CN112432693B - Tracing method and device for machine vision low-frequency vibration measurement - Google Patents
Tracing method and device for machine vision low-frequency vibration measurement Download PDFInfo
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Abstract
The invention discloses a tracing method and a tracing device for machine vision low-frequency vibration measurement, wherein in the tracing method, a computer is used for generating a moving target with standard sinusoidal vibration excitation, and the moving target is output in a video playing mode through high-refresh-rate output equipment; acquiring a motion sequence image of a target by an acquisition and imaging device, and measuring target displacement at different acquisition moments by using a machine vision method; then, correcting the target displacement measured by the machine vision method based on the accurate display output position rounding error model; finally, by evaluating the uncertainty component introduced by the machine vision method measurement uncertainty source, the magnitude tracing of the machine vision low-frequency vibration displacement peak value and the specific position phase measurement is realized. The method effectively avoids the influence of non-ideal sinusoidal vibration caused by factors such as machining, motion control and the like, and solves the defects that the quantity value tracing error chain length of the existing tracing method needs to be transmitted for multiple times.
Description
Technical Field
The invention belongs to the field of vibration measurement and calibration, and particularly relates to a tracing method for machine vision low-frequency vibration measurement.
Background
The low-frequency vibration measurement based on machine vision is widely applied to vibration monitoring in the fields of wind power generation, earthquake, deformation, bridge and building structure safety and the like. In order to better meet the requirements of practical engineering application, the machine vision measurement method needs to be subjected to magnitude tracing, and corresponding measurement uncertainty should be given when a measurement value is given. The tracing is a core link of vibration measurement, and has great significance for realizing international magnitude equivalence and domestic magnitude transmission and improving vibration measurement precision. Therefore, it is important to research a tracing method for machine vision low-frequency vibration measurement to improve the precision of machine vision measurement.
Currently, common low-frequency vibration measurement methods include a contact sensor measurement method and a non-contact optical measurement method, and the latter is further divided into a laser interferometry and a machine vision method. The sensor measurement method needs to trace the measurement value to the laser interferometry through multiple times of measurement value transmission. The laser interferometry generally uses factors such as laser interferometry, environmental noise, acquisition equipment and the like as main measurement uncertainty sources, and the measurement value tracing is realized by evaluating each uncertainty component through an evaluation method. The measurement quantity value of the laser interferometry is traced from the source to the length quantity (laser wavelength), secondary quantity value transmission is needed, and the requirement of tracing the field vibration measurement quantity value is difficult to meet. With the widespread use of machine vision methods in low frequency vibration measurement, it becomes critical to trace its magnitude. In general, uncertainty sources such as the resolution and frame rate of the acquisition and imaging device, the calibration of the acquisition and imaging device, the extraction of motion characteristics, and external illumination characteristics are considered, and a traceability error chain of a machine vision method is established to realize quantity value traceability. The traditional tracing method has to take the measured low-frequency vibration as a measurement uncertainty source, and then the measured low-frequency vibration is influenced by factors such as machining, motion control and the like, so that a tracing error chain is increased. The method provided by the invention provides the machine vision low-frequency vibration measurement magnitude tracing based on the standard sinusoidal vibration excitation, can shorten the tracing error chain of the machine vision low-frequency vibration measurement, and realizes the magnitude flattening transmission of the machine vision method.
Therefore, aiming at the defects that the tracing error chain of the current tracing method is long, multiple times of magnitude transmission have to be carried out and the like, the invention provides the machine vision low-frequency vibration measurement tracing method which is efficient, flexible and low in cost, and is beneficial to the field vibration measurement magnitude tracing of the machine vision method.
