AU2020101196A4 - Method and system for testing working modality of thin-walled member based on monocular visual optical flow tracking - Google Patents
Method and system for testing working modality of thin-walled member based on monocular visual optical flow tracking Download PDFInfo
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- 238000012360 testing method Methods 0.000 title claims abstract description 25
- 238000006073 displacement reaction Methods 0.000 claims abstract description 62
- 238000003384 imaging method Methods 0.000 claims abstract description 35
- 238000005314 correlation function Methods 0.000 claims abstract description 34
- 230000004044 response Effects 0.000 claims abstract description 34
- 239000011159 matrix material Substances 0.000 claims abstract description 28
- 238000005259 measurement Methods 0.000 claims abstract description 28
- 238000013178 mathematical model Methods 0.000 claims abstract description 17
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- 238000010586 diagram Methods 0.000 description 9
- 230000005284 excitation Effects 0.000 description 9
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/02—Arrangements for measuring frequency, e.g. pulse repetition rate; Arrangements for measuring period of current or voltage
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
- G01M5/0066—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by exciting or detecting vibration or acceleration
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R9/00—Instruments employing mechanical resonance
- G01R9/04—Instruments employing mechanical resonance using vibrating reeds, e.g. for measuring frequency
-
- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03B—APPARATUS OR ARRANGEMENTS FOR TAKING PHOTOGRAPHS OR FOR PROJECTING OR VIEWING THEM; APPARATUS OR ARRANGEMENTS EMPLOYING ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ACCESSORIES THEREFOR
- G03B19/00—Cameras
- G03B19/02—Still-picture cameras
- G03B19/16—Pin-hole cameras
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01H—ELECTRIC SWITCHES; RELAYS; SELECTORS; EMERGENCY PROTECTIVE DEVICES
- H01H50/00—Details of electromagnetic relays
- H01H50/64—Driving arrangements between movable part of magnetic circuit and contact
- H01H50/74—Mechanical means for producing a desired natural frequency of operation of the contacts, e.g. for self-interrupter
- H01H50/76—Mechanical means for producing a desired natural frequency of operation of the contacts, e.g. for self-interrupter using reed or blade spring
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Abstract
of Description
The present invention discloses a method for testing a working modality of a
thin-walled member based on monocular visual optical flow tracking, including:
establish an imaging mathematical model and an industrial camera pinhole imaging
model based on a monocular visual off-plane vibration measurement device;
determine a target actual displacement function; obtain a vibration sequence image of
the thin-walled member, determine a target angular point; determine a displacement
response signal where each the target angular point vibrates with each frame of an
image; calculate an average correlation function of the displacement response signal,
establish a Toeplitz matrix, and calculate a working modality parameter of the
thin-walled member. A test system includes a model building module, a target actual
displacement function determination module, a sequence image acquisition module,
an angular point determination module, a target angular point determination module, a
displacement response signal determination module, an average correlation function
calculation module, and a calculation module of a working modality parameter of the
thin-walled member,. The present invention significantly improves the efficiency and
accuracy of the identification of the working modality parameter of the thin-walled
member.
Drawings of Description
101
Establishan imagingmathematicalmdelandanindustrialcamerapinhole
gingm del based on a monocular visual off-planevibrationmeasurement
evice
102
Determine a target actual displacement function according to an imaging
mathematical mdel and an industrial camera pinhole imaging model
103
Obtain a vibration sequence image of a thin-walled member
104
I
Track acharacteristic point in avibration sequence image of athin-walled 1
ember by using an optical flow matching tracking algorithm to determine an
guar point
1 105
Adopt a ps eudo angular point removal algorithm to proces s a angular point
remove a pseudo angular point, and determine a target angular point
1106
Determine a displacement respons e signal where each a target angular point
vibrates with each frame of an image according to an actual displacement
function of a target
1107
Calculate an average correlation function of a displacement response signal
ss Toet -108
Establish a To eplitz matrix by using a Cov-SSI algorithm according to an
average correlation function, and calculate a working modality parameter
of a thin-walled member according to a To eplitz matrix
FIG.1
1/6
Description
Drawings of Description
101 Establishan imagingmathematicalmdelandanindustrialcamerapinhole gingm del based on a monocular visual off-planevibrationmeasurement evice
102 Determine a target actual displacement function according to an imaging mathematical mdel and an industrial camera pinhole imaging model
103
Obtain a vibration sequence image of a thin-walled member
104 I Track acharacteristic point in avibration sequence image of athin-walled 1 ember by using an optical flow matching tracking algorithm to determine an guar point
1 105 Adopt a ps eudo angular point removal algorithm to proces s a angular point remove a pseudo angular point, and determine a target angular point
1106 Determine a displacement respons e signal where each a target angular point vibrates with each frame of an image according to an actual displacement function of a target
1107 Calculate an average correlation function of a displacement response signal
ss Toet -108 Establish a To eplitz matrix by using a Cov-SSI algorithm according to an average correlation function, and calculate a working modality parameter of a thin-walled member according to a To eplitz matrix
FIG.1
1/6
Description
TECHNICAL FIELD The present invention relates to the measurement of a working modality parameter of a thin-walled member, particularly to a method and system for testing a working modality of a thin-walled member based on monocular visual optical flow tracking.
