CN116908222B - X-ray detection method and system for GIS equipment faults - Google Patents
X-ray detection method and system for GIS equipment faults Download PDFInfo
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Abstract
The invention relates to the field of GIS equipment X-ray detection, in particular to an X-ray detection method and system for GIS equipment faults, comprising the following steps: s1, acquiring an external surface image of GIS equipment to be detected through the camera; s2, based on the external surface image, the X-ray detector is moved to a region to be detected of GIS equipment by controlling the lifting assembly and the electric cradle head; s3, acquiring an internal X-ray image of a region to be detected of the GIS equipment through the imaging plate; s4, analyzing and evaluating the obtained internal X-ray image, and generating a GIS equipment fault report. The invention realizes remote adjustment of the X-ray detector, thereby reducing the frequency of manual on-site adjustment, reducing the labor intensity and improving the safety of detection personnel; meanwhile, the X-ray image of the GIS equipment can be analyzed and evaluated, so that the efficiency and accuracy of fault detection of the GIS equipment are improved.
Description
Technical Field
The invention relates to the field of GIS equipment X-ray detection, in particular to an X-ray detection method and system for GIS equipment faults.
Background
Gas insulated metal-enclosed switchgear (GIS) is widely used in electrical grids. At present, in the detection method of the gas-insulated metal-enclosed switchgear, nondestructive detection of power equipment is mostly adopted, and mainly comprises ray detection, infrared and ultraviolet detection, ultrasonic detection, vibroacoustic detection and the like.
The X-ray detection is widely used, and the X-ray imaging technology is applied to detection of the gas-insulated metal-enclosed switchgear, so that the detection accuracy is improved. Along with the wide application of X-ray in gas-insulated metal-enclosed switchgear detection, people have higher and higher use requirements, because before the use of X-ray technology detection equipment, a large amount of manpower is required to carry equipment and carry route planning, in the use, still need according to the position adjustment X-ray machine of fault point and imaging plate's position, when the operation, if there is the condition that shooting image is inconsistent with fault point, picture distortion etc., need to close X-ray machine, send the people to go to readjust and shoot again, often need adjust many times, later pure manual work carries out the mode inefficiency of discernment judgement to the detection image, and easily makeing mistakes.
Disclosure of Invention
In order to solve the problems, the invention provides an X-ray detection method and a system for GIS equipment faults, which can realize remote control and reduce the frequency of manual on-site adjustment; meanwhile, the X-ray image of the GIS equipment can be analyzed and evaluated, and a GIS equipment fault report is generated, so that the efficiency and the accuracy of GIS equipment fault detection are improved.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an X-ray detection method of a GIS device fault, the X-ray detection method of a GIS device fault being applied to a GIS device fault detection apparatus, the apparatus comprising: the X-ray detector is detachably arranged on the rotatable shooting platform with the camera and is electrically connected with the control component; the lifting assembly is arranged in the supporting rod so that the rotatable shooting platform fixedly arranged on the upper part of the supporting rod moves up and down, the electric cradle head is arranged between the rotatable shooting platform and the supporting rod, and the control assembly is electrically connected with the lifting assembly and the electric cradle head; the control component is in wireless communication connection with the user terminal equipment;
the detection method comprises the following steps:
s1, acquiring an external surface image of GIS equipment to be detected through the camera;
s2, based on the external surface image, the X-ray detector is moved to a region to be detected of GIS equipment by controlling the lifting assembly and the electric cradle head;
s3, acquiring an internal X-ray image of a region to be detected of the GIS equipment through the imaging plate;
s4, analyzing and evaluating the obtained internal X-ray image, and generating a GIS equipment fault report, wherein the method specifically comprises the following steps:
preprocessing the internal X-ray image to reduce noise and enhance target features;
extracting features related to faults in the internal X-ray images through an image processing algorithm;
performing GIS equipment fault detection and classification on the extracted features through a GIS equipment fault detection and classification model to obtain a fault region;
determining the position of the fault area in the GIS equipment;
and evaluating the detected and positioned GIS equipment faults according to a preset GIS equipment fault evaluation rule, displaying the evaluated results in a visual form, and generating a corresponding report.
