CN112179922A - Wire and cable defect detection system - Google Patents
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- CN112179922A CN112179922A CN202011019512.4A CN202011019512A CN112179922A CN 112179922 A CN112179922 A CN 112179922A CN 202011019512 A CN202011019512 A CN 202011019512A CN 112179922 A CN112179922 A CN 112179922A
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- 230000007547 defect Effects 0.000 title claims abstract description 94
- 238000001514 detection method Methods 0.000 title claims abstract description 32
- 239000013598 vector Substances 0.000 claims abstract description 49
- 238000000034 method Methods 0.000 claims abstract description 22
- 238000007781 pre-processing Methods 0.000 claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims abstract description 4
- 238000000605 extraction Methods 0.000 claims abstract description 4
- 238000013450 outlier detection Methods 0.000 claims description 23
- 238000013527 convolutional neural network Methods 0.000 claims description 16
- 238000004458 analytical method Methods 0.000 claims description 10
- 238000013500 data storage Methods 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 6
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- 230000002159 abnormal effect Effects 0.000 claims description 3
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- 238000001914 filtration Methods 0.000 claims description 3
- 239000002245 particle Substances 0.000 claims description 3
- 238000011176 pooling Methods 0.000 claims description 3
- 238000013528 artificial neural network Methods 0.000 claims description 2
- 238000001028 reflection method Methods 0.000 description 4
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- G06F18/00—Pattern recognition
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- G06F18/24—Classification techniques
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Abstract
The invention relates to defect detection, in particular to a wire and cable defect detection system, which comprises a controller, wherein the controller is connected with an image acquisition module for acquiring a cable image and a vertical axial X-ray image, the controller is connected with an image preprocessing module for preprocessing the acquired image, the controller is connected with a feature vector extraction module for extracting feature vectors of the processed cable image, the controller is connected with a feature vector combination module for randomly combining the extracted feature vectors, the controller is connected with a feature vector classification module for classifying and calculating the randomly combined feature vectors, and the controller is connected with a defect position determination module for determining defect positions according to calculation results of the feature vector classification module; the technical scheme provided by the invention can effectively overcome the problem that the defects on the surfaces of the wires and the cables and in the working process cannot be accurately and effectively detected in the prior art.
Description
Technical Field
The invention relates to defect detection, in particular to a wire and cable defect detection system.
Background
The electric wire and the cable play a great role in the construction process of the power system and directly influence the stable and reliable operation of the power system. In the manufacturing process of the electric wire and the electric cable, certain defects are generated on the surface of the electric wire and the electric cable inevitably. In order to ensure the quality of the electric wire and cable, the surface of the electric wire and cable and the defects existing in the working process need to be detected. The traditional method mostly adopts manual detection for the surface defects of the wires and the cables, but the manual detection mode has the defects of strong subjectivity, high cost, easy generation of visual fatigue and low detection efficiency and accuracy.
In addition, the defect detection in the cable working process can be divided into two types, one type is off-line detection, namely, power-off processing is required during detection, and the method comprises a bridge method, a pulse voltage method, a pulse current method, a secondary pulse method, a time domain reflection method and the like. Since the research of off-line detection starts earlier and has mature products, the off-line detection is the main method for detecting the cable fault at present. However, offline detection needs to be performed on the premise of power failure of the cable, which requires the power supply department to cut off the line in a certain area for troubleshooting, and inevitably causes great economic loss. In addition, intermittent faults occurring in the operation process of the cable are short in duration, and off-line detection is difficult to reproduce.
The second type is online detection, namely, the cable state can be continuously monitored without power failure during detection and influencing normal power supply of the cable, so that intermittent faults can be detected, and the method mainly comprises a noise reflection method, a carrier wave test method, a direct sequence time domain reflection method, a spread spectrum time domain reflection method and the like.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects in the prior art, the invention provides a wire and cable defect detection system which can effectively solve the problem that the defects on the surface of a wire and cable and in the working process cannot be accurately and effectively detected in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
a wire and cable defect detection system comprises a controller, wherein the controller is connected with an image acquisition module used for acquiring a cable image and a vertical axial X-ray image, the controller is connected with an image preprocessing module used for preprocessing the acquired image, the controller is connected with a feature vector extraction module used for extracting feature vectors of the processed cable image, the controller is connected with a feature vector combination module used for randomly combining the extracted feature vectors, the controller is connected with a feature vector classification module used for classifying and calculating the randomly combined feature vectors, and the controller is connected with a defect position determination module used for determining defect positions according to calculation results of the feature vector classification module;
the controller is connected with a defect identification model construction module for constructing a defect identification model for identifying cable defects from the vertical axial X-ray image, and the controller is connected with a defect identification result output module for outputting a defect identification model identification result;
the cable work data processing system is characterized by further comprising a fault data classification storage module used for storing historical outlier data corresponding to defect types and a normal data storage module used for storing normal cable work data, the controller is connected with the data acquisition module used for acquiring the cable work data, the controller is connected with an outlier detection module used for detecting outliers of the cable work data, and the controller is connected with an analysis and judgment module used for judging the defect types stored in the fault data classification storage module according to outlier detection results.
