CN117450413B - Engine oil leakage detection method of copper plate and strip cold rolling mill - Google Patents
Engine oil leakage detection method of copper plate and strip cold rolling mill Download PDFInfo
- Publication number
- CN117450413B CN117450413B CN202311786656.6A CN202311786656A CN117450413B CN 117450413 B CN117450413 B CN 117450413B CN 202311786656 A CN202311786656 A CN 202311786656A CN 117450413 B CN117450413 B CN 117450413B
- Authority
- CN
- China
- Prior art keywords
- oil leakage
- area
- engine oil
- weight
- region
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000010705 motor oil Substances 0.000 title claims abstract description 81
- 238000005097 cold rolling Methods 0.000 title claims abstract description 33
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 title claims abstract description 23
- 229910052802 copper Inorganic materials 0.000 title claims abstract description 23
- 239000010949 copper Substances 0.000 title claims abstract description 23
- 238000001514 detection method Methods 0.000 title claims abstract description 17
- 238000001931 thermography Methods 0.000 claims abstract description 36
- 230000008859 change Effects 0.000 claims abstract description 19
- 238000000034 method Methods 0.000 claims abstract description 13
- 238000012545 processing Methods 0.000 claims abstract description 7
- 238000012423 maintenance Methods 0.000 claims description 51
- 239000003921 oil Substances 0.000 claims description 50
- 238000004519 manufacturing process Methods 0.000 claims description 42
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 238000003708 edge detection Methods 0.000 claims description 3
- 238000010191 image analysis Methods 0.000 claims description 3
- 239000007788 liquid Substances 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000005096 rolling process Methods 0.000 description 7
- 238000007689 inspection Methods 0.000 description 4
- 238000011835 investigation Methods 0.000 description 4
- 238000005299 abrasion Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000010721 machine oil Substances 0.000 description 2
- 239000000047 product Substances 0.000 description 2
- 238000007789 sealing Methods 0.000 description 2
- 230000035882 stress Effects 0.000 description 2
- 238000005303 weighing Methods 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000001050 lubricating effect Effects 0.000 description 1
- 238000005461 lubrication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N29/00—Special means in lubricating arrangements or systems providing for the indication or detection of undesired conditions; Use of devices responsive to conditions in lubricating arrangements or systems
- F16N29/04—Special means in lubricating arrangements or systems providing for the indication or detection of undesired conditions; Use of devices responsive to conditions in lubricating arrangements or systems enabling a warning to be given; enabling moving parts to be stopped
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N21/00—Conduits; Junctions; Fittings for lubrication apertures
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/48—Thermography; Techniques using wholly visual means
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/002—Investigating fluid-tightness of structures by using thermal means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N2260/00—Fail safe
- F16N2260/02—Indicating
- F16N2260/06—Temperature
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10048—Infrared image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
Abstract
The invention relates to an engine oil leakage detection method of a copper plate and strip cold rolling mill, and in particular relates to the technical field of image data processing. According to the method, a cold rolling mill to be detected is divided into n areas according to the maximum shooting width of an infrared thermal imaging camera, an infrared thermal imaging image of a current area is acquired at a preset time node by using the infrared thermal imaging camera, the acquired infrared thermal imaging image is preprocessed, the temperature change of an engine oil leakage position of the two images is detected, an engine oil leakage alarm is sent out, an engine oil leakage area is output, different weights are given to the current area according to the temperature change value and the vibration frequency of the engine oil leakage area, and the priority of the processing area is divided according to the statistical weight.
Description
Technical Field
The invention relates to the technical field of image data processing, in particular to an engine oil leakage detection method of a copper plate-strip cold rolling mill.
Background
The production process of the copper plate strip is to firstly prepare a blank, roll-forming the blank into a copper plate with a corresponding thickness by using a cold rolling mill, and lubricating and cooling key parts by using engine oil in the working process of the cold rolling mill. However, due to factors such as long-time rolling vibration or equipment aging, the oil pipeline can have problems such as abrasion, corrosion and interface loosening, and the oil leakage is caused.
For example, when a copper plate with the thickness of 2.5 mm is rolled to be 0.4 mm during the working of the copper plate cold-rolling mill, the working pressure of the adopted large cold-rolling mill is large, the number of soft and hard pipe joints related to an oil pipeline is as large as several hundred, oil leakage is almost unavoidable under strong vibration, and the common practice is to overhaul when the oil filling is reduced to 70%, and supplement the oil leakage point to the oil filling after maintenance is finished again.