Further, the fault-related features include:
abnormal density or color areas, missing or broken lines, abnormal shapes or contours, abnormal connections or welds, abnormal electrical conduction paths, abnormal locations and distributions.
Further, the method for constructing the GIS equipment fault detection and classification model comprises the following steps:
acquiring a known GIS equipment fault data set, wherein the GIS equipment fault data set comprises: fault type, equipment information, X-ray images;
processing and cleaning the GIS equipment fault data set, including: removing repeated data, null values and standardized data to ensure the quality and accuracy of the data;
according to the purposes of fault detection and classification, selecting relevant characteristics of the processed and cleaned GIS equipment fault data set, wherein the relevant characteristics comprise X-ray image characteristics, equipment information and production date;
splitting relevant characteristics of the GIS equipment fault data set into a training set and a testing set;
training the training set by using a random forest algorithm, determining model parameters and decision quantity, and generating a GIS equipment fault detection and classification model;
evaluating the GIS equipment fault detection and classification model through a test set, wherein the evaluated indexes comprise accuracy, recall and F1-Score;
according to the model evaluation result, the GIS equipment fault detection and classification model is adjusted and optimized, including feature selection modification and model parameter adjustment, so as to improve the classification accuracy of the model;
and deploying the optimized GIS equipment fault detection and classification model into a GIS equipment fault detection and classification system.
Further, the determining the location of the fault region in the GIS device includes the steps of:
acquiring a CAD model of the GIS equipment, and marking each component in the internal X-ray image through the CAD model so as to facilitate subsequent positioning analysis;
determining a proportional relationship of image resolution between the CAD model and the internal X-ray image;
comparing the fault region with the CAD model according to the proportional relation of the image resolution ratio between the CAD model and the internal X-ray image so as to determine the position of the fault region in the CAD model;
and on the basis of a positioning result based on the CAD model, determining the position of the fault region in the internal X-ray image in the GIS by combining the topological relation of the GIS.
Further, the result of the evaluation includes: the severity of GIS equipment fault, the emergency degree of maintenance GIS equipment fault and cause the GIS equipment fault, the severity of GIS equipment fault contains: the emergency degree for maintaining the GIS equipment fault comprises the following steps: general and emergency.
Further, the preset GIS equipment fault evaluation rule includes: the severity assessment of GIS equipment faults, the emergency assessment of maintaining GIS equipment faults and the reason assessment of the GIS equipment faults, wherein,
the method for evaluating the severity of the GIS equipment fault comprises the following steps:
judging whether the fault area relates to a key component according to the position and the range of the fault area in the GIS equipment, if so, determining that the severity of the fault of the GIS equipment is serious, otherwise, determining that the severity of the fault of the GIS equipment is general;
or,
judging the severity of the fault according to the density and the color display condition of the fault area on the X-ray image, if the color is deep or the density is high, determining that the severity of the fault of the GIS equipment is serious, otherwise, determining that the severity of the fault of the GIS equipment is general.
Further, the method for evaluating the emergency of the fault of the maintenance GIS equipment comprises the following steps:
judging whether the fault area relates to a key component according to the position and the range of the fault area in the GIS equipment, if so, determining that the fault area is urgent, and otherwise, determining that the fault area is general.
Further, the method for evaluating the cause of the GIS equipment fault comprises the following steps:
judging possible fault reasons according to the fault characteristics of the fault area, wherein the possible fault reasons comprise: equipment aging, power problems, operational errors, and improper maintenance.
An X-ray detection system for a GIS device failure for performing the above-described GIS device failure X-ray detection method, the GIS device failure X-ray detection system comprising a control base station, the control base station being in wireless communication connection with the control assembly and a user terminal device; the control base station comprises a server, a wireless switch, a display and a memory, wherein the wireless switch is in connection communication with the server, and the GIS equipment fault detection device comprises a wireless communication module and is used for performing wireless communication connection with the wireless switch.
Further, the server comprises an image processing subsystem and a fault diagnosis subsystem,
the input end of the image processing subsystem is connected with the output end of the control component and is used for processing the image information of the X-ray detector and the camera;
the input end of the fault diagnosis subsystem is connected with the output end of the image processing subsystem and is used for carrying out fault feature identification and analysis.
