling and cable force identification according to claim 1, wherein in step (S3), the memory type peak screening algorithm specifically comprises the following steps:
(S301) setting a size S of the screening window;
(S302) defining a Boolean array B with initial values of true;
(S303) traversing each peak in the shift spectrum sequence, and for the current index i, if Bj is false, directly skipping the current value to reach the next value in the sequence;
(S304) for the current value, comparing it with the size of the following S/2 points and updating the B array;
(S305) comparing the current value with the previous S/2 points in size;
(S306) comparing, if the current value is larger than all surrounding S values, the point value is the peak point;
(S307) repeating the processes of steps (S303) to (S306) until the traversal is completed, and outputting all peak points.
6. The method for fully automatic cable frequency extraction, scaling and cable force identification of claim 1, wherein the size S of the screening window is initialized to 1.5 times the design value of the cable fundamental frequency.
7. The method for fully automatic cable frequency extraction, scaling and cable force identification according to claim 1, wherein in step (S4), the voting algorithm of the hash map specifically comprises the following steps:
(S401) determining a difference sequence of the characteristic frequency sequences in step (S3), i.e., a frequency difference sequence; meanwhile, the amplitude weight corresponding to each frequency difference value is calculated, the value of the amplitude weight is smaller in the frequency spectrum amplitude values corresponding to two adjacent frequencies, and the corresponding formula is as follows:
AW i ＝min(A i ,A i-1 )；
wherein, the liquid crystal display device comprises a liquid crystal display device,
i-index number of characteristic frequency sequence;
a-amplitude of the characteristic frequency;
AW-magnitude weights;
(S402) traversing each element in the sequence of frequency differences, multiplying the element by an integer factor to obtain a hash value; for two integers adjacent to the hash value, the distance weights of the two integers are respectively calculated, and the adopted formula is as follows:
PW floor (val)＝ceil(val)-val；
PW ceil (val)＝val-floor(val)；
wherein, the liquid crystal display device comprises a liquid crystal display device,
PW-distance weights;
floor-rounding down;
ceil-rounding up;
the value of an element in the val-sequence;
(S403) voting to two integers adjacent to the hash value, wherein the vote number is the product of the amplitude weight and the distance weight;
(S404) circularly performing the steps (S402) and (S403) until the hash voting result is counted after the traversal is completed, and finding out the hash value with the most votes;
(S405) traversing each element in the differential sequence again, multiplying the element by the integer factor in step (S402) to obtain a hash value, and comparing the hash value with the most ticket obtained in step (S404);
(S406) if the difference in step (S405) does not exceed 1, the corresponding difference may participate in the weighted average calculation of the fundamental frequency, the weight of which is the product of the amplitude weight and the distance weight;
(S407) looping through steps (S405) and (S406) until the traversal is completed; and meanwhile, the characteristic frequency difference value is weighted and averaged, and the obtained result is the nominal fundamental frequency.
8. The method for fully automatic cable frequency extraction, scaling and cable force identification of claim 7 wherein the hash value is obtained by multiplying 200 by the difference, and the ticket number is the result of the amplitude weight and the distance weight, and is generally not an integer.
9. The method for fully automatic cable frequency extraction, scaling and cable force identification according to claim 1, wherein in step (S5), the mode frequency scaling algorithm specifically comprises the following steps:
(S501) traversing the characteristic frequency sequence obtained in step (S3) with a front pointer;
(S502) dividing the current frequency by the nominal fundamental frequency, and if about an integer, obtaining a first effective modal order; otherwise, returning to the step (S501) to continue traversing;
(S503) traversing the remaining frequency sequence with a back pointer;
(S504) subtracting frequencies corresponding to the front pointer and the rear pointer, if the obtained difference is divided by the nominal fundamental frequency to be about an integer, obtaining an effective modal order, and updating the front pointer by the rear pointer;
(S505) steps (S503) and (S504) are circularly carried out until the traversal is completed, and all the identified mode orders are output.
10. Use of a fully automatic cable frequency extraction, sizing and cable force identification method according to any one of claims 1-9 in cable-stayed bridges, arch bridges and suspension bridge load tests, periodic detection and long term monitoring of the cable force identification of a pull-in sling.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117571184A (en) * | 2024-01-17 | 2024-02-20 | 四川省公路规划勘察设计研究院有限公司 | Bridge structure cable force identification method and equipment based on sliding window and cluster analysis |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117571184A (en) * | 2024-01-17 | 2024-02-20 | 四川省公路规划勘察设计研究院有限公司 | Bridge structure cable force identification method and equipment based on sliding window and cluster analysis |
| CN117571184B (en) * | 2024-01-17 | 2024-03-19 | 四川省公路规划勘察设计研究院有限公司 | Bridge structure cable force identification method and equipment based on sliding window and cluster analysis |
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