TECHNICAL BACKGROUND A thin-walled member has the advantages of light weight, a compact structure, a big bearing capacity, etc., and is widely used in various industrial fields. However, the thin-walled member has the characteristics of low rigidity, weak strength and large size and are prone to vibration and deformation, causing problems such as noise and instability, and even causing serious mechanical failures, which results in major security accidents. Therefore, it is necessary to identify a vibration modality parameter of the thin-walled member efficiently and accurately. However, the thin-walled member that cannot be tested with artificial excitation or whose excitation magnitude cannot be measured cannot be performed with experimental modality test. Since even an experimental modality analysis method has large difference from actual working conditions, the true working state cannot be reflected. However, the working modality analysis method can better solve this problem.
The traditional contact type working modality test method changes the original dynamic characteristics of the thin-walled member to a certain extent due to the additional mass introduced by the installation of a sensor. At the same time, spatial resolution is low and the sensor is difficultly arranged. However, test technology based on optical non-contact working modality makes up for the shortcomings of the contact type working modality test. In particular, a machine visual vibration measurement has gradually become the most flexible and practical technology in the field of non-contact test. A typical machine visual vibration test method generally requires auxiliary structure light and requires to past a sign. This method has relatively high requirement on equipment, a measurement environment and preliminary work, which is difficult to meet the requirement of low-cost working modality test. In addition, a machine visual vibration test method based on unmarking is emerging. Although the process of pasting the sign is omitted, such method is essentially to track the edge characteristics of a structure and also has low efficiency and accuracy, poor applicability, and other problems.
INVENTION SUMMARY The objective of the present invention is to provide a method and system for testing a working modality of a thin-walled member based on monocular visual optical flow tracking, to solve the problems in the prior art mentioned above, and to make the identification efficiency and accuracy of a working modality parameter of the thin-walled member obviously improved.
To achieve the above objective, the present invention provides the following solution: the present invention provides a method for testing a working modality of a thin-walled member based on monocular visual optical flow tracking, comprising the
Description
following steps:
Si. establish an imaging mathematical model and an industrial camera pinhole imaging model based on a monocular visual off-plane vibration measurement device, wherein the thin-walled member in the monocular visual off-plane vibration measurement device vibrates in a direction perpendicular to the planes of an industrial camera and a lens. The industrial camera is fixed in right front of the thin-walled member;
S2. determine a target actual displacement function according to the imaging mathematical model and the industrial camera pinhole imaging model; obtain a vibration sequence image of the thin-walled member to determine a target angular point;
S3. determine a displacement response signal where each the target angular point vibrates with each frame of an image according to the target actual displacement function;
S4. calculate the average correlation function of the displacement response signal, use a Cov-SSI algorithm to establish a Toeplitz matrix, and calculate the working modality parameter of the thin-walled member. The working modality parameter of the thin-walled member comprises a natural frequency, a damping ratio and a modality vibration mode matrix.
Preferably, the imaging mathematical model in the Si is: ax X(t )x w(t) x(t) 2 kX(t )w(t)
y(t) 2 kY(t )w(t) b2
Where k = a / b2 , a is an image distance, b is an object distance, x(t) and y(t) are image coordinates, X(t) and Y(t) are space coordinates, and w(t) is an image pixel displacement.
Preferably, the target actual displacement function in S2: x(t)= kX(t)N(t)g(t) y(t)= kY(t)N(t)g(t)
Where N(t) = G(X(t), Y(t)) , G(X,Y) is a vibration mode function of the monocular visual off-plane vibration measurement device, and g(t) is a unit impulse response of the monocular visual off-plane vibration measurement device.
