CN113554602A - Fan variable-pitch bearing monitoring method - Google Patents
Fan variable-pitch bearing monitoring method Download PDFInfo
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Abstract
The invention relates to a fan variable-pitch bearing monitoring method, which aims to solve the technical problems of high labor cost and hardware cost, and low detection efficiency and accuracy in the existing fan variable-pitch bearing health state monitoring. The method comprises the following steps: bolts for connecting the at least three cameras to the hub position towards the three variable pitch bearings of the fan respectively; monitoring bolts in a camera field of view in real time, and recording a first image as a pre-stored image; processing the monitoring image, and judging whether the monitoring image is an image when the variable pitch bearing rotates; if yes, recording the image as an effective image, and replacing the pre-stored image with the effective image; and transmitting the effective image information to a server. The invention adopts a video mode to obtain the bolt image of the position of the connecting hub of the variable pitch bearing, and matches with a unique algorithm to carry out rotation detection and identification, thereby accurately and efficiently monitoring the variable pitch bearing of the fan.
Description
Technical Field
The invention relates to a bearing monitoring method of a fan, in particular to a monitoring method of a fan variable pitch bearing health state.
Background
In recent years, with the increasing of the environmental improvement, coal power generation is gradually changed into environment-friendly wind power generation, and the wind power generation amount is increased year by year and becomes a main form of wind power utilization nowadays. With the increase of the fan loading amount and the lengthening of the fan time, the health operation and maintenance of the fan become more and more important, the detection of the health state of the fan through various sensors is particularly important, the condition of the fan can be forecasted in advance, safety accidents are avoided, and important losses of enterprises are recovered. The fan blade bearing can generate the phenomena of bolt breakage, nut falling, gear belt breakage and the like when the blade is worn for a long time under high-speed rotation and stress, if the fan blade bearing cannot be repaired in time, the generated energy is influenced by the unbalance of the impeller if the fan blade bearing is light, and the fan blade bearing sweeps the tower or falls to cause safety production accidents if the fan blade bearing is heavy, so that the healthy operation of the fan is seriously threatened, and the detection of the critical loss positions is very important.
In order to detect the health state of a fan blade bearing, the industry generally adopts a regular manual inspection mode to detect the key loss position of a fan. In addition, the tin foil paper, the lacquer thread and the like are distributed on the nut at the key loss position, and then whether the tin foil paper, the lacquer thread and the like are broken or not is judged for detection. In addition, some manufacturers adopt a video mode, the camera is fixed on the hub to monitor the bolt state of the variable-pitch bearing, as the fan vibrates all the time, a large amount of useless video information can be transmitted back by adopting a frame difference method, a large amount of storage resources are occupied, and the image information of each bolt state on each bearing is very difficult to retrieve from the data; and the adoption of artificial intelligence image recognition requires machine learning and code training in the early stage, and has higher requirements on hardware.
Disclosure of Invention
The invention aims to solve the technical problems of high labor cost and hardware cost, and low detection efficiency and accuracy in the existing fan variable pitch bearing health state monitoring, and provides a fan variable pitch bearing monitoring method which can realize remote real-time monitoring of the condition of a variable pitch bearing, and adopts a rotation detection algorithm to extract effective images from a large amount of image information and transmit the effective images back to a server, so that the monitoring efficiency and accuracy are improved, and the labor cost is reduced.
In order to achieve the purpose, the invention adopts the technical scheme that:
a monitoring method for a variable-pitch bearing of a fan is characterized by comprising the following steps:
1) respectively enabling at least three cameras to face the direction of bolts at the positions of three variable pitch bearings of the fan, which are connected with corresponding hubs;
2) monitoring bolts in a camera field of view in real time, and recording a first image as a pre-stored image;
3) processing the monitoring image, and judging whether the monitoring image is an image when the variable pitch bearing rotates; if yes, recording the image as an effective image, and replacing the pre-stored image with the effective image; if not, no recording is carried out;
4) and transmitting the effective image information to a server.
