CN112001327B - Valve hall equipment fault identification method and system - Google Patents
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Abstract
The invention discloses a valve hall equipment fault identification method and a system, wherein the method comprises the following steps: acquiring a video stream of equipment to be detected, which is shot by image acquisition equipment in real time, wherein the video stream comprises: infrared images and ultraviolet images; preprocessing any frame of image in the video stream, and extracting feature data of the preprocessed image; analyzing the feature data by at least one of a dynamic feature analysis algorithm and a heuristic classification algorithm to judge whether the equipment to be detected has faults or not; when a fault is detected, a fault type is generated and located to a specific location. According to the invention, the infrared image and the ultraviolet image are preprocessed and the characteristic data are extracted, at least one of a dynamic characteristic analysis algorithm and a heuristic classification algorithm is utilized to analyze the characteristic data, the type and the fault area position of the valve hall equipment fault are clear, and the reliability, the scientificity and the intellectualization of the operation and maintenance of the valve hall equipment are improved.
Description
Technical Field
The invention relates to the technical field of electric power inspection, in particular to a valve hall equipment fault identification method and system.
Background
At present, due to factors such as blindness, low maintenance level and the like of the electric equipment maintenance system based on time, good running state of the equipment can be damaged or the equipment is more and more damaged, especially the extra-high voltage converter valve, the converter transformer, the wall bushing and other complicated large-sized equipment can not be simulated at all due to the fact that the common preventive test is usually carried out by applying low voltage under off-line condition; under the offline condition, the characteristics of the equipment such as thermal stress and the like cannot be simulated; meanwhile, the extra-high voltage converter valve and other devices in the valve hall are complex in structure, waterway shuttling and high-low potential crossing exist in the valve body, the whole insulation characteristic is reliably guaranteed in the design and production of products, but along with long-term operation, the performance of elements is reduced, the operation environment is changed, especially water seepage is caused, potential threat is caused to the reliable operation of a converter station, the situation of abnormal oil seepage of a high-voltage terminal of the converter transformer and the like is caused, the abnormal situation is difficult to discover in time through the current artificial monitoring means, and the continuous and stable operation of the whole extra-high voltage direct current engineering forms potential risks.
The valve hall of the converter valve is a building unit of the core of the extra-high voltage converter station, the valve hall of the converter valve runs in a complex environment of interleaving of multiple physical fields such as electricity, magnetism and heat for a long time, the reliability of the operation of the valve hall of the converter valve is a key point which directly affects the stability of the whole direct current engineering, the current method for eliminating the hidden trouble of operation of equipment in the valve hall of the converter valve is regular maintenance, the regular maintenance in a power failure state cannot simulate the actual operation working condition of the equipment such as the converter valve, and meanwhile, certain operation defects or hidden trouble of the equipment cannot be reproduced, so that the problems that the type and the position of a fault area of the equipment in the valve hall cannot be clarified exist.
Disclosure of Invention
Therefore, the valve hall equipment fault identification method and system provided by the invention overcome the defect that the type of the valve hall equipment fault and the fault area position cannot be clearly determined in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
in a first aspect, an embodiment of the present invention provides a valve hall device fault identification method, including:
acquiring a video stream of equipment to be detected, which is shot by image acquisition equipment in real time, wherein the video stream comprises: infrared images and ultraviolet images;
preprocessing any frame of image in the video stream, and extracting feature data of the preprocessed image;
Analyzing the feature data by at least one of a dynamic feature analysis algorithm and a heuristic classification algorithm to judge whether the equipment to be detected has faults or not;
when a fault is detected, a fault type is generated and located to a specific location.
In an embodiment, after the step of generating the fault type and locating to a specific position when the fault is detected, the method further includes: generating alarm information, drawing a chart of the alarm information, and visually displaying the alarm information, wherein the alarm information comprises fault type, time, image and video information of the fault valve hall equipment.
In an embodiment, the fault types include: open fire, discharge, over-heat and water seepage.
In one embodiment, when the equipment fault is detected to be of an open flame, overheat or water seepage type, a fault area is identified in the infrared image; when a device failure is detected as a discharge type, a failure area is identified in the ultraviolet image.
In one embodiment, the step of open flame fault detection includes: the method comprises the steps of acquiring an infrared image shot by image acquisition equipment in real time, determining a flame suspicious region through a preset image processing algorithm, extracting a characteristic value of each flame at each moment of the suspicious region, inputting a dynamic characteristic pool, wherein the characteristic value comprises average circularity, average area change rate and average perimeter change rate, classifying and grouping the characteristic value by using a heuristic classification algorithm, and judging whether open flame faults exist in a plurality of equipment to be detected or not by combining a dynamic characteristic analysis algorithm.
In one embodiment, the step of discharge fault detection includes: the method comprises the steps of acquiring ultraviolet images shot by image acquisition equipment in real time, detecting suspicious discharge points in a single frame image through a preset image detection algorithm, counting ultraviolet noise distribution according to a discharge negative sample, fitting by using a model theory, obtaining a probability density function of noise, constructing a sequence of suspicious discharge areas in time by using a heuristic feature classification algorithm based on the probability density function, calculating the probability that the sequence is a noise sequence, and judging whether discharge faults exist in equipment to be detected through comparison with a corresponding preset threshold value.