An optical flow matching tracking algorithm in S2 is used to track a characteristic point in the vibration sequence image of the thin-walled member and
Description
determine the angular point;
a pseudo angular point removal algorithm is used to process the angular point, remove a pseudo angular point, and determine the target angular point.
Preferably, the average correlation function in the S4 is used as an input term of the Cov-SSI algorithm to establish a Hankel matrix; a covariance sequence is calculated according to a Hankel matrix; the covariance sequence is constituted into a block to establish a Toeplitz matrix.
Preferably, a singular value decomposition algorithm in the S4 is used to solve the Toeplitz matrix to obtain the working modality parameter of the thin-walled member. The working modality parameter of the thin-walled member comprises a natural frequency, a damping ratio and a modal vibration mode matrix.
A system for testing a working modality of a thin-walled member based on monocular visual optical flow tracking according to claim 1 comprises:
a model building module, configured to establish an imaging mathematical model and an industrial camera pinhole imaging model based on a monocular visual off-plane vibration measurement device;
a target actual displacement function determination module, configured to determine a target actual displacement function according to the imaging mathematical model and the industrial camera pinhole imaging model;
a sequence image acquisition module, configured to obtain a vibration sequence image of the thin-walled member;
an angular point determination module, configured to track a characteristic point in the vibration sequence image of the thin-walled member by using an optical flow matching tracking algorithm to determine an angular point;
a target angular point determination module, configured to adopt a pseudo angular point removal algorithm to process the angular point, remove a pseudo angular point, and determine a target angular point;
a displacement response signal determining module, configured to determine a displacement response signal where each the target angular point vibrates with each frame of an image according to the actual displacement function of the target;
a displacement response signal determining module, configured to determine a displacement response signal where each the target angular point vibrates with each frame of an image according to the actual displacement function of the target;
a calculation module of a working modality parameter of the thin-walled member, configured to establish a Toeplitz matrix by using a Cov-SSI algorithm according to the average correlation function, and calculate the working modality parameter of the thin-walled member.
D e s cr i p t i o n
The present invention discloses the following technical effects: the present invention does not require auxiliary structure light, does not require to paste any sign or mark, and has relatively low requirements on equipment, a measurement environment and preliminary work, can realize multi-view point non-contact vibration modality measurement, effectively improves low computational efficiency and low measurement accuracy of the machine visual vibration modality measurement method, better solves the problems of low signal-to-noise ratio and difficult working modality parameter identification, and provides a convenient and reliable new method for a working modality test of a thin-walled member. Therefore, the method or system provided by the present invention not only saves the process of pasting a logo, but also improves the efficiency and accuracy of the identification of the working modality parameter of the thin-walled member.
BRIEF DESCRIPTION OF DRAWINGS In order to more clearly explain the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly introduced in the following. Obviously, the drawings in the following description are only some embodiments of the present invention. The person skilled in the art can obtain other drawings based on these drawings without the needs for creative labor.
FIG.1 is a schematic flowchart of a method for determining a working modality parameter of a thin-walled member according to an embodiment of the present invention;
FIG. 2 is a block diagram of a flowchart of a method for determining a working modality parameter of a thin-walled member according to an embodiment of the present invention;
FIG. 3 is a diagram of an imaging mathematical model based on a monocular visual off-plane vibration measurement device of the present invention;
FIG. 4 is a structural diagram of a monocular visual off-surface vibration measurement device according to an embodiment of the present invention;
FIG. 5 is a diagram of an external dimension of a typical thin-walled member to be tested according to the present invention;
FIG. 6 is a vibration time-history displacement curve diagram obtained by tracking three adjacent optical flow points under random pulse excitation selected by the present invention;
FIG. 7 is a sample segmentation and an average correlation function of the present invention;
FIG. 8 is a stable diagram of a Cov-SSI algorithm and an Acs-Cov-SSI algorithm of the present invention;
FIG. 9 is a first-order vibration mode diagram of a cantilever beam of the present
Description
invention;
FIG. 10 is a second-order vibration mode diagram of a cantilever beam of the present invention;
FIG. 11 is a structural diagram of a system for determining a working modality parameter of a thin-walled member according to an embodiment of the present invention.