Not only does the oil leakage problem increase the maintenance costs of the equipment, but a number of other problems may also be caused. Firstly, the oil leakage can lead to insufficient lubrication of mechanical equipment, accelerate abrasion and damage of key parts and shorten the service life of the equipment. Therefore, timely finding and repairing the oil leakage problem is critical to the normal operation and production safety of the equipment. At present, a common engine oil leakage detection method in a factory is manual inspection. The manual inspection method is simple to operate, has strong subjectivity, depends on experience and observation capability of operators, and has risks of missed inspection and misjudgment. The production line of the cold rolling mill is not stopped for 24 hours for production, so that the cold rolling mill can be stably produced for maintenance, large maintenance work of stopping production can be performed, a great amount of time is wasted in searching maintenance points by manual inspection, and the real maintenance work only occupies shorter time and has low efficiency.
In order to find the maintenance points quickly and efficiently, the preparation work between maintenance is important, and if the important investigation of the potential leakage point areas is carried out in advance before the maintenance, the maintenance time is greatly reduced.
Prior to the technical transformation of the invention, the prior art does not adopt a technical combination of the important investigation and the additional infrared investigation in the leakage point area.
Disclosure of Invention
In view of the above, the invention provides an engine oil leakage detection method of a copper plate-strip cold-rolling mill, which uses an infrared thermal imaging technology to superimpose important investigation of leakage point areas, realizes engine oil leakage detection, and uses an image processing technology to quickly and accurately find out engine oil leakage problems and improve the reliability and production efficiency of equipment. The method does not need to contact the surface of the equipment, avoids misjudgment, omission and safety risks of operators, and has wide application prospect. The present invention will be described in further detail below.
The invention discloses an engine oil leakage detection method of a copper plate and strip cold rolling mill, which comprises the following steps:
s1: dividing the cold rolling mill into n areas according to the pixel size of the infrared thermal imaging camera, wherein n=Wherein L is the length of the cold rolling mill, and m is the maximum width which can be shot by the infrared thermal imaging camera at the set position; acquiring infrared thermal imaging images of each region through an infrared thermal imaging camera at a preset time node;
s2: preprocessing an infrared thermal imaging image of a current region Ri (i=1, 2 … n), carrying out threshold segmentation, extracting a characteristic value, calculating an engine oil leakage area, comparing engine oil leakage areas of two images adjacent to each other at a time interval, if the difference value of the engine oil leakage areas of the two images is DeltaS=0, continuing to detect a next region, and when the difference value DeltaS >0 of the engine oil leakage areas is calculated, carrying out a next step S3;
s3: and calculating the temperature change of the engine oil leakage positions of two images adjacent to each other at time intervals in the current region Ri, and if the temperature change value of the engine oil leakage positions is detected to exceed a preset threshold value, sending an alarm by the system and outputting the engine oil leakage region.
S4: according to the number of the engine oil leakage points and the temperature change of the engine oil leakage points in the current area Ri, corresponding weight is given to the current area Ri; installing a vibration sensor in each area, collecting the vibration frequency F of the current area Ri, and giving corresponding weight to the current area Ri according to a preset vibration frequency threshold; and accumulating and calculating a weight accumulated value, and dividing the maintenance priority of the engine oil leakage area in the process of stopping production maintenance according to a threshold value set by the weight.
As a further aspect of the present invention, in the step S1, two ir thermal imaging images are acquired at the beginning of each month, and the interval between the two ir thermal imaging images is 8 hours.
As a further aspect of the present invention, in the step S2, the specific steps of image preprocessing and oil leakage area calculation include:
s21: performing binarization processing on the infrared thermal imaging image, taking a region with the temperature higher than 40 ℃ as an engine oil leakage region, converting the gray value of the oil leakage region into 0, setting the gray value of the oil leakage region into white, converting the gray values of other regions into 255, and setting the gray value of the oil leakage region into black;
s22: extracting the characteristic values of the images by using an Ojin algorithm and a Canny edge detection algorithm to obtain the boundary contour of the engine oil leakage area;
s23: accumulating the number of pixel points in a white area by using an image analysis algorithm, recording as N, calculating the area of the white image area, and adopting the formula of the engine oil leakage area as follows:wherein x and y are the number of the long and wide pixels of the infrared thermal imaging image.