In one embodiment, the step of overheat fault detection comprises: the method comprises the steps of acquiring an infrared image shot by image acquisition equipment in real time, determining a heating suspicious region through a preset image processing algorithm, extracting a temperature value of the heating region, analyzing temperature rise, temperature difference and relative temperature difference of the heating region through a dynamic characteristic analysis algorithm, and judging whether the equipment to be detected has overheat faults or not through comparison with a corresponding preset threshold value.
In one embodiment, the step of water penetration failure detection comprises: the method comprises the steps of acquiring an infrared image shot by image acquisition equipment in real time, solving a water seepage suspicious region in a single frame image by utilizing an image segmentation algorithm, calculating the average temperature difference between the suspicious region and a region boundary outer region in the region, primarily screening the suspicious region, dividing different contours under a plurality of frames into different queues by utilizing a heuristic classification algorithm of the contours on a time sequence, each queue represents the change of a water seepage region on the time sequence, calculating the average change rate of the area and the average change rate of the circumference of each queue on the time sequence, comparing with a set change rate threshold, and screening the water seepage region.
In a second aspect, an embodiment of the present invention provides a valve hall device fault identification system, including:
the image acquisition module is used for acquiring a video stream of equipment to be detected, which is shot by the image acquisition equipment in real time, wherein the video stream comprises: infrared images and ultraviolet images;
the feature extraction module is used for preprocessing any frame of image in the video stream and extracting feature data of the preprocessed image;
the fault detection module is used for analyzing the feature data by utilizing at least one of a dynamic feature analysis algorithm and a heuristic classification algorithm and judging whether the equipment to be detected has faults or not;
and the fault identification module is used for generating a fault type and positioning the fault type to a specific position when the fault is detected.
In a third aspect, an embodiment of the present invention provides a terminal, including: the valve hall device fault identification system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the valve hall device fault identification method according to the first aspect of the embodiment of the invention.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium, where computer instructions are stored, where the computer instructions are configured to cause the computer to execute the valve hall device fault identification method according to the first aspect of the embodiment of the present invention.
The technical scheme of the invention has the following advantages:
the invention provides a valve hall equipment fault identification method and a system, which are used for acquiring a video stream of equipment to be detected, wherein the video stream is shot by image acquisition equipment in real time and comprises the following steps: infrared images and ultraviolet images; preprocessing any frame of image in the video stream, and extracting feature data of the preprocessed image; analyzing the feature data by at least one of a dynamic feature analysis algorithm and a heuristic classification algorithm to judge whether the equipment to be detected has faults or not; when a fault is detected, a fault type is generated and located to a specific location. The method comprises the steps of preprocessing an infrared image and an ultraviolet image, extracting feature data, analyzing the feature data by utilizing at least one of a dynamic feature analysis algorithm and a heuristic classification algorithm, and determining the type of the occurrence of the failure of the valve hall equipment and the position of a failure area, thereby improving the reliability, the scientificity and the intellectualization of the operation and maintenance of the valve hall equipment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a specific example of a valve hall device fault identification method according to an embodiment of the present invention;
fig. 2 is a flowchart of a specific login example of a valve hall device fault identification method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a dynamic feature pool of a specific example of a valve hall device fault identification method according to an embodiment of the present invention;
fig. 4 is a schematic diagram for solving fault overlapping of a specific example of a valve hall device fault identification method according to an embodiment of the present invention;
FIG. 5 is a block diagram of a valve hall device fault identification system according to an embodiment of the present invention;
fig. 6 is a composition diagram of a specific example of a terminal according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; the two components can be directly connected or indirectly connected through an intermediate medium, or can be communicated inside the two components, or can be connected wirelessly or in a wired way. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
Example 1
The method for identifying the equipment faults of the valve hall, provided by the embodiment of the invention, as shown in fig. 1, comprises the following steps:
step S1: acquiring a video stream of equipment to be detected, which is shot by image acquisition equipment in real time, wherein the video stream comprises: infrared images and ultraviolet images.
In the embodiment of the present invention, as shown in fig. 2, when a user acquires a video stream of a device to be detected, which is captured by an image capturing device in real time, the user needs to input a user name and a password to log in, and after the system logs in successfully, a login request is initiated to network devices such as a network video recorder (Network Video Recorder, NVR), which is only by way of example, but not by way of limitation, a corresponding network device is selected in practical application. After successful login, the device performs data initialization, including: initializing TCP connection, video stream, infrared original data stream, monitoring interface, fault detection rule parameters and the like, updating the equipment tree according to the network equipment information, and setting parameters such as alarm threshold value and the like for each equipment manager.
In an embodiment of the present invention, an image capturing apparatus includes: image capturing devices such as RGB camera, ultraviolet camera, infrared camera, etc. are only given as a secondary example, and not limited thereto, and the corresponding image capturing device is selected in practical application.