In the drawings, 1: Image Region of Interest, 2: Bar Light Source, 3: Vibration Exciter Control System, 4: Image Acquisition and Processing System, 5: Power Amplifier 5, 6: High-Speed Industrial Camera, 7: Acceleration Sensor, 8: Vibration Exciter, 9: Rubber Rope 9, 10: Thin-Walled Member
DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENTS The technical solutions in the embodiments of the present invention will be described clearly and completely in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by the person skilled in the art fall within the protection scope of the present invention without the need for creative effort.
In order to make the above objects, characteristics and advantages of the present invention more obvious and understandable, the present invention will be further described in details in conjunction with the accompanying drawings and specific embodiments.
Embodiment 1 The present invention provides a method for testing a working modality of a thin-walled member based on monocular visual optical flow tracking, comprising the following steps:
With reference to FIGS. 1-4, an imaging mathematical model based on a monocular visual off-plane vibration measurement device shown in FIG. 3 is established. The calibration of an industrial camera is completed, and lens distortion is corrected.
(1.1) Establish the imaging mathematical model based on the monocular visual off-plane vibration measurement device.
Referring to FIG. 4, the device comprises an image region of interest (Region of Interest, hereinafter referred to as ROI) 1, a bar light source 2, a vibration exciter control system 3, an image acquisition and processing system 4, a power amplifier 5, a high-speed industrial camera 6, an acceleration sensor 7, a vibration exciter 8, a rubber rope 9, a thin-walled member 10. One end of the thin-walled member 10 is fixed on a vibration excitation rod below the vibration vibrator 8 while the other end of the thin-walled member 10 is in a freely suspended state. The vibration exciter 8 is suspended on a fixed bracket with the rubber rope 9. A tripod for the industrial camera
Description
6 is fixed horizontally in right front of the thin-walled member 10, is connected to a USB3.0 high-speed expansion card on a PCI slot of a desktop computer through a data cable, and transmits a collected sequence image to the computer. The bar light source 2 is located in front of the industrial camera 6. A random excitation signal is sent from a built-in vibration controller of the vibration exciter control system 3 in the computer, and is transmitted to the power amplifier 5, and is then transmitted to the vibration excitation rod of the vibration exciter 8 by the power amplifier 5 to drive the thin-walled member 10 under test to vibrate. The acceleration sensor 7 arranged at the end of the vibration excitation rod of the vibration exciter 8 feeds back a vibration signal to the vibration exciter control system 3 to ensure the accuracy of the vibration excitation signal generated by the vibration exciter 8. The wall member 10 is performed with a vibration test by means of the vibration exciter 8, and at the same time, the image acquisition and processing system 4 is used to continuously collect an image during the vibration process of the thin-walled member 10.
Since an off-plane vibration thin-walled member 10 vibrates in a direction perpendicular to the planes of the industrial camera and lens, there is a mapping relationship between the off-plane vibration of the thin-walled member 10 and the position thereof on an imaging plane, and the vibration characteristics of an object can be obtained by analyzing an imaging change.
The changes of the geometric information and position coordinates of the thin-walled member 10 in a space are all mapped into the imaging plane of the industrial camera 6 and are presented as changes in pixel displacement and image gray scale. By quantitatively describing the mapping relationship between sequence image imaging information and spatial geometric information, the corresponding mathematical model can be established: ax X(t )x w(t) x(t) - 2 kX(t )w(t)
ax Y(t )x w(t) y(t)- 2 kY(t )w(t)
In formula (1), K=a/b 2 , a is an image distance. b is an object distance. x(t) and y(t) are image coordinates, and X(t) and Y(t) are space coordinates. A spatial off-plane displacement amount (a target actual displacement), and w(t) is the pixel displacement of a target image.
If G(X,Y) is set as the vibration mode function of the monocular off-plane vibration measurement device, and g(t) is a unit impulse response of the monocular off-plane vibration measurement device, then the vibration displacement w(t) can be expressed by formula (2). w(t)= G(X,Y)g(t) (2)
The coordinates of the image point corresponding to a point on a space plane is a function of time; and the spatial coordinate corresponding to a point on the sequence image is a function of time, that is, N(t)= G(X(t),Y(t)) can be defined. Combining with formulas (1) (2), we can get:
D e s c r i pt i o n
x(t)=kX(t)N(t)g(t) (3) y(t)=kY(t)N(t)g(t)
At this time, the pixel displacement reflects the off-plane motion of the thin-walled member 10, and X(t), Y(t), N(t)and g(t) have the same periodic component. As can be seen from the above, when the pixel displacement is used to detect the off-plane vibration of the thin-walled member 10, it is necessary to accurately know the change in the pixel position of the characteristics in an image.
