CN108506170A - Fan blade detection method, system, equipment and storage medium - Google Patents
Fan blade detection method, system, equipment and storage medium Download PDFInfo
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- CN108506170A CN108506170A CN201810190311.7A CN201810190311A CN108506170A CN 108506170 A CN108506170 A CN 108506170A CN 201810190311 A CN201810190311 A CN 201810190311A CN 108506170 A CN108506170 A CN 108506170A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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Abstract
The present invention provides a kind of fan blade detection method, system, equipment and storage mediums, include the following steps：Multiple images for acquiring each blade on wind turbine, identify the defect of blade in the picture, and mark out the number of the defective locations and defect type and defect place blade of each defect；Multiple image mosaics of each blade are gone out to the 3-D view of each blade, and defective locations and defect type are marked out on the 3-D view of blade；According to the number of the 3-D view of each blade and the blade, the 3-D view of wind turbine is established, to show the defective locations and defect type of each blade on the 3-D view of wind turbine.Multiple images for the blade being collected into can be identified in the present invention and mark out the defective locations on blade, defect type, and the 3-D view of wind turbine is established in turn, so as to show the defective locations and defect type of each blade in the 3-D view of wind turbine, in order to the convenient discovery defective locations in maintenance.
Description
Technical field
The present invention relates to aerator supervisions, and in particular, to a kind of fan blade detection method, system, equipment and storage are situated between
Matter.
Background technology
Wind-driven generator is to convert wind energy into mechanical work, and mechanical work drives rotor rotation, the electricity of final output alternating current
Power equipment.Wind-driven generator generally has the component groups such as blade, generator, direction-regulator, pylon, speed-limiting safety mechanism and energy storage device
At.
In the During Process of Long-term Operation of wind-driven generator, the surface of blade will present out various damages, such as blade protection
Membrane damage, blade fall paint, blade icing, blade cracks and blade greasy dirt etc..
Currently, when carrying out damage check to blade surface, generally use is manually climbed up wind-driven generator and is detected, not only
It can spend a large amount of manpower, and working at height, operating personnel are needed when being detected manually climb up wind-power electricity generation
Safety have certain risk.
Invention content
For the defects in the prior art, the object of the present invention is to provide a kind of fan blade detection method, system, equipment
And storage medium.
According to fan blade detection method provided by the invention, include the following steps：
Step S1：Multiple images for acquiring each blade on wind turbine, identify the defect of the blade in described image,
And mark out the number of the defective locations and defect type and defect place blade of each defect；
Step S2：Multiple image mosaics of each blade are gone out to the 3-D view of each blade, and described
The defective locations and defect type are marked out on the 3-D view of blade；
Step S3：According to the number of the 3-D view and the blade of each blade, the graphics of the wind turbine is established
Picture, to show the defective locations and defect type of each blade on the 3-D view of the wind turbine.
Preferably, the step S1 includes the following steps：
Step S101：The defect of the blade is categorized into several defect types, acquires the corresponding leaf of each defect type
Picture region generates multigroup training image；
Step S102：Defect recognition module is trained by multigroup training image；
Step S103：Collected multiple images input defect recognition module is identified and carries out defective locations
With the label of defect type.
Preferably, in training image that the corresponding leaf image Area generation of each described defect type is multigroup, by institute
State the background removal in leaf image region；
The background is to acquire the area adjacent in the plane with the leaf image region generated when leaf image region
Domain.
Preferably, multiple images of each blade include each blade two sides base region connected in sequence,
To tip region and tip region in region, leaf in blade root to leaf.
Preferably, the defect type includes following any or appoints a variety of：
Blade cracks；
Attachment falls off；
Surface corrosion；
Fall paint in surface；
Gel coat falls off；
Gel coat crackle.
Preferably, the step S2 includes the following steps：
Step S201：Multiple image mosaics of each blade are gone out to the 3-D view of each blade；
Step S202：It is established respectively using the length direction of blade as X-axis in the two sides of each blade, with the leaf
The width direction of piece is the coordinate system of Y-axis, and then generates the coordinate of each defective locations；
Step S203：Multiple defective locations of multiple Assembled distributions are generated into defect area, it is every in the defect area
A defective locations are less than presetting distance at least at a distance from another defective locations of the defect area；
Step S204：The defect area is identified on each blade.
