CN113962985A - Electrical cabinet fault analysis method, system and device - Google Patents
Electrical cabinet fault analysis method, system and device Download PDFInfo
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- CN113962985A CN113962985A CN202111346020.0A CN202111346020A CN113962985A CN 113962985 A CN113962985 A CN 113962985A CN 202111346020 A CN202111346020 A CN 202111346020A CN 113962985 A CN113962985 A CN 113962985A
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
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Abstract
本发明公开了一种电气柜故障分析方法、系统及装置,包括,S1、获取电气柜的热成像图;S2、对电气柜的热成像图进行预处理;S3、对预处理后的热成像图采用图像分割法得到热成像图的背景和前景图;S4、根据热成像图的背景和前景图判断发生故障位置及故障类型。本发明可以实现电气柜故障快速判断。
The invention discloses a fault analysis method, system and device for an electrical cabinet. The image segmentation method is used to obtain the background and foreground images of the thermal image; S4, according to the background and foreground images of the thermal image, the fault location and the fault type are judged. The present invention can realize rapid judgment of electrical cabinet faults.
Description
Technical Field
The invention relates to the field of electrical cabinet fault analysis, in particular to electrical cabinet fault analysis, a system and a device.
Background
The failure analysis of the electrical cabinet is usually realized through a monitoring circuit, but the monitoring circuit is unstable, and the monitoring circuit is easy to burn.
Disclosure of Invention
The invention aims to provide a fault analysis system, a fault analysis device and a fault analysis device for an electrical cabinet, and aims to solve the problem of fault analysis of the electrical cabinet.
The invention provides a fault analysis method for an electric cabinet, which comprises the following steps:
s1, acquiring a thermal imaging diagram of the electrical cabinet;
s2, preprocessing a thermal imaging graph of the electrical cabinet;
s3, obtaining a background image and a foreground image of the thermal imaging image by adopting an image segmentation method for the preprocessed thermal imaging image;
and S4, judging the fault position and fault type according to the background and foreground images of the thermal imaging image.
The invention also provides a fault analysis system of the electrical cabinet, which comprises:
an acquisition module: the thermal imaging system is used for acquiring a thermal imaging image of the electrical cabinet;
the preprocessing module is used for preprocessing a thermal imaging graph of the electrical cabinet;
a segmentation module: the image segmentation method is used for obtaining background images and foreground images of the thermal imaging images after the pretreatment by adopting an image segmentation method;
a judging module: and the method is used for judging the fault position and the fault type according to the background and foreground images of the thermal imaging image.
The embodiment of the present invention further provides an electrical cabinet fault analysis apparatus, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program implementing the steps of the above method when executed by the processor.
The embodiment of the invention also provides a computer readable storage medium, wherein an implementation program for information transmission is stored on the computer readable storage medium, and the implementation program realizes the steps of the method when being executed by a processor.
By adopting the embodiment of the invention, the fault can be identified by thermal imaging, and the method is convenient and quick.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a method for analyzing a fault of an electrical cabinet according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an electrical cabinet fault analysis system according to an embodiment of the invention;
fig. 3 is a schematic diagram of an electrical cabinet fault analysis apparatus according to an embodiment of the present invention.
Description of reference numerals:
310: an acquisition module; 320: a preprocessing module; 330: a segmentation module; 340: a judgment module; 40: a memory; 42: a processor.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. 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.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise. Furthermore, the terms "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Method embodiment
According to an embodiment of the present invention, a method for analyzing a fault of an electrical cabinet is provided, and fig. 1 is a flowchart of the method for analyzing the fault of the electrical cabinet according to the embodiment of the present invention, as shown in fig. 1, the method specifically includes:
s1, acquiring a thermal imaging diagram of the electrical cabinet;
s1 specifically includes: and acquiring thermal imaging graphs of the electric cabinet at a fixed point position when the electric cabinet fails or does not fail.