Disclosure of Invention
Aiming at the defects that the quantity value traceability error chain length of the machine vision low-frequency vibration measurement is long, the method is difficult to be used for on-site vibration measurement quantity value traceability and the like, the invention provides an effective and flexible machine vision low-frequency vibration measurement traceability method, which comprises the following steps:
generation of standard sinusoidal vibration excitation moving target: generating standard sinusoidal vibration excitation of different motion displacements within a frequency range of 0.01-10 Hz by using a computer, and outputting a motion target in a video playing mode through high-refresh-rate output equipment;
measuring and correcting the displacement of a moving target based on machine vision: acquiring a motion sequence image of a target by an acquisition and imaging device, resolving the motion target displacement at different acquisition moments from the motion sequence image by a machine vision method, and correcting the motion target displacement by an accurate display rounding position error model;
and (3) evaluating uncertainty components introduced by main uncertainty sources of machine vision low-frequency vibration measurement: taking external optical characteristics, acquisition and imaging equipment parameters and influence quantities such as calibration, environmental noise, edge extraction, straight line fitting and the like as main measurement uncertain sources, realizing the evaluation of uncertainty components according to the probability distribution of each uncertain source, and considering uncertainty introduced by repeated measurement;
the value tracing result of the machine vision low-frequency vibration measurement is as follows: and synthesizing and expanding the measurement uncertainty of the machine vision low-frequency vibration measurement through each calculated uncertainty component, establishing a traceability error chain of a machine vision method, and storing and displaying a traceability result.
Further, the tracing method comprises the following steps,
s1: generating standard sinusoidal vibration excitation moving targets with different frequencies and displacement peak values in a low-frequency range by using a computer, outputting the moving targets in a video playing mode through a display of high-refresh-rate output equipment, and acquiring moving sequence images of the targets by using a camera;
s2: resolving the displacement of a target at different acquisition moments from a sequence image by a machine vision method, outputting a target position rounding error model based on a display to correct the displacement, and fitting the corrected displacement by a sine approximation method to obtain the phase of a target displacement peak value and a specific position;
s3: uncertainty components introduced by measurement uncertain sources such as external optical characteristics, acquisition and imaging equipment parameters and calibration, environmental noise, edge extraction, straight line fitting and the like are considered, and different evaluation methods are selected according to probability distribution of each component to realize uncertainty evaluation of each component;
s4: and establishing a traceability error chain through the uncertainty component of each uncertainty source, obtaining the synthesis and expansion measurement uncertainty of the machine vision low-frequency vibration measurement, and storing and displaying the traceability result.
Further, the standard sine vibration excitation moving target is a video with specific frequency and displacement peak value generated based on Matlab, and the number T of images in a single period Num Vibration frequency f v And a video frame rate F r Satisfies the following conditions:
T Num ＝F r /f v (1)
the refreshing time of each frame of image is 1/F r 。f v The range is 0.01 to 10Hz, and the corresponding displacement peak value is set to be 40 to 400 pixels of the display.
Further, the displacement measurement of the standard sinusoidal vibration moving target based on the machine vision specifically includes:
(1) acquisition of images of a sequence of object movements
The acquisition and imaging equipment acquires a target motion sequence image F with a certain frame number and period j (x, y), j is 1, 2.. and N, the number N of images in the acquisition time can sufficiently reflect the motion characteristics of the target, and the field of view of the acquisition and imaging device contains the moving target in the whole period;
(2) moving target displacement measurement at different acquisition moments
Extracting the motion characteristic edge of a target motion sequence image by adopting a sub-pixel edge detection method, selecting the motion characteristic edge of the target image at a specific position as a zero displacement reference edge, and obtaining an acquisition time t by calculating the distance between the motion characteristic edge of the sequence image and the reference edge j S (t) of the moving object j )。
Further, the display can only output the moving target at the integer pixel position, the moving targets at other positions except the zero position and the maximum and minimum positions all have rounding errors, and in order to correct the error introduced by rounding the output position of the display, a rounding error sequence in a single period is defined as follows:
Δs(t j )＝int[s p-p sin(ω v (T Num t j )-π/2)]-s p-p sin(ω v (T Num t j )-π/2) (2)
wherein int is the rounding operation, s p-p Maximum displacement, omega, of moving object measured for machine vision methods v Is the angular frequency of vibration. The corrected machine vision method measures the displacement as follows:
s'(t j )＝s(t j )+Δs(t j ) (3)
in the formula: s' (t) j ) For corrected t j And moving the moving target at the moment. Fitting s' (t) by sinusoidal approximation j ) And obtaining the phase of the displacement peak value and the specific position of the moving target.
Furthermore, influence quantities such as external optical characteristics, acquisition and imaging equipment parameters and calibration, environmental noise, edge extraction, straight line fitting and the like are selected as main measurement uncertainty sources of the machine vision low-frequency vibration measurement, corresponding uncertainty is evaluated according to probability distribution, a traceability error chain of the machine vision low-frequency vibration measurement is established, the measurement uncertainty of a standard sinusoidal vibration excitation displacement peak value and a specific position phase of the machine vision method is resolved, and the magnitude traceability of the machine vision method is realized.
Further, the tracing device for measuring the machine vision low-frequency vibration, which is realized by the method, comprises the following steps: the device comprises a collecting and imaging device fixing device (1), a collecting and imaging device (2), a standard sine vibration excitation moving target (3), a high refresh rate output device (4), an image transmission device (5) and a processing and display unit (6).
The acquisition and imaging equipment fixing device (1) is used for fixing the acquisition and imaging equipment (2) and enabling the optical axis of the acquisition and imaging equipment to be perpendicular to a display of the high-refresh-rate output equipment (4); the standard sine vibration excitation moving target (3) is output by a display of the high-refresh-rate output device (4); the acquisition and imaging device (6) is used for acquiring a motion sequence image of a standard sine vibration excitation moving target (3); the image transmission device (5) transmits the acquired motion sequence images; and the processing and display unit (6) processes the acquired motion sequence images and stores and displays the tracing result.
The tracing method for measuring the low-frequency vibration of the machine vision has the following advantages that:
the method is stable, reliable and practical, and is suitable for acquisition of different models and parameters and low-frequency vibration measurement value tracing of imaging equipment.
The method corrects the standard sinusoidal vibration excitation displacement measured by the machine vision method through an accurate error model, and greatly improves the measurement accuracy.
The method has the advantages of simple and flexible traceability process and low system cost, reduces uncertain sources of measurement through standard sinusoidal vibration excitation, shortens traceability error chains, and does not need secondary quantity value transmission.
The method belongs to a source tracing method for low-frequency vibration measurement, and is beneficial to magnitude tracing of on-site vibration measurement by a machine vision method.
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FIG. 1 is a schematic diagram of an apparatus according to an embodiment of the method of the present invention;
FIG. 2 is a flow chart of a tracing method for machine vision low frequency vibration measurement;
FIG. 3 is a flow chart of measurement uncertainty assessment for machine vision low frequency vibration measurement;
FIG. 4 is a graph of the results of a standard sinusoidal vibration excitation displacement peak measured by machine vision methods;
FIG. 5 is a graph of the results of the phase at a specific location of a standard sinusoidal vibration excitation measured by machine vision methods.
Detailed Description
In order to solve the problem of magnitude traceability of the existing machine vision vibration measurement, the invention provides a traceability method for machine vision low-frequency vibration measurement.
Referring to fig. 1, a schematic diagram of an embodiment of the method of the present invention is shown, the apparatus mainly comprising: the device comprises a collecting and imaging device fixing device (1), a collecting and imaging device (2), a standard sine vibration excitation moving target (3), a high refresh rate output device (4), an image transmission device (5) and a processing and display unit (6). The method is characterized in that: the acquisition and imaging equipment fixing device (1) is used for fixing the acquisition and imaging equipment (2) and enabling the optical axis of the acquisition and imaging equipment to be perpendicular to a display of the high-refresh-rate output equipment (4); the standard sine vibration excitation moving target (3) is output by a display of the high-refresh-rate output device (4); the acquisition and imaging device (6) is used for acquiring a motion sequence image of a standard sine vibration excitation moving target (3); the image transmission device (5) transmits the acquired motion sequence images; and the processing and display unit (6) processes the acquired motion sequence images and stores and displays the tracing result.