Step S2: and preprocessing any frame of image in the video stream, and extracting the characteristic data of the preprocessed image.
In the embodiment of the invention, by setting a video stream callback function, updating the current frame image and the infrared original temperature data in the memory in real time, reading the current frame and the infrared original temperature data, firstly preprocessing any frame image in the video stream, wherein the preprocessing operation comprises filtering part of noise, sharpening edges, dividing the equipment area, selecting a corresponding preprocessing means in practical application, extracting suspicious candidate area characteristic data and carrying out simple filtering by taking the example as an illustration only.
Step S3: and analyzing the feature data by at least one of a dynamic feature analysis algorithm and a heuristic classification algorithm to judge whether the equipment to be detected has faults.
In an embodiment of the present invention, the fault types include: open fire, discharge, over-heat, water seepage; by way of example only, and not by way of limitation, the corresponding fault type is selected in the actual application; when the equipment faults are detected to be of open fire, overheat and water seepage types, a fault area is marked in the infrared image; when a device failure is detected as a discharge type, a failure area is identified in the ultraviolet image.
In the embodiment of the invention, the steps of open flame fault detection comprise: the method comprises the steps of acquiring an infrared image shot by image acquisition equipment in real time, determining a flame suspicious region through a preset image processing algorithm, extracting a characteristic value of each flame at each moment of the suspicious region, inputting a dynamic characteristic pool, wherein the characteristic value comprises average circularity, average area change rate and average perimeter change rate, classifying and grouping the characteristic value by using a heuristic classification algorithm, and judging whether open flame faults exist in a plurality of equipment to be detected or not by combining a dynamic characteristic analysis algorithm.
Specifically, when flame combustion is encountered, a large amount of electromagnetic wave radiation is generated, wherein the wave band of the electromagnetic wave is mainly concentrated in the infrared region and the visible light region, a certain amount of radiation exists in the ultraviolet region, the flame region is segmented by utilizing a preset image processing algorithm because the brightness of the flame region in the infrared image is far higher than that of other regions, but a heat source similar to an incandescent lamp, a halogen lamp and the like can also emit electromagnetic wave radiation similar to flame, and therefore the region initially identified by the infrared image serves as a suspicious region.
The outline of an incandescent lamp and other objects in an infrared image is similar to a circle, most of interference sources moving at high temperature, such as people and the like, also generally have a relatively regular shape, the change is relatively gentle, the shape of a flame is irregular, the circularity of the flame is not only low in average value, but also can change obviously with time, and therefore, the circularity and the change rate of the circularity are selected as important criteria of the flame.
The circularity calculation formula is as follows:
wherein A is the area of the suspected fire area; p is the perimeter of the suspected fire area.
The calculation formula of the average change rate of the circularity is as follows:
where n is the circularity eigenvector length.
Analyzing a flame sample, and setting a circularity threshold C according to the average circularity of the flame t Excluding part of interference profile, counting dynamic change rule of circularity, setting average change rate threshold V ct Removing the circleA disturbance profile with gentle shape changes.
Because the flame shape changes obviously with time, the area change rate and the perimeter change rate can also be used as important criteria for eliminating interference objects. The area change rate and the circumference change rate are calculated by the following formulas, respectively:
the area average change rate and the perimeter average change rate are calculated by the following formulas:
analyzing a flame sample, and setting a change rate threshold V according to the average change rate of the area and the average change rate of the circumference of the sample at And V is equal to lt The interference profile with gentle area and circumference variation is excluded.
In the dynamic change of the flame, the contour shape of the flame is changed continuously along with time, and the statistic of the change rate of the contour shape of the flame can also be used as a criterion of the flame. Extracting the outline v= (x (i), y (i)) of the suspicious region, the outline can be regarded as a one-dimensional complex sequence:
V(i)＝x(i)+jy(i)
Discrete Cosine Transform (DCT) is performed thereon:
wherein the low frequency part mainly reflects the overall shape of the region, and the shape description coefficients are defined by taking coefficients of the low frequency part:
calculating any two contours S by using Euclidean distance through the following formula 1 、S 2 Shape change between:
analyzing flame samples, setting an inter-frame shape change threshold D st . Counting that the shape change between two adjacent frames is larger than D for a feature queue containing M feature elements st If the total number of the feature elements is smaller than M/3, the profile is considered to be not obviously changed in time series, and only by way of example, but not by way of limitation, the corresponding numerical value is selected according to the actual requirement in the actual application, so that the heating target tracked by the feature array is not considered to be flame.
In the application scene of the embodiment of the invention, a plurality of heating sources can appear at the same time, and the traditional flame identification algorithm can be summarized into three steps: extracting suspicious regions; detecting dynamic characteristics; the feature data analysis and decision are only applicable to single-target analysis. The embodiment of the invention realizes a multi-target area tracking and optimizing algorithm, calculates the characteristic value of each suspicious area at each moment, inputs the characteristic value into a dynamic characteristic pool shown in figure 3, and classifies and returns the characteristic value by using a heuristic classification algorithm.