Further, the step 3) is specifically as follows:
3.1) blur processing
The image is blurred as follows:
wherein:
f (x, y) is the image after the blurring processing, wherein (x, y) is a pixel coordinate;
n is the coordinate variation range of the x-axis image;
m is the coordinate variation range of the y-axis image;
q is a proportional coefficient, and the value range is 0-100%;
3.2) frame difference processing
The monitoring image after the fuzzy processing is differed with a prestored image, then an absolute value is calculated, and the absolute value is stored as frame difference data; the pre-stored image is an image recorded for the first time or an image recorded when the pitch bearing rotates last time;
3.3) calculating the assignment threshold
Sorting all the frame difference data from large to small, averaging the maximum N data, and taking one value of 10-90% of the average value as an assignment threshold; wherein the value range of N is 1-50;
3.4) binarization processing
Binarizing the frame difference data by using an assignment threshold value to obtain a black-and-white image containing a plurality of white areas; wherein, the pixels less than or equal to the assignment threshold are assigned as black pixels 0, and the pixels greater than the assignment threshold are assigned as white pixels 255;
3.5) judging whether to move
3.5.1) respectively calculating the number of white pixels of each white area in the black-and-white image;
3.5.2) if the number of the white pixels is larger than a specified threshold value, adding 1 to the statistical value M, wherein the initial value of M is 0;
3.5.3) if the statistic M is larger than 1, respectively calculating the center coordinates of the corresponding white areas;
3.5.4) if the linear distance between the center coordinates of any two white areas is greater than one half of the length of the black-and-white image, judging that the monitoring image is an image when the pitch bearing rotates, recording the image as an effective image, and replacing the pre-stored image with the effective image; otherwise, no recording is performed.
Further, step 3.1) also comprises the step of reducing the image size and/or processing the gray scale before the blurring processing.
Further, the reducing the size of the image is to reduce the size of the image from 1920 × 1080 to 380 × 216; the algorithm of the gray processing is as follows:
P＝R*0.4+G*0.3+B*0.3
wherein: p is the target gray level value and R, G, B are the color components, respectively.
Further, in step 3.5.1), the number of white pixels in each white area in the black-and-white image is calculated by using an area statistical method, which specifically comprises:
firstly, finding a 255-value pixel point, searching other pixel points line by line on the basis of the pixel point, reassigning the found 255-value pixel point to 127 until the image boundary or the next line of pixel points are all 0 values or the pixel point value is 127, and finishing the searching;
when the image boundary or the next line of pixel points are found to be all 0 values, recording coordinates of 4 boundary points of the search area, and recording the number of 127-value pixel points of the search area, wherein the number is the number of white pixels of the white area;
when the pixel point value is 127, merging the search area and the area with the pixel point value of 127, recording the coordinates of 4 boundary points of the merged area, and recording the number of 127-value pixel points of the merged area, which is the number of white pixels of the white area.
Further, in step 3.5.3), the calculating the center coordinates of the corresponding white area specifically includes:
according to the 4 boundary point coordinates recorded in each white area, averaging the maximum value and the minimum value of the x coordinate to obtain the x-axis coordinate of the central coordinate; and averaging the maximum value and the minimum value of the y coordinate to obtain the y-axis coordinate of the central coordinate.
Further, the step 3.1) further includes a step of discarding the unclear image, which specifically includes:
subtracting gray values of two horizontally adjacent pixels in the monitored image, calculating an absolute value, squaring, accumulating and summing to obtain D; and if the D value is smaller than the image definition threshold value, discarding the monitoring image.
Further, in step 1), the camera is a web camera, and before step 3), a step of parsing an RTSP video stream of the camera into an image is further included.
Further, in the step 4), the transmission of the effective image information to the server is realized by adopting wireless Wifi connection wind field network transmission.
The invention has the beneficial effects that:
1) the fan pitch bearing monitoring method adopts a video mode, the plurality of cameras are respectively arranged on the three pitch bearings of the fan and used for observing bolts at the connecting positions of the pitch bearings, and the unique algorithm is matched for rotation detection and identification, so that the fan pitch bearing can be accurately and efficiently detected, and the healthy operation of the fan is monitored.
2) According to the monitoring method of the fan variable pitch bearing, the rotation detection algorithm is adopted for the shot image according to the rotation characteristics of the fan variable pitch bearing, the rotation and the vibration of the variable pitch bearing can be effectively identified, a large number of useless images caused by the vibration are abandoned, the effective image during the rotation of the variable pitch bearing is returned, and the pressure is relieved for the background analysis of the health state of the variable pitch bearing.
3) The camera is powered by the POE, the controller can receive the RTSP video stream of the network camera and analyze the RTSP video stream into pictures to carry out rotation detection, and the device has simple interface and low hardware cost.
Drawings
FIG. 1 is a schematic view of a camera shooting in a detection process of the fan pitch bearing monitoring method of the invention;
FIG. 2 is a schematic view of the equipment used in the method for monitoring a pitch bearing of a wind turbine according to the present invention;
FIG. 3 is a block diagram of a controller hardware system used in the method for monitoring a pitch bearing of a wind turbine according to the present invention;
FIG. 4 is a movement detection diagram of a detection bolt of a rotation detection module in the fan pitch bearing monitoring method of the invention; wherein, (a) is the picture of the bolt before rotating, (b) is the picture of the bolt after rotating, (c) is the result of the actual binary image;
FIG. 5 is a schematic diagram of a search area in a detection process of a rotation detection module in the monitoring method of the variable pitch bearing of the wind turbine according to the present invention;
FIG. 6 is a schematic view of the shape of a controller case used in the method for monitoring a pitch bearing of a wind turbine according to the present invention.
Description of reference numerals:
the system comprises a camera 1, a controller 2, an antenna 3 and a variable-pitch bearing 4.
Detailed Description
In order to more clearly explain the technical solution of the present invention, the following detailed description of the present invention is made with reference to the accompanying drawings and specific examples.
According to the scheme, a video mode is adopted, a plurality of cameras 1 are respectively arranged on three pitch bearings 4 of a fan and used for observing bolts at the connecting positions of the pitch bearings 4 and a hub, as shown in figure 1, pictures are taken every day and images are uploaded to a server, and the server performs post-processing. The difficulty of the scheme is that the variable pitch bearing 4 is round, the visual angle of the camera 1 can shoot two bolts at most, the number of the bolts is 50-150 according to different models, but the cameras 1 cannot be installed, so that the variable pitch bearing 4 can be changed by 0-90 degrees according to wind, and all the bolts can be observed by only installing 4 cameras 1 on each hub. And because the variable-pitch bearing 4 has a stress point, the bolts on the opposite stress points have the highest problem probability, and each hub can be only installed by 1 in consideration of the cost.
In order to shoot all the bolts for connecting the variable pitch bearing 4 and the hub, whether the hub rotates or not needs to be detected in real time, and when the hub rotates, the hub is shot (the monitoring image of the camera is recorded) once. The common rotation algorithm is a frame difference method or artificial intelligence image recognition, because the fan vibrates all the time, the vibration is judged to be movement by adopting the frame difference method, so that a large number of useless images are uploaded, machine learning and code training are required to be carried out in the early stage by adopting the artificial intelligence image recognition method, and the requirement on hardware is higher. The invention can accurately and efficiently detect the fan variable pitch bearing 4 by researching and developing special equipment and matching with a unique algorithm to carry out rotation detection and identification, and provides guarantee for monitoring the healthy operation of the fan.
Due to the special structure of the fan, if the hub detection signal is transmitted to the cabin, the equipment needs to support a wireless transmission function, so that the system can be connected with a wireless network of the cabin, and the wireless network in the cabin can be connected to a local area network of a wind field.
In the embodiment of the invention, the device used in the monitoring method of the wind turbine variable pitch bearing is shown in fig. 2 and comprises less than 3 cameras 1, 1 controller 2 and 1 antenna 3. The camera 1 mainly photographs the bolts connecting the hubs. The controller 2 is mainly connected with the camera 1, decodes the video stream of the camera 1 in real time, judges whether the hub rotates by adopting a rotation detection algorithm, records a bolt image during rotation, and finally transmits the image to the server through wireless Wifi in the controller 2. Wherein, the camera 1, the antenna 3 and the like are all mature and stable products in the market.
The controller 2 of the present invention is designed as follows:
1. hardware design
The hardware design block diagram of the controller 2 is shown in fig. 3, and the hardware design block diagram includes a processing module, a wireless wifi module, a network power supply module, an RTC module and a power supply system, which are arranged in a chassis, wherein a hardware platform of the processing module selects 3399K with a small core, and configures 4G RAM and 16G flash. The method supports hardware decoding of at least 12 paths of 1080P and h.26420 frame video streams, supports jpge hardware coding and has 1T calculation power. The network power supply module is used for controlling the camera and adopts the POE (Power over Ethernet) power supply of the network camera. The maximum power of Wifi can reach 24dbm, the maximum speed can reach 150Mbp, and real-time access to the camera is met. In addition, a network switch, power management, a hardware watchdog, an RTC clock, SD storage, an HDMI interface, a USB interface and the like are designed. The protection grade is IP66, and the working temperature is-40 to 75 ℃.
The controller 2 is connected with the camera 1 through a network interface, 6 network interfaces support and connect 6-path network cameras to the maximum extent, and the network cameras can be expanded into 12 paths through a POE (power over Ethernet) switch; the controller 2 is connected with the antenna 3 through a radio frequency interface and is used for being connected with a wireless network of the fan cabin, so that the wind field network is accessed, and a user can remotely access the device.
2. Functional design
Video parsing
The video analysis is designed by relying on hardware circuits such as a processing module and a network switching module in the circuit, the controller 2 can be simultaneously connected with 12 paths of RTSP video streams, a 3399K self-contained hardware decoder is adopted, 12 paths of simultaneous decoding can be completed, and the maximum frame rate can reach 20 frame rates.
② rotation detection
In the control program of the controller 2, the video analysis module analyzes the RTSP video stream into pictures and then sends the pictures to the rotation detection module for detection.
The detection process is as follows:
1) to speed up the process, the image is first reduced in size by 1920 x 1080 to 380 x 216. The image scaling adopts a bilinear interpolation method commonly used in image processing.
2) The image is processed in gray scale to further reduce data, and the gray scale processing algorithm is
P＝R*0.4+G*0.3+B*0.3
Where P is the target gray level value and R, G, B are the color components, respectively.
3) And calculating the definition, wherein the shot image is unclear sometimes due to vibration of the installation position, the definition of the image needs to be judged, and the judgment algorithm is to subtract gray values of two horizontally adjacent pixels to calculate an absolute value and then square the absolute value, namely to calculate the variance of the pixel values, and then to add up to calculate D, wherein the reference formula is as follows. Before using, D is calculated for a period of image, then threshold is taken according to the definition of the image, if the threshold is less than the threshold, the program abandons calculation, and next frame image calculation is carried out.
D(f0)＝ΣyΣx|f(x-1,y)–f(x+1,y)|2
Wherein f (x-1, y) is the gray value of the pixel point adjacent to the left side of the pixel point (x, y); f (x +1, y) is the gray value of the pixel point adjacent to the right of the pixel point (x, y).
4) And (4) image blurring processing, wherein image blurring adopts the intermediate image gray value and the peripheral gray value to be multiplied by different coefficients respectively to form a new intermediate image value. The formula is shown below, where q is the scaling factor.
Wherein f (x, y) is the image after the blurring processing, and (x, y) is the pixel coordinate; n is the coordinate variation range of the x-axis image; m is the coordinate variation range of the y-axis image; q is a proportionality coefficient, and the value range of q is 0-100%, and the value of q in the embodiment is 50%.
5) And (4) image frame difference processing, namely, performing difference between the image subjected to the blurring processing and a pre-stored image, then calculating an absolute value, and storing the absolute value as new data M03. The pre-stored image is an image recorded for the first time or an image recorded when the pitch bearing rotates last time.
6) And (3) calculating a threshold value of the frame difference images, sequencing the frame difference images from large to small, then averaging the 10 largest data, and then taking 50% of the average value as a threshold value (thresh) for the following calculation.
7) The binarization processing is to binarize M03 by a found threshold value (thresh), assign values of 0 to values less than or equal to thresh, and assign values of 255 to values greater than thresh.
8) Judging whether the image moves or not, generating various irregular white areas after binarization, wherein the areas are generated by bolt movement, FIG. 4 is an actual binarization image result, the value of the white area is 255, then calculating the area of each white area, wherein the area is the number of pixels, if the area of the pixel area is larger than a specified threshold value, the area is considered to be an area generated by object movement, counting the number of the white areas and adding 1, if the number of the areas is larger than 1, then calculating the center coordinates of the area surface, then calculating the linear distance of the coordinates, and if the coordinates are larger than one half of the image length, considering that the image moves.
The area statistical method is to find a 255 value, then to search the area formed by the 255 value based on the 255 value, to assign the 255 value to 127 again, and to record the number. Then, the values of 255 sequentially adjacent to the row are searched, the boundary coordinates f0(x0, y0) and f1(x1 and y1) of the left side and the right side of the row are recorded, the next row is continuously searched according to the left boundary coordinates and the right boundary coordinates, the searched area becomes f0(x0-1, y0+1) and f1(x1+1 and y1+1), then the coordinates of the searched area of the next row are updated according to the 255 values searched by the row, and the like until the image boundary is searched, or the next row is 0, or 127 is met, the search is ended, the statistical number is recorded, and the 4 maximum and minimum coordinates of the searched area are recorded. When 127 is encountered, it is indicated that the area belongs to an area which has been previously found, the two areas are coincident and this area is mainly the case as shown in fig. 5, the light and dark colors are themselves an area, but no dark color area is counted for algorithmic reasons, when the dark color area starts to be found down independently, the light color area already marked 127 is encountered, which is the need to merge the two areas, by counting whether the coordinates next to the coordinates marked 127 are in the saved coordinates area, if so, in the corresponding area, and if not, discarding.
The calculation of the center coordinate is based on the result of searching the area of the region, and the maximum value and the minimum value of the x-axis coordinate are summed and averaged, namely: x ═ xmin+xmax)/2，xminIs the minimum x-axis coordinate, x, of the zone recordmaxIs the maximum x-axis coordinate of the zone record; the y-axis is calculated as: y ═ ymin+ymax) 2; f (x, y) is the center coordinate.
Third, uploading the file
The controller 2 has an ftp file upload function, and when it is judged that the designated folder has the recorded image, the controller uploads the image to the designated server by the ftp protocol.
Disk management
When a network is disconnected, the recorded images cannot be uploaded to the server, so that the backlog is increased, and in order to prevent the system from being crashed due to the fact that the disk is full, the controller 2 has a disk management function and controls the disk to have the capacity of the remaining 20% in real time.
Control of camera
The controller 2 has a function of controlling the camera 1 to power on and power off, and when the camera 1 is found to be dead, the camera 1 can be restarted to power on and power off through the control circuit, so that the camera is restarted.
Hardware watchdog
Because the controller 2 is installed in a special position, non-special personnel cannot operate the equipment on site, and in order to prevent system operation accidents, a hardware watchdog is specially configured and has the functions of detecting a power supply and resetting the system.
In addition, the controller 2 also has common functions such as timing and logging.
3. Structural design
The structural design mainly considers from four aspects, respectively for the cost is low, and the protection level is required to reach IP66, and the installation of being convenient for is pleasing to the eye light. Through the investigation on the shell structure in the market, the section bar is finally selected, the four-point requirements are met, and the aluminum alloy provides a shielding effect for an internal circuit, further improves the anti-interference capacity of the product, and provides the heat dissipation capacity.
Fig. 6 is a schematic diagram of the external form of the chassis, on the panel of the chassis, there are 4 kinds of connectors, which are 1 power switch, 1 power interface, 1 radio frequency interface, 6 network interfaces, except for the network interface, the network interface adopts LP16-RJ45 of the ling ke electrical appliance, the connector can directly use the network cable, and can also reach the IP66 grade requirement, it is not necessary to process the cable, and the network cable can be directly used to connect the camera and the device, thus saving the product cost and the labor cost.
The monitoring method for the variable-pitch bearing of the fan in the embodiment of the invention comprises the following steps:
1) each hub is provided with 2 cameras 1 which are respectively connected with bolts at the corresponding hub position towards the corresponding variable-pitch bearing 4;
2) monitoring bolts in the field of view of the camera 1 in real time; recording the first image as a pre-stored image; the controller 2 analyzes the RTSP video stream of the camera 1 into images;
3) processing the monitoring image, judging whether the image is the image when the variable pitch bearing 4 rotates, if so, recording the image as an effective image, and replacing the prestored image with the effective image; otherwise, not storing; judging whether the image is rotated by using a rotation detection method of a rotation detection module of the controller 2;
4) and transmitting the effective image information to a server by using Wifi wireless transmission.
The above description is only for the purpose of describing the preferred embodiments of the present invention and is not intended to limit the technical solutions of the present invention, and any known modifications made by those skilled in the art based on the main technical concepts of the present invention are within the technical scope of the present invention.
Claims (9)
1. A monitoring method for a variable-pitch bearing of a fan is characterized by comprising the following steps:
1) at least three cameras (1) are respectively towards the direction of bolts at the positions where three variable pitch bearings (4) of the fan are connected with corresponding hubs;
2) monitoring bolts in a field of view of the camera (1) in real time, and recording a first image as a pre-stored image;
3) processing the monitoring image, and judging whether the image is the image when the variable pitch bearing (4) rotates; if yes, recording the image as an effective image, and replacing the pre-stored image with the effective image; if not, no recording is carried out;
4) and transmitting the effective image information to a server.
2. The fan pitch bearing monitoring method according to claim 1, wherein the step 3) specifically comprises:
3.1) blur processing
The monitoring image is blurred according to the following formula:
wherein:
f (x, y) is the image after the blurring processing, wherein (x, y) is a pixel coordinate;
n is the coordinate variation range of the x-axis image;
m is the coordinate variation range of the y-axis image;
q is a proportional coefficient, and the value range is 0-100%;
3.2) frame difference processing
The monitoring image after the fuzzy processing is differed with a prestored image, then an absolute value is calculated, and the absolute value is stored as frame difference data;
3.3) calculating the assignment threshold
Sorting all the frame difference data from large to small, averaging the maximum N data, and taking one value of 10-90% of the average value as an assignment threshold; wherein the value range of N is 1-50;
3.4) binarization processing
Binarizing the frame difference data by using an assignment threshold value to obtain a black-and-white image containing a plurality of white areas; wherein, the pixels less than or equal to the assignment threshold are assigned as black pixels 0, and the pixels greater than the assignment threshold are assigned as white pixels 255;
3.5) judging whether to move
3.5.1) respectively calculating the number of white pixels of each white area in the black-and-white image;
3.5.2) if the number of the white pixels is larger than a specified threshold value, adding 1 to the statistical value M, wherein the initial value of M is 0;
3.5.3) if the statistic M is larger than 1, respectively calculating the center coordinates of the corresponding white areas;
3.5.4) if the linear distance between the center coordinates of any two white areas is greater than one half of the length of the black-and-white image, judging that the monitoring image is an image when the pitch bearing (4) rotates, recording the image as an effective image, and replacing the pre-stored image with the effective image; otherwise, no recording is performed.
3. The fan pitch bearing monitoring method according to claim 2, wherein: the step 3.1) also comprises the step of reducing the size of the image and/or carrying out gray scale processing before blurring processing.
4. The fan pitch bearing monitoring method according to claim 3, wherein: the reducing the image size is reducing the image size from 1920 x 1080 to 380 x 216;
the algorithm of the gray processing is as follows:
P＝R*0.4+G*0.3+B*0.3
wherein: p is the target gray level value and R, G, B are the color components, respectively.
5. The fan pitch bearing monitoring method according to claim 2, wherein:
in step 3.5.1), the number of white pixels in each white area in the black-and-white image is calculated by adopting an area statistical method, which specifically comprises the following steps:
firstly, finding a 255-value pixel point, searching other pixel points line by line on the basis of the pixel point, reassigning the found 255-value pixel point to 127 until the image boundary or the next line of pixel points are all 0 values or the pixel point value is 127, and finishing the searching;
when the image boundary or the next line of pixel points are found to be all 0 values, recording coordinates of 4 boundary points of the search area, and recording the number of 127-value pixel points of the search area, wherein the number is the number of white pixels of the white area;
when the pixel point value is 127, merging the search area and the area with the pixel point value of 127, recording the coordinates of 4 boundary points of the merged area, and recording the number of 127-value pixel points of the merged area, which is the number of white pixels of the white area.
6. The fan pitch bearing monitoring method according to claim 5, wherein in step 3.5.3), the calculating the center coordinates of the corresponding white areas specifically comprises:
according to the 4 boundary point coordinates recorded in each white area, averaging the maximum value and the minimum value of the x coordinate to obtain the x-axis coordinate of the central coordinate; and averaging the maximum value and the minimum value of the y coordinate to obtain the y-axis coordinate of the central coordinate.
7. The fan pitch bearing monitoring method according to claim 2, wherein the step 3.1) further comprises the step of discarding unclear images, and specifically comprises the steps of:
subtracting gray values of two horizontally adjacent pixels in the monitored image, calculating an absolute value, squaring, accumulating and summing to obtain D; and if the D value is smaller than the image definition threshold value, discarding the monitoring image.
8. The fan pitch bearing monitoring method according to any one of claims 1 to 7, characterized by comprising the steps of: in the step 1), the camera (1) is a network camera, and the step 3) further comprises a step of analyzing the RTSP video stream of the camera (1) into an image.
9. The fan pitch bearing monitoring method of claim 8, wherein: in the step 4), the transmission of the effective image information to the server is realized by adopting wireless Wifi connection wind field network transmission.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116557225A (en) * | 2023-04-06 | 2023-08-08 | 中广核全椒风力发电有限公司 | Monitoring device, monitoring system and wind generating set for monitoring variable-pitch bearing |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008294805A (en) * | 2007-05-25 | 2008-12-04 | Ryukoku Univ | Monitoring camera terminal, control method thereof, control program thereof, computer readable recording medium bearing recorded program, monitoring camera terminal managing device, and monitoring camera system |
| CN102252623A (en) * | 2011-06-24 | 2011-11-23 | 西安工程大学 | Measurement method for lead/ground wire icing thickness of transmission line based on video variation analysis |
| CN103343728A (en) * | 2013-06-09 | 2013-10-09 | 上海乾祺电子科技有限公司 | Wind generating set remote on-line multi-mode health state monitoring and fault diagnosis system |
| JP2016009896A (en) * | 2014-06-23 | 2016-01-18 | 三菱電機株式会社 | Video monitoring system, and video retrieval method of video monitoring system |
| CN107061338A (en) * | 2017-05-27 | 2017-08-18 | 山东罗泰风机有限公司 | A kind of fan condition real-time monitoring system |
| CN107345857A (en) * | 2017-06-09 | 2017-11-14 | 昆明理工大学 | A kind of electro spindle condition monitoring and failure diagnosis system and its monitoring, diagnosing method |
| CN107657250A (en) * | 2017-10-30 | 2018-02-02 | 四川理工学院 | Bearing fault detection and localization method and detection location model realize system and method |
| CN109389566A (en) * | 2018-10-19 | 2019-02-26 | 辽宁奇辉电子系统工程有限公司 | Subway height adjusting valve fastening nut defective mode detection method based on boundary characteristic |
| CN109946075A (en) * | 2018-12-25 | 2019-06-28 | 东北大学 | A kind of bearing condition monitoring and method for diagnosing faults |
| CN112399150A (en) * | 2020-11-16 | 2021-02-23 | 广州顶新时代电子系统科技有限公司 | Method for optimizing imaging picture of monitoring camera |
-
2021
- 2021-06-30 CN CN202110737418.0A patent/CN113554602B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008294805A (en) * | 2007-05-25 | 2008-12-04 | Ryukoku Univ | Monitoring camera terminal, control method thereof, control program thereof, computer readable recording medium bearing recorded program, monitoring camera terminal managing device, and monitoring camera system |
| CN102252623A (en) * | 2011-06-24 | 2011-11-23 | 西安工程大学 | Measurement method for lead/ground wire icing thickness of transmission line based on video variation analysis |
| CN103343728A (en) * | 2013-06-09 | 2013-10-09 | 上海乾祺电子科技有限公司 | Wind generating set remote on-line multi-mode health state monitoring and fault diagnosis system |
| JP2016009896A (en) * | 2014-06-23 | 2016-01-18 | 三菱電機株式会社 | Video monitoring system, and video retrieval method of video monitoring system |
| CN107061338A (en) * | 2017-05-27 | 2017-08-18 | 山东罗泰风机有限公司 | A kind of fan condition real-time monitoring system |
| CN107345857A (en) * | 2017-06-09 | 2017-11-14 | 昆明理工大学 | A kind of electro spindle condition monitoring and failure diagnosis system and its monitoring, diagnosing method |
| CN107657250A (en) * | 2017-10-30 | 2018-02-02 | 四川理工学院 | Bearing fault detection and localization method and detection location model realize system and method |
| CN109389566A (en) * | 2018-10-19 | 2019-02-26 | 辽宁奇辉电子系统工程有限公司 | Subway height adjusting valve fastening nut defective mode detection method based on boundary characteristic |
| CN109946075A (en) * | 2018-12-25 | 2019-06-28 | 东北大学 | A kind of bearing condition monitoring and method for diagnosing faults |
| CN112399150A (en) * | 2020-11-16 | 2021-02-23 | 广州顶新时代电子系统科技有限公司 | Method for optimizing imaging picture of monitoring camera |
Non-Patent Citations (2)
| Title |
|---|
| PORNCHAI NIVESRANGSAN ET AL: "Bearing fault monitoring by comparison with main bearing frequency components using vibration signal", 《 2018 5TH INTERNATIONAL CONFERENCE ON BUSINESS AND INDUSTRIAL RESEARCH (ICBIR)》 * |
| 申振腾: "风力机叶片在线视觉监测与故障诊断系统研究", 《中国优秀硕士学位论文全文数据库（电子期刊）》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116557225A (en) * | 2023-04-06 | 2023-08-08 | 中广核全椒风力发电有限公司 | Monitoring device, monitoring system and wind generating set for monitoring variable-pitch bearing |
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|---|---|
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