Referring to fig. 2, a flow chart of a tracing method for machine vision low-frequency vibration measurement is shown. The tracing method mainly comprises the following steps:
step S10: generating standard sinusoidal vibration excitation moving targets with different displacement peak values within the range of 0.01-10 Hz by using a computer, outputting the standard sinusoidal vibration excitation moving targets by a display of high-refresh-rate equipment, and acquiring a moving sequence image of the moving targets by using a camera;
step S20: resolving the displacement of the moving target at different acquisition moments from the sequence image by a machine vision method, correcting the displacement of the moving target at different acquisition moments based on a display output position rounding error model, and fitting the corrected displacement by a sine approximation method to obtain a moving target displacement peak value and a specific position phase;
step S30: according to the probability distribution, different evaluation methods are selected to evaluate uncertainty components introduced by external optical characteristics, acquisition and imaging equipment parameters, calibration, environmental noise, edge extraction, straight line fitting and the like;
step S40: and solving the synthesis and expansion measurement uncertainty of the machine vision low-frequency vibration measurement through the obtained uncertainty components, and storing and displaying a tracing result.
Referring to fig. 3, a flow chart for assessing measurement uncertainty of a machine vision low-frequency vibration measurement is shown. The measurement uncertainty evaluation of the invention comprises the following steps:
step S51: solving the moving target displacement peak value average value and the specific position phase value average value measured by a machine vision method for multiple times;
step S52: analyzing a measurement model of the displacement peak value and the specific position phase of the moving target;
step S53: introducing a measurement uncertainty source of machine vision low-frequency vibration measurement;
step S54: whether the probability distribution of uncertainty components introduced by each measurement uncertainty source is known or not;
step S55: if the probability distribution of the uncertainty component is unknown, evaluating the uncertainty by adopting a Monte Carlo method based on multiple experiments, otherwise, skipping to the step S56;
step S56: selecting an A-type or B-type evaluation method to evaluate the uncertainty of the probability distribution according to different types of the probability distribution;
step S57: solving a corresponding sensitivity coefficient according to the measurement model of the moving target;
step S58: calculating a correlation coefficient between each uncertain component, wherein if the correlation coefficients are independent, the correlation coefficient is 0;
step S59: resolving uncertainty of a machine vision method by using the sensitivity coefficient and the correlation coefficient;
step S60: calculating the standard deviation of repeated measurement of the displacement peak value of the moving target and the phase at the specific position for multiple times;
step S61: solving the uncertainty of the synthetic measurement of the machine vision method by using the standard deviation of the repeated measurement and each uncertainty component;
step S62: the extended measurement uncertainty is calculated when the factor k is comprised of 2.
The specific parameters of the device of the embodiment are as follows: the method comprises the steps of selecting a standard sinusoidal vibration excitation moving target with the frequency range of 0.01-10 Hz and the maximum single-peak displacement of 400DP, outputting the standard sinusoidal vibration excitation moving target by using a display device with the G-SYN technology and the highest refresh rate of 200 frames, selecting IDT OS10-V3-4K with the maximum resolution of 9 million pixels and the maximum frame rate of 1000fps by using an industrial camera, and enabling the focal length of a lens to be 25 mm.
