CN112667952B - Structure dynamic displacement non-integral reconstruction method - Google Patents
Structure dynamic displacement non-integral reconstruction method Download PDFInfo
- Publication number
- CN112667952B CN112667952B CN202011097110.6A CN202011097110A CN112667952B CN 112667952 B CN112667952 B CN 112667952B CN 202011097110 A CN202011097110 A CN 202011097110A CN 112667952 B CN112667952 B CN 112667952B
- Authority
- CN
- China
- Prior art keywords
- signal
- acceleration
- displacement
- term
- prooni
- 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.)
- Active
Links
- 238000006073 displacement reaction Methods 0.000 title claims abstract description 91
- 238000000034 method Methods 0.000 title claims abstract description 40
- 230000001133 acceleration Effects 0.000 claims abstract description 115
- 108010076504 Protein Sorting Signals Proteins 0.000 claims abstract description 10
- 238000012360 testing method Methods 0.000 claims description 25
- 229920001983 poloxamer Polymers 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 8
- 238000012216 screening Methods 0.000 claims description 2
- 230000007613 environmental effect Effects 0.000 claims 1
- 230000010354 integration Effects 0.000 abstract description 15
- 238000001914 filtration Methods 0.000 abstract description 13
- 238000006243 chemical reaction Methods 0.000 abstract description 5
- 230000000737 periodic effect Effects 0.000 abstract description 4
- 238000000354 decomposition reaction Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 230000003321 amplification Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000003199 nucleic acid amplification method Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Landscapes
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention provides a structure dynamic displacement non-integral reconstruction method, which has higher displacement reconstruction precision. According to the invention, the actually measured acceleration signal of the structure is expressed as the fitting signal with the characteristics of the Proonil signal, so that the effective stripping of the baseline drift term, the structural vibration term and the noise term in the actually measured acceleration signal is realized, and the Proonil signal sequence of the residual acceleration without the baseline drift term is obtained. The method deduces and establishes the acceleration-displacement relation based on the Proonil signal parameters, and realizes the accurate reconstruction of the structural speed and displacement based on the residual Proonil signal sequence. The displacement reconstruction method inherits the advantage of the Prooni signal on the non-periodic signal representation on the displacement reconstruction, and avoids drift term errors. According to the invention, the displacement reconstruction is realized by establishing the conversion relation between the acceleration and the displacement through the characteristic parameters of the acceleration signal, so that the loss of a signal low-frequency term existing in the traditional method based on integration and high-pass filtering is avoided.
Description
Technical Field
The invention relates to a structure dynamic displacement non-integral reconstruction method.
Background
The dynamic displacement is one of the most important measurement indexes for describing the dynamic characteristics of the structural engineering, and can be directly converted into the strain and deformation of the structure to provide effective information of the structure operation. Especially when the structure has a nonlinear characteristic or permanent deformation, the dynamic displacement of the structure is critical. In addition to traditional applications, dynamic displacement caused by structural vibration is also widely used in engineering, such as structural vibration control, health monitoring, system identification, and other fields. While displacement is very useful, direct measurement of displacement is often difficult and challenging. This is because displacement is a relative quantity and when displacement measurements are made using conventional equipment, such as laser displacement sensors, linear variable differential transformers, etc., a static reference point is often required to support the equipment, which is often difficult to achieve in a practical environment. Although the Global Positioning System (GPS) does not require a fixed reference point for measurement, it has a low sampling frequency (typically 1hz to 20 hz) and limited accuracy (about 1.0 cm). In addition, the use of GPS also entails high costs. Furthermore, the GPS recorded signals may lose the necessary information in some cases.
Compared with the measurement of the dynamic displacement of the structure, the acceleration test does not need a fixed reference point, is more mature and accurate, and is widely applied to the structure test. Based on the advantages of acceleration testing, it is presently preferred to reconstruct the true dynamic displacement information of the structure from the measured acceleration. In theory, due to the strict mathematical transformation relationship between acceleration and velocity, the displacement information of the structure can be reconstructed through quadratic integration. However, since the initial velocity and initial displacement of the structure during operation are unknown, there will be severe drift terms in the reconstructed dynamic displacement, and it is often not feasible to integrate the acceleration directly.
At present, two main methods are aimed at the problem of drift term in displacement obtained after integration. The first method is to perform Fourier transform on the acceleration signal to convert to a frequency domain, integrate the acceleration signal in the frequency domain, and finally convert the integrated displacement to a time domain through a transfer function and Fourier inverse transform. Since the measured acceleration signal often has difficulty satisfying the periodic assumption of fourier transform, the displacement obtained using the frequency domain integration method inevitably generates a large truncation error. The time domain integration method directly processes acceleration in the time domain, avoids errors caused by Fourier transformation, but inevitably generates drift terms in reconstruction displacement due to the influence of unknown initial conditions. Most of the current research is therefore focused on the removal of drift terms in the time-domain reconstruction displacement. Trifunac and Lee (1973) combine baseline correction and high-pass filtering to propose a time-domain displacement reconstruction method. Converse and Brady (1992) pad the acceleration signal at the beginning and end stages and reconstruct the displacement using least squares linear fit and high pass filtering. Chiu (1997) achieves displacement reconstruction by performing a least squares fit and high pass filtering on the acceleration and subtracting the initial velocity therefrom. Boore et al (2002) performed a least squares fit to the velocity, differentiated the adjustment function and removed from the acceleration, thereby effecting adjustment of the baseline. Darragh et al (2004) fit the velocities using three functions, linear fit, bilinear piecewise fit, and quadratic fit, respectively, and reconstruct the displacement without applying high pass filtering. Park et al (2005) effectively realized the reconstruction of bridge displacements by subtracting the mean adjustment baseline of acceleration and velocity. However, in the above method, the displacement reconstruction is realized by adopting a method of combining baseline fitting and high-pass filtering, however, in the filtering process, low-frequency components in the signals are inevitably filtered, which leads to inaccuracy of the reconstruction displacement.
Disclosure of Invention
The invention aims to provide a structure dynamic displacement non-integral reconstruction method, which is based on decomposition and reconstruction of acceleration signals, characterizes the actually measured acceleration signals into a form meeting the characteristics of a Prooni signal, and removes components causing drift. The true velocity and displacement of the structure is then reconstructed by using the remaining Prooni signal parameters and the initial velocity and displacement of the structure is calculated. For the reconstruction of the speed and the displacement, the invention establishes the mathematical relationship between the acceleration and the speed and the displacement by using the Proonil signal parameters based on the decomposition and the reconstruction of the acceleration, avoids the error caused by integration, simultaneously solves the problem of low-frequency item loss in the signal caused by the traditional high-pass filtering method, and finally realizes the high-precision reconstruction of the structural displacement.
The invention is realized by adopting the following technical scheme:
the structure dynamic displacement non-integral reconstruction method is characterized by comprising the following specific steps:
(1) Decomposing and characterizing the actually measured acceleration signal into a structure real acceleration signal, a noise signal and an acceleration baseline drift signal;
(2) Fitting the structural real acceleration signal, the noise signal and the acceleration baseline drift signal by using the Prooni signal parameters;
(3) Obtaining a Prooni signal parameter representation form of the actually measured acceleration signal;
(4) Solving a Prooni signal parameter of the actually measured acceleration signal;
(5) Stripping an acceleration baseline drift signal by utilizing a Prooni signal parameter of the obtained actual measurement acceleration signal, and removing the acceleration baseline drift signal;
(6) Establishing a mathematical relationship between the acceleration signal after the baseline drift term is removed and the real speed of the structure;
(7) Obtaining an expression of the real speed of the structure, and solving the initial speed of the structure; integrating the real speed expression obtained by solving to obtain a real displacement expression of the structure;
(8) Obtaining a reconstructed structural displacement and an initial displacement;
Further, in steps (1) - (3):
In order to consider errors in the measured acceleration data due to the effects of test and noise, the measured acceleration signal is Characterized by:
Wherein a (t) is a structural real acceleration signal, epsilon (t) is a noise signal contained in the test signal, u is a baseline drift component in the test acceleration signal, and t represents a corresponding moment.
Assuming that three signal components of the structural real acceleration signal a (t), the noise signal epsilon (t) and the baseline drift component u all meet the characteristics of the Prooni signal, and the Prooni signal sequence is used for fitting the acceleration, the three signal components can be written as follows:
wherein N p represents the number of components in the structural signal, N n represents the number of components in the noise signal, and γ and λ are the parameters of the pluronic signal for the corresponding components;
the three parts in the above formula are collectively expressed as
Where N l +1 represents the number of components in the measured acceleration signal, N l＝Np+Nn.
The innovation of the steps (1) - (3) is as follows: in the setting of the acceleration signal, structural vibrations, ambient noise and baseline drift problems in the test are taken into account at the same time. And then dividing the acceleration into a drift term, a structural information term and a noise component term, and uniformly fitting the drift term, the structural information term and the noise component term by using a Prooni signal sequence, so that the measured acceleration signal is uniformly expressed in a form meeting the characteristics of the Prooni signal, and the defect that the drift term and the noise term cannot be fitted with high precision due to periodic assumption in the traditional Fourier sequence is overcome.
Further, in steps (4) - (5):
solving the formula (3), and calculating to obtain a Prooni signal parameter as AndThe signal can be divided into a component term and a noise component term which respectively correspond to drift terms in the signal; wherein the component frequency representing the baseline drift is much smaller than the frequencies of the structural signal component and the noise signal component (approximately equal to 0), obtained by calculationThe frequencies of the corresponding components can be calculated as follows:
in the formula, img represents an imaginary part.
By aligningJudging not only the corresponding oneAndScreening to reconstruct the acceleration signal using the remaining pluronic signal sequence:
The acceleration signal contaminated by noise after the baseline drift is removed is represented by the formula (5).
The innovation in the steps (4) - (5) is as follows: the drift term is removed by combining the fitted Proonil signal parameters and the signal characteristics of the drift term, so that the problem that the low-frequency components in the signal are inevitably lost when the drift term is removed by the traditional high-pass filtering method is avoided, and the accuracy of the calculation result is improved.
Further, in (6) to (7):
Integrating the expression (5) to obtain the expression of the corresponding structure true speed, namely
It can be found that the true velocity corresponds to the true acceleration signal of the structureThe method comprises the following steps:
Meanwhile, the corresponding initial speed can be calculated by the formula (6) The method comprises the following steps:
the innovation of the steps (6) - (7) is as follows: the mathematical relationship between the acceleration and the speed is established by using the Proonil signal parameters, the problem of noise amplification caused by using integration is solved, the mathematical relationship between the acceleration after removing the trend term and the initial speed of the structure is also established by using the fitted Proonil signal parameters, and the initial speed of the structure is also directly solved by using the acceleration signals.
Further, in (7) to (8):
Integrating the true speed obtained by solving, i.e
Can obtain the reconstructed structural displacementInitial displacementI.e.
And
The innovation of the steps (7) - (8) is as follows: by using the Prooni signal sequence, a mathematical relationship between the actually measured acceleration signal and the structural real displacement is established, so that displacement drift caused by unknown initial conditions and acceleration baseline drift problems after secondary integration is avoided, and the problem of low-frequency loss caused by high-pass filtering in the traditional method is solved. Meanwhile, a mathematical relationship between the Prooni signal parameters of the fitting acceleration signals and the initial displacement of the structure is also established, so that the reconstructed displacement result is more accurate when the method is used for reconstructing the real displacement of the structure.
The invention provides a new structure dynamic displacement reconstruction method, which is mainly based on decomposition and reconstruction of acceleration, and establishes a conversion relation between the acceleration and displacement by using a Prooni signal parameter on the basis of carrying out Prooni signal characteristic fitting on the acceleration. The method first characterizes the measured acceleration signal as satisfying the form of the Proonine signal characteristics and separates therefrom a sequence of Proonine signals representing the drift term. The displacement is then reconstructed using the remaining pluronic parameters. By using the decomposed Prooni parameters, a mathematical relationship between the true speed and displacement of the structure and the acceleration is established, including an estimation of the initial speed and displacement values at the moment of the start of the measurement. Meanwhile, the method of the invention utilizes the decomposition and reconstruction technology of acceleration, thereby avoiding errors caused by integration and effectively avoiding drift problems caused by integration. In addition, noise contained in the acceleration record is also decomposed and converted into speed and displacement reconstruction, so that the method has good robustness. As can be seen from the introduction of the background art, no research work similar to the present method exists.
In summary, compared with the prior art, the invention has the advantages and positive effects that:
1) The invention establishes the conversion relation between the acceleration and the speed and displacement based on the decomposition and reconstruction of the acceleration signal by using the Prooni signal fitting test to obtain the acceleration signal, avoids the error caused by integration, and has higher precision. Meanwhile, the actual initial displacement and speed of the structure can be reconstructed through the acceleration obtained through testing, so that the detailed information of the vibration of the structure is revealed.
2) According to the method, the actually measured acceleration signal is divided into the drift term, the structure vibration information term and the noise term, so that the problem of poor fitting precision caused by the fact that the actually measured signal does not meet periodic assumption of Fourier transformation is solved, high-precision removal of the drift term is realized, and feasibility and practicability of the method in an engineering actual structure are improved.
3) The traditional displacement reconstruction method is based on integration and high-pass filtering, but due to the defect of the high-pass filtering, not only can the drift term in the signal be filtered, but also the loss of low-frequency components in the signal can be caused, and errors are caused. By means of the Prooni signal parameter fitting method, accurate isolation of drift items in signals is achieved, meanwhile, mathematical relations between acceleration and displacement are established, errors caused by integration are avoided, and high-precision reconstruction of structural dynamic displacement is achieved.
4) According to the invention, based on decomposition and reconstruction of the acceleration signal, the Prooni signal parameters are used for fitting the acceleration signal, so that drift items caused by integration are removed, a conversion relation between acceleration and speed and displacement is established, the problems of high-pass filtering loss of low-frequency items, amplification errors caused by integration and the like are avoided, the higher calculation precision of a calculation method is ensured, a novel analysis method is provided for vibration displacement calculation of an engineering structure in engineering, novel technical means can be provided for monitoring and control of related structures and the like, and a certain engineering application prospect is provided.
Drawings
FIG. 1 is a schematic diagram of a test layout;
FIG. 2 is a graph of measured signals using an acceleration sensor and a laser displacement sensor, wherein (a) is an acceleration signal graph and (b) is a displacement signal graph;
FIG. 3 is a graph of the results of a fit to measured acceleration using a Prooni parameter, wherein (a) is the result of a fit to the whole range of acceleration using a Prooni signal, (b) is the result of a fit to a2 to 2.1 second acceleration signal, and (c) is the result of a fit to a 10 to 10.1 second acceleration signal;
FIG. 4 is a graph of the results of dynamic structural displacement reconstructed by the method of the present invention, wherein (a) is a graph comparing structural displacement obtained by the reconstruction of the method of the present invention with test displacement, and (b) is a graph of the results of 2 to 3 second displacement reconstruction; (c) 12 to 13 second displacement reconstructing the result.
Detailed Description
The present invention will be described in further detail below with reference to the drawings and specific examples to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the specific embodiments, and various changes made to the present invention should be construed as falling within the spirit and scope of the present invention as defined and defined by the appended claims as apparent to those skilled in the art.
Model information:
In this embodiment, the vibration test data of the cantilever beam 2 placed on the hydraulic vibration table 1 is used for calculation and analysis, the control table 3 is used for controlling the hydraulic vibration table 1 to generate random vibration in the test, and the acceleration sensor 4 is used for testing the acceleration data of the top end of the cantilever beam 2. The cantilever beam 2 is of a steel structure, the size is 1.89m multiplied by 40mm multiplied by 20mm, and the elastic modulus is 2.1 multiplied by 10 11 N/m. In order to check the accuracy of the conversion result, the displacement at the acceleration center is measured using the laser displacement sensor 5. A schematic of the test arrangement is shown in figure 1. The sampling frequency of the laser displacement sensor and the acceleration sensor in the test is set to be 500Hz.
In this embodiment, the acceleration data in the x direction of the mounted acceleration sensor is selected for analysis, and the acceleration signal and the displacement signal obtained by the test in the experiment are shown in fig. 2 (a) and 2 (b). As can be seen from the figure, the test is divided into three phases, namely a pre-test phase, a formal test phase and a test stopping phase. The first two phases of data are selected for analysis, namely the first 22s of acceleration signal for displacement reconstruction. Assuming that the acceleration signal of the first 22s satisfies the formula (1), before using the acceleration signal to perform displacement reconstruction, the acceleration signal is first fitted by using the pluronic signal, namely formulas (2) and (3), and in the fitting process, the mode order needs to be adjusted so as to achieve the best fitting effect, in this experiment, the mode order is set to 3000, and the fitting result is shown in fig. 3 (a). Fig. 3 (b) and (c) show the fitting results for 2 to 2.1 seconds and 10 to 10.1 seconds. It can be seen that the solved Prooni signal parameters fit better to the test acceleration, which also proves to be representative of the original acceleration signal.
After obtaining the parameters of the pluronic signal, which can represent the measured acceleration, the actual acceleration of the structure can be reconstructed by using the formula (5). Since the mode order is set to 3000, 1500 components are fitted together. The frequency corresponding to each component can be calculated by the formula (4), and the frequency of the total 8 components is found to be infinitely close to 0Hz through calculation. After these 8 components are removed, the true and initial velocities of the structure can be solved by using the remaining Proonil signal parameters through formulas (6), (7) and (8), and the true and initial displacements of the structure can be reconstructed by formulas (9), (10) and (11).
Comparison of results:
the reconstructed displacement results are shown in fig. 4 (a) using the decomposed pluronic signal parameters and using the established mathematical relationship of acceleration and displacement. Meanwhile, fig. 4 (b) and (c) show the reconstruction results for 2 to 3 seconds and 12 to 13 seconds. From the results, the reconstruction results have better consistency with the test results of the laser displacement sensor. This also demonstrates the correctness of the method of the invention.
Claims (3)
1. The structure dynamic displacement non-integral reconstruction method is characterized by comprising the following specific steps:
(1) Decomposing and characterizing the actually measured acceleration signal into a structure real acceleration signal, a noise signal and an acceleration baseline drift signal;
(2) Fitting the structural real acceleration signal, the noise signal and the acceleration baseline drift signal by using the Prooni signal parameters;
(3) Obtaining a Prooni signal parameter representation form of the actually measured acceleration signal;
(4) Solving a Prooni signal parameter of the actually measured acceleration signal;
(5) Stripping an acceleration baseline drift signal by utilizing a Prooni signal parameter of the obtained actual measurement acceleration signal, and removing the acceleration baseline drift signal;
(6) Establishing a mathematical relationship between the acceleration signal after the baseline drift term is removed and the real speed of the structure;
(7) Obtaining an expression of the real speed of the structure, and solving the initial speed of the structure; integrating the real speed expression obtained by solving to obtain a real displacement expression of the structure;
(8) Obtaining a reconstructed structural displacement and an initial displacement;
In the steps (1) - (3), the structural vibration, the environmental noise and the baseline drift problem of the acceleration sensor are simultaneously considered; in the steps (1) - (3), the acceleration signal is divided into a drift term, a structural information term and a noise component term, and is uniformly fitted by using a Proonil signal sequence, and the actually measured acceleration signal is uniformly expressed in a form for meeting the characteristics of the Proonil signal, specifically as follows:
Will actually measure acceleration signal Characterized by:
Wherein a (t) is a structural real acceleration signal, epsilon (t) is a noise signal contained in a test signal, u is a baseline drift component in the test acceleration signal, and t represents a corresponding moment;
Assuming that three signal components of a structural real acceleration signal a (t), a noise signal epsilon (t) and a baseline drift component u all meet the characteristics of a Prooni signal, and fitting the acceleration by using a Prooni signal sequence, and writing the signals into the following form:
wherein N p represents the number of components in the structural signal, N n represents the number of components in the noise signal, and γ and λ are the parameters of the pluronic signal for the corresponding components;
the three parts in the above formula are collectively expressed as
Wherein N l +1 represents the component quantity in the measured acceleration signal, namely N l＝Np+Nn;
steps (4) - (5) isolate the pluronic signal parameters representing the drift term and reconstruct the acceleration signal using the remaining pluronic signal sequence, as follows:
solving the formula (3), and calculating to obtain a Prooni signal parameter as AndParameters of the obtained Prooni signalsAndDividing the signal into drift items respectively corresponding to the signals, and constructing component items and noise component items; wherein the component frequency representing the baseline drift is far smaller than the frequencies of the structural signal component and the noise signal component, and is obtained by calculationThe frequencies of the corresponding components are calculated as follows:
Wherein imag represents an imaginary part;
By aligning Judging and corresponding toAndScreening to reconstruct the acceleration signal using the remaining pluronic signal sequence:
The acceleration signal contaminated by noise after the baseline drift is removed is represented by the formula (5).
2. The method of claim 1, wherein the step (6) establishes a mathematical relationship between the acceleration signal after removal of the drift term and the true speed of the structure, and solves the initial speed of the structure using the mathematical relationship.
3. The method of non-integral reconstruction of dynamic displacement of a structure according to claim 1, wherein the steps (7) - (8) solve for the displacement expression and the initial displacement of the structure based on the actual displacement of the structure and the mathematical relationship between acceleration and velocity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011097110.6A CN112667952B (en) | 2020-10-14 | 2020-10-14 | Structure dynamic displacement non-integral reconstruction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011097110.6A CN112667952B (en) | 2020-10-14 | 2020-10-14 | Structure dynamic displacement non-integral reconstruction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112667952A CN112667952A (en) | 2021-04-16 |
| CN112667952B true CN112667952B (en) | 2024-08-06 |
Family
ID=75403262
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011097110.6A Active CN112667952B (en) | 2020-10-14 | 2020-10-14 | Structure dynamic displacement non-integral reconstruction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112667952B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114692669A (en) * | 2021-12-16 | 2022-07-01 | 浙江华东测绘与工程安全技术有限公司 | Forecasting method and application of three-dimensional space attitude of offshore wind turbine |
| CN114296150B (en) * | 2021-12-27 | 2024-12-20 | 河北环鼎石油设备有限责任公司 | An acceleration correction method and correction device for an array lateral instrument |
| CN117128956B (en) * | 2023-08-30 | 2024-03-26 | 中国海洋大学 | Dynamic tilt angle acquisition method based on angular velocity conversion and equipment applying this method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101221066A (en) * | 2008-01-25 | 2008-07-16 | 太原理工大学 | Engineering Nonlinear Vibration Testing Method |
| CN104142219A (en) * | 2014-07-17 | 2014-11-12 | 浙江工业大学 | Spindle system operation modal analysis method based on multi-point pulse excitation |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104154893A (en) * | 2014-08-20 | 2014-11-19 | 中国科学技术大学 | Vibration displacement response reconstruction method based on discrete singular convolution |
| CN105654062B (en) * | 2016-01-07 | 2019-10-01 | 中国海洋大学 | The weak modal idenlification of ocean structure and time domain reconstruction method |
| CN108180983A (en) * | 2017-12-29 | 2018-06-19 | 燕山大学 | The emergency management and rescue vehicle vibration displacement reconstructing method of adaptive time-frequency domain mixed filtering |
| CN108764073B (en) * | 2018-05-14 | 2021-09-21 | 华南理工大学 | Acceleration noise filtering and integrating method combined with spectrum energy form fitting |
-
2020
- 2020-10-14 CN CN202011097110.6A patent/CN112667952B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101221066A (en) * | 2008-01-25 | 2008-07-16 | 太原理工大学 | Engineering Nonlinear Vibration Testing Method |
| CN104142219A (en) * | 2014-07-17 | 2014-11-12 | 浙江工业大学 | Spindle system operation modal analysis method based on multi-point pulse excitation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112667952A (en) | 2021-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112667952B (en) | Structure dynamic displacement non-integral reconstruction method | |
| Park et al. | Displacement estimation using multimetric data fusion | |
| Liu et al. | Displacement estimation from measured acceleration for fixed offshore structures | |
| CN100561162C (en) | A kind of virtual oscillating table detection signal processing method and equipment thereof | |
| US8775114B2 (en) | Method and apparatus for determining phase sensitivity of an accelerometer based on an analysis of the harmonic components of the interference signal | |
| CN107329932B (en) | Time-frequency domain modal parameter identification method based on nonlinear frequency modulation component decomposition | |
| Arias-Lara et al. | Assessment of methodologies to estimate displacements from measured acceleration records | |
| CN102980756A (en) | Method and system for testing dynamic stiffness | |
| CN114186577A (en) | Vibration acceleration signal denoising method and device based on wavelet transformation | |
| CN113820003A (en) | Acceleration real-time reconstruction dynamic displacement method suitable for bridge vibration monitoring | |
| CN110907827B (en) | Motor transient distortion measuring method and system | |
| US20230129913A1 (en) | Method for predicting heaving motion parameters of semi-submersible offshore platform based on heaving acceleration | |
| Yang et al. | Nonlinear time-varying vibration system identification using parametric time–frequency transform with spline kernel | |
| He et al. | Displacement reconstruction of beams subjected to moving load using data fusion of acceleration and strain response | |
| CN114964673B (en) | Structural frequency response function correction method for frequency spectrum leakage error | |
| CN108680247B (en) | Vibration signal conversion method based on low frequency filter correction of vibration intensity | |
| CN106980722B (en) | Method for detecting and removing harmonic component in impulse response | |
| Li et al. | A study of joint time-frequency analysis-based modal analysis | |
| Lu et al. | Cepstral operational modal analysis for multiple-input systems based on the real cyclic cepstrum | |
| Cheng et al. | AR model-based crosstalk cancellation method for operational transfer path analysis | |
| Gu et al. | Research on dynamic calibration and compensation method of strain-gauge type force sensor | |
| Smyth et al. | Integration of measured response signals for nonlinear structural health monitoring | |
| JP3045759B2 (en) | Settling characteristics measurement method | |
| Czop | Application of advanced data-driven parametric models to load reconstruction in mechanical structures and systems | |
| Link | Calibration of accelerometers by shock excitation and laser interferometry |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |