CN111652154A - Underdetermined system mode identification method based on automatic frequency band segmentation - Google Patents

Underdetermined system mode identification method based on automatic frequency band segmentation Download PDF

Info

Publication number
CN111652154A
CN111652154A CN202010499791.2A CN202010499791A CN111652154A CN 111652154 A CN111652154 A CN 111652154A CN 202010499791 A CN202010499791 A CN 202010499791A CN 111652154 A CN111652154 A CN 111652154A
Authority
CN
China
Prior art keywords
frequency
time
mode
short
point
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.)
Granted
Application number
CN202010499791.2A
Other languages
Chinese (zh)
Other versions
CN111652154B (en
Inventor
姚小俊
杨欣
赵少伟
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hebei University of Technology
Original Assignee
Hebei University of Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Hebei University of Technology filed Critical Hebei University of Technology
Priority to CN202010499791.2A priority Critical patent/CN111652154B/en
Publication of CN111652154A publication Critical patent/CN111652154A/en
Application granted granted Critical
Publication of CN111652154B publication Critical patent/CN111652154B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)

Abstract

The invention belongs to the technical field of civil engineering structure health monitoring data analysis, and particularly provides a method for identifying an underdetermined system mode based on automatic frequency band segmentation; the response of the vibration system is transformed into a time-frequency domain through short-time Fourier transform, so that the window length of the short-time Fourier is adaptively determined by utilizing information entropy, the minimum value of a frequency spectrum is automatically searched according to a scale space peak detection method and is determined as a segmentation point in the frequency range, then a single-mode point on a time-frequency plane is detected in each frequency sub-band, the single-mode point is clustered to obtain a vibration mode matrix, and the number measured in each frequency sub-band is greater than that of an active mode, so that time-domain modal response is obtained through short-time Fourier inverse transformation, and finally the frequency and the damping ratio can be estimated by utilizing a logarithmic attenuation technology; the invention automatically positions the division point on the frequency axis, and uses sparse component analysis in the sub-frequency band, so that the division point can be accurately identified under the condition of larger under-determined range.