Preferably, the image preprocessing module preprocesses the acquired image, including performing tilt correction on the acquired image; filtering the collected image to reduce quantum noise and particle noise; and carrying out gray level adjustment and sharpening on the acquired image.
Preferably, the feature vector extracting module extracts the feature vectors of the processed cable image through a convolutional neural network, and the feature vector combining module randomly combines the extracted feature vectors through a pooling layer and a regional convolutional neural network.
Preferably, the feature vector classification module classifies the randomly combined feature vectors by a golden section method, calculates values of the classified feature vectors, and the defect position determination module finds abnormal values by a fast regional convolutional neural network and determines the defect position.
Preferably, the convolutional neural network, the regional convolutional neural network and the fast regional convolutional neural network share one convolutional layer.
Preferably, the defect identification model identifies the position of the cable to be detected in the vertical axial X-ray image, identifies the structure of the cable to be detected in the vertical axial X-ray image according to the model of the preset cable to be detected, judges the defect of the cable to be detected according to the structure of the cable to be detected in the vertical axial X-ray image, and marks the defect in the vertical axial X-ray image.
Preferably, the training method of the defect recognition model includes:
manually collecting vertical axial X-ray images of cables of various types, marking the position of a defect on a structure corresponding to the cable to be detected in the image, inputting the marked vertical axial X-ray images into a defect recognition model for training, and obtaining the trained defect recognition model.
Preferably, the outlier detection module performs outlier detection on the cable working data acquired by the data acquisition module by using an outlier detection algorithm based on density and distance parameters.
Preferably, if the outlier detection result of the cable working data by the outlier detection module is smaller than a threshold value, the analysis and judgment module judges that the cable working data belongs to normal data and stores the cable working data into a normal data storage module;
otherwise, the analysis and judgment module judges that the cable working data belongs to fault data, stores the cable working data into a fault data classification storage module, and simultaneously judges the defect type corresponding to the cable working data according to the defect type corresponding to the historical outlier data.
(III) advantageous effects
Compared with the prior art, the wire and cable defect detection system provided by the invention can accurately and effectively analyze and judge the defects on the surface and the structure of the cable by acquiring the cable image and the vertical axial X-ray image and identifying the image, and can accurately and effectively identify the fault data in the cable working process and the defect type corresponding to the fault data by means of outlier detection and analysis, so that the defect condition in the cable working process can be timely and effectively found under the condition of no power failure.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic diagram of the system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A wire and cable defect detection system comprises a controller, wherein the controller is connected with an image acquisition module used for acquiring a cable image and a vertical axial X-ray image, the controller is connected with an image preprocessing module used for preprocessing the acquired image, the controller is connected with a feature vector extraction module used for extracting feature vectors of the processed cable image, the controller is connected with a feature vector combination module used for randomly combining the extracted feature vectors, the controller is connected with a feature vector classification module used for classifying and calculating the randomly combined feature vectors, and the controller is connected with a defect position determination module used for determining defect positions according to calculation results of the feature vector classification module.
The image preprocessing module is used for preprocessing the acquired image, including inclination correction of the acquired image; filtering the collected image to reduce quantum noise and particle noise; and carrying out gray level adjustment and sharpening on the acquired image.
The feature vector extracting module extracts feature vectors of the processed cable image through a convolutional neural network, and the feature vector combining module randomly combines the extracted feature vectors through a pooling layer and a regional convolutional neural network.
The feature vector classification module classifies the randomly combined feature vectors by adopting a golden section method, calculates numerical values of the classified feature vectors, and the defect position determination module searches abnormal numerical values through a fast regional convolution neural network and determines the defect positions.
The convolutional neural network, the regional convolutional neural network and the fast regional convolutional neural network share one convolutional layer.
The controller is connected with a defect identification model building module used for building a defect identification model for identifying cable defects from the vertical axial X-ray image, and the controller is connected with a defect identification result output module used for outputting the identification result of the defect identification model.
Firstly, a defect recognition model needs to be constructed and trained, and the training method of the defect recognition model comprises the following steps:
manually collecting vertical axial X-ray images of cables of various types, marking the position of a defect on a structure corresponding to the cable to be detected in the image, inputting the marked vertical axial X-ray images into a defect recognition model for training, and obtaining the trained defect recognition model.
The defect identification model identifies the position of the cable to be detected in the vertical axial X-ray image, identifies the structure of the cable to be detected in the vertical axial X-ray image according to the model of the preset cable to be detected, judges the defect of the cable to be detected according to the structure of the cable to be detected in the vertical axial X-ray image, and marks the defect in the vertical axial X-ray image.
In the technical scheme, the defect identification model adopts a depth residual error network ResNet to identify the position of the cable to be detected in the vertical axial X-ray image, and comprises a cable body, a cable terminal and a cable connector.
The cable work data processing system is characterized by further comprising a fault data classification storage module used for storing historical outlier data corresponding to defect types and a normal data storage module used for storing normal cable work data, the controller is connected with the data acquisition module used for acquiring the cable work data, the controller is connected with an outlier detection module used for performing outlier detection on the cable work data, and the controller is connected with an analysis judgment module used for judging the defect types stored in the fault data classification storage module according to outlier detection results.
The outlier detection module performs outlier detection on the cable working data acquired by the data acquisition module by using an outlier detection algorithm based on density and distance parameters.
The outlier detection result of the cable working data by the outlier detection module is smaller than a threshold value, the analysis and judgment module judges that the cable working data belongs to normal data, and stores the cable working data into a normal data storage module;
otherwise, the analysis and judgment module judges that the cable working data belongs to fault data, stores the cable working data into a fault data classification storage module, and simultaneously judges the defect type corresponding to the cable working data according to the defect type corresponding to the historical outlier data.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (9)
1. A wire and cable defect detection system is characterized in that: the cable image detection device comprises a controller, wherein the controller is connected with an image acquisition module used for acquiring a cable image and a vertical axial X-ray image, the controller is connected with an image preprocessing module used for preprocessing the acquired image, the controller is connected with a feature vector extraction module used for extracting feature vectors of the processed cable image, the controller is connected with a feature vector combination module used for randomly combining the extracted feature vectors, the controller is connected with a feature vector classification module used for classifying and calculating the randomly combined feature vectors, and the controller is connected with a defect position determination module used for determining defect positions according to calculation results of the feature vector classification module;
the controller is connected with a defect identification model construction module for constructing a defect identification model for identifying cable defects from the vertical axial X-ray image, and the controller is connected with a defect identification result output module for outputting a defect identification model identification result;
the cable work data processing system is characterized by further comprising a fault data classification storage module used for storing historical outlier data corresponding to defect types and a normal data storage module used for storing normal cable work data, the controller is connected with the data acquisition module used for acquiring the cable work data, the controller is connected with an outlier detection module used for detecting outliers of the cable work data, and the controller is connected with an analysis and judgment module used for judging the defect types stored in the fault data classification storage module according to outlier detection results.
2. The wire and cable defect detection system of claim 1, wherein: the image preprocessing module is used for preprocessing the acquired image, including inclination correction of the acquired image; filtering the collected image to reduce quantum noise and particle noise; and carrying out gray level adjustment and sharpening on the acquired image.
3. The wire and cable defect detection system of claim 1, wherein: the feature vector extracting module extracts feature vectors of the processed cable image through a convolutional neural network, and the feature vector combining module randomly combines the extracted feature vectors through a pooling layer and a regional convolutional neural network.
4. The wire and cable defect detection system of claim 3, wherein: the feature vector classification module classifies the randomly combined feature vectors by adopting a golden section method, calculates numerical values of the classified feature vectors, and the defect position determination module searches abnormal numerical values through a fast regional convolution neural network and determines defect positions.
5. The wire and cable defect detection system of claim 4, wherein: the convolutional neural network, the regional convolutional neural network and the fast regional convolutional neural network share one convolutional layer.
6. The wire and cable defect detection system of claim 1, wherein: the defect identification model identifies the position of the cable to be detected in the vertical axial X-ray image, identifies the structure of the cable to be detected in the vertical axial X-ray image according to the model of the preset cable to be detected, judges the defect of the cable to be detected according to the structure of the cable to be detected in the vertical axial X-ray image, and marks the defect in the vertical axial X-ray image.
7. The wire and cable defect detection system of claim 6, wherein: the training method of the defect recognition model comprises the following steps:
manually collecting vertical axial X-ray images of cables of various types, marking the position of a defect on a structure corresponding to the cable to be detected in the image, inputting the marked vertical axial X-ray images into a defect recognition model for training, and obtaining the trained defect recognition model.
8. The wire and cable defect detection system of claim 1, wherein: the outlier detection module performs outlier detection on the cable working data acquired by the data acquisition module by using an outlier detection algorithm based on density and distance parameters.
9. The wire and cable defect detection system of claim 8, wherein: the outlier detection result of the cable working data by the outlier detection module is smaller than a threshold value, the analysis and judgment module judges that the cable working data belongs to normal data, and stores the cable working data into a normal data storage module;
otherwise, the analysis and judgment module judges that the cable working data belongs to fault data, stores the cable working data into a fault data classification storage module, and simultaneously judges the defect type corresponding to the cable working data according to the defect type corresponding to the historical outlier data.
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| CN202011019512.4A CN112179922A (en) | 2020-09-24 | 2020-09-24 | Wire and cable defect detection system |
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Cited By (1)
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| CN116912234A (en) * | 2023-09-06 | 2023-10-20 | 青岛理研电线电缆有限公司 | Cable stranded wire quality detection method based on image features |
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Application publication date: 20210105 |