Preferably, when the defect type is blade cracking and gel coat crackle, the step S101 includes the following steps：
Step S1011：The corresponding leaf image region of each defect type is acquired, the defect type includes blade cracking
With gel coat crackle；
Step S1012：The corresponding leaf image region of each defect type is divided into according to residing defect area several
A defect group；
Step S1013：Leaf image region in each defect group is sorted successively from short to long according to defect length；
Step S1014：Two leaf image regions for tracking Adjacent defect length successively, when the forward leaf image of arrangement
Area defects length is generated to identical as arranged adjacent in the defect group and the leaf image area defects length of arrangement rearward
When, generate the ageing time in two leaf image regions of the Adjacent defect length.
Fan blade detecting system provided by the invention is used for the fan blade detection method, including：
Defect recognition module, for identification on collected wind turbine each blade multiple images, know in described image
Do not go out the defect of the blade, and mark out each defect defective locations and defect type and the defect where leaf
The number of piece；
Image mosaic module, the graphics for multiple image mosaics of each blade to be gone out to each blade
Picture, and the defective locations and defect type are marked out on the 3-D view of the blade；
3-D view module is used for the number of the 3-D view and the blade according to each blade, establishes the wind
The 3-D view of machine, to show the defective locations and defect type of each blade on the 3-D view of the wind turbine.
Fan blade detection device provided by the invention, including：
Processor；
Memory, wherein being stored with the executable instruction of the processor；
Wherein, the processor is configured to execute the fan blade detection method via the executable instruction is executed
The step of.
Computer readable storage medium provided by the invention, for storing program, which is characterized in that described program is performed
Described in Shi Shixian the step of fan blade detection method.
Compared with prior art, the present invention has following advantageous effect：
Multiple images for the blade being collected into can be identified in the present invention and mark out the defect on the blade
Position, defect type, and it is spliced into the 3-D view of each blade in turn, the 3-D view of the wind turbine is established, to
The defective locations and defect type that each blade can be shown in the 3-D view of wind turbine, can easily show defective bit
It sets, in order to the convenient discovery defective locations in maintenance；
The classification of the defect of the blade is acquired into the corresponding blade of each defect type in several defect types in the present invention
Image-region generates multigroup training image, defect recognition module is trained by multigroup training image, to improve defect
The recognition efficiency of type.
Description of the drawings
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, other feature of the invention,
Objects and advantages will become more apparent upon：
Fig. 1 is the step flow chart of wind turbine crop leaf measuring method in the present invention；
Fig. 2 is the step flow chart of defect recognition in the present invention；
Fig. 3 is the step flow chart that defect area identifies in the present invention；
Fig. 4 is the ageing time calculation flow chart of Adjacent defect length in the present invention；
Fig. 5 is the structural schematic diagram of blade in the present invention；
Fig. 6 is the module diagram of wind turbine crop leaf measuring system in the present invention；
Fig. 7 is the structural schematic diagram of wind turbine crop leaf measuring equipment in the present invention；And
Fig. 8 is the structural schematic diagram of this present invention Computer readable storage medium storing program for executing.
In figure：
1 is root zone domain；
2 be region in blade root to leaf；
3 in leaf to tip region；
4 be tip region.
Specific implementation mode
With reference to specific embodiment, the present invention is described in detail.Following embodiment will be helpful to the technology of this field
Personnel further understand the present invention, but the invention is not limited in any way.It should be pointed out that the ordinary skill of this field
For personnel, without departing from the inventive concept of the premise, various modifications and improvements can be made.These belong to the present invention
Protection domain.
In the present embodiment, Fig. 1 shows the step flow chart of wind turbine crop leaf measuring method in the present invention, as shown in Figure 1,
Fan blade detection method provided by the invention, includes the following steps：
Step S1：Multiple images for acquiring each blade on wind turbine, identify the defect of the blade in described image,
And mark out the number of the defective locations and defect type and defect place blade of each defect；
Step S2：Multiple image mosaics of each blade are gone out to the 3-D view of each blade, and described
The defective locations and defect type are marked out on the 3-D view of blade；
Step S3：According to the number of the 3-D view and the blade of each blade, the graphics of the wind turbine is established
Picture, to show the defective locations and defect type of each blade on the 3-D view of the wind turbine.
In the present embodiment, the defect type includes following any or appoints a variety of：
Blade cracks；
Attachment falls off；
Surface corrosion；
Fall paint in surface；
Gel coat falls off；
Gel coat crackle.
In variation, blade lightning damage, surface contamination, structural failure or leading edge protection membrane damage etc. can also be increased
Other damages.