Description

Underdetermined system mode identification method based on automatic frequency band segmentation
Technical Field
The invention belongs to the technical field of analysis of health monitoring data of civil engineering structures, relates to a modal identification method under the condition of large underdetermined degree, and particularly relates to an underdetermined system modal identification method based on automatic frequency band segmentation.
Background
The modal parameters comprise modal frequency, vibration mode and damping ratio, and are important parameters for representing the dynamic characteristics of the civil engineering structure. The process of identifying modal parameters from vibration data is consistent with the principle of a blind source separation method, so that the modal identification method based on the blind source separation theory is developed. The number of active modalities of the actual structure is often unknown and the number of sensors may be limited, so the underdetermined blind source separation method is suitable for modality identification of large structures.
Sparse component analysis is suitable for handling underdetermined problems. The time-frequency transformation is a necessary step for realizing time-frequency response sparseness through sparse component analysis, and mainly adopts short-time Fourier transformation to transform time-domain data to a time-frequency domain, and then utilizes a clustering technology to estimate the mode shape after the time-frequency transformation. When the underrun of the blind source separation system is large, meaning that the number of available measurements is much smaller than the number of modes participating in the vibration, the accuracy of the sparse component analysis method will be reduced.
To address this problem, by dividing the frequency range into subbands, using sparse component analysis in the subbands may improve the accuracy of modal identification when the degree of underplanning is large. However, dividing the frequency range often requires manual selection from the spectrum.
Therefore, it is important to research an automatic band segmentation technique for the sparse component analysis-based mode identification. .
Disclosure of Invention
The invention aims to provide an underdetermined system mode identification method based on automatic frequency band segmentation so as to improve the mode identification precision of a sparse component analysis method under the condition of larger underdetermined degree.
The invention adopts the following technical scheme:
an underdetermined system mode identification method based on automatic frequency band segmentation comprises the following steps:
the first step is as follows: converting the acceleration response to a time-frequency domain;
converting the response of the vibration system to a time-frequency domain by a short-time Fourier transform; the expression in the time-frequency domain is
Figure BDA0002524278950000021
In the formula (I), the compound is shown in the specification,
Figure BDA0002524278950000022
and
Figure BDA0002524278950000023
are respectively responses
Figure BDA0002524278950000024
And modal response
Figure BDA0002524278950000025
A short-time fourier transform at the frequency ω at time i.
Figure BDA0002524278950000026
Is that
Figure BDA0002524278950000027
The ith element in (1);
the second step is that: determining the window length of short-time Fourier transform by using the information entropy;
coefficient of time frequency
Figure BDA0002524278950000028
Considered as a probability distribution sequence, the probability of each time-frequency coefficient can be calculated as:
Figure BDA0002524278950000029
then, the entropy of the time-frequency representation is obtained:
Figure BDA00025242789500000210
calculating entropy values of time-frequency coefficients under different window lengths, determining the window length when the entropy takes the minimum value as an optimal window length parameter, and calculating a short-time Fourier transform coefficient of structural response under the window length;
the third step: automatically segmenting frequency sub-bands using scale space peak detection;
based on the theory that the vicinity of the peak value of the frequency response function is dominant in a certain order mode, selecting a minimum value between two peak values of a power spectrum as a dividing point of a frequency band; t is tkThe amplitude of the short-time Fourier transform coefficient at the ith position at time and frequency of omega is
Figure BDA00025242789500000211
The target amplitude of the peak pickup is represented as
Figure BDA00025242789500000212
Wherein AMP isi(tkω) is a search
Figure BDA00025242789500000213
Target amplitude of minimum value. Automated detection of AMPs using a scale-space approachi(tkω) to obtain a separation point of the sub-band division, and performing frequency sub-band division on the corresponding short-time Fourier transform coefficient;
the fourth step: selecting a single-mode point in each sub-band;
selecting time frequency points which mainly contribute to the j-th order mode from a time frequency plane, and adopting a formula:
Figure BDA00025242789500000214
choosing is performed where Δ θ is a given threshold, Re {. cndot.) denotes the real part of the extracted data, Im {. cndot.) denotes the imaginary part of the extracted data, and the subscript ΩjRepresenting the jth time-frequency subspace, and marking the single mode point corresponding to the jth order mode as the mode point
Figure BDA0002524278950000031
The fifth step: estimating the vibration mode by using the short-time Fourier coefficient of the single-mode point through a clustering technology;
within each sub-frequency band, for the time-frequency coefficient at the single-mode point
Figure BDA0002524278950000032
Clustering is carried out, wherein N is the number of modes participating in vibration, and the obtained clustering center is the vibration mode of each order of modes;
and a sixth step: frequency and damping ratio identification;
calculating the modal response of the time-frequency domain through an inverse operation:
Figure BDA0002524278950000033
wherein
Figure BDA0002524278950000034
Is shown as
Figure BDA0002524278950000035
The pseudo-inverse of (a) is,
Figure BDA0002524278950000036
and further performing short-time inverse Fourier transform on the time-frequency coefficient of the j-th order modal response to obtain a time-domain modal response, and finally estimating the damping ratio by using a logarithmic attenuation method.
Compared with the prior art, the invention has the following beneficial effects:
the response of the vibration system is transformed into a time-frequency domain through short-time Fourier transform, and the window length of the short-time Fourier is adaptively determined by utilizing the information entropy; automatically searching a minimum value of a frequency spectrum by using a scale space peak value detection method, and determining the minimum value as a segmentation point in a frequency range; detecting single-mode points on a time-frequency plane in each frequency sub-band, and clustering the single-mode points to obtain a vibration mode matrix; since the number of measurements is greater than the number of active modes in each frequency subband, the time domain modal response is obtained by short-time inverse fourier transform, and the frequency and damping ratio are further estimated using logarithmic attenuation techniques.
According to the invention, the frequency range is divided into sub-frequency bands, the underdetermined problem is converted into the overdetermined problem which is easy to solve, the division point on the frequency axis can be automatically positioned without manual selection based on the scale space peak value detection method, and the accuracy of mode identification under the condition of larger underdetermined range is improved.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the present invention will be further described in detail with reference to specific examples.
Example one
Taking a 10-layer linear shear building model, wherein the floor mass and the interlayer rigidity of each layer are respectively 1000Kg and 1.76108N/m; the mass proportion damping is adopted, the damping coefficient is 2, and the obtained maximum damping ratio is 1.5%. And generating free vibration response by applying initial conditions, and performing modal identification by adopting the first two structural responses.
Based on the above conditions, the present embodiment provides an underdetermined system mode identification method based on automatic band segmentation, including the following steps:
the first step is as follows: converting the acceleration response to a time-frequency domain;
the response of the vibrating system is converted to the time-frequency domain by a short-time fourier transform. The expression in the time-frequency domain is
Figure BDA0002524278950000041
In the formula (I), the compound is shown in the specification,
Figure BDA0002524278950000042
and
Figure BDA0002524278950000043
are respectively responses
Figure BDA0002524278950000044
And modal response
Figure BDA0002524278950000045
Short-time Fourier transform at time t at frequency ω;
Figure BDA0002524278950000046
is that
Figure BDA0002524278950000047
The ith element in (1); acceleration data is acquired by an acceleration sensor in a structural health monitoring system;
the second step is that: determining the window length of short-time Fourier transform by using the information entropy;
coefficient of time frequency
Figure BDA0002524278950000048
Considered as a probability distribution sequence, the probability of each time-frequency coefficient can be calculated as:
Figure BDA0002524278950000049
then obtaining the time-frequency meterEntropy of the graph:
Figure BDA00025242789500000410
calculating information entropy values of different window lengths (0.2s to 5s), wherein the information entropy is minimum when the window length is 1s, so that the optimal window length is selected to be 1s, and calculating a short-time Fourier transform coefficient of structural response under 1 s;
the third step: automatically segmenting frequency sub-bands using scale space peak detection;
based on the theory that the vicinity of the peak of the frequency response function is dominant for a certain order mode, the minimum value between two peaks of the power spectrum is selected as the dividing point of the frequency band. t is tkThe amplitude of the short-time Fourier transform coefficient at the ith position at time and frequency of omega is
Figure BDA00025242789500000411
The target amplitude of the peak pickup is represented as
Figure BDA00025242789500000412
Wherein AMP isi(tkω) is a search
Figure BDA00025242789500000413
Target amplitude of minimum value. Automated detection of AMPs using a scale-space approachi(tkω) to obtain a separation point of the sub-band division, so that the frequency axis is divided into ten bands;
the fourth step: selecting a single-mode point in each sub-band;
selecting time frequency points which mainly contribute to the j-th order mode from a time frequency plane, and adopting a formula:
Figure BDA00025242789500000414
selecting, and marking the single mode point corresponding to the j-th order mode as the single mode point
Figure BDA0002524278950000051
The fifth step: estimating the vibration mode by using the short-time Fourier coefficient of the single-mode point through a clustering technology;
within each sub-frequency band, for the time-frequency coefficient at the single-mode point
Figure BDA0002524278950000052
Clustering is carried out, wherein N is the number of modes participating in vibration, and the obtained clustering center is the vibration mode of each order of modes;
and a sixth step: frequency and damping ratio identification;
calculating the modal response of the time-frequency domain through an inverse operation:
Figure BDA0002524278950000053
estimating the damping ratio by using a logarithmic attenuation method, wherein the finally identified modal frequency of each order is as follows: 9.97Hz, 29.73Hz, 48.76Hz, 66.75Hz, 83.27Hz, 97.60Hz, 110.34Hz, 120.76Hz, 127.62Hz and 132.26Hz, and the damping ratio of each step is: 1.52%, 0.52%, 0.31%, 0.23%, 0.18%, 0.15%, 0.11%, 0.13% and 0.12%.
The seventh step: and determining the health state of the civil engineering structure based on the modal parameters obtained by the identification.
Example two
The embodiment provides a civil engineering structure health state monitoring system based on automatic frequency band division, which comprises a remote server and an acceleration sensor which is arranged at a main body part of a civil engineering structure and is used for acquiring acceleration data; the acceleration sensor can comprise a controller, a GPS module, a thermometer, an accelerometer, a gyroscope, a 3D compass, a wireless transceiver and a power supply module, wherein the controller is respectively connected with the wireless transceiver, the accelerometer and the power supply module, and the GPS module, the thermometer, the gyroscope and the 3D compass are all connected with the accelerometer; the acceleration sensor is connected with the remote server in a wired or wireless mode, and acceleration data acquired by the acceleration sensor is transmitted to the remote server for modal identification.
The remote server is used for executing the following steps:
the first step is as follows: converting the acceleration response to a time-frequency domain;
transforming the response of the vibrating system by short-time Fourier transformConverting to a time-frequency domain; the expression in the time-frequency domain is
Figure BDA0002524278950000054
In the formula (I), the compound is shown in the specification,
Figure BDA0002524278950000055
and
Figure BDA0002524278950000056
are respectively responses
Figure BDA0002524278950000057
And modal response
Figure BDA0002524278950000058
Short-time Fourier transform at a frequency ω at time i;
Figure BDA0002524278950000059
is that
Figure BDA00025242789500000510
The ith element in (1);
the second step is that: determining the window length of short-time Fourier transform by using the information entropy;
coefficient of time frequency
Figure BDA0002524278950000061
Considered as a probability distribution sequence, the probability of each time-frequency coefficient can be calculated as:
Figure BDA0002524278950000062
then, the entropy of the time-frequency representation is obtained:
Figure BDA0002524278950000063
calculating entropy values of time-frequency coefficients under different window lengths, determining the window length when the entropy takes the minimum value as an optimal window length parameter, and calculating a short-time Fourier transform coefficient of structural response under the window length;
the third step: automatically segmenting frequency sub-bands using scale space peak detection;
frequency-basedThe method comprises the following steps that a theory that a certain order mode is dominant is formed near the peak value of a rate response function, and a minimum value between two peak values of a power spectrum is selected as a dividing point of a frequency band; t is tkThe amplitude of the short-time Fourier transform coefficient at the ith position at time and frequency of omega is
Figure BDA0002524278950000064
The target amplitude of the peak pickup is represented as
Figure BDA0002524278950000065
Wherein AMP isi(tkω) is a search
Figure BDA0002524278950000066
Target amplitude of minimum value. Automated detection of AMPs using a scale-space approachi(tkω) to obtain a separation point of the sub-band division, and performing frequency sub-band division on the corresponding short-time Fourier transform coefficient;
the fourth step: selecting a single-mode point in each sub-band;
selecting time frequency points which mainly contribute to the j-th order mode from a time frequency plane, and adopting a formula:
Figure BDA0002524278950000067
choosing is performed where Δ θ is a given threshold, Re {. cndot.) denotes the real part of the extracted data, Im {. cndot.) denotes the imaginary part of the extracted data, and the subscript ΩjRepresenting the jth time-frequency subspace, and marking the single mode point corresponding to the jth order mode as the mode point
Figure BDA0002524278950000068
The fifth step: estimating the vibration mode by using the short-time Fourier coefficient of the single-mode point through a clustering technology;
within each sub-frequency band, for the time-frequency coefficient at the single-mode point
Figure BDA0002524278950000069
Clustering is carried out, N is the number of the modes participating in vibration, and the obtained clustering center is the mode of each orderThe vibration mode of (3);
and a sixth step: frequency and damping ratio identification;
calculating the modal response of the time-frequency domain through an inverse operation:
Figure BDA00025242789500000610
wherein
Figure BDA00025242789500000611
Is shown as
Figure BDA00025242789500000612
The pseudo-inverse of (a) is,
Figure BDA00025242789500000613
and further performing short-time inverse Fourier transform on the time-frequency coefficient of the j-th order modal response to obtain a time-domain modal response, and finally estimating the damping ratio by using a logarithmic attenuation method.
And finally, determining the health state of the civil engineering structure based on the modal parameters obtained by the identification.
Finally, the principle and embodiments of the present invention are explained by using specific examples, and the above descriptions of the examples are only used to help understand the core idea of the present invention, and the present invention can be modified and modified without departing from the principle of the present invention, and the modified and modified examples also fall into the protection scope of the present invention.