The invention has the beneficial effects that:
the user terminal equipment can remotely send a control command to the control component, and the control component drives the lifting component and the electric cradle head according to the control command so as to remotely adjust the shooting angle and/or the shooting height of the X-ray detector, thereby reducing the frequency of manual on-site adjustment, reducing the labor intensity and improving the safety of detection personnel and the accuracy of detection results. Meanwhile, the X-ray image of the GIS equipment can be analyzed and evaluated, and a GIS equipment fault report is generated, so that the efficiency and the accuracy of GIS equipment fault detection are improved.
Drawings
Fig. 1 is a flowchart of an X-ray detection method for a GIS device failure according to the present invention.
Fig. 2 is a schematic structural diagram of a fault detection device for GIS equipment according to the present invention.
Reference numerals illustrate: 1. a rotatable shooting platform; 2. an electric cradle head; 3. a lifting assembly; 4. and (5) supporting the rod.
Detailed Description
Referring to fig. 1-2, the present invention provides an X-ray detection method for GIS equipment failure and a system thereof, which can realize remote control and reduce frequency of manual on-site adjustment; meanwhile, the X-ray image of the GIS equipment can be analyzed and evaluated, and a GIS equipment fault report is generated, so that the efficiency and the accuracy of GIS equipment fault detection are improved.
Example 1
An X-ray detection method of a GIS device fault, the X-ray detection method of a GIS device fault being applied to a GIS device fault detection apparatus, the apparatus comprising: the X-ray detector is detachably arranged on the rotatable shooting platform 1 with the camera and is electrically connected with the control component; the lifting assembly 3 is arranged inside the supporting rod 4 so that the rotatable shooting platform 1 fixedly arranged on the upper part of the supporting rod 4 moves up and down, the electric tripod head 2 is arranged between the rotatable shooting platform 1 and the supporting rod 4, and the control assembly is electrically connected with the lifting assembly 3 and the electric tripod head 2; the control component is in wireless communication connection with user terminal equipment, and the user terminal equipment comprises: computers, tablets, cell phones and special equipment; the electric cradle head 2 is configured to receive an instruction of the control assembly to electrically adjust the position of the rotatable shooting platform 1, thereby adjusting shooting angles of the X-ray detector and the camera. The lifting assembly 3 is used for receiving instructions of the control assembly to adjust shooting heights of the X-ray detector and the camera.
The detection method comprises the following steps:
s1, acquiring an external surface image of GIS equipment to be detected through the camera;
s2, based on the external surface image, the X-ray detector is moved to a region to be detected of GIS equipment by controlling the lifting assembly 3 and the electric cradle head 2;
s3, acquiring an internal X-ray image of a region to be detected of the GIS equipment through the imaging plate;
s4, analyzing and evaluating the obtained internal X-ray image, and generating a GIS equipment fault report, wherein the method specifically comprises the following steps:
preprocessing the internal X-ray image, including denoising, contrast enhancement, image smoothing and the like, so as to reduce noise and enhance target characteristics and improve image quality;
features associated with faults in the internal X-ray image, which may include texture, color, shape, edges, etc., are extracted by computer vision techniques or image processing algorithms. The goal of feature extraction is to transform complex image information into values or descriptors that are easy to understand and analyze;
performing GIS equipment fault detection and classification on the extracted features through a GIS equipment fault detection and classification model to obtain a fault region;
determining the position of the fault area in the GIS equipment;
according to a preset GIS equipment fault evaluation rule, evaluating the detected and positioned GIS equipment faults, displaying the evaluated results in a visual form, and generating a corresponding report; the purpose defined herein is to help operators or decision makers to better understand and solve the problem of failure.
In the above scheme, the user terminal device can remotely send a control command to the control component, and the control component drives the lifting component 3 and the electric cradle head 2 according to the control command, so as to remotely adjust the shooting angle and/or shooting height of the X-ray detector, thereby reducing the frequency of manual on-site adjustment, reducing the labor intensity and improving the safety of detection personnel and the accuracy of detection results. Meanwhile, the X-ray image of the GIS equipment can be analyzed and evaluated, and a GIS equipment fault report is generated, so that the efficiency and the accuracy of GIS equipment fault detection are improved.