The respective feature values of each suspicious region can be obtained by using the algorithm described above, for example: the circularity, area, circumference and shape describing coefficient of a flame suspicious region can be calculated, the group of characteristic values is called as characteristic element q of the suspicious region, and the position attribute (X q ,Y q )：
Wherein (X (i), y (i)) is a point constituting the outline of the suspicious region, (X) q ,Y q ) The position of the feature element q is the outline center of the suspicious region.
Inputting the characteristic elements into a dynamic characteristic pool, arranging a plurality of characteristic elements in time sequence to form a characteristic queue Q, wherein one characteristic queue corresponds to one heating source in the infrared image, and the central coordinate value of a heating target area tracked by the current queue in the image is defined as the position attribute (X Q ,Y Q ) The method comprises the steps of carrying out a first treatment on the surface of the When a new feature element q is added to the queue at time t, the position of the queue is updated using the "weighted average motion" by the following formula:
when lambda is a smoothing factor and lambda is larger, the newly added element has larger influence on the position attribute of the queue, the queue can rapidly track the position change of the element, but the jitter is larger, and the tracking failure is easily caused by the influence of individual elements; when λ is small, the newly added element has a small influence on the position attribute of the queue, the history element has a large influence on the position of the queue, and the jitter of the position of the queue is small, but when λ is too small, the position change of the element cannot be effectively tracked, so that λ needs to be a proper value.
Calculating the distance of the feature element or the feature queue as Euclidean distance by the following formula:
three operations between feature elements and feature queues are defined:
element return: the characteristic element is added to the tail of a certain characteristic queue, and the position of the queue is updated;
creating a queue: creating a new feature queue, namely a new feature time sequence of the heating area; queue fusion:
the two queues are combined into one queue, the elements are arranged in time sequence, and if a plurality of elements appear at the same time, the reserved area is larger. When new element is added into dynamic feature pool, calculating distance D between new element and all queues by using distance formula n Taking the minimum distance and the corresponding queue D min 、Q min . Setting a maximum enqueue distance threshold D t When D min ≤D t At the time, the element is classified into Q min A queue, otherwise, creating a new queue; checking the distance between the new queue and other queues, when the existing distance is less than D t In the case of (2), the two queues are merged.
And finally, each queue is respectively analyzed by utilizing a dynamic characteristic algorithm, so that the tracking and detection of multiple targets are realized, and the heuristic characteristic classification algorithm is also used for water seepage detection and discharge detection.
In an embodiment of the present invention, the step of detecting a discharge failure includes: the method comprises the steps of acquiring ultraviolet images shot by image acquisition equipment in real time, detecting suspicious discharge points in a single frame image through a preset image detection algorithm, counting ultraviolet noise distribution according to a discharge negative sample, fitting by using a model theory, obtaining a probability density function of noise, constructing a sequence of suspicious discharge areas in time by using a heuristic feature classification algorithm based on the probability density function, calculating the probability that the sequence is a noise sequence, and judging whether discharge faults exist in equipment to be detected through comparison with a corresponding preset threshold value.
In the embodiment of the invention, the discharge suspicious region can be easily obtained in the ultraviolet image by utilizing the pixel brightness information, the key point of the distinction is that the discharge point and the ultraviolet noise point are detected by utilizing a simple image detection technology, then the suspicious region where the suspicious discharge point is positioned in a single frame image is calculated by utilizing a statistical algorithm, the probability of continuously appearing the discharge point around the suspicious region on a time sequence is calculated, and when the probability value is larger than the set prior probability, the suspicious region is judged to be the discharge region.
The brightness of the discharge point in the ultraviolet image is obviously higher than that of the background environment, the shape is random, no texture information exists, but the noise of the ultraviolet image also has similar characteristics, the prior discharge detection based on the ultraviolet imaging is focused on researching the relationship between the discharge intensity and parameters such as ultraviolet gain, light spot area, morphology, distance, photon number and the like, and the purpose is to quantify the discharge intensity, and the corresponding research parameters are selected according to actual requirements in practical application by way of example only and not limitation; when the discharge intensity is large, the detection distance is short, the light spot area is large, and the light spot area can be well distinguished from background noise, but when the discharge intensity is small, shielding exists between a discharge point and an ultraviolet camera, and when the detection distance is long, the area of the discharge point in an ultraviolet image can be small, and the light spot area cannot be effectively distinguished from the background noise.
The positions of the ultraviolet noise in the image are randomly distributed, the discharge points always have equipment fault areas, the positions are relatively fixed, the discharge points have a certain frequency, the discharge points appear in the ultraviolet image in a certain rule along with the time sequence, the time sequence of the light spots is considered, the adjacent frames of the discharge points in the ultraviolet image continuously appear, and the probability of the continuous occurrence of the noise in one area is low.
Firstly, because the discharge points under the ultraviolet image have the characteristic of high brightness, suspicious discharge points in a single frame image can be detected by using a simple image detection technology, the distribution of ultraviolet noise is counted according to a discharge negative sample, and a probability density function f (t) of the noise can be obtained by using a model fitting of a principle.
Constructing a sequence of suspicious discharge areas in time by using a heuristic feature classification algorithm, and calculating the probability that the sequence is a noise sequence:
wherein t is the time difference between the current light spot and the last light spot in the same queue, whether the equipment to be detected has discharge faults or not is judged by comparing the time difference with the corresponding preset threshold value,when P Q <0.01, the sequence is determined to be a discharge sequence, which is only used as an example, but not limited to, and the corresponding preset threshold is selected according to the actual requirement in the practical application.
In an embodiment of the present invention, the step of overheat fault detection includes: the method comprises the steps of acquiring an infrared image shot by image acquisition equipment in real time, determining a heating suspicious region through a preset image processing algorithm, extracting a temperature value of the heating region, analyzing temperature rise, temperature difference and relative temperature difference of the heating region through a dynamic characteristic analysis algorithm, and judging whether the equipment to be detected has overheat faults or not through comparison with a corresponding preset threshold value.
In the embodiment of the invention, when the power transmission and transformation equipment normally operates, the conductive loop, the insulating medium and the iron core all have heating and temperature rise within the normal design range; when the device is in various bad states such as bad contact, short circuit, pollutant coverage, etc., various components outside and inside the power device may generate different thermal effects exceeding the design standard. The infrared energy emitted by the thermal infrared imager is detected by the thermal defect of the electric equipment, and once the tested equipment has the defect, the temperature field of the corresponding part changes, the change can be accurately captured by the thermal infrared imager, and the equipment fault can be diagnosed in time by detecting the temperature distribution condition of the equipment in the running state.
Overheat detection by infrared imaging is very important in power equipment inspection, and a plurality of potential accidents have been found and prevented, but at present, infrared detection relies on a field engineer to hold a thermal infrared imager, and according to infrared images, the state of equipment is manually analyzed. According to the embodiment of the invention, the online infrared imager is utilized, the temperature rise, the temperature difference and the relative temperature difference of the heating area are analyzed by utilizing the dynamic characteristic analysis algorithm, whether the equipment to be detected has overheat faults or not is judged by comparing the temperature rise, the temperature difference and the relative temperature difference with the corresponding preset threshold values, all-weather monitoring of the temperature state of the power equipment is realized, and meanwhile, the image processing technology is combined, and real-time autonomous fault positioning and accurate analysis of the overheat of the equipment are realized.
According to the embodiment of the invention, the infrared image of the equipment is acquired in real time, and the heating area is determined by utilizing an image processing technologyAnd (3) simultaneously reading the infrared original data stream, performing mathematical transformation on the original data to obtain temperature data, and counting the temperature data of the heating area to obtain the temperature T of the heating point. Comparing the heating point temperature T with the normal working temperature threshold T t When T>T t An alarm is issued. For example, the specification of "infrared temperature measurement standardization operation Instructions in Jiangsu province": when the hot spot temperature of the metal wire is higher than 80 ℃, the metal wire is judged to be a serious defect, and the corresponding alarm standard is selected in practical application by way of example only and not limitation.
According to T and the ambient temperature reference surface temperature T e And (5) calculating the temperature rise:
θ＝T-T e 。
calculating the temperature difference between different tested devices or different parts of the same tested device:
D＝T 1 -T 2
comparing the current temperature difference D with a normal working temperature difference threshold D t When D>D t An alarm is issued, for example: specifying temperature rise of metal wire>At 15K, it is determined as a serious defect, which is only taken as an example, but not limited to, and the corresponding threshold is selected according to the actual requirement in the practical application.
The relative temperature difference is calculated according to the temperature rise stomach and the temperature difference D:
comparing the current relative temperature difference delta t Threshold delta of temperature difference relative to normal operation t When delta t >δ t An alarm is issued. For example: when the relative temperature difference of the metal wires is more than or equal to 95%, the critical defect is judged, and the method is not limited by the critical defect, and the corresponding threshold value is selected according to the actual requirement in the practical application.
And an engineer responsible for the operation safety of equipment formulates a corresponding infrared characteristic detection standard, an infrared characteristic value of the computing equipment is acquired in real time by using the thermal infrared imager, equipment faults are diagnosed according to the standard, all-weather automatic monitoring of equipment heating conditions is realized, and once the heating faults occur, an alarm can be timely sent out.
In the embodiment of the invention, the water seepage fault detection step comprises the following steps: the method comprises the steps of acquiring an infrared image shot by image acquisition equipment in real time, solving a water seepage suspicious region in a single frame image by utilizing an image segmentation algorithm, calculating the average temperature difference between the suspicious region and a region boundary outer region in the region, primarily screening the suspicious region, dividing different contours under a plurality of frames into different queues by utilizing a heuristic classification algorithm of the contours on a time sequence, each queue represents the change of a water seepage region on the time sequence, calculating the average change rate of the area and the average change rate of the circumference of each queue on the time sequence, comparing with a set change rate threshold, and screening the water seepage region.
In the embodiment of the invention, the water seepage refers to the condition that the water in the pipeline permeates and diffuses to the surface of the equipment and even drips due to the damage of the pipeline, the poor sealing of the joint and the like; when cooling water seeps out, when the temperature of the cooling water is lower than the temperature of operating equipment, a continuous and isolated low-temperature area is displayed under the thermal infrared imager, and the conventional water seepage detection is performed by using water detection test paper or a mode that a field person holds the thermal infrared imager to observe the equipment. The embodiment of the invention provides an online real-time water seepage detection algorithm by combining low-temperature characteristics and dynamic characteristic changes of water seepage, and can detect the seepage failure of cooling water with the temperature lower than that of equipment.
Firstly, calculating a water seepage suspicious region in a single frame image by using an image segmentation algorithm, calculating the average temperature difference between the region and a region boundary outer region, and primarily screening the suspicious region. And dividing different contours under multiple frames into different queues by using a heuristic classification algorithm of the contours on a time sequence, wherein each queue represents the change of a water seepage area on the time sequence. And calculating the average change rate of the area and the average change rate of the perimeter of each queue on the time sequence, and comparing the average change rate with a set change rate threshold value so as to screen out the water seepage area.
Step S4: when a fault is detected, a fault type is generated and located to a specific location.
In the embodiment of the present invention, after the step of generating the fault type and locating to a specific position when the fault is detected, the method further includes: generating alarm information, drawing a chart of the alarm information, and visually displaying the alarm information, wherein the alarm information comprises fault type, time, image and video information of the fault valve hall equipment.
In the embodiment of the invention, four faults of overheat, open fire, water seepage and discharge are synchronously detected in real time by analyzing the infrared and ultraviolet channel images, but other three faults are not mutually independent, when the open fire fault occurs, a high-temperature flame area in the infrared image can be detected as overheat fault, radiation released by flame in an ultraviolet band can be detected as discharge fault, and the phenomenon is called overlapping among faults. The overlap of faults may trigger a false type of fault alert. The following table lists the overlap of four fault detections.
| Fault type | Involving channels | Overlap condition |
| Superheating | Infrared ray | Overlap with open flame |
| Open flame | Ultraviolet and infrared | Overlap with open fire and discharge |
| Discharge of electric power | Ultraviolet ray | Overlap with open flame |
| Water seepage | Infrared ray | Independent non-overlapping |
In order to eliminate fault type error detection caused by fault overlapping, comprehensive analysis decisions are needed to be combined with various fault detection. When an open fire fault occurs, overheat and discharge detection immediately judges that the fault occurs before open fire detection due to a detection algorithm, and if overheat or discharge alarms are immediately sent out, alarm type errors occur. To eliminate such errors, it is necessary to wait for a period of time, called "collision wait time", when overheat or discharge is detected, and comprehensively analyze the type of failure in combination with the result of open fire detection, and if open fire is not detected after the collision wait, a response type alarm is issued. When an open fire fault is detected, the detection results of the overheat and discharge faults are combined, and if the overheat and discharge faults are waiting, an open fire alarm is sent out, and the first two alarms are cancelled.
Setting a sending buffer zone, generating a fault message, a fault screenshot and a fault short video immediately when discharge or overheat is detected, adding fault information into the buffer zone to start conflict waiting, sending the fault information after waiting, checking the fault information in the sending buffer zone when open fire fault is detected, generating and sending the open fire fault information when discharge and overheat fault information exists, and emptying the buffer zone. The method has the advantages that fault information can be timely captured and stored when overheat and discharge occur, corresponding fault image, short video and other information are prevented from being lost after waiting is finished, the fault information can be immediately sent after waiting is finished, system delay is reduced, comprehensive analysis of overheat and discharge detection results is combined in flame detection, and false detection rate can be further reduced.
According to the valve hall equipment fault identification method provided by the embodiment of the invention, the video stream of the equipment to be detected, which is shot by the image acquisition equipment in real time, is acquired, and the video stream comprises the following components: infrared images and ultraviolet images; preprocessing any frame of image in the video stream, and extracting feature data of the preprocessed image; analyzing the feature data by at least one of a dynamic feature analysis algorithm and a heuristic classification algorithm to judge whether the equipment to be detected has faults or not; when a fault is detected, a fault type is generated and located to a specific location. The method has the advantages that the type and the fault area position of the valve hall equipment fault are clear by preprocessing the infrared image and the ultraviolet image and analyzing the characteristic data by at least one of a dynamic characteristic analysis algorithm and a heuristic classification algorithm, and the reliability, the scientificity and the intellectualization of the operation and the maintenance of the valve hall equipment are improved.
Example 2
An embodiment of the present invention provides a valve hall device fault recognition system, as shown in fig. 5, including:
the image acquisition module 1 is configured to acquire a video stream of a device to be detected, which is shot by the image acquisition device in real time, where the video stream includes: infrared images and ultraviolet images; this module performs the method described in step S1 in embodiment 1, and will not be described here again.
The feature extraction module 2 is used for preprocessing any frame of image in the video stream and extracting feature data of the preprocessed image; this module performs the method described in step S2 in embodiment 1, and will not be described here.
The fault detection module 3 is used for analyzing the feature data by utilizing at least one of a dynamic feature analysis algorithm and a heuristic classification algorithm and judging whether the equipment to be detected has faults or not; this module performs the method described in step S3 in embodiment 1, and will not be described here.
A fault identification module 4, configured to generate a fault type and locate a specific position when a fault is detected; this module performs the method described in step S4 in embodiment 1, and will not be described here.
The embodiment of the invention provides a valve hall equipment fault identification system, which provides a video stream of equipment to be detected, which is shot in real time by an image acquisition equipment, wherein the video stream comprises the following components: infrared images and ultraviolet images; preprocessing any frame of image in the video stream, and extracting feature data of the preprocessed image; analyzing the feature data by at least one of a dynamic feature analysis algorithm and a heuristic classification algorithm to judge whether the equipment to be detected has faults or not; when a fault is detected, a fault type is generated and located to a specific location. The infrared image and the ultraviolet image are preprocessed, the characteristic data are extracted, the characteristic data are analyzed by at least one of a dynamic characteristic analysis algorithm and a heuristic classification algorithm, the type of the occurrence of the faults of the valve hall equipment and the positions of fault areas are clear, and the reliability, the scientificity and the intellectualization of the operation and maintenance of the valve hall equipment are improved.
Example 3
An embodiment of the present invention provides a terminal, as shown in fig. 6, including: at least one processor 401, such as a CPU (Central Processing Unit ), at least one communication interface 403, a memory 404, at least one communication bus 402. Wherein communication bus 402 is used to enable connected communications between these components. The communication interface 403 may include a Display screen (Display) and a Keyboard (Keyboard), and the optional communication interface 403 may further include a standard wired interface and a wireless interface. The memory 404 may be a high-speed RAM memory (Random Access Memory) or a nonvolatile memory (nonvolatile memory), such as at least one magnetic disk memory. The memory 404 may also optionally be at least one storage device located remotely from the aforementioned processor 401. Wherein the processor 401 may perform the valve hall device fault identification method of embodiment 1. A set of program codes is stored in the memory 404, and the processor 401 calls the program codes stored in the memory 404 for executing the valve hall device failure recognition method in embodiment 1. The communication bus 402 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. Communication bus 402 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one line is shown in fig. 6, but not only one bus or one type of bus. Wherein the memory 404 may include volatile memory (English) such as random-access memory (RAM); the memory may also include a nonvolatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviated as HDD) or a solid-state drive (english: SSD); memory 404 may also include a combination of the above types of memory. The processor 401 may be a central processor (English: central processing unit, abbreviated: CPU), a network processor (English: network processor, abbreviated: NP) or a combination of CPU and NP.
Wherein the memory 404 may include volatile memory (English) such as random-access memory (RAM); the memory may also include a nonvolatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviated as HDD) or a solid state disk (english: solid-state drive, abbreviated as SSD); memory 404 may also include a combination of the above types of memory.
The processor 401 may be a central processor (English: central processing unit, abbreviated: CPU), a network processor (English: network processor, abbreviated: NP) or a combination of CPU and NP.
Wherein the processor 401 may further comprise a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof (English: programmable logic device). The PLD may be a complex programmable logic device (English: complex programmable logic device, abbreviated: CPLD), a field programmable gate array (English: field-programmable gate array, abbreviated: FPGA), a general-purpose array logic (English: generic array logic, abbreviated: GAL), or any combination thereof.
Optionally, the memory 404 is also used for storing program instructions. The processor 401 may call program instructions to implement the valve hall device failure recognition method in embodiment 1 as performed by the present application.
The embodiment of the application also provides a computer readable storage medium, and the computer readable storage medium stores computer executable instructions, wherein the computer executable instructions can execute the valve hall equipment fault identification method in the embodiment 1. Wherein the storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), or a Solid State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present application.
Claims (10)
1. A valve hall apparatus fault identification method, comprising:
acquiring a video stream of equipment to be detected, which is shot by image acquisition equipment in real time, wherein the video stream comprises: infrared images and ultraviolet images;
preprocessing any frame of image in the video stream, and extracting feature data of the preprocessed image;
analyzing the feature data by at least one of a dynamic feature analysis algorithm and a heuristic classification algorithm to judge whether the equipment to be detected has faults or not;
generating a fault type and locating to a specific position when a fault is detected, wherein when a device fault is detected as a discharge type, the step of detecting the discharge fault comprises: acquiring an ultraviolet image shot by image acquisition equipment in real time, detecting suspicious discharge points in a single frame image through a preset image detection algorithm, counting ultraviolet noise distribution according to a discharge negative sample, fitting by using a model of a profile, obtaining a probability density function of noise, constructing a sequence of suspicious discharge areas in time by using a heuristic feature classification algorithm based on the probability density function, calculating the probability that the sequence is a noise sequence, judging whether discharge faults exist in equipment to be detected by comparing the probability with a corresponding preset threshold value, wherein the sequence of suspicious discharge areas in time is constructed by using the heuristic feature classification algorithm, and calculating the probability that the sequence is the noise sequence:
wherein ,tis the time difference between the current spot and the last spot in the same queue,and judging whether the equipment to be detected has discharge faults or not by comparing the probability density function with a corresponding preset threshold value.
2. The valve hall device fault identification method of claim 1, wherein when a fault is detected, the step of generating a fault type and locating to a specific location further comprises: generating alarm information, drawing a chart of the alarm information, and visually displaying the alarm information, wherein the alarm information comprises fault type, time, image and video information of the fault valve hall equipment.
3. The valve hall device fault identification method of claim 1, wherein the fault type comprises: open fire, discharge, over-heat and water seepage.
4. A valve hall equipment failure recognition method according to claim 3, wherein when an equipment failure is detected as an open flame, overheat, water seepage type, a failure area is identified in the infrared image; when a device failure is detected as a discharge type, a failure area is identified in the ultraviolet image.
5. The valve hall device fault identification method of claim 4, wherein the step of open flame fault detection comprises: the method comprises the steps of acquiring an infrared image shot by image acquisition equipment in real time, determining a flame suspicious region through a preset image processing algorithm, extracting a characteristic value of each flame at each moment of the suspicious region, inputting a dynamic characteristic pool, wherein the characteristic value comprises average circularity, average area change rate and average perimeter change rate, classifying and grouping the characteristic value by using a heuristic classification algorithm, and judging whether open flame faults exist in a plurality of equipment to be detected or not by combining a dynamic characteristic analysis algorithm.
6. The valve hall device fault identification method of claim 4 wherein the step of overheat fault detection comprises: the method comprises the steps of acquiring an infrared image shot by image acquisition equipment in real time, determining a heating suspicious region through a preset image processing algorithm, extracting a temperature value of the heating region, analyzing temperature rise, temperature difference and relative temperature difference of the heating region through a dynamic characteristic analysis algorithm, and judging whether the equipment to be detected has overheat faults or not through comparison with a corresponding preset threshold value.
7. The valve hall device fault identification method of claim 4, wherein the step of water seepage fault detection comprises: the method comprises the steps of acquiring an infrared image shot by image acquisition equipment in real time, solving a water seepage suspicious region in a single frame image by utilizing an image segmentation algorithm, calculating the average temperature difference between the suspicious region and a region boundary outer region in the region, primarily screening the suspicious region, dividing different contours under a plurality of frames into different queues by utilizing a heuristic classification algorithm of the contours on a time sequence, each queue represents the change of a water seepage region on the time sequence, calculating the average change rate of the area and the average change rate of the circumference of each queue on the time sequence, comparing with a set change rate threshold, and screening the water seepage region.
8. A valve hall device fault identification system, comprising:
the image acquisition module is used for acquiring a video stream of equipment to be detected, which is shot by the image acquisition equipment in real time, wherein the video stream comprises: infrared images and ultraviolet images;
the feature extraction module is used for preprocessing any frame of image in the video stream and extracting feature data of the preprocessed image;
the fault detection module is used for analyzing the feature data by utilizing at least one of a dynamic feature analysis algorithm and a heuristic classification algorithm and judging whether the equipment to be detected has faults or not;
the fault identification module is used for generating a fault type and positioning the fault type to a specific position when a fault is detected, wherein when the equipment fault is detected to be a discharge type, the step of detecting the discharge fault comprises the following steps: acquiring an ultraviolet image shot by image acquisition equipment in real time, detecting suspicious discharge points in a single frame image through a preset image detection algorithm, counting ultraviolet noise distribution according to a discharge negative sample, fitting by using a model of a profile, obtaining a probability density function of noise, constructing a sequence of suspicious discharge areas in time by using a heuristic feature classification algorithm based on the probability density function, calculating the probability that the sequence is a noise sequence, judging whether discharge faults exist in equipment to be detected by comparing the probability with a corresponding preset threshold value, wherein the sequence of suspicious discharge areas in time is constructed by using the heuristic feature classification algorithm, and calculating the probability that the sequence is the noise sequence:
wherein ,tis the time difference between the current spot and the last spot in the same queue,and judging whether the equipment to be detected has discharge faults or not by comparing the probability density function with a corresponding preset threshold value.
9. A terminal, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the valve hall device fault identification method of any one of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing the computer to perform the valve hall apparatus fault identification method of any one of claims 1-7.
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| CN113191313B (en) * | 2021-05-20 | 2024-07-09 | 国能大渡河沙坪发电有限公司 | Video stream discharge identification method and device based on hydropower plant and computer equipment |
| CN113340352A (en) * | 2021-06-08 | 2021-09-03 | 国网浙江省电力有限公司 | Valve hall monitoring method, device and system |
| CN116168464A (en) * | 2022-12-22 | 2023-05-26 | 国网河南省电力公司郑州供电公司 | Unmanned aerial vehicle inspection data identification and management method and system based on distributed storage |
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