S2, preprocessing a thermal imaging graph of the electrical cabinet;
s3, obtaining a background image and a foreground image of the thermal imaging image by adopting an image segmentation method for the preprocessed thermal imaging image;
s3 specifically includes: and obtaining a hot point diagram of the thermal imaging diagram by adopting a histogram threshold selection segmentation method for the preprocessed thermal imaging diagram. The image segmentation can enable the image to observe the hot spot position more clearly, so that the subsequent image comparison and analysis are facilitated, and which part of the circuit is in question can be analyzed.
Or obtaining a heat point diagram of the heat imaging diagram by adopting a region segmentation method based on color YCbCr to the preprocessed heat imaging diagram.
Or obtaining a thermal image of the thermal image by adopting a region segmentation method based on color hsv characteristics on the preprocessed thermal image.
And S4, judging the fault position and fault type according to the background and foreground images of the thermal imaging image.
S4 specifically includes: and comparing the background and the foreground of the thermal imaging graph with the background and the foreground of the thermal imaging graph without faults to obtain fault positions, and judging the fault types according to the electrical cabinet layout graph of the fixed point positions and the fault positions.
The white part of the thermal imaging image is a circuit with excessively high heat productivity, and the overheating of the chip causes the overheating of the middle main chip.
One of the histogram threshold value selection segmentation methods is to utilize the distribution condition of the histogram, then perform zero processing and one processing according to a threshold value, and multiply the processed gray image data with the double data of the original image correspondingly to obtain a segmented original image. Several infrared thermographic images are used for background and foreground segmentation.
The histogram threshold value selecting and dividing method basically separates the background from the foreground, the dividing contour is smooth, and the small holes in the image are divided, but the defects are that the dividing of the method has certain artificial factors, an operator needs to judge the number of gray level images in the histogram to judge which threshold value should be selected so as to obtain a corresponding calling range, the threshold value range is deviated due to the artificial selection of the threshold value range, if the selected threshold value range is too large, the over-dividing phenomenon is caused, and if the selected threshold value range is too small, the separation is not complete, and a plurality of impurities cannot be filtered out by filtering. The basic form of image segmentation can be obtained from the method, and the segmented image looks complete and has a great relation with the selection parameters because the segmentation purpose is clear. And because the histogram and the parameters need to be manually analyzed, the total processing time is increased, and the method can quickly process simple pictures.
Region segmentation based on color YCbCr, where Y refers to the luminance component, Cb refers to the blue chrominance component, and Cr refers to the red chrominance component;
since color features are the most widely used visual features in image retrieval, the main reason is that color tends to be quite correlated with objects or scenes contained in the image. In addition, compared with other visual features, the color features have smaller dependence on the size, direction and visual angle of the image, so that the robustness is higher.
The color features are brightness, chroma and density, a global threshold value is judged according to an Otsu method after data under a corresponding channel are obtained, a corresponding binary image is obtained according to the threshold value, the background and the foreground are separated from the binary image, then the original image is processed by double, and then the original image is multiplied by the obtained binary image data one by one to obtain a foreground image with the background being segmented.
The method mainly comprises the steps of distinguishing a background and a foreground by utilizing the hue, the saturation and the lightness of an image, carrying out rgb conversion on data according to the data of an original image, converting the data into a binary image by carrying out global threshold processing on the data after converting the image, and then segmenting the background and an ROI of the image by utilizing double data of the original image for analysis;
according to the invention, the thermal imaging image is obtained through the thermal imager, the thermal imager can be installed to monitor the electrical cabinet in real time, or can be independently used as a fault analyzer, the thermal imaging image is shot at a fixed point position, and the fault position is obtained according to the analysis of the layout of the electrical cabinet.
The invention judges the fault position by thermal imaging, and has high speed and accuracy; the originally designed monitoring circuit is easy to damage due to overlarge current when in fault, and the monitoring circuit is prevented from being connected with the fault circuit by collecting a thermal imaging graph for analysis, so that the safety is high.
System embodiment
According to an embodiment of the present invention, an electrical cabinet fault analysis system is provided, and fig. 2 is a schematic diagram of an electrical cabinet fault analysis system according to an embodiment of the present invention, as shown in fig. 2, specifically including:
an acquisition module: the thermal imaging system is used for acquiring a thermal imaging image of the electrical cabinet;
the acquisition module is specifically configured to: acquiring thermal imaging graphs of the electric cabinet at a fixed point position when the electric cabinet fails and does not fail;
the preprocessing module is used for preprocessing a thermal imaging graph of the electrical cabinet;
a segmentation module: the image segmentation method is used for obtaining background images and foreground images of the thermal imaging images after the pretreatment by adopting an image segmentation method;
the segmentation module is specifically configured to: obtaining a hot spot graph of the thermal imaging graph by adopting a histogram threshold selection segmentation method for the preprocessed thermal imaging graph;
or obtaining a hot spot diagram of the thermal imaging diagram by adopting a region segmentation method based on color YCbCr to the preprocessed thermal imaging diagram;
or obtaining a hot spot graph of the thermal imaging graph by adopting a region segmentation method based on color hsv characteristics on the preprocessed thermal imaging graph;
a judging module: and the method is used for judging the fault position and the fault type according to the background and foreground images of the thermal imaging image.
The judgment module is specifically used for: and comparing the background and the foreground of the thermal imaging graph with the background and the foreground of the thermal imaging graph without faults to obtain fault positions, and judging the fault types according to the electrical cabinet layout graph of the fixed point positions and the fault positions.
The embodiment of the present invention is a system embodiment corresponding to the above method embodiment, and specific operations of each module may be understood with reference to the description of the method embodiment, which is not described herein again.
Apparatus embodiment one
An embodiment of the present invention provides an electrical cabinet fault analysis apparatus, as shown in fig. 3, including: a memory 40, a processor 42 and a computer program stored on the memory 40 and executable on the processor 42, the computer program, when executed by the processor, implementing the steps of the above-described method embodiments.
Device embodiment II
The embodiment of the present invention provides a computer-readable storage medium, on which an implementation program for information transmission is stored, and when being executed by the processor 42, the implementation program implements the steps in the above method embodiments.
The computer-readable storage medium of this embodiment includes, but is not limited to: ROM, RAM, magnetic or optical disks, and the like.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; however, these modifications or alternative technical solutions of the embodiments of the present invention do not depart from the scope of the present invention.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119131035A (en) * | 2024-11-13 | 2024-12-13 | 西安重装韩城煤矿机械有限公司 | Belt conveyor motor overheating detection method based on infrared image |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109916637A (en) * | 2019-01-25 | 2019-06-21 | 深圳市元征科技股份有限公司 | A kind of failure analysis methods and device based on thermal imaging |
| CN110988663A (en) * | 2019-11-25 | 2020-04-10 | 广东电网有限责任公司 | A fault location method, device and equipment for gas-insulated switchgear |
| CN111798405A (en) * | 2019-11-21 | 2020-10-20 | 南京航空航天大学 | Fault diagnosis method of casing infrared image based on deep learning |
| CN113515655A (en) * | 2021-06-24 | 2021-10-19 | 国网山东省电力公司邹城市供电公司 | A kind of fault identification method and device based on image classification |
-
2021
- 2021-11-15 CN CN202111346020.0A patent/CN113962985A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109916637A (en) * | 2019-01-25 | 2019-06-21 | 深圳市元征科技股份有限公司 | A kind of failure analysis methods and device based on thermal imaging |
| CN111798405A (en) * | 2019-11-21 | 2020-10-20 | 南京航空航天大学 | Fault diagnosis method of casing infrared image based on deep learning |
| CN110988663A (en) * | 2019-11-25 | 2020-04-10 | 广东电网有限责任公司 | A fault location method, device and equipment for gas-insulated switchgear |
| CN113515655A (en) * | 2021-06-24 | 2021-10-19 | 国网山东省电力公司邹城市供电公司 | A kind of fault identification method and device based on image classification |
Non-Patent Citations (1)
| Title |
|---|
| 川流的记忆: "热成像分割", pages 1, Retrieved from the Internet <URL:https://blog.csdn.net/dsbbjx/article/details/105610142> * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119131035A (en) * | 2024-11-13 | 2024-12-13 | 西安重装韩城煤矿机械有限公司 | Belt conveyor motor overheating detection method based on infrared image |
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