Further, the fault-related features include:
abnormal density or color areas, missing or broken lines, abnormal shapes or contours, abnormal connections or welds, abnormal electrical conduction paths, abnormal locations and distributions; in particular to a special-shaped ceramic tile,
abnormal density or color region: the dark or dark areas present in the X-ray image may represent density anomalies or material anomalies due to faults.
Missing or broken lines: the presence of breaks, missing or broken lines in the X-ray image may indicate component breakage or connection problems caused by the failure.
Abnormal shape or profile: the abnormal shape or contour that occurs in an X-ray image may represent a component deformation, damage, or material anomaly caused by a fault.
Abnormal connections or welds: abnormalities in the joints or welds shown in the X-ray images occur, such as broken welds, weak welds, etc.
Abnormal electrical conduction path: an abnormality in the electrical conduction path shown in the X-ray image, such as an open circuit or a short circuit, occurs between the conductive members.
Abnormal location and distribution: the specific position of the fault, such as the critical position of the current collecting circuit, the insulator and the like, is abnormal.
These features can help identify faults in the GIS equipment X-ray images and help assess the severity of the fault, urgency of repair, and possible cause of the fault. It should be noted that the specific fault characteristics may vary depending on the type of GIS device and the specific fault.
Further, the method for constructing the GIS equipment fault detection and classification model comprises the following steps:
acquiring a known GIS equipment fault data set, wherein the GIS equipment fault data set comprises: fault type, equipment information, X-ray images;
processing and cleaning the GIS equipment fault data set, including removing repeated data, null values and standardized data to ensure data quality and accuracy;
according to the purposes of fault detection and classification, selecting relevant characteristics of the processed and cleaned GIS equipment fault data set, wherein the relevant characteristics comprise X-ray image characteristics, equipment information and production date; to improve the accuracy and robustness of the model;
and splitting relevant features of the GIS equipment fault data set into a training set and a testing set, and carrying out necessary data preprocessing, such as feature scaling, dimension reduction and the like.
Training the training set by using a random forest algorithm, determining model parameters and decision quantity, and generating a GIS equipment fault detection and classification model;
and evaluating the GIS equipment fault detection and classification model through a test set, wherein the evaluated indexes comprise accuracy, recall rate and F1-Score so as to evaluate the performance and accuracy of the model.
And adjusting and optimizing the GIS equipment fault detection and classification model according to the model evaluation result, wherein the method comprises the steps of modifying feature selection and adjusting model parameters so as to improve the classification accuracy of the model.
And deploying the optimized GIS equipment fault detection and classification model into a GIS equipment fault detection and classification system to realize real-time detection and classification.
Further, the determining the location of the fault area in the GIS device includes the following steps:
acquiring a CAD model of the GIS equipment, and marking each component in the internal X-ray image through the CAD model so as to facilitate subsequent positioning analysis; the CAD model refers to a three-dimensional model of Computer-Aided Design (CAD), which is a product of a designer's Design using various drawing software on a Computer. The visual display device can contain information such as the geometric shape, the size, the material and the like of the object, and can carry out visual presentation and editing modification;
determining the proportional relation of the image resolution between the CAD model and the internal X-ray image, wherein the emphasis is that the CAD model generally adopts the real size which is not necessarily the same as the physical size corresponding to each pixel in the X-ray image, so that the proportional relation of the CAD model and the internal X-ray image needs to be determined firstly;
comparing the fault region with the CAD model according to the proportional relation of the image resolution between the CAD model and the internal X-ray image so as to determine the position of the fault region in the CAD model, wherein the emphasis is that the embodiment can adopt various methods for positioning, such as a method based on contour matching, a method based on low-threshold scanning and the like;
based on the positioning result based on the CAD model, the topological relation of the GIS equipment (the topological relation of the GIS equipment describes the connection relation among all components, and the position of the fault area can be deduced by analyzing the consistency between the position of the fault area in the X-ray image and the topological relation), so that the position of the fault area in the internal X-ray image in the GIS equipment is determined.
