CN113295732A - Pipeline robot capable of detecting pipeline defects and control method and control system thereof - Google Patents

Pipeline robot capable of detecting pipeline defects and control method and control system thereof Download PDF

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CN113295732A
CN113295732A CN202110435345.XA CN202110435345A CN113295732A CN 113295732 A CN113295732 A CN 113295732A CN 202110435345 A CN202110435345 A CN 202110435345A CN 113295732 A CN113295732 A CN 113295732A
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pipeline
data
matrix
thermal image
processor
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CN113295732B (en
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熊俊杰
杨克己
吴海腾
黎勇跃
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Hangzhou Shenhao Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N25/00—Investigating or analyzing materials by the use of thermal means
    • G01N25/72—Investigating presence of flaws
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
    • H05B3/00—Ohmic-resistance heating
    • H05B3/0033—Heating devices using lamps

Abstract

The invention provides a pipeline robot capable of detecting pipeline defects, a control method and a control system thereof, which are mainly characterized in that a pipeline robot body is arranged in a pipeline, the pipeline is subjected to pulse heating through a flash lamp, meanwhile, a thermal imager is matched for thermal image acquisition, after acquisition, each thermal image acquisition data is made into two-dimensional thermal image matrix data, then the operations of neutralization, whitening, Gaussian operation, orthogonalization, normalization and the like are sequentially carried out, finally, an independent component matrix is obtained, each internal independent component is rearranged to obtain thermal image analysis data, and therefore, the part of the pipeline with the structural defects can be clearly shown.