Multiple images for the blade being collected into can be identified in the present invention and mark out the defect on the blade
Position, defect type, and it is spliced into the 3-D view of each blade in turn, the 3-D view of the wind turbine is established, to
The defective locations and defect type that each blade can be shown in the 3-D view of wind turbine, can easily show defective bit
It sets, in order to the convenient discovery defective locations in maintenance.
Fig. 2 shows the step flow charts of defect recognition in the present invention, as shown in Fig. 2, the step S1 includes following step
Suddenly：
Step S101：The defect of the blade is categorized into several defect types, acquires the corresponding leaf of each defect type
Picture region generates multigroup training image；
Step S102：Defect recognition module is trained by multigroup training image；
Step S103：Collected multiple images input defect recognition module is identified and carries out defective locations
With the label of defect type.
In the present embodiment, it is in several defect types by the classification of the defect of the blade in the present invention, acquires each defect
The corresponding leaf image region of type, generates multigroup training image, and defect recognition module is trained by multigroup training image,
To improve the recognition efficiency of defect type.
In the present embodiment, the label for carrying out defective locations and defect type, specially by defective locations in blade upper ledge
Go out, the defect type is gone out by words identification or is gone out by character mark.
In training image that the corresponding leaf image Area generation of each described defect type is multigroup, by the blade figure
As the background removal in region；
The background is to acquire the area adjacent in the plane with the leaf image region generated when leaf image region
Domain.
When multiple collected described images are inputted the defect recognition module identification, the background of described image is gone
It removes.
In the present embodiment, when the background can shoot wind turbine image, the backgrounds such as ground, meadow, sky of introducing will
The background removal in the leaf image region removes the blades image-region in described image.By the back of the body of described image
Scape remove, i.e., by the image non-wind turbine and leaf area remove.
Fig. 5 is the structural schematic diagram of blade in the present invention, multiple images of each blade include the two of each blade
To tip region 3 and tip region 4 in region 2, leaf in side base region 1 connected in sequence, blade root to leaf.
In the present embodiment, in the base region 1, the blade root to leaf in region 2, the leaf to tip region 3 with
And the length of the tip region 4 can carry out the adjustment of adaptability as needed.
In variation, multiple images of each blade can also be sequentially connected with leaf including the two sides of each blade
To region 2, Ye Zhongzhi in tip region 3 and tip region 4 or blade root to leaf in region 2, leaf in root zone domain 1, blade root to leaf
Tip region 3.
Fig. 3 shows the step flow chart that defect area identifies in the present invention, as shown in figure 3, the step S2 includes such as
Lower step：
Step S201：Multiple image mosaics of each blade are gone out to the 3-D view of each blade；
Step S202：It is established respectively using the length direction of blade as X-axis in the two sides of each blade, with the leaf
The width direction of piece is the coordinate system of Y-axis, and then generates the coordinate of each defective locations；
Step S203：Multiple defective locations of multiple Assembled distributions are generated into defect area, it is every in the defect area
A defective locations are less than presetting distance at least at a distance from another defective locations of the defect area；
Step S204：The defect area is identified on each blade.
In the present embodiment, the present invention is X by being established with the length direction of blade in the two sides of each blade
Axis, the width direction with the blade are Y, can accurately determine the coordinate of each defective locations, and then mutual distance is less than
The defective locations of presetting distance generate defect area, so that determine the defective locations concentration zones of each blade, to
Emphasis detection can be carried out to the region, improve the efficiency of detection.
In the present embodiment, the presetting distance be according to need carry out setpoint distance, it is described in the present embodiment pre-
Setpoint distance is 10 centimetres.
In the present embodiment, the defect area is identified on each blade, specially by the defect area
Domain outlines on the blade.
Fig. 4 is the ageing time calculation flow chart of Adjacent defect length in the present invention, as shown in figure 4, working as the defect class
When type is blade cracking and gel coat crackle, the step S101 includes the following steps：
Step S1011：The corresponding leaf image region of each defect type is acquired, the defect type includes blade cracking
With gel coat crackle；
Step S1012：The corresponding leaf image region of each defect type is divided into according to residing defect area several
A defect group；
Step S1013：Leaf image region in each defect group is sorted successively from short to long according to defect length；
Step S1014：Two leaf image regions for tracking Adjacent defect length successively, when the forward leaf image of arrangement
Area defects length is generated to identical as arranged adjacent in the defect group and the leaf image area defects length of arrangement rearward
When, generate the ageing time in two leaf image regions of the Adjacent defect length.