In some embodiments, calibration between the image and the GIS device coordinate system may also be performed using known reference points or calibration data, and the location of the fault region in the device may be directly determined from the calibrated image.
Further, the result of the evaluation includes: the severity of GIS equipment fault, the emergency degree of maintenance GIS equipment fault and cause the GIS equipment fault, the severity of GIS equipment fault contains: the emergency degree for maintaining the GIS equipment fault comprises the following steps: general and emergency. It should be noted that specific evaluation results should be quantified or marked according to specific requirements to support subsequent decisions and actions.
Further, the preset GIS equipment fault evaluation rule includes: the severity assessment of GIS equipment faults, the emergency assessment of maintaining GIS equipment faults and the reason assessment of the GIS equipment faults, wherein,
the method for evaluating the severity of the GIS equipment fault comprises the following steps:
judging whether the fault area relates to a key component according to the position and the range of the fault area in the GIS equipment, if so, determining that the severity of the fault of the GIS equipment is serious, otherwise, determining that the severity of the fault of the GIS equipment is general;
or,
judging the severity of the fault according to the density and the color display condition of the fault area on the X-ray image, if the color is deep or the density is high, determining that the severity of the fault of the GIS equipment is serious, otherwise, determining that the severity of the fault of the GIS equipment is general;
in the above-described solution, it is possible to evaluate the position and extent of the occurrence of faults in the X-ray image, such as whether critical components are involved or the core functions of the device are affected. The degree of the fault can also be judged according to the density and the color display condition of the fault on the X-ray image, and the condition that the fault is serious can be indicated by dark color and high density. The impact of a fault on the performance and stability of the device, such as whether it may result in complete failure of the device or a serious impact on critical operations, may also be considered.
The method for evaluating the emergency of the fault of the maintenance GIS equipment comprises the following steps:
judging whether the fault area relates to a key component according to the position and the range of the fault area in the GIS equipment, if so, determining that the fault area is urgent, and otherwise, determining that the fault area is general.
The method for evaluating the causes of the GIS equipment faults comprises the following steps:
judging possible fault reasons according to the fault characteristics of the fault area, wherein the possible fault reasons comprise: equipment aging, power problems, operational errors, and improper maintenance.
In conclusion, the X-ray detection method for the GIS equipment faults adopts the movable detection device, can remotely regulate and control different heights, angles and terrains, can realize remote visual operation, and simultaneously, detects, analyzes and evaluates the equipment in real time, improves the GIS equipment fault detection efficiency, reduces manual operation, reduces labor intensity and improves the safety of detection personnel and the accuracy of detection results.
Example 2
An X-ray detection system for a GIS device failure for performing the GIS device failure X-ray detection method of embodiment 1, the GIS device failure X-ray detection system comprising a control base station in wireless communication connection with the control assembly and a user terminal device; the control base station comprises a server, a wireless switch, a display and a memory, wherein the wireless switch is in connection communication with the server, and the GIS equipment fault detection device comprises a wireless communication module and is used for performing wireless communication connection with the wireless switch. Further, the server comprises an image processing subsystem and a fault diagnosis subsystem, wherein the input end of the image processing subsystem is connected with the output end of the control component and is used for processing the image information of the X-ray detector and the camera; the input end of the fault diagnosis subsystem is connected with the output end of the image processing subsystem and is used for carrying out fault feature identification and analysis.
In the above scheme, the X-ray detection system for the GIS equipment fault is based on the X-ray detection method for the GIS equipment fault described in embodiment 1, and a movable detection device is adopted, so that remote control can be performed on different heights, angles and terrains, remote visual operation can be realized, meanwhile, equipment is detected, analyzed and evaluated in real time, the GIS equipment fault detection efficiency is improved, manual operation is reduced, labor intensity is reduced, and the safety of detection personnel and the accuracy of detection results are improved.
The above embodiments are merely illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the scope of protection defined by the claims of the present invention without departing from the spirit of the design of the present invention.