As a further aspect of the present invention, in the step S3, a change in temperature at an engine oil leakage position of the two infrared thermal imaging images is calculated, and a calculation formula thereof is as follows:wherein->Indicating the size of the oil leakage area detected at the beginning of the production cycle,/->Indicating the size of the oil leakage area detected at the end of the production cycle,/->The liquid expansion coefficient is represented by k, k is a constant coefficient, and t is the detection interval time; when DeltaT>T 0 At the time T 0 And sending out an engine oil leakage alarm for presetting an alarm temperature threshold value, and outputting an engine oil leakage area. After the field engineer receives the alarm, the engine oil leakage area is overhauled.
As a further scheme of the invention, in the step S4, the copper plate-strip cold rolling mill has a plurality of rolling processes, the rolling force requirements of different products are different, the rolling force can reach hundreds of tons to thousands of tons, the production is not stopped for 24 hours, vibration is transmitted to the engine oil pipeline through mechanical conduction, the engine oil pipeline receives additional vibration and stress, a fastener and a sealing piece at the joint of the pipeline are loosened, the vibration can further cause the friction between the engine oil pipeline and other parts to be aggravated, and the pipeline is damaged, so the vibration of the engine oil cold rolling mill is one of key factors causing engine oil leakage. Installing vibration sensors in each divided area of the cold rolling mill body, collecting vibration frequency F of each area, adding weight information for the current detection area, setting initial weight to W=0, and when F is larger than a set frequency threshold value 2When the current region weight is increased by 3; when F is greater than the set frequency threshold +.>Less than the set frequency 2->When the current area weight is increased by 2; when F is smaller than the set frequency threshold +.>When the current region weight increases by 1。
As a further aspect of the present invention, in the step S4, the area Ri may include a plurality of oil leakage points, and the weight of Ri is increased by 1 each time an oil leakage point is detected. Temperature change at oil leakage of region Ri>At 15 ℃, the weight W of Ri increases by 3, and the temperature at the oil leakage of region Ri changes by 6 °cΔT/>At 15 ℃, the weight W of Ri increases by 2, the temperature change Δt at the oil leakage of region Ri<At 6 ℃, the weight W of Ri increases by 1.
Counting the total weight value of the area Ri within one year, and weighing W150 are marked as priority maintenance areas, weight 80<W<150 is marked as secondary maintenance area, weight W +.>80 is marked as a low-level maintenance area; in the centralized maintenance of the production stopping of the annual end, the priority treatment is marked as a priority maintenance area, the secondary maintenance area is maintained after the whole maintenance of the priority treatment area is completed, and the low-level maintenance area is maintained in the production process of putting the production line into production again.
The invention has the beneficial effects that: according to the engine oil leakage detection method for the copper plate-strip cold rolling mill, the engine oil leakage points are detected through infrared thermal imaging on the engine oil pipeline of the cold rolling mill, the engine oil leakage points which need to be maintained can be detected rapidly under the conditions that the surfaces of equipment are not required to be contacted and manual intervention is not required, the risk of manual operation is reduced, the risks of misjudgment and missed judgment are reduced, different weights are given to the current area according to the difference of temperature change values and vibration frequencies of the engine oil leakage areas, the priority degree of the treatment area is divided according to the statistical weight, the time for a field engineer to check the engine oil pipeline maintenance points in the process of stopping production maintenance is greatly reduced, and the production efficiency is improved.
Drawings
FIG. 1 is a schematic flow chart of an engine oil leakage detection method of a copper strip cold rolling mill.
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully hereinafter with reference to 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.