In the present embodiment, in the present invention when arrange forward leaf image area defects length generate to the defect
Arranged adjacent and when identical arrangement leaf image area defects length rearward in group, determines the ageing time of the defect type,
So as to determine the defect type aging tendency, convenient for arrange maintenance time, as early as possible the defect smaller to ageing time into
Row repair, avoids larger loss caused by postponing to repair.
Fig. 6 is the module diagram of wind turbine crop leaf measuring system in the present invention, as shown in fig. 6, wind turbine provided by the invention
Crop leaf measuring system, for realizing the fan blade detection method, including：
Defect recognition module, for identification on collected wind turbine each blade multiple images, know in described image
Do not go out the defect of the blade, and mark out each defect defective locations and defect type and the defect where leaf
The number of piece；
Image mosaic module, the graphics for multiple image mosaics of each blade to be gone out to each blade
Picture, and the defective locations and defect type are marked out on the 3-D view of the blade；
3-D view module is used for the number of the 3-D view and the blade according to each blade, establishes the wind
The 3-D view of machine, to show the defective locations and defect type of each blade on the 3-D view of the wind turbine.
A kind of fan blade detection device, including processor are also provided in the embodiment of the present invention.Memory, wherein being stored with
The executable instruction of processor.Wherein, processor is configured to be performed fan blade detection side via execution executable instruction
The step of method.
As above, multiple images for the blade being collected into can be identified in the present invention in the embodiment and marks out institute
Defective locations, the defect type on blade are stated, and is spliced into the 3-D view of each blade in turn, establishes the wind turbine
3-D view, so as to show the defective locations and defect type of each blade in the 3-D view of wind turbine, with can be square
Just displaying defective locations, in order to the convenient discovery defective locations in maintenance.
Person of ordinary skill in the field it is understood that various aspects of the invention can be implemented as system, method or
Program product.Therefore, various aspects of the invention can be embodied in the following forms, i.e.,：It is complete hardware embodiment, complete
The embodiment combined in terms of full Software Implementation (including firmware, microcode etc.) or hardware and software, can unite here
Referred to as " circuit ", " module " or " platform ".
Fig. 7 is the structural schematic diagram of the fan blade detection device of the present invention.It is described according to the present invention referring to Fig. 7
This embodiment electronic equipment 600.The electronic equipment 600 that Fig. 7 is shown is only an example, should not be to of the invention real
The function and use scope for applying example bring any restrictions.
As shown in fig. 7, electronic equipment 600 is showed in the form of universal computing device.The component of electronic equipment 600 can wrap
It includes but is not limited to：At least one processing unit 610, at least one storage unit 620, (including the storage of connection different platform component
Unit 620 and processing unit 610) bus 630, display unit 640 etc..
Wherein, storage unit has program stored therein code, and program code can be executed by processing unit 610 so that processing is single
Member 610 execute described in this specification above-mentioned electronic prescription circulation processing method part according to the various exemplary implementations of the present invention
The step of mode.For example, processing unit 610 can execute step as shown in fig. 1.
Storage unit 620 may include the readable medium of volatile memory cell form, such as Random Access Storage Unit
(RAM) 6201 and/or cache memory unit 6202, it can further include read-only memory unit (ROM) 6203.
Storage unit 620 can also include program/utility with one group of (at least one) program module 6205
6204, such program module 6205 includes but not limited to：Operating system, one or more application program, other program moulds
Block and program data may include the realization of network environment in each or certain combination in these examples.
Bus 630 can be to indicate one or more in a few class bus structures, including storage unit bus or storage
Cell controller, peripheral bus, graphics acceleration port, processing unit use the arbitrary bus structures in a variety of bus structures
Local bus.
Electronic equipment 600 can also be with one or more external equipments 700 (such as keyboard, sensing equipment, bluetooth equipment
Deng) communication, can also be enabled a user to one or more equipment interact with the electronic equipment 600 communicate, and/or with make
Any equipment that the electronic equipment 600 can be communicated with one or more of the other computing device (such as router, modulation /demodulation
Device etc.) communication.This communication can be carried out by input/output (I/O) interface 650.Also, electronic equipment 600 can be with
By network adapter 660 and one or more network (such as LAN (LAN), wide area network (WAN) and/or public network,
Such as internet) communication.Network adapter 660 can be communicated by bus 630 with other modules of electronic equipment 600.It should
Understand, although being not shown in Fig. 8, other hardware and/or software module can be used in conjunction with electronic equipment 600, including unlimited
In：Microcode, device driver, redundant processing unit, external disk drive array, RAID system, tape drive and number
According to backup storage platform etc..