In order to verify the effectiveness of the tracing method for measuring the machine vision low-frequency vibration, the method provided by the invention is utilized to realize the magnitude tracing of the machine vision method in the frequency range of 0.01-10 Hz. Referring to fig. 4-5, which are graphs of the measured peak displacement values and the measured phase values at specific positions of the moving object by the machine vision method, ten measurements are performed at each selected frequency, fig. 4(a) and 4(b) show the measured peak displacement values and the relative standard deviations, respectively, and fig. 5(a) and 5(b) show the measured phase values at 0 ° and the standard deviations, respectively. Within the whole frequency range, the relative standard deviation of the displacement peak value measured by a machine vision method does not exceed 0.3 percent. The measured 0 ° position phase is less than 0.04 ° out of phase with the true 0 °, and the maximum standard deviation of the measured 0 ° position phase is less than 0.06 °.
Table 1 shows the source of uncertainty in the measurement of the standard sinusoidal vibration displacement peak with the specific position phase by the machine vision method and the corresponding uncertainty component. And if all uncertainty components are not correlated, the uncertainty of the extended measurement of the displacement peak value and the phase at the specific position is calculated to be 0.38 percent and 0.09 degrees respectively.
TABLE 1 uncertainty sources and uncertainty components of low frequency vibration measurements by machine vision methods
The above description is a detailed description of an example embodiment of the invention and is not intended to limit the invention in any way. The invention is capable of many modifications, improvements and adaptations by those skilled in the art. Accordingly, the scope of the invention should be limited only by the attached claims.
Claims (5)
1. A tracing method for machine vision low-frequency vibration measurement is characterized in that: the tracing method comprises the following steps of,
s1: generating standard sinusoidal vibration excitation moving targets with different frequencies and displacement peak values in a low-frequency range by using a computer, outputting the moving targets in a video playing mode through a display of high-refresh-rate output equipment, and acquiring moving sequence images of the targets by using a camera;
s2: resolving the displacement of a target at different acquisition moments from a sequence image by a machine vision method, outputting a target position rounding error model based on a display to correct the displacement, and fitting the corrected displacement by a sine approximation method to obtain the phase of a target displacement peak value and a specific position;
s3: considering external optical characteristics, collecting and imaging equipment parameters and calibrating, environmental noise, edge extraction and uncertainty component introduced by a straight line fitting measurement uncertain source, and selecting different evaluation methods according to probability distribution of each component to realize uncertainty evaluation of each component;
s4: establishing a traceability error chain through uncertainty components of each uncertainty source, obtaining the synthesis and expansion measurement uncertainty of machine vision low-frequency vibration measurement, and storing and displaying traceability results;
the display can only output moving targets at integer pixel positions, the moving targets at other positions except zero position and maximum and minimum positions have rounding errors, and in order to correct the error introduced by rounding at the output position of the display, a rounding error sequence in a single period is defined as follows:
Δs(t j )＝int[s p-p sin(ω v (T Num t j )-π/2)]-s p-p sin(ω v (T Num t j )-π/2) (2)
wherein int is the rounding operation, s p-p Maximum displacement, omega, of moving object measured for machine vision methods v Is the vibration angular frequency; corrected machine vision methodThe measured displacement is:
s'(t j )＝s(t j )+Δs(t j ) (3)
in the formula: s' (t) j ) For corrected t j Moving the target at the moment; fitting s' (t) by sinusoidal approximation j ) And obtaining the phase of the displacement peak value and the specific position of the moving target.
2. The tracing method for machine vision low-frequency vibration measurement according to claim 1, characterized in that:
the standard sinusoidal vibration excitation moving target is a video which is generated based on Matlab and has specific frequency and displacement peak value, and the number T of images in a single period Num Vibration frequency f v And a video frame rate F r Satisfies the following conditions:
T Num ＝F r /f v (1)
the refreshing time of each frame of image is 1/F r ；f v The range is 0.01 to 10Hz, and the corresponding displacement peak value is set to be 40 to 400 display pixels.