(1.2) calibrate the industrial camera 6 according to the correlation relationship between the three-dimensional geometric position of a certain point in the space on the surface of the above thin-walled member 10 and the corresponding point thereof in the image, that is, a geometric model parameter is determined. The parameter of the industrial camera is determined by extracting the coordinates of an angular point on a checkerboard from the image with a calibration board and by minimizing the distance between coordinates calculated by projection. In order to obtain the unique solution of an internal parameter, a plurality of pairs of images with the calibration board is required to be jointly solved.
(2) Use an optical flow matching tracking algorithm to track the characteristic point in a vibration sequence image of the thin-walled member to determine the angular point, use a pseudo angular point removal algorithm to remove a pseudo angular point, determine a target angular point, and then combine a target actual displacement function to determine a displacement response signal where each target angular point vibrates with each frame of the image.
(2.1) Use a monocular industrial camera to collect the vibration sequence image of the thin-walled member 10, and perform image preprocessing such as ROI selection and filtering on the vibration sequence image to ensure the efficiency and accuracy of a subsequent operation.
(2.2) Process the vibration sequence image of the thin-walled member according to the characteristics of the optical flow matching tracking algorithm, to determine the tracking angular point.
(2.3) Inversely derive a conditional inequation VITW + It 2t2(4) that
meets an optical flow estimation result (4) according to the solution of the least square method in optical flow estimation, VIT represents [II I ; w represents
u ; u represents a velocity vector in the x-axis direction of an image plane, v
represents a velocity vector of the y-axis direction in the image plane; I.represents
the gradient of an image grayscale in time; represents a norm. A reasonable value range of a correction term in a pyramid iteration is derived from this inequation. A
D e s c r ip t i o n
first estimation result is a conditional inequation of a first-order optical flow calculation result at a top layer.
(2.4) Determine an optical flow calculation result of each layer and each order of the angular point in a pyramid iterative process.
(2.5) Compare a calculation result of each the layer and each the order of the angular point with the reasonable value range. If the calculation result is not within the reasonable range, the angular point can be regarded as the pseudo angular point, which is removed.
(3) Use an average correlation function of the displacement response signal where each the target angular point vibrates with each frame the image to replace the displacement response signal as an input item of a covariance-driven random subspace (Cov-SSI) algorithm to identify a working modality parameter.
The average correlation function of the displacement response signal where each the target angular point vibrates with each frame of the image replaces this response signal as an input of the Cov-SSI algorithm, and the average correlation function is used to improve a signal-to-noise ratio. The correlation function of a plurality of sets of test data or the same set of data is divided into the correlation function of a plurality of segments of the data that is averaged and denoised, thereby improving the signal-to-noise ratio of the correlation function, obtaining the average correlation function, and finally taking the average correlation function as the input item of the Cov-SSI Algorithm to identify modality parameter.
(3.1) Determine the target angular point in step 2, and obtain the displacement response signal of 1 point on the thin-walled member according to the actual displacement function, and obtain the average correlation function of the displacement response signal:
K data segments are obtained from 1 channel, and each data segment comprises N data samples, that is, N is the number of data samples in one data segment, and there are K*N data samples in total.
One channel from 1 channels is used as a reference channel. The reference channel selects the channel with higher signal to noise through spectrum analysis or optimal analysis.
An autocorrelation function of the reference channel in the N data samples and the correlation function of the relative reference channel in the i-th (i=1, 2L 1) channel are calculated respectively.
(3.2) Establish a Hankel matrix using the average correlation function as the input item of the Cov-SSI algorithm.
According to the Hankel matrix, a covariance sequence is calculated and output, and then a Toeplitz matrix is established according to the covariance sequence.
Description
(4) Solve the Toeplitz matrix by a singular value decomposition (SVD) algorithm to obtain the working modality parameter of the thin-walled member; wherein the working modality parameter of the thin-walled member comprises a natural frequency, a damping ratio and a modality vibration mode matrix.
Embodiment 2 The cantilever beam in FIG. 5 is taken as an object, 15 strong characteristic points are sequentially selected in the y-direction on the cantilever beam as an optical flow matching tracking point. The response signal of the optical flow matching tracking point is divided into 13 segments to calculate the correlation function, that is, the correlation function is averaged by 13 times. It is assumed that the reference channel is the channel with the number 15, that is, the characteristic point 15 is used as a reference point. FIG. 6 shows the response signals of three measurement points under random pulse excitation. It can be known from the average correlation function in FIG. 7 that the response signal that the average correlation function represents is smoother and has a more obvious attenuation characteristic, and can obtain more accurate results by replacing the original response signal.