A kind of computer readable storage medium is also provided in the embodiment of the present invention, for storing program, program is performed
The step of fan blade detection method of realization.In some possible embodiments, various aspects of the invention can also be real
It is now a kind of form of program product comprising program code, when program product is run on the terminal device, program code is used
Showing according to the present invention is various described in this specification above-mentioned electronic prescription circulation processing method part is executed in making terminal device
The step of example property embodiment.
As it appears from the above, the program of the computer readable storage medium of the embodiment is when being executed, it can be to receiving in the present invention
Multiple images of the blade collected are identified and mark out defective locations, defect type on the blade, and splice in turn
The 3-D view for going out each blade, establishes the 3-D view of the wind turbine, is shown so as to the 3-D view in wind turbine
The defective locations and defect type for going out each blade, can easily show defective locations, in order to the convenient hair in maintenance
Existing defective locations.
Fig. 8 is the structural schematic diagram of the computer readable storage medium of the present invention.Refering to what is shown in Fig. 8, describing according to this
The program product 800 for realizing the above method of the embodiment of invention, may be used the read-only storage of portable compact disc
Device (CD-ROM) and include program code, and can be run on terminal device, such as PC.However, the journey of the present invention
Sequence product is without being limited thereto, in this document, readable storage medium storing program for executing can be any include or storage program tangible medium, the journey
Sequence can be commanded the either device use or in connection of execution system, device.
The arbitrary combination of one or more readable mediums may be used in program product.Readable medium can be that readable signal is situated between
Matter or readable storage medium storing program for executing.Readable storage medium storing program for executing for example can be but be not limited to electricity, magnetic, optical, electromagnetic, infrared ray or partly lead
System, device or the device of body, or the arbitrary above combination.More specific example (the non exhaustive row of readable storage medium storing program for executing
Table) include：Electrical connection, portable disc, hard disk, random access memory (RAM), read-only storage with one or more conducting wires
Device (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read only memory (CD-
ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
Computer readable storage medium may include in a base band or as the data-signal that a carrier wave part is propagated,
In carry readable program code.The data-signal of this propagation may be used diversified forms, including but not limited to electromagnetic signal,
Optical signal or above-mentioned any appropriate combination.Readable storage medium storing program for executing can also be any readable Jie other than readable storage medium storing program for executing
Matter, which can send, propagate either transmission for used by instruction execution system, device or device or and its
The program of combined use.The program code for including on readable storage medium storing program for executing can transmit with any suitable medium, including but not
It is limited to wireless, wired, optical cable, RF etc. or above-mentioned any appropriate combination.
It can be write with any combination of one or more programming languages for executing the program that operates of the present invention
Code, programming language include object oriented program language-Java, C++ etc., further include conventional process
Formula programming language-such as " C " language or similar programming language.Program code can be calculated fully in user
It executes in equipment, partly execute on a user device, being executed, partly in user calculating equipment as an independent software package
Upper part executes or is executed in remote computing device or server completely on a remote computing.It is being related to remotely counting
In the situation for calculating equipment, remote computing device can pass through the network of any kind, including LAN (LAN) or wide area network
(WAN), it is connected to user calculating equipment, or, it may be connected to external computing device (such as utilize ISP
To be connected by internet).
In the present embodiment, multiple images for the blade being collected into can be identified and is marked out described in the present invention
Defective locations, defect type on blade, and it is spliced into the 3-D view of each blade in turn, establish the three of the wind turbine
Image is tieed up, so as to show the defective locations and defect type of each blade in the 3-D view of wind turbine, can facilitate
Displaying defective locations, in order to the convenient discovery defective locations in maintenance；
The classification of the defect of the blade is acquired into the corresponding blade of each defect type in several defect types in the present invention
Image-region generates multigroup training image, defect recognition module is trained by multigroup training image, to improve defect
The recognition efficiency of type.
Specific embodiments of the present invention are described above.It is to be appreciated that the invention is not limited in above-mentioned
Particular implementation, those skilled in the art can make various deformations or amendments within the scope of the claims, this not shadow
Ring the substantive content of the present invention.
Claims (10)
1. a kind of fan blade detection method, which is characterized in that include the following steps：
Step S1：Multiple images for acquiring each blade on wind turbine, identify the defect of the blade, and mark in described image
Outpour the number of the defective locations and defect type and defect place blade of each defect；
Step S2：Multiple image mosaics of each blade are gone out to the 3-D view of each blade, and in the blade
3-D view on mark out the defective locations and defect type；
Step S3：According to the number of the 3-D view and the blade of each blade, the 3-D view of the wind turbine is established, with
The defective locations and defect type of each blade are shown on the 3-D view of the wind turbine.