Referring to FIG. 1, the invention discloses a method for detecting oil leakage of a copper plate-strip cold rolling mill, which comprises the following steps:
s1: dividing the cold rolling mill into n areas according to the pixel size of the infrared thermal imaging camera, wherein n=Wherein L is the length of the cold rolling mill, and m is the maximum width which can be shot by the infrared thermal imaging camera at the set position; acquiring infrared thermal imaging images of each region through an infrared thermal imaging camera at a preset time node;
s2: preprocessing an infrared thermal imaging image of a current region Ri (i=1, 2 … n), carrying out threshold segmentation, extracting a characteristic value, calculating an engine oil leakage area, comparing engine oil leakage areas of two images adjacent to each other at a time interval, if the difference value of the engine oil leakage areas of the two images is DeltaS=0, continuing to detect a next region, and when the difference value DeltaS >0 of the engine oil leakage areas is calculated, carrying out a next step S3;
s3: and calculating the temperature change of the engine oil leakage positions of two images adjacent to each other at time intervals in the current region Ri, and if the temperature change value of the engine oil leakage positions is detected to exceed a preset threshold value, sending an alarm by the system and outputting the engine oil leakage region.
S4: according to the number of the engine oil leakage points and the temperature change of the engine oil leakage points in the current area Ri, corresponding weight is given to the current area Ri; installing a vibration sensor in each area, collecting the vibration frequency F of the current area Ri, and giving corresponding weight to the current area Ri according to a preset vibration frequency threshold; and accumulating and calculating a weight accumulated value, and dividing the maintenance priority of the engine oil leakage area in the process of stopping production maintenance according to a threshold value set by the weight.
Further, in the step S1, two ir thermal imaging images are acquired at the beginning of the month, and the interval between the two ir thermal imaging images is 8 hours.
Further, in the step S2, the specific steps of image preprocessing and oil leakage area calculation include:
s21: performing binarization processing on the infrared thermal imaging image, taking a region with the temperature higher than 40 ℃ as an engine oil leakage region, converting the gray value of the oil leakage region into 0, setting the gray value of the oil leakage region into white, converting the gray values of other regions into 255, and setting the gray value of the oil leakage region into black;
s22: extracting the characteristic values of the images by using an Ojin algorithm and a Canny edge detection algorithm to obtain the boundary contour of the engine oil leakage area;
s23: accumulating the number of pixel points in a white area by using an image analysis algorithm, recording as N, calculating the area of the white image area, and adopting the formula of the engine oil leakage area as follows:wherein x and y are the number of the long and wide pixels of the infrared thermal imaging image.
Further, in the step S3, the temperature change at the engine oil leakage position of the two infrared thermal imaging images is calculated, and the calculation formula is as follows:wherein->Indicating the size of the oil leakage area detected at the beginning of the production cycle,/->Indicating the size of the oil leakage area detected at the end of the production cycle,/->The liquid expansion coefficient is represented by k, k is a constant coefficient, and t is the detection interval time; when DeltaT>T 0 At the time T 0 And sending out an engine oil leakage alarm for presetting an alarm temperature threshold value, and outputting an engine oil leakage area. After the field engineer receives the alarm, the engine oil leakage area is overhauled.
Further, in step S4, the copper strip cold rolling mill has a plurality of rolling processes, the requirements of different products on rolling force are different, the rolling force can reach hundreds of tons to thousands of tons, and the production is not stopped for 24 hours, vibration is transmitted to the engine oil pipeline through mechanical conduction, so that the engine oil pipeline receives additional vibration and stress, a fastener and a sealing element at the joint of the pipeline are loosened, the vibration can further cause the friction between the engine oil pipeline and other components to be aggravated, and the pipeline is damaged, so that the vibration of the cold rolling mill is one of key factors causing engine oil leakage. Installing vibration sensors in each divided area of the cold rolling mill body, collecting vibration frequency F of each area, adding weight information for the current detection area, setting initial weight to W=0, and when F is larger than a set frequency threshold value 2When the current region weight is increased by 3; when F is greater than the set frequency threshold +.>Less than the set frequency 2->When the current area weight is increased by 2; when F is smaller than the set frequency threshold +.>When the current region weight increases by 1.
Further, in the step S4, the area Ri may include a plurality of oil leakage points, and the weight of Ri is increased by 1 each time an oil leakage point is detected. Temperature change at oil leakage of region Ri>At 15 ℃, the weight W of Ri increases by 3, and the temperature at the oil leakage of region Ri changes by 6 °cΔT/>At 15 ℃, the weight W of Ri increases by 2, the temperature change Δt at the oil leakage of region Ri<At 6 ℃, the weight W of Ri increases by 1.