As shown in FIG. 8, a identification result of the traditional Cov-SSI algorithm in the steady-state graph after the 20th order is basically stable, but due to noise interference, more false modalities are generated. Compared with the traditional Cov-SSI algorithm, the Acs-Cov-SSI algorithm of the present invention has obvious denoising effect, its identification result is stable, and the accuracy of mode identification is guaranteed. Modality identification results of the Cov-SSI algorithm and the Acs-Cov-SSI algorithm are shown in Table 1:
Table 1 Cov-SSI Identification Acs-Co V-SSI Theoretical Identification Calculated Order Value Frequenc Damping Frequenc Damping Frequency y y The First 3.3698 0.0105 3.377 0.010 3.42 Order The Seco 20.23 0.0581 20.33 0.0612 21.02 nd Order
Referring to Table 1 and FIG. 8, the identification result of the Cov-SSI algorithm is relatively poor stability, appears to be divergent in the vicinity of the second order, and show false modality especially in the first and second orders. To facilitate comparison, one of the points is selected as the identification result of the original algorithm. Relative to a theoretical calculation value, the first-order fixed frequency errors of the Cov-SSI algorithm and Acs-Cov-SSI identification are 1.75% and 1.4%, respectively, and the identification result is basically credible. However, the natural frequency in Table 1 only selects a candidate point close to the theoretical calculation as the result rather than the stable value of the identification thereof. The second-order result recognized by the Cov-SSI algorithm obviously has divergence, and Acs-Cov-SSI is relatively stable for the modality identification result of a system structure, and the error of a randomly selected candidate point is basically within a
Description
tolerance range. The comparison between FIG. 9 and FIG. 10 shows that the modality identification result of the Acs-Cov-SSI algorithm is basically consistent with theoretical calculation. The above verification shows the effectiveness and superiority of the working modality test method of the thin-walled member based on the monocular optical flow tracking. The results show that the method improves the denoising ability of the Cov-SSI algorithm and enables the Cov-SSI algorithm to recognize the working modality of a system with the low signal-to-noise ratio.
To achieve the above objectives, the present invention also provides a system for determining a working modality parameter of a thin-walled member.
Referring to FIG. 11, the system for determining a working modality parameter of a thin-walled member provided by an embodiment of the present invention comprises:
a model building module, configured to establish an imaging mathematical model and an industrial camera pinhole imaging model based on a monocular visual off-plane vibration measurement device;
a target actual displacement function determination module, configured to determine a target actual displacement function according to the imaging mathematical model and the industrial camera pinhole imaging model;
a sequence image acquisition module, configured to obtain a vibration sequence image of the thin-walled member;
an angular point determination module, configured to track a characteristic point in the vibration sequence image of the thin-walled member by using an optical flow matching tracking algorithm to determine an angular point;
a target angular point determination module, configured to adopt a pseudo angular point removal algorithm to process the angular point, remove a pseudo angular point, and determine a target angular point;
a displacement response signal determining module, configured to determine a displacement response signal where each the target angular point vibrates with each frame of an image according to the actual displacement function of the target;
an average correlation function calculation module, configured to calculate an average correlation function of the displacement response signal; and
a calculation module of a working modality parameter of the thin-walled member, configured to establish a Toeplitz matrix by using a Cov-SSI algorithm according to the average correlation function, and calculate the working modality parameter of the thin-walled member according to the Toeplitz matrix.
The present invention does not need auxiliary devices such as a laser light source, a complex interference optical path, etc. Various measurements can be completed by analyzing an image sequence with the help of a common camera device, which has
D e s cr i p t i o n
strong applicability, high spatial resolution in the whole field, and a single measurement point multi-directional vibration measurement and other advantages. The core algorithm thereof is improved, that is, an optical flow algorithm based on the matching and removal of a pseudo-angular point is proposed, which better solves the problems of optical flow point mismatch and low operation efficiency. The average correlation function of the displacement response signal tracked by an optical flow is used as the input of the Cov-SSI algorithm to identify a modality parameter. This method improves the denoising ability of the Cov-SSI algorithm and effectively removes the false modality introduced by noise or improper ordering, so that the method can identify the working modality of a system with a low signal-to-noise ratio.