2. fan blade detection method according to claim 1, which is characterized in that the step S1 includes the following steps：
Step S101：The defect of the blade is categorized into several defect types, acquires the corresponding blade figure of each defect type
As region, multigroup training image is generated；
Step S102：Defect recognition module is trained by multigroup training image；
Step S103：Collected multiple images input defect recognition module is identified and carried out defective locations and is lacked
Fall into the label of type.
3. fan blade detection method according to claim 2, which is characterized in that corresponded to by each described defect type
Leaf image Area generation multigroup training image when, by the background removal in the leaf image region；
The background is to acquire the region adjacent in the plane with the leaf image region generated when leaf image region.
4. fan blade detection method according to claim 1, which is characterized in that multiple image packets of each blade
It includes in the two sides base region connected in sequence of each blade, blade root to leaf in region, leaf to tip region and blade tip area
Domain.
5. fan blade detection method according to claim 2, which is characterized in that the defect type includes following any
Kind is appointed a variety of：
Blade cracks；
Attachment falls off；
Surface corrosion；
Fall paint in surface；
Gel coat falls off；
Gel coat crackle.
6. fan blade detection method according to claim 1, which is characterized in that the step S2 includes the following steps：
Step S201：Multiple image mosaics of each blade are gone out to the 3-D view of each blade；
Step S202：It is established respectively using the length direction of blade as X-axis in the two sides of each blade, with the blade
Width direction is the coordinate system of Y-axis, and then generates the coordinate of each defective locations；
Step S203：Multiple defective locations of multiple Assembled distributions are generated into defect area, are each lacked in the defect area
Sunken position is less than presetting distance at least at a distance from another defective locations of the defect area；
Step S204：The defect area is identified on each blade.
7. fan blade detection method according to claim 2, which is characterized in that when the defect type cracks for blade
When with gel coat crackle, the step S101 includes the following steps：
Step S1011：The corresponding leaf image region of each defect type is acquired, the defect type includes blade cracking and glue
Clothing crackle；
Step S1012：The corresponding leaf image region of each defect type is divided into several according to residing defect area to lack
Fall into group；
Step S1013：Leaf image region in each defect group is sorted successively from short to long according to defect length；
Step S1014：Two leaf image regions for tracking Adjacent defect length successively, when the forward leaf image region of arrangement
It is raw when defect length is generated to arranged adjacent in the defect group and the identical leaf image area defects length of arrangement rearward
At the ageing time in two leaf image regions of the Adjacent defect length.
8. a kind of fan blade detecting system, for realizing the fan blade detection side described in any one of claim 1 to 7
Method, which is characterized in that including：
Defect recognition module, for identification on collected wind turbine each blade multiple images, identified in described image
The defect of the blade, and the defective locations for marking out each defect and blade where defect type and the defect
Number；
Image mosaic module, the 3-D view for multiple image mosaics of each blade to be gone out to each blade, and
The defective locations and defect type are marked out on the 3-D view of the blade；
3-D view module is used for the number of the 3-D view and the blade according to each blade, establishes the wind turbine
3-D view, to show the defective locations and defect type of each blade on the 3-D view of the wind turbine.
9. a kind of fan blade detection device, which is characterized in that including：
Processor；
Memory, wherein being stored with the executable instruction of the processor；
Wherein, the processor is configured to come any one of perform claim requirement 1 to 7 institute via the execution executable instruction
The step of stating fan blade detection method.