Counting the total weight value of the area Ri within one year, and weighing W150 are marked as priority maintenance areas, weight 80<W<150 is marked as secondary maintenance area, weight W +.>80 is marked as a low-level maintenance area; in the centralized maintenance of the production stopping of the annual end, the priority treatment is marked as a priority maintenance area, the secondary maintenance area is maintained after the whole maintenance of the priority treatment area is completed, and the low-level maintenance area is maintained in the production process of putting the production line into production again.
In a preferred embodiment of the invention, the oil leakage points are detected through infrared thermal imaging, and a field engineer timely solves the oil leakage points according to alarm information after one production period is finished, so that the quantity of the oil needed to be supplemented by the cold rolling mill is effectively reduced, and compared with the quantity of the oil needed to be supplemented by the cold rolling mill which is not used for 1-6 months in the first year, the quantity of the oil supplemented by the cold rolling mill which is used for 1-6 months in the second year is averagely reduced by 39.9 percent, and specific data are shown in the following table:
| first year copper coil machine oil replenishment quantity (unit: L) | The second year copper coil machine oil replenishment quantity (unit: L) | Reduction rate of engine oil replenishment quantity of copper coil machine | |
| 1 month | 520 | 300 | 42.3% |
| 2 months of | 550 | 320 | 41.8% |
| 3 months of | 580 | 360 | 37.9% |
| 4 months of | 470 | 280 | 40.4% |
| 5 months of | 510 | 310 | 39.2% |
| 6 months of | 450 | 280 | 37.8% |
Removing the maintenance of large intensity of about 20 days of centralized production stoppage once a year. Before the invention is adopted, the medium-sized intensity maintenance of the oil leakage point is carried out for 5 to 6 times per year, and the maintenance time is about 15 days on average.
The priority treatment is marked as the priority maintenance area, the secondary maintenance area is maintained after the priority treatment area is completely maintained, the low-level maintenance area is maintained in the production process of putting the production line into production again, and the shutdown maintenance time of the production line is reduced.
The 15-day maintenance time of the medium-sized intensity per year, which is required to stop production of the production line, is shortened to only 7 days per year, the maintenance time is greatly shortened, the production efficiency is improved, and the production can be carried out for 8 days per year compared with the prior production plan.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (5)
1. The engine oil leakage detection method of the copper plate and strip cold rolling mill is characterized by comprising the following steps of:
s1: dividing the cold rolling mill into n areas according to the pixel size of the infrared thermal imaging camera, wherein n=Wherein L is the length of the cold rolling mill, and m is the maximum width which can be shot by the infrared thermal imaging camera at the set position; acquiring infrared thermal imaging images of each region through an infrared thermal imaging camera at a preset time node;
s2: preprocessing an infrared thermal imaging image of a current region Ri (i=1, 2 … n), carrying out threshold segmentation, extracting a characteristic value, calculating an engine oil leakage area, comparing engine oil leakage areas of two images adjacent to each other at a time interval, if the difference value of the engine oil leakage areas of the two images is DeltaS=0, continuing to detect a next region, and when the difference value DeltaS >0 of the engine oil leakage areas is calculated, carrying out a next step S3;
s3: the temperature change of the engine oil leakage positions of two images adjacent to each other at time intervals in the current region Ri is calculated by the following calculation formula:wherein->Indicating the size of the oil leakage area detected at the beginning of the production cycle,/->Indicating the size of the oil leakage area detected at the end of the production cycle,/->The liquid expansion coefficient is represented by k, k is a constant coefficient, and t is the detection interval time; when DeltaT>T 0 At the time T 0 Sending out an engine oil leakage alarm for a preset alarm temperature threshold value, and outputting an engine oil leakage area;
s4: according to the number of the engine oil leakage points and the temperature change of the engine oil leakage points in the current area Ri, corresponding weight is given to the current area Ri; installing a vibration sensor in each area, collecting the vibration frequency F of the current area Ri, and giving corresponding weight to the current area Ri according to a preset vibration frequency threshold; and accumulating and calculating a weight accumulated value, and dividing the maintenance priority of the engine oil leakage area in the process of stopping production maintenance according to a threshold value set by the weight.
2. The method for detecting oil leakage in a cold-rolled copper strip mill according to claim 1, wherein in the step S1, the infrared thermal imaging images are acquired twice at the beginning of the month, respectively, and the interval between the two infrared thermal imaging images is 8 hours.