The above-mentioned embodiments are only for describing the preferred modes of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various variations and improvements made by the person skilled in the art should fall within the scope of protection determined by the claims of the present invention.
Claims (7)
1. A method for testing a working modality of a thin-walled member based on monocular visual optical flow tracking, comprising the following steps:
Si. establish an imaging mathematical model and an industrial camera pinhole imaging model based on a monocular visual off-plane vibration measurement device, wherein the thin-walled member in the monocular visual off-plane vibration measurement device vibrates in a direction perpendicular to the planes of an industrial camera and a lens, the industrial camera is fixed in right front of the thin-walled member;
S2. determine a target actual displacement function according to the imaging mathematical model and the industrial camera pinhole imaging model; obtain a vibration sequence image of the thin-walled member to determine a target angular point;
S3. determine a displacement response signal where each the target angular point vibrates with each frame of an image according to the target actual displacement function;
S4. calculate the average correlation function of the displacement response signal, use a Cov-SSI algorithm to establish a Toeplitz matrix, and calculate the working modality parameter of the thin-walled member.
2. The method for testing the working modality of the thin-walled member based on the monocular visual optical flow tracking according to claim 1, wherein the imaging mathematical model in the Si is: ax X(t )x w(t) x(t) 2 kX(t )w(t)
y(t) 2 kY(t )w(t) b2 In the formula,, k a / b 2 , a is an image distance, b is an object distance, x(t) and y(t) are image coordinates, X(t) and Y(t) are space coordinates, and w(t) is an image pixel displacement.
3. The method for testing the working modality of the thin-walled member based on the monocular visual optical flow tracking according to claim 2, wherein the target actual displacement function in the S2 is:
x(t) = kX(t)N(t)g(t) y(t) = kY(t)N(t)g(t)
Where N(t)= G(X(t), Y(t)), G(X,Y) is a vibration mode function of the
Claims monocular visual off-plane vibration measurement device, and g(t) is a unit impulse response of the monocular visual off-plane vibration measurement device.
4. The method for testing the working modality of the thin-walled member based on the monocular visual optical flow tracking according to claim 1, wherein an optical flow matching tracking algorithm in S2 is used to track a characteristic point in the vibration sequence image of the thin-walled member and determine the angular point;
a pseudo angular point removal algorithm is used to process the angular point, remove a pseudo angular point, and determine the target angular point.
5. The method for testing the working modality of the thin-walled member based on the monocular visual optical flow tracking according to claim 1, wherein the average correlation function in the S4 is used as an input term of the Cov-SSI algorithm to establish a Hankel matrix; a covariance sequence is calculated according to a Hankel matrix; the covariance sequence is constituted into a block to establish a Toeplitz matrix.
6. The method for testing the working modality of the thin-walled member based on the monocular visual optical flow tracking according to claim 1, wherein a singular value decomposition algorithm in the S4 is used to solve the Toeplitz matrix to obtain the working modality parameter of the thin-walled member; the working modality parameter of the thin-walled member comprises a natural frequency, a damping ratio and a modal vibration mode matrix.
7. A system for testing a working modality of a thin-walled member based on monocular visual optical flow tracking according to claim 1, comprising:
a model building module, configured to establish an imaging mathematical model and an industrial camera pinhole imaging model based on a monocular visual off-plane vibration measurement device;
a target actual displacement function determination module, configured to determine a target actual displacement function according to the imaging mathematical model and the industrial camera pinhole imaging model;
a sequence image acquisition module, configured to obtain a vibration sequence image of the thin-walled member;
an angular point determination module, configured to track a characteristic point in the vibration sequence image of the thin-walled member by using an optical flow matching tracking algorithm to determine an angular point;
a target angular point determination module, configured to adopt a pseudo angular point removal algorithm to process the angular point, remove a pseudo angular point, and determine a target angular point;
a displacement response signal determining module, configured to determine a displacement response signal where each the target angular point vibrates with each frame of an image according to the actual displacement function of the target;
Claims
an average correlation function calculation module, configured to calculate an average correlation function of the displacement response signal; and
a calculation module of a working modality parameter of the thin-walled member, configured to establish a Toeplitz matrix by using a Cov-SSI algorithm according to the average correlation function, and calculate the working modality parameter of the thin-walled member according to the Toeplitz matrix.
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