Description

Pipeline robot capable of detecting pipeline defects and control method and control system thereof
Technical Field
The invention relates to the technical field of robots, in particular to a pipeline robot capable of detecting pipeline defects and a control method and a control system thereof.
Background
At present to the detection of pipeline, maintenance, or state exploration in the pipeline, perhaps clear up the interior siltation of pipeline again, mostly adopt pipeline robot to accomplish. The detection of the pipeline is mostly done by relying on image recognition. However, image recognition can only recognize defects on the inner wall surface of the pipe, and defects on the pipe structure are difficult to recognize, and therefore, the present inventors considered that such problems need to be solved, and began to think about solutions.
Disclosure of Invention
The invention solves the problem that the pipeline robot can only judge the defects on the surface of the pipeline when identifying the defects of the pipeline, and the structural defects are difficult to identify.
In order to solve the above problems, the present invention provides a pipeline robot for detecting pipeline defects, a control method and a control system thereof, wherein the technical scheme is as follows:
the pipeline robot body is arranged in a pipeline and can move in the pipeline according to preset routes or remote control modes, when the pipeline robot body is used for detecting defects of the pipeline, a flash lamp is mainly used for conducting pulse heating on the pipeline for about 0.2 second in 2000W, the thermal imager is used for conducting thermal image capturing on the pipeline with 30 frames/second while conducting pulse heating, a plurality of thermal image capturing data are obtained, and then the processor integrates all the thermal image capturing data into two-dimensional thermal image matrix data nxnyThen, the two-dimensional thermal image matrix data is subjected to a series of operations to obtain an orthogonal separation matrix, then the orthogonal separation matrix is used for calculating an independent component matrix, and finally the processor rearranges each independent component in the independent component matrix into nx*nyIn this way, a plurality of thermal image analysis data can be obtained.
Therefore, whether the pipeline has structural defects can be clearly shown through the thermal image analysis data, and if the pipeline has structural defects, corresponding measures such as remedy, prevention and the like can be carried out in advance to maintain the safety of the pipeline.
Drawings
FIG. 1 is a schematic view of an embodiment of the present invention shown in FIG. 1;
FIG. 2 is a schematic view of an embodiment of the present invention FIG. 2;
FIG. 3 is a schematic view of an embodiment of the present invention shown in FIG. 3;
FIG. 4 is a schematic view of the linkage of the components of the present invention;
FIG. 5 is a schematic diagram of the thermal test results of the present invention;
FIG. 6 is a flow chart of the present invention;
FIG. 7 is a schematic diagram of three-dimensional thermal image matrix data according to the present invention;
FIG. 8 is a schematic diagram of two-dimensional thermal image matrix data according to the present invention.
Description of reference numerals:
a-a pipeline; b1-plate body; b2-structural defects; 1-a pipeline robot body; 11-tip; 2-a flash lamp; 3-a thermal imager; 4-a processor; 5-a positioning unit; 6-a power wheel set; 7-a tilting unit; 8, a telescopic rod; 9-inclination angle identification unit.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Referring to fig. 4, the present invention includes a pipe robot body 1, a flash lamp 2, a thermal imager 3, a processor 4, a positioning unit 5, a power wheel set 6, an inclined unit 7, an expansion link 8, and an inclination angle detecting unit 9. Wherein, flash light 2 reach thermal imager 3 set up in the pipeline robot body 1 outside, in the bottom of pipeline robot body 1 is equipped with power wheelset 6, in the top surface of pipeline robot body 1 then sets up according to the preface from bottom to top slope unit 7 reaches telescopic link 8, positioning unit 5 reaches processor 4 then locates inside the pipeline robot body 1. Referring to fig. 1 to 3, the front side of the pipeline robot body 1 is gradually tapered from the left half and the right half toward the center to form a tip 11, and the resistance of the fluid can be reduced through the tip 11, so that when there is fluid in the pipeline a, the resistance to the advance can be reduced, and the impact of the fluid on the pipeline robot body 1 can be reduced.
Referring to fig. 1 to 3, the pipeline robot body 1 can be operated in the pipeline a through a preset moving route or by using a remote control method, so that the pipeline robot body 1 moves in the pipeline a. When pipeline robot body 1 need carry out the hot image and pick up, permeable control the elongation of telescopic link 8 makes 8 one end of telescopic link withstands pipeline A's roof, makes pipeline robot body 1 is fixed in the pipeline A, in order to avoid pipeline robot body 1 receives external force to influence and produces and rock, and then influences the effect of whole hot image capture. In addition, when the pipeline robot body 1 moves to the inclined plane and needs to capture the thermal image, in order to make the pipeline robot body 1 have a better fixing effect, the inclination unit 7 can adjust the inclination angle according to the detection result of the inclination angle detection unit 9, so that the telescopic rod 8 can keep vertically and upwards propping against the top wall of the pipeline a, so that the pipeline robot body 1 can be stabilized in the pipeline a.
Referring to fig. 4 and 6, when detecting whether the pipeline a has a structural defect, the flash lamp 2 is first controlled to perform pulse heating on the pipeline a, the pulse heating process is mainly performed at 2000W for about 0.2 second to perform pulse heating on the pipeline a, and the thermal imager 3 is used to perform thermal image capturing on the pipeline a while performing pulse heating, so as to obtain a plurality of thermal image capturing data ntReferring to FIG. 7, each thermal image capture data includes a plurality of horizontal pixel data nxAnd a plurality of vertical pixel data nyThen, the processor 4 will make each thermal image capturing data into a three-dimensional thermal image matrix data nt*nx*nyThen, referring to fig. 8, the processor 4 converts the three-dimensional thermal image matrix data into two-dimensional thermal image matrix data n based on the three-dimensional thermal image matrix datat*nxnyWherein n isxnyRepresenting the number of variables.
In order to speed up the whole instruction cycle, the complexity of the whole operation needs to be reduced to reduce the operation amount, so the two hot image matrix data needs to be processed with operation procedures such as neutralization and whitening. First, the processor 4 is based on the neutralizationCalculating an average value of each variable quantity of the two-dimensional thermal image matrix data, and subtracting the average value from each variable quantity of the two-dimensional thermal image matrix data to obtain a centralized matrix
Figure BDA0003032753310000041
Then, the whitening operation is performed, in which the processor 4 sequentially uses the centering matrices according to the formula:
Figure BDA0003032753310000042
Cx＝EDET、M＝ED-1/2ET、
Figure BDA0003032753310000043
performing operation to obtain a whitening matrix Z, wherein E represents an unit orthogonal matrix, M represents a whitening matrix, Z represents a whitening matrix, D represents a diagonal matrix, and CxRepresenting a co-variance matrix.
Then, the processor 4 selects a quadratic gaussian function g (y) y4The overall operation is simple without additional parameter selection, and then the random vector b is selectediAnd b i1, and according to the formula: bi＝E{Zg(bi TZ)}-E{Zg'(bi TZ)}biPerforming iterative operation from i to 1 until i to m to obtain a complex random vector; wherein m is the number of the thermal image capturing data, G (y) is a non-quadratic Gaussian function, y is a probability variable, g is a first order differential of G (y), and g' is a second order differential of G (y).
After each random vector is calculated, the processor 4 performs a process of orthogonalizing and normalizing each random vector, in which the processor 4 firstly performs the following steps according to the formula:
Figure BDA0003032753310000044
performing an orthogonalization operation according to the following formula:
Figure BDA0003032753310000045
performing normalization operation, wherein the orthogonalization and normalization operation is performed in the process of starting from i to 1 until i to m, and if b isiForm a convergent output biFinally, each b isiAn orthogonal separation matrix B is formed.
After the orthogonal separation matrix is obtained after the operation, the part of the data belonging to the independent component is separated through the orthogonal separation matrix, and the processor 4 is respectively according to the formula:
Figure BDA0003032753310000046
and the formula: w is BTM, calculating a separation matrix W and independent component matrix
Figure BDA0003032753310000047
Rearranging each independent component in the independent component matrix into nx*nyThen, a thermal image analysis data is obtained.
As shown in fig. 5, the board C1 and the default structural defect C2 are on the left, and the defect part in the thermal image analysis data can be clearly highlighted by the present invention.
Since the number of thermal image analysis data generated through the above steps is too large, it is quite difficult for the manager to quickly find whether a structural defect occurs in the pipeline a, and in order to increase the inspection speed, the processor 4 may further calculate the following formula: k ═ E { y4}-3(E{y2})2Calculating peak value k of each independent component, taking the highest and the second highest values from each peak value as the highest peak value and the second highest value, calculating an average peak value according to each peak value, when the difference value between the highest peak value and the average peak value exceeds a default value, indicating that the pipeline A generates structural defects, then taking out the independent components corresponding to the highest peak value and the second highest value, and rearranging the independent components into nx*nyAnd a defect data is obtained, as shown in fig. 5, which will obviously show a defective portion, so that the associated tubeThe management personnel can rapidly carry out the countermeasures such as remedy, emergency repair and the like.
In order to allow the relevant management personnel to dispatch to quickly go to process when the pipeline a detects the structural defect, the defect data preferably includes positioning data, and the positioning data is obtained by using the positioning unit 5 to detect the position of the pipeline robot body 1.
Although the present disclosure has been described above, the scope of the present disclosure is not limited thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present disclosure, and these changes and modifications are intended to be within the scope of the present disclosure.

