CN112115776A - Method for monitoring lubrication state of large open gear of ship lock hoist - Google Patents
Method for monitoring lubrication state of large open gear of ship lock hoist Download PDFInfo
- Publication number
- CN112115776A CN112115776A CN202010790805.6A CN202010790805A CN112115776A CN 112115776 A CN112115776 A CN 112115776A CN 202010790805 A CN202010790805 A CN 202010790805A CN 112115776 A CN112115776 A CN 112115776A
- Authority
- CN
- China
- Prior art keywords
- area
- oil
- image
- oil shortage
- tooth surface
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000012544 monitoring process Methods 0.000 title claims abstract description 25
- 238000005461 lubrication Methods 0.000 title claims abstract description 22
- 239000003921 oil Substances 0.000 claims abstract description 104
- 238000001931 thermography Methods 0.000 claims abstract description 13
- 239000010687 lubricating oil Substances 0.000 claims abstract description 10
- 230000002950 deficient Effects 0.000 claims abstract description 8
- 238000012216 screening Methods 0.000 claims abstract description 6
- 230000007812 deficiency Effects 0.000 claims description 12
- 238000001514 detection method Methods 0.000 claims description 12
- 239000002184 metal Substances 0.000 claims description 8
- 230000007797 corrosion Effects 0.000 claims description 4
- 238000005260 corrosion Methods 0.000 claims description 4
- 239000013589 supplement Substances 0.000 claims description 4
- 230000003044 adaptive effect Effects 0.000 claims description 3
- 239000011248 coating agent Substances 0.000 claims description 3
- 238000000576 coating method Methods 0.000 claims description 3
- 230000001050 lubricating effect Effects 0.000 abstract description 7
- 238000005299 abrasion Methods 0.000 abstract description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 239000004519 grease Substances 0.000 description 2
- 235000003642 hunger Nutrition 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000037351 starvation Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Probability & Statistics with Applications (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention discloses a method for monitoring the lubrication state of a large open gear of a ship lock hoist, which comprises the following steps: acquiring an RGB (red, green and blue) image and a thermography of a gear tooth surface by using an infrared thermography camera when the gear runs; dividing the tooth surface RGB image and the thermography to obtain a complete and single pure tooth surface RGB image and a pure tooth surface thermography image; screening out a suspected oil shortage area from the pure tooth surface RGB image by adopting a preset algorithm; analyzing the pure tooth surface thermal image to obtain characteristic data; judging the oil shortage degree of the relevant area according to the thermal image characteristic data corresponding to the suspected oil shortage area and a preset logic judgment step; and obtaining the position of the oil-deficient area and the grade of the oil-deficient degree, and taking corresponding measures. The invention can monitor and accurately judge the tooth surface lubricating effect in real time, greatly reduces equipment abrasion and lubricating oil loss, and reduces labor cost.
Description
Technical Field
The invention relates to the field of lubricating oil state monitoring, in particular to a method for monitoring the lubricating state of a large-scale open gear of a ship lock hoist.
Background
The ship lock herringbone door is generally controlled to be opened and closed by large-scale open gear transmission, and lubrication of open gear teeth is an important work content for ensuring safe operation of the ship lock. The open gear is generally used in large heavy-duty transmission occasions, is completely exposed outside, and the tooth surface is easy to wear, so that a better lubricating state needs to be kept. The lubrication of open gears is generally protected by applying grease to the tooth surfaces and forming a lubricating oil film after running in for several minutes, the thickness of the oil film being generally less than 5 μm. With the operation time of the open gear being longer and longer, the oil film can be gradually thinned and even damaged, and the lubricating effect can be lost when the oil film is seriously damaged, so that the risk of pitting corrosion of the tooth surface is increased. Therefore, the lubricating state of the open gear needs to be accurately judged, and oil is timely supplemented to avoid equipment abrasion.
Whether the large-scale open gear needs oil supplement under the traditional industrial environment is judged by engineers according to field observation and self experience, and quantitative judgment can not be given to the oil shortage degree and the oil shortage area by judgment, so that the lubricating effect is ensured, the oil supplement is usually carried out by adopting a regular full-tooth-surface coating mode, and a large amount of lubricating grease is wasted. In addition, the traditional method needs manual regular inspection with certain experience to make judgment, the labor intensity of the manual inspection is high, and online real-time monitoring and judgment cannot be realized.
Therefore, there is a need to develop a real-time, on-line, accurate method for monitoring lubrication status of large-scale open gears.
Disclosure of Invention
The invention aims to solve the technical problems and provides a method for monitoring the lubrication state of a large-scale open gear of a ship lock hoist.
The technical scheme of the invention is a method for monitoring the lubrication state of a large open tooth of a ship lock hoist, which comprises the following steps:
step 1: acquiring an RGB (red, green and blue) image and a thermography of a gear tooth surface by using an infrared thermography camera when the gear runs;
step 2: dividing the tooth surface RGB image and the thermography obtained in the step 1 to obtain a complete and single pure tooth surface RGB image and a single thermography image;
and step 3: screening out a suspected oil shortage area from the pure tooth surface image in the step 2 by adopting a preset algorithm;
and 4, step 4: analyzing the pure tooth surface thermal image in the step 2 to obtain heat distribution characteristic data;
and 5: judging the oil shortage degree of the relevant area according to the heat distribution characteristic data of the thermal image corresponding to the suspected oil shortage area and a preset logic judgment step;
step 6: and (5) determining the position of the oil shortage region and the grade of the oil shortage degree according to the oil shortage degree of the relevant region judged in the step (5), and giving corresponding prompts according to different oil shortage grades.
In the step 3, the preset algorithm for screening out the suspected oil shortage area comprises the following steps:
step 3.1: graying the pure tooth surface image obtained in the step 2 to obtain a pure tooth surface gray image;
step 3.2: dividing the gray level image in the step 3.1 by using a maximum inter-class variance method adaptive threshold to obtain a binary image;
step 3.3: carrying out corrosion operation on the binary image, then carrying out expansion operation, removing image noise, and filling holes to obtain a complete black-white connected domain;
step 3.4: setting a threshold T1Searching all white connected domains in the image, and reducing pixel points to be smaller than T1Removing the connected domain, and leaving a white connected domain with a larger area;
step 3.5: marking a corresponding area in the gray-scale image according to the position coordinates of the remaining white connected domain obtained in the step 3.4;
step 3.6: calculating a gradient value image of the marking area in the step 3.5;
step 3.7: setting a threshold T2Calculating the gradient mean value of the selected area image obtained in the step 3.6, namely summing all elements in the area gradient image and dividing the sum by the total number of the elements to obtain the gradient mean value of the selected area, if the value is more than T2That is, the region is considered to have uneven light reflection characteristics, and is determined as a lubricating oil surface, otherwise, the region is determined as a metal surface, and the region determined as the metal surface is determined as a suspected oil-deficient region.
In the step 4, the step of analyzing the gear thermal image to obtain the characteristic data is as follows:
step 4.1: calculating a thermal image mean value I according to the pure tooth surface thermal image pixel values obtained in the step 21I.e. the sum of all pixel values divided by the total number of pixels;
step 4.2: marking and extracting corresponding areas in the thermal image according to the area coordinates marked in the step 3.5, and calculating the thermal image mean value I of the extracted areas2Calculating the heat difference I ═ I2-I1；
Step 4.3: and taking a suspected oil-starved area, namely a metal visible area, as an approximate contact surface, and calculating the weighted heat difference E of the contact area to be I M, wherein M is the area of the area, and the numerical value is the number of pixel points in the area.
In the step 5, the judgment step for determining the oil shortage degree through logic judgment comprises the following steps:
step 5.1: from the beginning of the first operation, storing each monitored suspected oil shortage area according to the gear number and the area position, and setting an oil shortage index K with an initial value of 0 for each area;
step 5.2: setting a high threshold Th, a medium threshold Tm and a low threshold Tl of the heat difference, and comparing the relation between the weighted heat difference E obtained in the step 4.3 and the set threshold;
step 5.3: setting the values Kh, Km and Kl of the oil shortage, and updating the value of the oil shortage index K of the area according to the relationship between the weighted heat difference E and the set high threshold Th, medium threshold Tm and low threshold Tl of the heat difference;
step 5.4: and updating the oil shortage index K value during each operation, judging the oil shortage degree according to the size of the oil shortage index K value, and resetting the K value of the lubricating oil coating area to 0 after the gear is replenished with lubricating oil.
And step 6, grading the oil shortage degree into: severe oil deficiency, moderate oil deficiency, mild oil deficiency, and no oil deficiency. Taking corresponding treatment measures according to different oil shortage grades: the detection area is in a severe oil shortage grade, and oil is supplemented; the detection area is in a medium oil shortage grade, and early warning is carried out; reminding when the detection area is in a mild oil shortage grade; the detection zone is at a no starvation level and is ignored.
Compared with the prior art, the invention has the beneficial effects that:
1) the monitoring method of the invention realizes the automatic monitoring of the lubrication state of the large-scale open gear, automatically judges the oil shortage degree of the gear according to the thermal image of the gear, is convenient for carrying out continuous monitoring on the lubrication state of the gear, replaces manpower, and saves manpower;
2) the monitoring method can distinguish and judge the oil shortage degree of the gear, and sends different prompts to gear managers according to different oil shortage grades, so that the gear managers can conveniently make oil supply plans, severe oil shortage is prevented, damage to the gear is avoided, and the risk of unplanned shutdown and damage of ship lock equipment is reduced.
Drawings
The invention is further described below with reference to the figures and examples.
FIG. 1 is a flow chart of a method for monitoring lubrication status of a large-sized open gear of a ship lock hoist according to an embodiment;
FIG. 2 is a schematic view of the procedure for calculating K value and judging the grade of oil shortage in the example.
Detailed Description
As shown in fig. 1 and 2, the method for monitoring the lubrication state of the large open gear of the ship lock hoist comprises the following steps which are sequentially executed:
step 1: and acquiring an RGB image and a thermographic image of the tooth surface of the gear by using an infrared thermographic camera when the gear runs. In this embodiment, after the thermal imaging camera is set at a proper position, the gear rotation angle data obtained by the angle sensor is used as a standard for controlling the camera photographing time, and each tooth surface image is photographed one by one when each tooth surface and the camera form a fixed angle, so that each tooth surface image is guaranteed to be at a fixed position where the camera photographs the image.
Step 2: and (3) dividing the tooth surface RGB image and the thermography obtained in the step (1) to obtain a complete and single pure tooth surface RGB image and a single thermography image. In the embodiment, the gear tooth surface position is calibrated, the area image is fixedly intercepted, and the rectangular area image with the abscissa 480 and the ordinate 750 is fixedly intercepted under the erection position;
and step 3: and (3) screening out a suspected oil shortage area from the pure tooth surface image in the step (2) by adopting a preset algorithm. In this embodiment, the step of screening the suspected oil-deficient area comprises the following steps:
step 3.1: graying the pure tooth surface image obtained in the step 2 to obtain a pure tooth surface gray image;
step 3.2: dividing the gray level image in the step 3.1 by using a maximum inter-class variance method adaptive threshold to obtain a binary image;
step 3.3: carrying out corrosion operation on the binary image, then carrying out expansion operation, removing image noise, and filling holes to obtain a complete black-white connected domain, wherein the hole filling step is as follows:
step 3.3.1: filling the image with water, filling colors from the pixel (0, 0);
step 3.3.2: reversing the image after flood filling, such as black to white and white to black;
step 3.3.3: combining the threshold image with the reverse flood fill image using a bitwise OR operation;
step 3.4: setting a threshold T1Searching all white connected domains in the image, and reducing pixel points to be smaller than T1Removing the connected domain, and leaving a white connected domain with a larger area; in this example, T is selected1Searching all white connected domains in the image, eliminating the connected domains with the pixel points smaller than 1000, and leaving the white connected domains with larger areas;
step 3.5: marking a corresponding area in the gray-scale image according to the position coordinates of the remaining white connected domain obtained in the step 3.4, and marking the marked area;
step 3.6: and (3) calculating a gradient value image of the marking area in the step 3.5, wherein the calculation process is as follows:
step 3.6.1: for processing boundary points, expanding the boundary of the image in the selected area by adopting boundary mirror image expansion;
step 3.6.2: sequentially selecting pixel points from the pixel positions before the expansion of the expanded regional image obtained in the step 3.6.1;
step 3.6.3: obtaining eight surrounding pixel values of the selected point, namely a 3 x 3 matrix taking the selected point as the center, searching the maximum value and the minimum value of the matrix element, and taking the absolute value of the difference between the maximum value and the minimum value as a new value of the selected point;
step 3.6.4: and repeating the step 3.6.2 and the step 3.6.3 until all pixel points in the selected area are traversed, and obtaining the gradient value image.
Step 3.7: setting a threshold T2Calculating the gradient mean value of the selected area image obtained in the step 3.6, namely summing all elements in the area gradient image and dividing the sum by the total number of the elements to obtain the gradient mean value of the selected area, if the sum is not equal to the total number of the elements, calculating the gradient mean value of the selected areaThe value is greater than T2That is, the region is considered to have uneven light reflection characteristics, and is determined as a lubricating oil surface, otherwise, the region is determined as a metal surface, and the region determined as the metal surface is determined as a suspected oil-deficient region.
And 4, step 4: analyzing the pure tooth surface thermal image in the step 2 to obtain characteristic data, wherein the process is as follows:
step 4.1: calculating a thermal image mean value I according to the pure tooth surface thermal image pixel values obtained in the step 21I.e. the sum of all pixel values divided by the total number of pixels;
step 4.2: marking and extracting corresponding areas in the thermal image according to the area coordinates marked in the step 3.5, and calculating the thermal image mean value I of the extracted areas2Calculating the heat difference I ═ I2-I1；
Step 4.3: taking a suspected oil-starved area, namely a metal visible area, as an approximate contact surface, and calculating the weighted heat difference E of the contact area to be I M, wherein M is the area of the area, and the numerical value is the number of pixel points in the area;
and 5: and judging the oil shortage degree of the relevant area according to the preset logic judgment step according to the thermal image characteristic data corresponding to the suspected oil shortage area. In this embodiment, the logical determination of the degree of oil starvation is as follows:
step 5.1: from the beginning of the first operation, storing each monitored suspected oil shortage area according to the gear number and the area position, and setting an oil shortage index K with an initial value of 0 for each area;
step 5.2: setting a high threshold Th, a medium threshold Tm and a low threshold Tl of the heat difference, and comparing the relation between the weighted heat difference E obtained in the step 4.3 and the set threshold;
step 5.3: setting the oil shortage scores Kh, Km and Kl, and updating the oil shortage index K value of the area according to the relationship between the weighted heat difference E and the set high threshold Th, medium threshold Tm and low threshold Tl of the heat difference.
The default score Kh, Km, Kl is set mainly for distinguishing the strength of the temperature change, and in principle Kh > Km > Kl, for example, Kh 20, Km 6, Kl 1, but not limited thereto.
Step 5.4: and updating the oil shortage index K value during each operation, and judging the oil shortage degree according to the size of the oil shortage index K value. K-value calculation as shown in fig. 2, the K-value of the oiled area is initialized to 0 after the oil is reapplied.
Step 6: and obtaining the position of the oil-deficient area and the grade of the oil-deficient degree, and taking corresponding measures. The grade of the oil shortage degree is divided into: severe oil deficiency, moderate oil deficiency, mild oil deficiency, and no oil deficiency. Giving corresponding prompts according to different oil shortage grades: the detection area is in a severe oil shortage grade, and an oil supplement prompt is given; the detection area is in a medium oil shortage grade, and an early warning prompt is given; the detection area is in a mild oil shortage grade, and an early warning prompt is given; the detection area is in the grade of no oil shortage and shows normal.
The implementation result shows that the gear lubrication state monitoring method can accurately detect the oil shortage degree of the large open gear of the ship lock, is time-saving and labor-saving, is convenient for managers to make an oil supplementing plan, prevents severe oil shortage, avoids the damage of the gear, and reduces the risk of unplanned shutdown and damage of ship lock equipment.
The method for monitoring the lubrication state of the gear is also suitable for monitoring the lubrication state of large gears except ship locks.
Claims (7)
1. The method for monitoring the lubrication state of the large-scale open gear of the ship lock hoist is characterized by comprising the following steps of:
step 1: acquiring an RGB (red, green and blue) image and a thermography of a gear tooth surface by using an infrared thermography camera when the gear runs;
step 2: dividing the acquired tooth surface RGB image and the acquired thermal image to obtain a complete and single pure tooth surface RGB image and a pure tooth surface thermal image;
and step 3: screening out suspected oil-starved areas from the pure tooth surface RGB image in the step 2;
and 4, step 4: processing and analyzing the pure tooth surface thermal image in the step 2 to obtain heat distribution characteristic data;
and 5: and judging the oil shortage degree of the related area according to the heat distribution characteristic data of the thermal image corresponding to the suspected oil shortage area.
2. The method for monitoring the lubrication state of the large-scale open gear of the ship lock hoist according to claim 1, wherein in the step 3, the suspected oil shortage area is screened out, and the method comprises the following specific steps:
step 3.1: graying the pure tooth surface RGB image obtained in the step 2 to obtain a pure tooth surface gray image;
step 3.2: dividing the pure tooth surface gray level image by using a maximum inter-class variance method adaptive threshold to obtain a binary image;
step 3.3: carrying out corrosion operation on the binary image, then carrying out expansion operation to remove image noise, and filling holes to obtain a complete black-white connected domain;
step 3.4: setting a threshold T1Searching all white connected domains in the image, and reducing pixel points to be smaller than T1Removing the connected domain, and leaving a white connected domain with a larger area;
step 3.5: marking a corresponding area in the gray-scale image according to the position coordinate of the white connected domain with a larger area in the step 3.4; step 3.6: calculating a gradient value image of the marking area in the step 3.5;
step 3.7: setting a threshold T2Calculating the gradient mean value of the selected area image obtained in the step 3.6, namely summing all elements in the area gradient image and dividing the sum by the total number of the elements to obtain the gradient mean value of the selected area, if the value is more than T2That is, the region is considered to have uneven light reflection characteristics, and is determined as a lubricating oil surface, otherwise, the region is determined as a metal surface, and the region determined as the metal surface is determined as a suspected oil-deficient region.
3. The method for monitoring the lubrication state of the large-scale open gear of the ship lock hoist according to claim 2, wherein the step 4 comprises the following substeps:
step 4.1: calculating the mean value of the thermal image according to the pure tooth surface thermal image pixel values obtained in the step 2I1I.e. the sum of all pixel values divided by the total number of pixels;
step 4.2: marking and extracting corresponding areas in the thermal image according to the area coordinates marked in the step 3.5, and calculating the thermal image mean value I of the extracted areas2Calculating the heat difference I ═ I2-I1；
Step 4.3: and taking the suspected oil-starved area as an approximate contact surface, and calculating the weighted heat difference E of the contact area to be I M, wherein M is the area of the area, and the numerical value is the number of pixel points in the area.
4. The method for monitoring the lubrication state of the large-scale open gear of the ship lock hoist according to claim 3, wherein in the step 5, the logic determination step for determining the oil shortage degree comprises the following steps:
step 5.1: from the beginning of the first operation, storing each monitored suspected oil shortage area according to the gear number and the area position, and setting an oil shortage index K with an initial value of 0 for each area;
step 5.2: setting a high threshold Th, a medium threshold Tm and a low threshold Tl of the heat difference, and comparing the relation between the weighted heat difference E obtained in the step 4.3 and the set threshold;
step 5.3: setting the values Kh, Km and Kl of the oil shortage, and updating the value of the oil shortage index K of the area according to the relationship between the weighted heat difference E and the set high threshold Th, medium threshold Tm and low threshold Tl of the heat difference; k value is calculated as follows
Step 5.4: and updating the oil shortage index K value during each operation, and judging the oil shortage degree according to the size of the oil shortage index K value.
5. The method for monitoring the lubrication state of the large-scale open gear of the ship lock hoist according to claim 4, wherein in step 5.4, the oil shortage index K value is updated, and after the gear is replenished with lubricating oil, the K value of a lubricating oil coating area is reset to 0.
6. The method for monitoring the lubrication state of the large-scale open gear of the ship lock hoist according to claim 1, characterized by further comprising the step 6: and (5) determining the position of the oil shortage region and the grade of the oil shortage degree according to the oil shortage degree of the relevant region judged in the step (5), and giving corresponding prompts according to different oil shortage grades.
7. The method for monitoring the lubrication state of the large-scale open gear of the ship lock hoist according to claim 6, characterized in that: and step 6, grading the oil shortage degree into: severe oil deficiency, moderate oil deficiency, mild oil deficiency, no oil deficiency; giving corresponding prompts according to different oil shortage grades: the detection area is in a severe oil shortage grade, and an oil supplement prompt is given; the detection area is in a medium oil shortage grade, and an early warning prompt is given; the detection area is in a mild oil shortage grade, and an early warning prompt is given; the detection area is in the grade of no oil shortage and shows normal.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010790805.6A CN112115776B (en) | 2020-08-07 | 2020-08-07 | Method for monitoring lubrication state of large open gear of ship lock hoist |
| CN202311854658.4A CN117893491A (en) | 2020-08-07 | 2020-08-07 | Ship lock large gear tooth surface oil shortage degree judging method based on oil shortage index |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010790805.6A CN112115776B (en) | 2020-08-07 | 2020-08-07 | Method for monitoring lubrication state of large open gear of ship lock hoist |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311854658.4A Division CN117893491A (en) | 2020-08-07 | 2020-08-07 | Ship lock large gear tooth surface oil shortage degree judging method based on oil shortage index |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112115776A true CN112115776A (en) | 2020-12-22 |
| CN112115776B CN112115776B (en) | 2024-03-12 |
Family
ID=73803697
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311854658.4A Pending CN117893491A (en) | 2020-08-07 | 2020-08-07 | Ship lock large gear tooth surface oil shortage degree judging method based on oil shortage index |
| CN202010790805.6A Active CN112115776B (en) | 2020-08-07 | 2020-08-07 | Method for monitoring lubrication state of large open gear of ship lock hoist |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311854658.4A Pending CN117893491A (en) | 2020-08-07 | 2020-08-07 | Ship lock large gear tooth surface oil shortage degree judging method based on oil shortage index |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN117893491A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8755597B1 (en) * | 2011-02-24 | 2014-06-17 | Exelis, Inc. | Smart fusion of visible and infrared image data |
| KR101785202B1 (en) * | 2016-04-29 | 2017-10-13 | 한국과학기술원 | Automatic Calibration for RGB-D and Thermal Sensor Fusion and Method thereof |
| CN107729907A (en) * | 2016-08-12 | 2018-02-23 | 南京理工大学 | A kind of fault recognition method based on infra-red thermal imaging system |
| CN110261436A (en) * | 2019-06-13 | 2019-09-20 | 暨南大学 | Rail deformation detection method and system based on infrared thermal imaging and computer vision |
| CN110334750A (en) * | 2019-06-21 | 2019-10-15 | 西安工程大学 | Iron tower of power transmission line bolt corrosion degree image classification recognition methods |
-
2020
- 2020-08-07 CN CN202311854658.4A patent/CN117893491A/en active Pending
- 2020-08-07 CN CN202010790805.6A patent/CN112115776B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8755597B1 (en) * | 2011-02-24 | 2014-06-17 | Exelis, Inc. | Smart fusion of visible and infrared image data |
| KR101785202B1 (en) * | 2016-04-29 | 2017-10-13 | 한국과학기술원 | Automatic Calibration for RGB-D and Thermal Sensor Fusion and Method thereof |
| CN107729907A (en) * | 2016-08-12 | 2018-02-23 | 南京理工大学 | A kind of fault recognition method based on infra-red thermal imaging system |
| CN110261436A (en) * | 2019-06-13 | 2019-09-20 | 暨南大学 | Rail deformation detection method and system based on infrared thermal imaging and computer vision |
| CN110334750A (en) * | 2019-06-21 | 2019-10-15 | 西安工程大学 | Iron tower of power transmission line bolt corrosion degree image classification recognition methods |
Non-Patent Citations (1)
| Title |
|---|
| 杨亚;陶红艳;余成波;: "SURF与灰度差分在小模数塑料齿轮缺陷检测中的研究与应用", 机械传动, no. 05 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117893491A (en) | 2024-04-16 |
| CN112115776B (en) | 2024-03-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Raadnui | Wear particle analysis—utilization of quantitative computer image analysis: a review | |
| CN108921839A (en) | Continuous casting billet quality detection method, device, electronic equipment and storage medium | |
| CN106683099A (en) | Product surface defect detection method | |
| CN115409741B (en) | Machine vision identification method for measuring sediment content by using river surface color difference | |
| CN102622763A (en) | Method for detecting and eliminating shadow | |
| Tsai et al. | Morphology-based defect detection in machined surfaces with circular tool-mark patterns | |
| CN108305259B (en) | Multi-texture feature fusion type automatic abrasive particle type identification method | |
| Ichi et al. | Effectiveness of infrared thermography for delamination detection in reinforced concrete bridge decks | |
| CN116452598B (en) | Axle production quality rapid detection method and system based on computer vision | |
| JP2010139317A (en) | Method and device for inspecting defect on surface of shaft-like tool | |
| CN112330593A (en) | Building surface crack detection method based on deep learning network | |
| CN111028229A (en) | Metal or coating corrosion detection method based on image processing technology | |
| CN110136210A (en) | A kind of intelligent early-warning system of monitoring device leakage | |
| CN111192273A (en) | Digital shot blasting coverage rate measuring method based on computer vision technology | |
| Ivasenko et al. | Detection of rust defects of protective coatings based on HSV color model | |
| CN115689533A (en) | Intelligent operation and maintenance system data management method for waste gas monitoring and treatment | |
| CN112115776A (en) | Method for monitoring lubrication state of large open gear of ship lock hoist | |
| CN116883408B (en) | Integrating instrument shell defect detection method based on artificial intelligence | |
| Lee | Color image-based defect detection method and steel bridge coating | |
| CN112232307B (en) | Method for detecting wearing of safety helmet in night vision environment | |
| Prasetio et al. | Design of Defect Classification on Clay Tiles using Support Vector Machine (SVM) | |
| CN116710763A (en) | Image analysis device, image analysis method, and program | |
| CN114820383A (en) | Pollution grade evaluation method based on insulator visible light image | |
| CN112808782A (en) | Automatic detection method for rust removal grade of hot rolled plate | |
| Lee et al. | Digital image processing methods for assessing bridge painting rust defects and their limitations |
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 |