CN113432592A - Automatic winding defect identification and correction system of optical fiber ring winding machine - Google Patents
Automatic winding defect identification and correction system of optical fiber ring winding machine Download PDFInfo
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- CN113432592A CN113432592A CN202110698179.2A CN202110698179A CN113432592A CN 113432592 A CN113432592 A CN 113432592A CN 202110698179 A CN202110698179 A CN 202110698179A CN 113432592 A CN113432592 A CN 113432592A
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- 238000004804 winding Methods 0.000 title claims abstract description 166
- 230000007547 defect Effects 0.000 title claims abstract description 129
- 239000013307 optical fiber Substances 0.000 title claims abstract description 82
- 238000012937 correction Methods 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 claims abstract description 30
- 230000008569 process Effects 0.000 claims abstract description 23
- 238000013500 data storage Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 6
- 239000000835 fiber Substances 0.000 claims description 25
- 238000003860 storage Methods 0.000 claims description 12
- 238000013135 deep learning Methods 0.000 claims description 10
- 238000007726 management method Methods 0.000 claims description 8
- 238000012549 training Methods 0.000 claims description 8
- 239000011159 matrix material Substances 0.000 claims description 7
- 238000013473 artificial intelligence Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 4
- 230000001360 synchronised effect Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 5
- 230000009471 action Effects 0.000 description 4
- 238000007599 discharging Methods 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 230000004913 activation Effects 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 238000010923 batch production Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 210000002364 input neuron Anatomy 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C19/00—Gyroscopes; Turn-sensitive devices using vibrating masses; Turn-sensitive devices without moving masses; Measuring angular rate using gyroscopic effects
- G01C19/58—Turn-sensitive devices without moving masses
- G01C19/64—Gyrometers using the Sagnac effect, i.e. rotation-induced shifts between counter-rotating electromagnetic beams
- G01C19/72—Gyrometers using the Sagnac effect, i.e. rotation-induced shifts between counter-rotating electromagnetic beams with counter-rotating light beams in a passive ring, e.g. fibre laser gyrometers
- G01C19/721—Details
- G01C19/722—Details of the mechanical construction
Abstract
The invention relates to an automatic winding defect identification and correction system of an optical fiber ring winding machine, which comprises an optical fiber ring winding image acquisition unit, an automatic winding defect identification unit and an automatic winding defect correction unit; the optical fiber ring winding image acquisition unit comprises an acquisition module and a data storage module; the acquisition module is used for acquiring optical fiber arrangement images of winding spaces at various positions in the optical fiber ring winding process and collecting optical fiber arrangement information in the whole working process; the data storage module is used for storing the acquired image information and the acquired optical fiber arrangement information and outputting the image information and the optical fiber arrangement information to the winding defect automatic identification unit; the winding defect automatic identification unit is used for amplifying and analyzing the optical fiber arrangement image input by the image acquisition unit, identifying different types of winding defects and synchronously sending the types of the winding defects to the defect automatic error correction unit; and the automatic winding defect error correction unit is used for automatically processing the winding defect type and the occurrence position information identified by the automatic winding defect identification system.
Description
Technical Field
The invention belongs to the technical field of winding production of optical fiber rings, and particularly relates to an automatic identification and error correction system for winding defects of an optical fiber ring winding machine.
Background
The optical fiber gyroscope, as a novel optical gyroscope instrument, has the advantages of high reliability, impact vibration resistance, long service life, high starting speed and the like, and is widely applied to a plurality of military and civil fields. However, when the temperature of the operating environment of the fiber-optic gyroscope changes, thermally induced non-reciprocal phase noise, i.e., a SHUPE error, is generated in the fiber-optic ring sensor (for short, a fiber-optic ring) which is a core component of the fiber-optic gyroscope. The error cannot be distinguished from the SAGNAC effect of sensing the earth rotation speed by the fiber-optic gyroscope, and the actual detection precision of the fiber-optic gyroscope is seriously reduced. For a high-precision fiber-optic gyroscope, a high-precision fiber-optic ring is needed, and the high-precision fiber-optic ring has high looping difficulty, long winding period and high cost. In order to improve the precision and yield of the high-precision optical fiber ring, high-precision optical fiber ring winding equipment is required to be adopted for winding. The higher the automation degree of the high-precision ring winding machine is, the more the influence of human interference on the precision and the quality of the high-precision optical fiber ring can be reduced, and the winding precision of the optical fiber ring is further improved.
However, in the actual process of winding the optical fiber loop, it has been found that although the mechanical precision of the winding machine can be made high by using a high precision motor, a mechanical spindle, a lead screw, and the like, the diameter of the optical fiber has a certain tolerance (about ± 0.003mm in the case of a 0.135mm diameter thin optical fiber), which is unavoidable. The high-precision optical fiber ring is generally wound by several kilometers, and under the condition of ideal other conditions, the tolerance can also cause the defects of generating intervals among optical fibers in the winding process, overlapping during the winding of the optical fibers and the like, and in addition, the winding defects of the optical fibers can be caused by the problems of non-uniform winding stress, matching of fiber arrangement precision and fiber diameter of a winding machine and the like. In the past, the defects need to be amplified by an industrial-grade CCD and then identified by naked eyes of surrounding workers, so that very accurate fault criteria are difficult to formulate. And after the fault is found, the worker controls the winding machine to pour the optical fiber at the position with the winding defect through the key to perform winding again. The method has low accuracy and high labor intensity, the help to the winding quality of the ring needs to be improved, and the method is difficult to adapt to the requirement of batch production of high-precision optical fiber rings. Therefore, it is necessary to improve the accuracy of judging the winding defect of the high-precision optical fiber loop and reduce the labor intensity of judging and correcting the winding fault, thereby effectively improving the winding accuracy, efficiency and yield of the high-precision optical fiber loop.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides the automatic identification and error correction system for the winding defects of the optical fiber ring winding machine, which can greatly reduce the human intervention in the winding process of the high-precision optical fiber ring, thereby improving the winding precision and greatly reducing the labor intensity.
The above object of the present invention is achieved by the following technical solutions:
the utility model provides an automatic identification of optic fibre ring circling machine coiling defect and error correction system which characterized in that: the system comprises an optical fiber ring winding image acquisition unit, a winding defect automatic identification unit and a winding defect automatic error correction unit;
the optical fiber ring winding image acquisition unit comprises an acquisition module and a data storage module; the acquisition module is used for acquiring optical fiber arrangement images of winding spaces at various positions in the optical fiber ring winding process and collecting optical fiber arrangement information in the whole working process; the data storage module is used for storing the acquired image information and the acquired optical fiber arrangement information and outputting the image information and the optical fiber arrangement information to the winding defect automatic identification unit;
the winding defect automatic identification unit is used for amplifying and analyzing the optical fiber arrangement image input by the image acquisition unit, identifying different types of winding defects and synchronously sending the types of the winding defects to the defect automatic error correction unit;
and the winding defect automatic error correction unit is used for respectively controlling the fiber supply unit, the auxiliary fiber discharge unit and the main shaft rotation unit of the winding machine according to the winding defect type and the occurrence position information identified by the winding defect automatic identification system so as to realize automatic processing of the winding defect.
Further: the image acquisition unit is a multi-degree-of-freedom multi-position image acquisition unit, the acquisition module is a synchronous acquisition module and is provided with a plurality of industrial grade high-magnification CCD cameras, and the cameras are arranged at different positions of an optical fiber ring winding working interface at different angles.
Further: the automatic winding defect identification unit completes automatic defect identification by means of artificial intelligence deep learning, and comprises two operation models, namely a training model and a working model; in the process of operating the training model, amplifying a large amount of image information which is acquired by the optical fiber ring winding image acquisition unit and contains no winding defects and various winding defects, coding the amplified image information, and constructing a defect classification reference standard of the operating working model; in the process of operating the working model, the optical fiber ring winding image acquisition unit acquires image information of an actual winding scene, the image information is amplified and processed, the image information is coded, the coded information is compared with a constructed winding defect standard, and whether the winding defect exists or not and the specific type of the winding defect are judged.
Further, the method comprises the following steps: fiber winding defects include fiber overlap and excessive winding gaps.
Further, the method comprises the following steps: the winding defect automatic identification unit comprises a local storage module, an operation matrix core module, a storage management module, a clock management module and a communication output module; the operation matrix module is a deep learning core operation module.
The invention has the advantages and positive effects that:
by adopting the automatic winding defect identification and correction system of the optical fiber ring winding machine, when optical fibers are wound, a winding image is synchronously stored through the image acquisition unit and is sent to the automatic defect identification system, whether defects exist or not and the types of the defects are automatically identified through an artificial intelligence deep learning algorithm, and if the defects exist, the types of the defects are sent to the automatic defect correction system. And after receiving the defect type signal, the defect automatic error correction system respectively sends a winding machine spindle control signal, an auxiliary fiber discharge control signal and a tension control signal to control each module of the winding machine to process the winding defect through a preset defect error correction program, reversely winds the optical fiber with the defect, and quits the operation of the whole winding defect automatic identification and error correction system after the winding defect automatic identification system has a failure mode and disappears. The self-checking, self-interpretation and self-processing of the winding fiber defects are realized, so that the human intervention in the winding process of the high-precision optical fiber ring is greatly reduced, the winding precision can be improved, and the labor intensity is greatly reduced.
Drawings
FIG. 1 is a schematic diagram of an automatic winding defect identification and correction system of an optical fiber ring winding machine according to the present invention;
FIG. 2 is a schematic view of a multi-degree-of-freedom multi-position winding image acquisition unit according to the present invention;
FIG. 3 is a schematic diagram of an automatic winding defect identification unit according to the present invention;
FIG. 4 is a schematic diagram of an automatic winding defect correction system according to the present invention;
FIG. 5a is a schematic view of a defect-free structure of a normally wound optical fiber according to the present invention;
FIG. 5b is a schematic structural diagram of a defect in a wound and stacked optical fiber according to the present invention;
FIG. 5c is a schematic diagram of a defect of an excessive gap in the winding of an optical fiber according to the present invention;
FIG. 6 is a defect handling workflow of the present invention.
Detailed Description
The structure of the present invention will be further described by way of examples with reference to the accompanying drawings. It is to be understood that this embodiment is illustrative and not restrictive.
Referring to fig. 1-6, an automatic identification and error correction system for winding defects of an optical fiber ring winding machine is characterized in that: the automatic winding defect identification unit and the automatic winding defect correction unit are included.
The optical fiber ring winding image acquisition unit adopts a multi-degree-of-freedom multi-position image acquisition unit and mainly comprises an acquisition module and a data storage module. The acquisition module is a synchronous acquisition module which is provided with a plurality of industrial grade high-magnification CCD cameras, and the cameras are arranged at different positions of a winding working interface of the optical fiber ring at different angles. The acquisition module is used for acquiring optical fiber arrangement images of winding spaces at all positions in the optical fiber ring winding process and collecting optical fiber arrangement information in the whole working process. The data storage module is used for storing the collected image information and the collected optical fiber arrangement information and outputting the image information and the optical fiber arrangement information to the winding defect automatic identification unit. The optical fiber ring winding image acquisition unit monitors the winding process, provides data for subsequent winding defect identification and automatic error correction, and can provide a large amount of training data and data for artificial intelligence deep learning.
And the winding defect automatic identification unit is used for amplifying and analyzing the optical fiber arrangement image input by the image acquisition unit, identifying different types of winding defects and synchronously sending the types of the winding defects to the defect automatic error correction unit. Specifically, the winding defect automatic identification unit automatically identifies defects by means of artificial intelligence deep learning, and comprises two operation models, namely a training model and a working model. In the process of operating the training model, characteristic learning and training based on a convolutional neural network or deep belief network method are carried out, and different winding defect conditions are learned and classified in a supervision or semi-supervision learning mode. The method comprises the steps of amplifying a large amount of image information which is acquired by an optical fiber ring winding image acquisition unit and contains no winding defects and various winding defects, coding the amplified image information, and constructing a defect classification reference standard for operating a working model. In the process of operating the working model, the optical fiber ring winding image acquisition unit acquires image information of an actual winding scene, the image information is amplified and processed, the image information is coded, the coded information is compared with a constructed winding defect standard, and whether the winding defect exists or not and the specific type of the winding defect are judged. The winding defect automatic identification unit mainly comprises a local storage module, an operation matrix core module, a storage management module, a clock management module and a communication output module; the operation matrix module is a deep learning core operation module.
The local storage module is a main cache unit of the automatic identification unit and comprises an input neuron cache block, an input weight cache block, an input offset parameter cache block and an intermediate result cache block.
The operation matrix core module is a deep learning core operation module. The method mainly comprises a convolution operation block, a sampling operation block and an activation function block, and is mainly used for finishing the graphic amplification processing, identifying different types of winding defects through convolution, finishing the deep learning operation of various defects and extracting the defect position information from a local storage unit.
The storage management module is a control unit for the local storage module, and is mainly used for controlling the local storage module and comprises a local controller and a priority controller. The local controller is used for transmitting different input information to different cache block units, and the priority controller is used for coordinating the storage position and the sequence priority of the calculation result.
The clock management module is matched with clock signals with different frequencies according to the execution efficiency of different module circuits to carry out the work of different clock domains.
And the communication output module is a bridge connected with the computer. And orderly transmitting the network parameters, the original data, the control instructions and the operation results.
And the winding defect automatic error correction unit automatically processes the winding defect type and the occurrence position information identified by the winding defect automatic identification system. The main work content is as follows: firstly, the defect type is received, and then different processing modes are selected according to different defect types. And respectively controlling the fiber supply unit, the auxiliary fiber discharge unit and the main shaft rotation unit of the winding machine to treat winding defects. The winding defect types which are easy to appear at present are as follows: fiber overlap, excessive winding gaps, etc.
When the defect identification automatic identification module identifies the defect type: when the optical fiber is overlapped and has a defect and the position of the fault occurs, the information is provided for an automatic error correction unit, then a control module is edited through action, a winding stopping command is sent to a main shaft control module at first, then a lifting command is sent to an auxiliary fiber discharging module, then a rewinding command is sent to the main shaft control module until the rewinding action of the position of the overlapped fiber defect is completed, then an advancing command is sent to the main shaft control module, the main shaft is fed for a certain distance, the distance is given by a depth learning result of the actual optical fiber overlapping position through an automatic defect identification system, then a putting down command is sent to the auxiliary fiber discharging module, the auxiliary fiber discharging module is made to approach a winding working interface again, and the tension control module keeps relatively stable tension all the time. And finishing the automatic error correction process of the optical fiber overlapping defect.
When the defect identification automatic identification module identifies the defect type: when the winding interval is too large and the position of a fault occurs, the information is provided for an automatic error correction unit, then a control module is edited through action, a winding stopping command is sent to a main shaft control module firstly, then a lifting command is sent to an auxiliary fiber arrangement module, then a rewinding command is sent to the main shaft control module until the rewinding action of the position of the fiber stacking defect is completed, then a retreating command is sent to the main shaft control module, the main shaft is retreated for a certain distance, the distance is given by a depth learning result of the actual optical fiber gap through an automatic defect identification system, then a lowering command is sent to the auxiliary fiber arrangement module, the auxiliary fiber arrangement module is made to approach the winding working interface again, and the tension control module keeps the tension relatively stable all the time. And finishing the automatic error correction process with overlarge optical fiber winding intermittence.
Although the embodiments of the present invention and the accompanying drawings are disclosed for illustrative purposes, those skilled in the art will appreciate that: various substitutions, changes and modifications are possible without departing from the spirit of the invention and the scope of the appended claims, and therefore the scope of the invention is not limited to the disclosure of the embodiments and the accompanying drawings.
Claims (5)
1. The utility model provides an automatic identification of optic fibre ring circling machine coiling defect and error correction system which characterized in that: the system comprises an optical fiber ring winding image acquisition unit, a winding defect automatic identification unit and a winding defect automatic error correction unit;
the optical fiber ring winding image acquisition unit comprises an acquisition module and a data storage module; the acquisition module is used for acquiring optical fiber arrangement images of winding spaces at various positions in the optical fiber ring winding process and collecting optical fiber arrangement information in the whole working process; the data storage module is used for storing the acquired image information and the acquired optical fiber arrangement information and outputting the image information and the optical fiber arrangement information to the winding defect automatic identification unit;
the winding defect automatic identification unit is used for amplifying and analyzing the optical fiber arrangement image input by the image acquisition unit, identifying different types of winding defects and synchronously sending the types of the winding defects to the defect automatic error correction unit;
and the winding defect automatic error correction unit is used for respectively controlling the fiber supply unit, the auxiliary fiber discharge unit and the main shaft rotation unit of the winding machine according to the winding defect type and the occurrence position information identified by the winding defect automatic identification system so as to realize automatic processing of the winding defect.
2. The automatic identification and correction system for winding defects of an optical fiber ring winding machine according to claim 1, characterized in that: the image acquisition unit is a multi-degree-of-freedom multi-position image acquisition unit, the acquisition module is a synchronous acquisition module and is provided with a plurality of industrial grade high-magnification CCD cameras, and the cameras are arranged at different positions of an optical fiber ring winding working interface at different angles.
3. The automatic identification and correction system for winding defects of an optical fiber ring winding machine according to claim 1, characterized in that: the automatic winding defect identification unit completes automatic defect identification by means of artificial intelligence deep learning, and comprises two operation models, namely a training model and a working model; in the process of operating the training model, amplifying a large amount of image information which is acquired by the optical fiber ring winding image acquisition unit and contains no winding defects and various winding defects, coding the amplified image information, and constructing a defect classification reference standard of the operating working model; in the process of operating the working model, the optical fiber ring winding image acquisition unit acquires image information of an actual winding scene, the image information is amplified and processed, the image information is coded, the coded information is compared with a constructed winding defect standard, and whether the winding defect exists or not and the specific type of the winding defect are judged.
4. The automatic identification and correction system for winding defects of an optical fiber ring winding machine according to claim 3, characterized in that: fiber winding defects include fiber overlap and excessive winding gaps.
5. The automatic identification and correction system for winding defects of an optical fiber ring winding machine according to claim 3, characterized in that: the winding defect automatic identification unit comprises a local storage module, an operation matrix core module, a storage management module, a clock management module and a communication output module; the operation matrix module is a deep learning core operation module.
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Cited By (3)
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| CN114046802A (en) * | 2021-09-28 | 2022-02-15 | 中国船舶重工集团公司第七0七研究所 | Step-by-step temperature compensation method for fiber-optic gyroscope |
| CN114441552A (en) * | 2022-02-28 | 2022-05-06 | 中国船舶重工集团公司第七0七研究所 | High-precision optical fiber loop glue filling defect detection and correction method |
| CN114777816A (en) * | 2022-06-20 | 2022-07-22 | 中国船舶重工集团公司第七0七研究所 | Method and system for early warning and inhibiting hollow-core microstructure fiber winding breakpoint |
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| CN114777816A (en) * | 2022-06-20 | 2022-07-22 | 中国船舶重工集团公司第七0七研究所 | Method and system for early warning and inhibiting hollow-core microstructure fiber winding breakpoint |
| CN114777816B (en) * | 2022-06-20 | 2022-09-06 | 中国船舶重工集团公司第七0七研究所 | Method and system for early warning and inhibiting hollow microstructure fiber winding breakpoint |
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Application publication date: 20210924 |