Claims (7)
1. An X-ray detection method for a GIS equipment fault is characterized in that the X-ray detection method for the GIS equipment fault is applied to a GIS equipment fault detection device, and the device comprises: the X-ray detector is detachably arranged on the rotatable shooting platform with the camera and is electrically connected with the control component; the lifting assembly is arranged in the supporting rod so that the rotatable shooting platform fixedly arranged on the upper part of the supporting rod moves up and down, the electric cradle head is arranged between the rotatable shooting platform and the supporting rod, and the control assembly is electrically connected with the lifting assembly and the electric cradle head; the control component is in wireless communication connection with the user terminal equipment;
the detection method comprises the following steps:
s1, acquiring an external surface image of GIS equipment to be detected through the camera;
s2, based on the external surface image, the X-ray detector is moved to a region to be detected of GIS equipment by controlling the lifting assembly and the electric cradle head;
s3, acquiring an internal X-ray image of a region to be detected of the GIS equipment through the imaging plate;
s4, analyzing and evaluating the obtained internal X-ray image, and generating a GIS equipment fault report, wherein the method specifically comprises the following steps:
preprocessing the internal X-ray image to reduce noise and enhance target features;
extracting features related to faults in the internal X-ray images through an image processing algorithm;
performing GIS equipment fault detection and classification on the extracted features through a GIS equipment fault detection and classification model to obtain a fault region;
determining the position of the fault area in the GIS equipment;
according to a preset GIS equipment fault evaluation rule, evaluating the detected and positioned GIS equipment faults, displaying the evaluated results in a visual form, and generating a corresponding report;
wherein,
the fault-related features include:
abnormal density or color areas, missing or broken lines, abnormal shapes or contours, abnormal connections or welds, abnormal electrical conduction paths, abnormal locations and distributions;
the construction method of the GIS equipment fault detection and classification model comprises the following steps:
acquiring a known GIS equipment fault data set, wherein the GIS equipment fault data set comprises: fault type, equipment information, X-ray images;
processing and cleaning the GIS equipment fault data set, including: removing repeated data, null values and standardized data to ensure the quality and accuracy of the data;
according to the purposes of fault detection and classification, selecting relevant characteristics of the processed and cleaned GIS equipment fault data set, wherein the relevant characteristics comprise X-ray image characteristics, equipment information and production date;
splitting relevant characteristics of the GIS equipment fault data set into a training set and a testing set;
training the training set by using a random forest algorithm, determining model parameters and decision quantity, and generating a GIS equipment fault detection and classification model;
evaluating the GIS equipment fault detection and classification model through a test set, wherein the evaluated indexes comprise accuracy, recall and F1-Score;
according to the model evaluation result, the GIS equipment fault detection and classification model is adjusted and optimized, including feature selection modification and model parameter adjustment, so as to improve the classification accuracy of the model;
deploying the optimized GIS equipment fault detection and classification model into a GIS equipment fault detection and classification system;
the determining the location of the fault region in the GIS device comprises the following steps:
acquiring a CAD model of the GIS equipment, and marking each component in the internal X-ray image through the CAD model so as to facilitate subsequent positioning analysis;
determining a proportional relationship of image resolution between the CAD model and the internal X-ray image;
comparing the fault region with the CAD model according to the proportional relation of the image resolution ratio between the CAD model and the internal X-ray image so as to determine the position of the fault region in the CAD model;
on the basis of a positioning result based on a CAD model, determining the position of a fault region in the internal X-ray image in the GIS by combining the topological relation of the GIS; the topological relation of the GIS equipment describes the connection relation among all components, and the position of the fault area can be deduced by analyzing the consistency between the position of the fault area in the X-ray image and the topological relation;
the result of the evaluation includes: the severity of GIS equipment failure, the emergency degree of maintaining GIS equipment failure and the cause of GIS equipment failure.
2. The method for detecting a GIS device failure according to claim 1, wherein the severity of the GIS device failure comprises: the emergency degree for maintaining the GIS equipment fault comprises the following steps: general and emergency.
3. The method for detecting a GIS equipment failure according to claim 2, wherein,
the method for evaluating the severity of the GIS equipment fault comprises the following steps:
judging whether the fault area relates to a key component according to the position and the range of the fault area in the GIS equipment, if so, determining that the severity of the fault of the GIS equipment is serious, otherwise, determining that the severity of the fault of the GIS equipment is general;
or,
judging the severity of the fault according to the density and the color display condition of the fault area on the X-ray image, if the color is deep or the density is high, determining that the severity of the fault of the GIS equipment is serious, otherwise, determining that the severity of the fault of the GIS equipment is general.
4. The method for detecting the fault X-ray of the GIS device according to claim 2, wherein the method for evaluating the urgency of repairing the GIS device fault comprises:
judging whether the fault area relates to a key component according to the position and the range of the fault area in the GIS equipment, if so, determining that the fault area is urgent, and otherwise, determining that the fault area is general.
5. The method for detecting the failure of the GIS device according to claim 2, wherein the method for evaluating the cause of the failure of the GIS device comprises:
judging possible fault reasons according to the fault characteristics of the fault area, wherein the possible fault reasons comprise: equipment aging, power problems, operational errors, and improper maintenance.
6. An X-ray detection system for a GIS device failure, wherein the GIS device failure X-ray detection system is configured to perform the GIS device failure X-ray detection method according to any one of claims 1 to 5, and the GIS device failure X-ray detection system includes a control base station, and the control base station is connected to the control component and a user terminal device in a wireless communication manner; the control base station comprises a server, a wireless switch, a display and a memory, wherein the wireless switch is in connection communication with the server, and the GIS equipment fault detection device comprises a wireless communication module and is used for performing wireless communication connection with the wireless switch.
7. The GIS device failure X-ray detection system of claim 6, wherein the server includes an image processing subsystem and a failure diagnosis subsystem,
the input end of the image processing subsystem is connected with the output end of the control component and is used for processing the image information of the X-ray detector and the camera;
the input end of the fault diagnosis subsystem is connected with the output end of the image processing subsystem and is used for carrying out fault feature identification and analysis.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004093279A (en) * | 2002-08-30 | 2004-03-25 | Hitachi Kenki Fine Tech Co Ltd | X-ray inspection apparatus and x-ray inspection program |
| CN206546417U (en) * | 2016-11-08 | 2017-10-10 | 广东电网有限责任公司电力科学研究院 | A kind of GIS switch fault automatic recognition systems based on picture recognition |
| CN107389705A (en) * | 2017-07-20 | 2017-11-24 | 云南电网有限责任公司电力科学研究院 | A kind of power equipment X ray Non-Destructive Testing three-D imaging method |
| CN113777484A (en) * | 2021-11-11 | 2021-12-10 | 四川赛康智能科技股份有限公司 | GIS defect detection device and method |
| CN114402360A (en) * | 2019-07-12 | 2022-04-26 | 布鲁克纳米公司 | Method and system for defect detection and classification using X-rays |
| CN218646874U (en) * | 2022-06-24 | 2023-03-17 | 四川赛康智能科技股份有限公司 | X-ray defect detection device and system based on image recognition |
-
2023
- 2023-09-13 CN CN202311174478.1A patent/CN116908222B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004093279A (en) * | 2002-08-30 | 2004-03-25 | Hitachi Kenki Fine Tech Co Ltd | X-ray inspection apparatus and x-ray inspection program |
| CN206546417U (en) * | 2016-11-08 | 2017-10-10 | 广东电网有限责任公司电力科学研究院 | A kind of GIS switch fault automatic recognition systems based on picture recognition |
| CN107389705A (en) * | 2017-07-20 | 2017-11-24 | 云南电网有限责任公司电力科学研究院 | A kind of power equipment X ray Non-Destructive Testing three-D imaging method |
| CN114402360A (en) * | 2019-07-12 | 2022-04-26 | 布鲁克纳米公司 | Method and system for defect detection and classification using X-rays |
| CN113777484A (en) * | 2021-11-11 | 2021-12-10 | 四川赛康智能科技股份有限公司 | GIS defect detection device and method |
| CN218646874U (en) * | 2022-06-24 | 2023-03-17 | 四川赛康智能科技股份有限公司 | X-ray defect detection device and system based on image recognition |
Non-Patent Citations (1)
| Title |
|---|
| X 射线影像识别技术在GIS 缺陷诊断中的作用;刘荣海 等;高压电器;第55卷(第6期);章节1-4 * |
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