Claims (9)

1. Can supply to detect pipeline robot of pipeline defect, its characterized in that includes:
the system comprises a pipeline robot body, a flash lamp, a thermal imager and a processor; wherein the content of the first and second substances,
the pipeline robot body is provided with the flash lamp and the thermal imager;
the heating unit can be used for carrying out pulse heating on the pipeline;
the thermal imager is used for capturing thermal images of the pipeline while the pipeline is heated by the pulse to obtain a plurality of thermal image capturing data, wherein each thermal image capturing data respectively comprises a plurality of horizontal pixel data and a plurality of vertical pixel data;
the processor makes the plurality of thermal image acquisition data into three-dimensional thermal image matrix data nt*nx*nyWherein n istRepresenting the thermal image capture data, nxRepresenting said horizontal pixel data, nyRepresenting the vertical pixel data; then converting the three-dimensional thermal image matrix data into two-dimensional thermal image matrix data nt*nxnyWherein n isxnyRepresents the number of variables; and the number of the first and second groups,
the processor centralizes the two-dimensional thermal image matrix data, and the processor firstly performs the centralization according to each variable quantity of the two-dimensional thermal image matrix dataCalculating an average value, and subtracting the average value from each variable number to obtain a centering matrix
Figure FDA0003032753300000011
Then according to the formula:
Figure FDA0003032753300000012
Cx＝EDET、M＝ED-1/2ET、
Figure FDA0003032753300000013
obtaining a whitening matrix Z, wherein E represents an unit orthogonal matrix, M represents a whitening matrix, Z represents a whitening matrix, D represents a diagonal matrix, CxRepresents a common variation matrix;
the processor selects a random vector biAnd bi1, and according to the formula:
bi＝E{Zg(bi TZ)}-E{Zg'(bi TZ)}biand g (y) ═ y4Performing iterative operation from i to 1 until i to m to obtain a complex random vector; wherein m is the number of the thermal image capturing data, G (y) is a non-quadratic Gaussian function, y is a probability variable, g is a first order differential of G (y), and g' is a second order differential of G (y);
the processor then:
Figure FDA0003032753300000014
and the formula:
Figure FDA0003032753300000015
the orthogonalization and normalization operations are sequentially performed from i equal to 1 until i equal to m, and if b isiForm a convergent output biFinally, each b isiOrder as an orthogonal separation matrix B;
the processor respectively uses the orthogonal separation matrixes to:
Figure FDA0003032753300000021
and the formula: w is BTM, calculating a separation matrix W and independent component matrix
Figure FDA0003032753300000022
Rearranging each independent component in the independent component matrix into nx*nyThen, a thermal image analysis data is obtained.
2. The pipeline robot for detecting pipeline defects according to claim 1, wherein each independent component in the independent component matrix is determined according to the formula:
k＝E{y4}-3(E{y2})2calculating peak value k of each independent component, taking the highest and the second highest values from each peak value as the highest peak value and the second highest value, calculating an average peak value according to each peak value, taking out the independent components corresponding to the highest peak value and the second highest value when the difference value between the highest peak value and the average peak value exceeds a default value, and rearranging the independent components into nx*nyThereby obtaining a defect data.
3. The pipeline robot for detecting the pipeline defects as claimed in claim 2, wherein a positioning unit is provided inside the pipeline robot body, and the positioning unit is used for detecting the position of the pipeline robot body to obtain positioning data; when the processor calculates the defect data, the processor stores the positioning data in the defect data.
4. The pipeline robot for detecting the pipeline defect as claimed in claim 3, wherein a power wheel set is provided at the bottom of the pipeline robot body for driving the pipeline robot body to travel; the top surface of the pipeline robot body is sequentially provided with an inclined unit and a telescopic rod from bottom to top; the pipeline robot body is internally provided with an inclination angle detecting unit, the processor is in information connection with the inclination angle detecting unit and the telescopic rod, the processor can be used for controlling the inclination unit according to the detecting result of the inclination angle detecting unit, and the processor can be used for controlling the elongation of the telescopic rod.
5. A control method of a pipeline robot for detecting a defect of a pipeline, which is applied to the pipeline robot of any one of claims 1 to 4, the pipeline robot comprising: the system comprises a pipeline robot body, a flash lamp, a thermal imager and a processor; the control method comprises the following steps:
(A) the pipeline robot body is arranged in the pipeline, and the pipeline is subjected to pulse heating by using the flash lamp; controlling the thermal imager to work while the pulse heating is carried out so as to carry out thermal image acquisition on the pipeline to obtain a plurality of thermal image acquisition data, wherein each thermal image acquisition data respectively comprises a plurality of horizontal pixel data and a plurality of vertical pixel data;
(B) the processor makes the plurality of thermal image acquisition data into three-dimensional thermal image matrix data nt*nx*nyWherein n istRepresenting the thermal image capture data, nxRepresenting said horizontal pixel data, nyRepresenting the vertical pixel data; then converting the three-dimensional thermal image matrix data into two-dimensional thermal image matrix data nt*nxnyWherein n isxnyRepresents the number of variables;
(C) the processor centralizes the two-dimensional thermal image matrix data, the processor calculates an average value according to each variable quantity of the two-dimensional thermal image matrix data, and then subtracts the average value from each variable quantity to obtain a centralized matrix
Figure FDA0003032753300000031
The processor then sequentially according to a formula:
Figure FDA0003032753300000032
Cx＝EDET、M＝ED-1/2ET、
Figure FDA0003032753300000033
obtaining a whitening matrix Z, wherein E represents a unit orthogonal matrix, M represents a whitening matrix, Z represents a whitening matrix, D represents a diagonal matrix, CxRepresents a common variation matrix;
(D) the processor selects a random vector biAnd bi1, according to the formula:
bi＝E{Zg(bi TZ)}-E{Zg'(bi TZ)}biand g (y) ═ y4Performing iterative operation from i to 1 until i to m to obtain a complex random vector; wherein m is the number of the thermal image capturing data, G (y) is a non-quadratic Gaussian function, y is a probability variable, g is a first order differential of G (y), and g' is a second order differential of G (y);
(E) the processor is configured to:
Figure FDA0003032753300000034
and the formula:
Figure FDA0003032753300000035
the orthogonalization and normalization operations are sequentially performed from i equal to 1 until i equal to m, and if b isiForm a convergent output biFinally, each b isiOrder as an orthogonal separation matrix B;
(F) the processor respectively uses the orthogonal separation matrixes to:
Figure FDA0003032753300000036
and the formula: w is BTM, calculating a separation matrix W and independent component matrix
Figure FDA0003032753300000037
Rearranging each independent component in the independent component matrix into nx*nyThen a heat shadow is obtainedThe data is analyzed.
6. The method for controlling a pipeline robot capable of detecting a pipeline defect according to claim 5, further comprising the step (G): the processor compares each independent component in the independent component matrix according to a formula: k ═ E { y4}-3(E{y2})2Calculating peak value k of each independent component, taking the highest and the second highest values from each peak value as the highest peak value and the second highest value, calculating an average peak value according to each peak value, and taking out the independent components corresponding to the highest peak value and the second highest value by the processor when the difference value between the highest peak value and the average peak value exceeds a default value and rearranging the independent components into nx*nyThereby obtaining a defect data.
7. The method for controlling a pipeline robot capable of detecting a pipeline defect according to claim 6, further comprising the step (H): when the defect data is calculated, the processor controls the positioning unit of the pipeline robot body to work so as to detect the position of the pipeline robot body to obtain positioning data, and the booking data is stored in the defect data.
8. The method for controlling a pipeline robot capable of detecting a pipeline defect according to claim 7, further comprising the step (I): when the inclination angle detecting unit of the pipeline robot body detects that the pipeline robot body is in an inclined state, the processor controls the inclination unit of the pipeline robot body to change the inclination angle according to the detection result of the inclination angle detecting unit.
9. Robot control system, its characterized in that includes:
the pipeline robot of any one of the preceding claims 1-4;
a control center, wherein the control center is connected to the pipeline robot in an information mode;
the control center controls the pipeline robot according to the control method of any one of the above claims 5 to 8.
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