3. The tracing method for machine vision low-frequency vibration measurement as claimed in claim 1, wherein:
the displacement measurement of the standard sinusoidal vibration moving target based on the machine vision specifically comprises the following steps:
(1) acquisition of images of a sequence of object movements
The acquisition and imaging equipment acquires a target motion sequence image F with a certain frame number and period j (x, y), j is 1,2, …, N, the number of images N in the acquisition time can sufficiently reflect the motion characteristics of the object, and the field of view of the acquisition and imaging device contains the moving object in the whole period;
(2) moving target displacement measurement at different acquisition moments
Extracting the motion characteristic edge of a target motion sequence image by adopting a sub-pixel edge detection method, selecting the motion characteristic edge of the target image at a specific position as a zero displacement reference edge, and calculating the motion characteristic of the sequence imageThe distance between the edge and the reference edge is used to obtain the acquisition time t j S (t) of the moving object j )。
4. The tracing method for machine vision low-frequency vibration measurement as claimed in claim 1, wherein:
the method comprises the steps of selecting external optical characteristics, collecting and imaging equipment parameters and calibrating, environmental noise, edge extraction and straight line fitting influence quantity as a measurement uncertainty source of machine vision low-frequency vibration measurement, evaluating corresponding uncertainty according to probability distribution, establishing a traceability error chain of the machine vision low-frequency vibration measurement, resolving the measurement uncertainty of a standard sinusoidal vibration excitation displacement peak value and a specific position phase of a machine vision method, and achieving magnitude traceability of the machine vision method.
5. The tracing device for machine vision low-frequency vibration measurement realized by the method of claim 1 is characterized in that:
the method comprises the following steps: the device comprises a collecting and imaging device fixing device (1), a collecting and imaging device (2), a standard sine vibration excitation moving target (3), a high refresh rate output device (4), an image transmission device (5) and a processing and display unit (6);
the acquisition and imaging equipment fixing device (1) is used for fixing the acquisition and imaging equipment (2) and enabling the optical axis of the acquisition and imaging equipment to be perpendicular to a display of the high-refresh-rate output equipment (4); the standard sine vibration excitation moving target (3) is output by a display of the high-refresh-rate output device (4); the acquisition and imaging device (2) is used for acquiring a motion sequence image of a standard sine vibration excitation moving target (3); the image transmission device (5) transmits the acquired motion sequence images; and the processing and display unit (6) processes the acquired motion sequence images and stores and displays the tracing result.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109632085A (en) * | 2018-12-29 | 2019-04-16 | 中国计量科学研究院 | A kind of low-frequency vibration calibration method based on monocular vision |
| CN109727267A (en) * | 2018-12-29 | 2019-05-07 | 中国计量科学研究院 | A kind of virtual sinusoidal straight-line oscillation measurement method of standard |
| CN109883533A (en) * | 2019-01-21 | 2019-06-14 | 哈尔滨工业大学（深圳） | Low frequency vibration measurement method based on machine vision |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7705996B2 (en) * | 2005-07-18 | 2010-04-27 | Ohio State University | Methods and systems for ultra-precise measurement and control of object motion in six degrees of freedom by projection and measurement of interference fringes |
| CN102288282B (en) * | 2011-05-04 | 2012-10-31 | 浙江大学 | Vibration table system with feedback control device based on linear optical grating ruler |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109632085A (en) * | 2018-12-29 | 2019-04-16 | 中国计量科学研究院 | A kind of low-frequency vibration calibration method based on monocular vision |
| CN109727267A (en) * | 2018-12-29 | 2019-05-07 | 中国计量科学研究院 | A kind of virtual sinusoidal straight-line oscillation measurement method of standard |
| CN109883533A (en) * | 2019-01-21 | 2019-06-14 | 哈尔滨工业大学（深圳） | Low frequency vibration measurement method based on machine vision |
Non-Patent Citations (1)
| Title |
|---|
| 地震传感器的量值溯源方法;杨振宇等;《世界地震工程》;20110930;第27卷(第03期);157-162 * |
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