3. The method for detecting oil leakage in a cold-rolled copper strip mill according to claim 1, wherein in the step S2, the specific steps of image preprocessing and oil leakage area calculation include:
s21: performing binarization processing on the infrared thermal imaging image, taking a region with the temperature higher than 40 ℃ as an engine oil leakage region, converting the gray value of the engine oil leakage region into 0, setting the gray value of the engine oil leakage region into white, converting the gray values of other regions into 255, and setting the gray value of the other regions into black;
s22: extracting the characteristic values of the images by using an Ojin algorithm and a Canny edge detection algorithm to obtain the boundary contour of the engine oil leakage area;
s23: accumulating the number of pixel points in a white area by using an image analysis algorithm, recording as N, calculating the area of the white image area, and adopting the formula of the engine oil leakage area as follows:wherein x and y are the number of the long and wide pixels of the infrared thermal imaging image.
4. The method for detecting oil leakage in a cold-rolled copper strip mill according to claim 1, wherein a vibration sensor is installed in each divided region of the mill body, vibration frequency F of each region is collected, weight information is added to the current detection region, the initial weight is set to w=0, and when F is greater than a set frequency threshold value 2When the current region weight is increased by 3; when F is greater than the set frequency threshold +.>Less than the set frequency 2->When the current area weight is increased by 2; when F is smaller than the set frequency threshold +.>When the current region weight increases by 1.
5. The method for detecting oil leakage in a cold-rolled copper strip mill according to claim 1, characterized in thatCharacterized in that in the step S4, if an oil leakage point is detected in the area Ri, the weight of Ri is added with 1; temperature change at oil leakage of region Ri>At 15 ℃, the weight W of Ri increases by 3, and the temperature at the oil leakage of region Ri changes by 6 °cΔT/>At 15 ℃, the weight W of Ri increases by 2, the temperature change Δt at the oil leakage of region Ri<At 6 ℃, the weight W of Ri increases by 1; counting the total weight value of the area Ri within one year, and adding the weight W +.>150 are marked as priority maintenance areas, weight 80<W<150 is marked as secondary maintenance area, weight W +.>80 is marked as a low-level maintenance area; in the centralized maintenance of the production stopping of the annual end, the priority treatment is marked as a priority maintenance area, the secondary maintenance area is maintained after the whole maintenance of the priority treatment area is completed, and the low-level maintenance area is maintained in the production process of putting the production line into production again.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311786656.6A CN117450413B (en) | 2023-12-25 | 2023-12-25 | Engine oil leakage detection method of copper plate and strip cold rolling mill |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311786656.6A CN117450413B (en) | 2023-12-25 | 2023-12-25 | Engine oil leakage detection method of copper plate and strip cold rolling mill |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117450413A CN117450413A (en) | 2024-01-26 |
| CN117450413B true CN117450413B (en) | 2024-03-15 |
Family
ID=89580285
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311786656.6A Active CN117450413B (en) | 2023-12-25 | 2023-12-25 | Engine oil leakage detection method of copper plate and strip cold rolling mill |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117450413B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110034780A (en) * | 2009-09-29 | 2011-04-06 | 동명대학교산학협력단 | Image detection system for leakage regions of hydraulic fluid in foring press machine |
| CN102636313A (en) * | 2012-04-11 | 2012-08-15 | 浙江工业大学 | Leakage source detecting device based on infrared thermal imaging processing |
| CN111751762A (en) * | 2020-05-29 | 2020-10-09 | 江苏方天电力技术有限公司 | Power transformer state identification and safety prevention and control system |
| CN112284619A (en) * | 2020-11-06 | 2021-01-29 | 重庆大唐国际彭水水电开发有限公司 | Oil leakage real-time monitoring device and detection method |
| CN112729688A (en) * | 2021-01-12 | 2021-04-30 | 南京大学 | Oil-gas pipeline leakage detection method based on vibration and temperature double parameters |