10. a kind of computer readable storage medium, for storing program, which is characterized in that described program is performed realization power
Profit requires the step of any one of 1 to 7 fan blade detection method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201810190311.7A CN108506170A (en) | 2018-03-08 | 2018-03-08 | Fan blade detection method, system, equipment and storage medium |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201810190311.7A CN108506170A (en) | 2018-03-08 | 2018-03-08 | Fan blade detection method, system, equipment and storage medium |
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| Publication Number | Publication Date |
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| CN108506170A true CN108506170A (en) | 2018-09-07 |
Family
ID=63377254
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|---|---|---|---|
| CN201810190311.7A Pending CN108506170A (en) | 2018-03-08 | 2018-03-08 | Fan blade detection method, system, equipment and storage medium |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109741238A (en) * | 2018-11-23 | 2019-05-10 | 上海扩博智能技术有限公司 | Fan blade image split-joint method, system, equipment and storage medium |
| CN110608137A (en) * | 2019-08-30 | 2019-12-24 | 华电电力科学研究院有限公司 | On-site rapid diagnosis method for wind turbine generator |
| CN111537532A (en) * | 2020-06-11 | 2020-08-14 | 全球能源互联网研究院有限公司 | Membrane electrode defect detection method and device |
| CN111654642A (en) * | 2020-07-22 | 2020-09-11 | 上海扩博智能技术有限公司 | Exposure value adjusting method, system, device and storage medium for shooting fan blade |
| CN111852792A (en) * | 2020-09-10 | 2020-10-30 | 东华理工大学 | Fan blade defect self-diagnosis positioning method based on machine vision |
| CN111858553A (en) * | 2020-07-10 | 2020-10-30 | 天津智惠未来科技有限责任公司 | Construction method of wind power blade inspection database management system |
| CN117514646A (en) * | 2023-11-22 | 2024-02-06 | 辽宁高比科技有限公司 | Dynamic inspection analysis method and system for ground type fan blade |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101657839A (en) * | 2007-03-23 | 2010-02-24 | 汤姆森许可贸易公司 | System and method for region classification of 2D images for 2D-to-3D conversion |
| CN101714262A (en) * | 2009-12-10 | 2010-05-26 | 北京大学 | Method for reconstructing three-dimensional scene of single image |
| CN102519407A (en) * | 2011-12-05 | 2012-06-27 | 西北工业大学 | Method for establishing three-dimensional tolerance model of blade |
| CN103148784A (en) * | 2013-03-14 | 2013-06-12 | 哈尔滨鹰瑞达科技开发有限公司 | Full size detection method for large vane |
| CN103323461A (en) * | 2013-06-14 | 2013-09-25 | 上海大学 | On-line detection method for movement of non-contact type wind driven generator blade |
| CN103679674A (en) * | 2013-11-29 | 2014-03-26 | 航天恒星科技有限公司 | Method and system for splicing images of unmanned aircrafts in real time |
| CN104180789A (en) * | 2014-09-12 | 2014-12-03 | 北京航空航天大学 | Blade detection method based on graphic matching algorithm |
| CN104730091A (en) * | 2015-02-10 | 2015-06-24 | 西安交通大学 | Gas turbine blade defects extraction and analysis method based on region segmenting detection |
| CN105447910A (en) * | 2015-12-31 | 2016-03-30 | 河北工业大学 | Method for three-dimensional reconstruction of defected part of tip of aeroengine compressor blade |
| CN105973161A (en) * | 2016-06-17 | 2016-09-28 | 西安交通大学 | Three-dimensional full-field deformation measurement method of paddle |
| CN106156780A (en) * | 2016-06-29 | 2016-11-23 | 南京雅信科技集团有限公司 | The method getting rid of wrong report on track in foreign body intrusion identification |
| CN106338521A (en) * | 2016-09-22 | 2017-01-18 | 华中科技大学 | Additive manufacturing surface defect, internal defect and shape composite detection method and device |
| CN206092295U (en) * | 2016-09-23 | 2017-04-12 | 西安热工研究院有限公司 | At labour wind generating set blade internal defect detector |
| CN106762451A (en) * | 2016-12-05 | 2017-05-31 | 北京金风科创风电设备有限公司 | Fan blade damage detection method, device and system based on unmanned aerial vehicle |
| CN106767524A (en) * | 2016-11-22 | 2017-05-31 | 江苏大学 | A kind of hydraulic spoon of blade detection method and device |
| CN106767467A (en) * | 2017-03-05 | 2017-05-31 | 贵州大学 | A kind of indexable insert tip, throw away tip blunt circle detecting system based on machine vision |
| CN107292865A (en) * | 2017-05-16 | 2017-10-24 | 哈尔滨医科大学 | A kind of stereo display method based on two dimensional image processing |
| CN107735794A (en) * | 2015-08-06 | 2018-02-23 | 埃森哲环球服务有限公司 | Use the condition detection of image procossing |
-
2018
- 2018-03-08 CN CN201810190311.7A patent/CN108506170A/en active Pending
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101657839A (en) * | 2007-03-23 | 2010-02-24 | 汤姆森许可贸易公司 | System and method for region classification of 2D images for 2D-to-3D conversion |
| CN101714262A (en) * | 2009-12-10 | 2010-05-26 | 北京大学 | Method for reconstructing three-dimensional scene of single image |
| CN102519407A (en) * | 2011-12-05 | 2012-06-27 | 西北工业大学 | Method for establishing three-dimensional tolerance model of blade |
| CN103148784A (en) * | 2013-03-14 | 2013-06-12 | 哈尔滨鹰瑞达科技开发有限公司 | Full size detection method for large vane |
| CN103323461A (en) * | 2013-06-14 | 2013-09-25 | 上海大学 | On-line detection method for movement of non-contact type wind driven generator blade |
| CN103679674A (en) * | 2013-11-29 | 2014-03-26 | 航天恒星科技有限公司 | Method and system for splicing images of unmanned aircrafts in real time |
| CN104180789A (en) * | 2014-09-12 | 2014-12-03 | 北京航空航天大学 | Blade detection method based on graphic matching algorithm |
| CN104730091A (en) * | 2015-02-10 | 2015-06-24 | 西安交通大学 | Gas turbine blade defects extraction and analysis method based on region segmenting detection |
| CN107735794A (en) * | 2015-08-06 | 2018-02-23 | 埃森哲环球服务有限公司 | Use the condition detection of image procossing |
| CN105447910A (en) * | 2015-12-31 | 2016-03-30 | 河北工业大学 | Method for three-dimensional reconstruction of defected part of tip of aeroengine compressor blade |
| CN105973161A (en) * | 2016-06-17 | 2016-09-28 | 西安交通大学 | Three-dimensional full-field deformation measurement method of paddle |
| CN106156780A (en) * | 2016-06-29 | 2016-11-23 | 南京雅信科技集团有限公司 | The method getting rid of wrong report on track in foreign body intrusion identification |
| CN106338521A (en) * | 2016-09-22 | 2017-01-18 | 华中科技大学 | Additive manufacturing surface defect, internal defect and shape composite detection method and device |
| CN206092295U (en) * | 2016-09-23 | 2017-04-12 | 西安热工研究院有限公司 | At labour wind generating set blade internal defect detector |
| CN106767524A (en) * | 2016-11-22 | 2017-05-31 | 江苏大学 | A kind of hydraulic spoon of blade detection method and device |
| CN106762451A (en) * | 2016-12-05 | 2017-05-31 | 北京金风科创风电设备有限公司 | Fan blade damage detection method, device and system based on unmanned aerial vehicle |
| CN106767467A (en) * | 2017-03-05 | 2017-05-31 | 贵州大学 | A kind of indexable insert tip, throw away tip blunt circle detecting system based on machine vision |
| CN107292865A (en) * | 2017-05-16 | 2017-10-24 | 哈尔滨医科大学 | A kind of stereo display method based on two dimensional image processing |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109741238A (en) * | 2018-11-23 | 2019-05-10 | 上海扩博智能技术有限公司 | Fan blade image split-joint method, system, equipment and storage medium |
| CN109741238B (en) * | 2018-11-23 | 2020-08-11 | 上海扩博智能技术有限公司 | Fan blade image splicing method, system, equipment and storage medium |
| US11466666B2 (en) | 2018-11-23 | 2022-10-11 | Shanghai Clobotics Technology Co., Ltd. | Method and device for stitching wind turbine blade images, and storage medium |
| CN110608137A (en) * | 2019-08-30 | 2019-12-24 | 华电电力科学研究院有限公司 | On-site rapid diagnosis method for wind turbine generator |
| CN111537532A (en) * | 2020-06-11 | 2020-08-14 | 全球能源互联网研究院有限公司 | Membrane electrode defect detection method and device |
| CN111858553A (en) * | 2020-07-10 | 2020-10-30 | 天津智惠未来科技有限责任公司 | Construction method of wind power blade inspection database management system |
| CN111654642A (en) * | 2020-07-22 | 2020-09-11 | 上海扩博智能技术有限公司 | Exposure value adjusting method, system, device and storage medium for shooting fan blade |
| CN111852792A (en) * | 2020-09-10 | 2020-10-30 | 东华理工大学 | Fan blade defect self-diagnosis positioning method based on machine vision |
| CN117514646A (en) * | 2023-11-22 | 2024-02-06 | 辽宁高比科技有限公司 | Dynamic inspection analysis method and system for ground type fan blade |
| CN117514646B (en) * | 2023-11-22 | 2024-06-07 | 辽宁高比科技有限公司 | Dynamic inspection analysis method and system for ground type fan blade |
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Application publication date: 20180907 |