Claims (1)

1. An underdetermined system mode identification method based on automatic frequency band segmentation is characterized by comprising the following steps:
the first step is as follows: converting the acceleration response to a time-frequency domain;
converting the response of the vibration system to a time-frequency domain by a short-time Fourier transform; the expression in the time-frequency domain is
Figure FDA0002524278940000011
In the formula (I), the compound is shown in the specification,
Figure FDA0002524278940000012
and
Figure FDA0002524278940000013
are respectively responses
Figure FDA0002524278940000014
And modal response
Figure FDA0002524278940000015
Short-time Fourier transform at time t at frequency ω;
Figure FDA0002524278940000016
is that
Figure FDA0002524278940000017
The ith element in (1);
the second step is that: determining the window length of short-time Fourier transform by using the information entropy;
coefficient of time frequency
Figure FDA0002524278940000018
Considered as a probability distribution sequence, the probability of each time-frequency coefficient can be calculated as:
Figure FDA0002524278940000019
then, the entropy of the time-frequency representation is obtained:
Figure FDA00025242789400000110
calculating entropy values of time-frequency coefficients under different window lengths, determining the window length when the entropy takes the minimum value as an optimal window length parameter, and calculating a short-time Fourier transform coefficient of structural response under the window length;
the third step: automatically segmenting frequency sub-bands using scale space peak detection;
based on the theory that the vicinity of the peak value of the frequency response function is dominant in a certain order mode, selecting a minimum value between two peak values of a power spectrum as a dividing point of a frequency band; t is tkThe ith time with the frequency of omegaThe short-time Fourier transform coefficients of a position have an amplitude of
Figure FDA00025242789400000111
The target amplitude of the peak pickup is represented as
Figure FDA00025242789400000112
Wherein AMP isi(tkω) is a search
Figure FDA00025242789400000113
A target amplitude of minima; automated detection of AMPs using a scale-space approachi(tkω) to obtain a separation point of the sub-band division, and performing frequency sub-band division on the corresponding short-time Fourier transform coefficient;
the fourth step: selecting a single-mode point in each sub-band;
selecting time frequency points which mainly contribute to the j-th order mode from a time frequency plane, and adopting a formula:
Figure FDA00025242789400000114
choosing is performed where Δ θ is a given threshold, Re {. cndot.) denotes the real part of the extracted data, Im {. cndot.) denotes the imaginary part of the extracted data, and the subscript ΩjRepresenting the jth time-frequency subspace, and marking the single mode point corresponding to the jth order mode as the mode point
Figure FDA0002524278940000021
The fifth step: estimating the vibration mode by using the short-time Fourier coefficient of the single-mode point through a clustering technology;
within each sub-frequency band, for the time-frequency coefficient at the single-mode point
Figure FDA0002524278940000022
j is 1,2, …, and N is clustering, wherein N is the number of modes participating in vibration, and the obtained clustering center is the mode shape of each order of mode;
and a sixth step: frequency and damping ratio identification;
calculating the modal response of the time-frequency domain through an inverse operation:
Figure FDA0002524278940000023
wherein
Figure FDA0002524278940000024
Is shown as
Figure FDA0002524278940000025
The pseudo-inverse of (a) is,
Figure FDA0002524278940000026
and further performing short-time inverse Fourier transform on the time-frequency coefficient of the j-th order modal response to obtain a time-domain modal response, and finally estimating the damping ratio by using a logarithmic attenuation method.
CN202010499791.2A 2020-06-04 2020-06-04 Underdetermined system modal identification method based on automatic frequency band segmentation Active CN111652154B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010499791.2A CN111652154B (en) 2020-06-04 2020-06-04 Underdetermined system modal identification method based on automatic frequency band segmentation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010499791.2A CN111652154B (en) 2020-06-04 2020-06-04 Underdetermined system modal identification method based on automatic frequency band segmentation

Publications (2)

Publication Number Publication Date
CN111652154A true CN111652154A (en) 2020-09-11
CN111652154B CN111652154B (en) 2023-05-30

Family

ID=72351135

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010499791.2A Active CN111652154B (en) 2020-06-04 2020-06-04 Underdetermined system modal identification method based on automatic frequency band segmentation

Country Status (1)

Country Link
CN (1) CN111652154B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112348829A (en) * 2020-11-02 2021-02-09 东华理工大学 Method for separating branches and leaves of ground LiDAR point cloud based on modal point evolution
CN115982625A (en) * 2023-01-06 2023-04-18 哈尔滨工业大学（深圳） Long-term working mode analysis method and detection method based on prior information

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104809097A (en) * 2015-04-30 2015-07-29 吴伟 Multi-window function selection method for time-frequency domain signal processing
CN108009584A (en) * 2017-12-01 2018-05-08 西安交通大学 Deficient based on the detection of single source point determines blind source separation method
CN108491608A (en) * 2018-03-06 2018-09-04 大连理工大学 The Sparse Component Analysis method of distinguishing structural mode when number of sensors is incomplete

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104809097A (en) * 2015-04-30 2015-07-29 吴伟 Multi-window function selection method for time-frequency domain signal processing
CN108009584A (en) * 2017-12-01 2018-05-08 西安交通大学 Deficient based on the detection of single source point determines blind source separation method
CN108491608A (en) * 2018-03-06 2018-09-04 大连理工大学 The Sparse Component Analysis method of distinguishing structural mode when number of sensors is incomplete

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
苏欣;孟晓风;: "基于信息熵的自适应窗长STFT算法在AFVISAR中的应用", 现代电子技术 *
贾晓健;尹涛;: "基于模态参数的钢框架螺栓连接节点损伤识别理论与实验研究", 武汉大学学报(工学版) *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112348829A (en) * 2020-11-02 2021-02-09 东华理工大学 Method for separating branches and leaves of ground LiDAR point cloud based on modal point evolution
CN112348829B (en) * 2020-11-02 2022-06-28 东华理工大学 Method for separating branches and leaves of ground LiDAR point cloud based on modal point evolution
CN115982625A (en) * 2023-01-06 2023-04-18 哈尔滨工业大学（深圳） Long-term working mode analysis method and detection method based on prior information
CN115982625B (en) * 2023-01-06 2023-10-03 哈尔滨工业大学（深圳） Priori information-based long-term working mode analysis method and detection method

Also Published As

Publication number Publication date
CN111652154B (en) 2023-05-30

Similar Documents

Publication Publication Date Title
CN104166804B (en) A kind of operation mode discrimination method based on time-frequency domain list source point sparse component analysis
CN109541028B (en) Method and system for positioning and detecting crack position of wind turbine blade
WO2018188432A1 (en) Model ordering method for modal identification of engineering structure
CN111652154B (en) Underdetermined system modal identification method based on automatic frequency band segmentation
CN106772227B (en) A kind of unmanned plane direction determining method based on the identification of vocal print multiple-harmonic
CN109188362B (en) Microphone array sound source positioning signal processing method
WO2019119195A1 (en) Target signal detection method and device, unmanned aerial vehicle, and agricultural unmanned aerial vehicle
CN111273297A (en) Horizontal array moving target depth estimation method based on AR wave number spectrum
CN109459745B (en) Method for estimating speed of moving sound source by using radiation noise
CN108491608B (en) Sparse component analysis method for structural modal identification when sensor number is incomplete
CN103020479A (en) Signal instantaneous frequency estimation method based on nonlinear frequency modulation wavelet transformation
CN102999473A (en) Detection and parameter estimation method for linear frequency modulation signals
CN109916434B (en) Optical fiber Fabry-Perot sensor cavity length multiple frequency related demodulation method
JP6762276B2 (en) Movement locus forming device, abnormality diagnosis system, movement locus forming method and program
WO2019104904A1 (en) Method for determining order in sparse component analysis-based modal identification
CN103743435A (en) Multi-sensor data fusion method
CN101587007A (en) Output-only wavelet analytical method for recognizing flexible bridge structure kinetic parameter
CN112405072A (en) On-line monitoring method and device for cutting chatter of machine tool
CN111168569B (en) Grinding material removal amount prediction method, device, equipment and storage medium
CN110702042B (en) Thickness estimation method under signal aliasing condition in ultrasonic pulse echo thickness measurement
CN109410178A (en) A kind of workpiece crack detection method and system
CN113302511B (en) Interference processing method and device
CN107728104A (en) A kind of improved satellite navigation interference direction-finding method
CN108646248B (en) Passive acoustic speed and distance measuring method for low-speed moving sound source
CN106679659A (en) Signal denoising method based on parameter-adjustable nonlinear track differentiator

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