| CN114092437A (en) * | 2021-11-18 | 2022-02-25 | 国网四川省电力公司电力科学研究院 | Method for detecting oil leakage of transformer |
| CN115311623A (en) * | 2022-08-11 | 2022-11-08 | 杭州舜程智能检测技术有限公司 | Equipment oil leakage detection method and system based on infrared thermal imaging |
-
2023
- 2023-12-25 CN CN202311786656.6A patent/CN117450413B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110034780A (en) * | 2009-09-29 | 2011-04-06 | 동명대학교산학협력단 | Image detection system for leakage regions of hydraulic fluid in foring press machine |
| CN102636313A (en) * | 2012-04-11 | 2012-08-15 | 浙江工业大学 | Leakage source detecting device based on infrared thermal imaging processing |
| CN111751762A (en) * | 2020-05-29 | 2020-10-09 | 江苏方天电力技术有限公司 | Power transformer state identification and safety prevention and control system |
| CN112284619A (en) * | 2020-11-06 | 2021-01-29 | 重庆大唐国际彭水水电开发有限公司 | Oil leakage real-time monitoring device and detection method |
| CN112729688A (en) * | 2021-01-12 | 2021-04-30 | 南京大学 | Oil-gas pipeline leakage detection method based on vibration and temperature double parameters |
| CN114092437A (en) * | 2021-11-18 | 2022-02-25 | 国网四川省电力公司电力科学研究院 | Method for detecting oil leakage of transformer |
| CN115311623A (en) * | 2022-08-11 | 2022-11-08 | 杭州舜程智能检测技术有限公司 | Equipment oil leakage detection method and system based on infrared thermal imaging |
Non-Patent Citations (1)
| Title |
|---|
| 隧道渗漏水红外辐射特征模型试验及图像处理;豆海涛;黄宏伟;薛亚东;;岩石力学与工程学报;20110915(S2);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117450413A (en) | 2024-01-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109870617B (en) | Intelligent power plant electrical equipment fault diagnosis method based on width learning and infrared image space-time characteristics | |
| EP2131258A1 (en) | A Method for Prognostic Maintenance in Semiconductor Manufacturing Equipments | |
| CN116839682A (en) | Cable processing and manufacturing real-time monitoring system based on Internet of things | |
| CN105760883A (en) | Belt conveyer key component automatic identification method based on infrared thermography | |
| CN107749058A (en) | A kind of machine vision detection method and system of boiler tubing surface defect | |
| CN103984952A (en) | Method for diagnosing surface crack fault of blade of wind turbine generator of electric power system based on machine vision image | |
| NO20230279A1 (en) | Method and system for diagnosing structural damage of overwater building on basis of flow-state video monitoring | |
| CN117450413B (en) | Engine oil leakage detection method of copper plate and strip cold rolling mill | |
| CN116468427B (en) | Equipment operation and maintenance intelligent supervision system and method based on big data | |
| CN115060491A (en) | Fan bearing operation health degree assessment method and system based on multi-source data | |
| CN117173187A (en) | Intelligent valve fault detection system | |
| Janssens et al. | Data-driven imbalance and hard particle detection in rotating machinery using infrared thermal imaging | |
| CN114896735A (en) | Partial least square improved hot-rolled strip steel head wave defect reason identification method | |
| CN108508860B (en) | Process industrial production system data monitoring method based on coupling relation | |
| CN105760672B (en) | A kind of Trouble Diagnostic Method of Machinery Equipment | |
| CN115861294A (en) | Computer vision-based concrete production abnormity detection method and device | |
| CN113530619A (en) | Steam turbine trip symptom capturing method based on lubricating oil system parameter correlation analysis | |
| CN110516323B (en) | Pressure pipeline damage prediction method based on time sequence analysis | |
| CN115358137A (en) | Bearing metal temperature prediction method based on CNN-GRU algorithm | |
| CN109376451B (en) | Automatic equipment failure rate calculation method based on fitting association | |
| CN111967127A (en) | Small satellite delivery reliability problem screening rate calculation method | |
| CN114002222B (en) | Intelligent flaw detection device, system and method for in-service pipeline | |
| Goel et al. | Exploring the Diagnostic Potential of Infrared Thermography for Experimental Assessment of Cavitation and Air Entrainment-induced Faults in Centrifugal Pumps | |
| Wiliani et al. | Statistical Characteristics For Identification Defect of Solar Panel with Naive Bayes | |
| CN117730341A (en) | Method for determining heat and reflected heat in thermal images |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |