CN113160909A - Method for rapidly identifying metal corrosion failure - Google Patents
Method for rapidly identifying metal corrosion failure Download PDFInfo
- Publication number
- CN113160909A CN113160909A CN202110485476.9A CN202110485476A CN113160909A CN 113160909 A CN113160909 A CN 113160909A CN 202110485476 A CN202110485476 A CN 202110485476A CN 113160909 A CN113160909 A CN 113160909A
- Authority
- CN
- China
- Prior art keywords
- metal corrosion
- metal
- corrosion
- data
- library
- 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.)
- Pending
Links
- 239000002184 metal Substances 0.000 title claims abstract description 155
- 229910052751 metal Inorganic materials 0.000 title claims abstract description 155
- 230000007797 corrosion Effects 0.000 title claims abstract description 143
- 238000005260 corrosion Methods 0.000 title claims abstract description 143
- 238000000034 method Methods 0.000 title claims abstract description 14
- 238000003384 imaging method Methods 0.000 claims abstract description 7
- 230000036314 physical performance Effects 0.000 claims abstract description 5
- 238000012360 testing method Methods 0.000 claims abstract description 5
- 239000000463 material Substances 0.000 claims description 12
- 230000000704 physical effect Effects 0.000 claims description 8
- 230000000717 retained effect Effects 0.000 claims description 5
- 238000002372 labelling Methods 0.000 claims description 3
- 150000002739 metals Chemical class 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 abstract description 2
- 238000005070 sampling Methods 0.000 abstract description 2
- 239000006185 dispersion Substances 0.000 abstract 1
- 238000005259 measurement Methods 0.000 abstract 1
- 238000002474 experimental method Methods 0.000 description 2
- 239000007769 metal material Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 150000003839 salts Chemical class 0.000 description 1
- 239000007921 spray Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/90—Programming languages; Computing architectures; Database systems; Data warehousing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Computing Systems (AREA)
- Bioinformatics & Computational Biology (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Chemical & Material Sciences (AREA)
- Crystallography & Structural Chemistry (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Computational Linguistics (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Testing Resistance To Weather, Investigating Materials By Mechanical Methods (AREA)
Abstract
The invention discloses a method for rapidly identifying metal corrosion failure, which adopts a series of physical performance parameters of an ultrasonic imaging picture, resistance, tensile property, fatigue resistance and hardness, a digital corrosion state picture, metal corrosion texture, metal corrosion color, metal corrosion shape, metal corrosion total amount and metal surface damage condition as reference data, constructs a metal corrosion data rapid search library and obtains the grade of corrosion grade after fuzzification reasoning of a reasoning machine, directly introduces each parameter of a metal piece to be detected into the metal corrosion data rapid search library after measurement and compares the parameters to obtain the corresponding corrosion grade, and has the advantages that universal data are obtained through a large number of dispersion tests, the metal corrosion data rapid search library is constructed on the basis of the universal data, and rapid sampling detection and data output of a single metal piece to be detected can be realized, the analysis efficiency is improved.
Description
Technical Field
The invention relates to the field of industrial analysis, in particular to a method for rapidly identifying metal corrosion failure.
Background
In the domestic metal corrosion experiment, manual grading is generally adopted, and on one hand, the problems of low control precision, unstable grading, no provision of related image analysis materials and the like exist; on the other hand, the quality grade of the corrosion resistance of the batch of metal needs to be manually analyzed and provided according to the current national standard related to the metal corrosion resistance rating, and the information such as the metal corrosion image after the salt spray experiment of the metal corrosion is carried out, and the experience and the expectation of the user.
In addition, in an actual usage scenario of a metal product, metal is generally processed into a workpiece and becomes a component of a certain mechanical device or a mechanical system, and the whole machine necessarily contains multiple metal materials, so that it is impossible to sample and grade and evaluate the metal material with data of a single metal component, and it is necessary to integrally characterize a target machine and find out a corrosion site of the metal and grade and evaluate a corrosion state of the corrosion site without disassembling the whole machine.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a method for rapidly identifying metal corrosion failure aiming at the defects of the prior art.
The technical scheme is as follows: the invention relates to a method for rapidly identifying metal corrosion failure, which comprises the following steps:
s1, establishing a material library of industrial metals, establishing a metal corrosion state and parameter comparison model according to categories contained in the material library, setting a plurality of corrosion states according to general experience aiming at each metal category in the material library, retaining a photo for each corrosion state, testing an ultrasonic imaging graph, a resistance value, a tensile property, fatigue resistance and hardness of the photo, and establishing a physical property model corresponding to each corrosion state;
s2, digitally storing the retained photos of each corrosion state, measuring and calculating the metal corrosion texture, the metal corrosion color, the metal corrosion shape, the metal corrosion total amount and the metal surface damage condition of the retained photos, recording the data into a database, corresponding the data to the digitized photos one by one, constructing a metal corrosion data rapid search library, and introducing the physical performance model in the S1 into the metal corrosion data rapid search library to match and correspond to the metal corrosion data rapid search library;
s3, introducing an inference machine, introducing the metal corrosion data rapid search library into the inference machine, carrying out fuzzification inference, and establishing a corrosion grade in the metal corrosion data rapid search library after the inference is finished;
s4, measuring the ultrasonic imaging graph, the resistance value, the tensile property, the fatigue resistance and the hardness of the metal piece to be measured, guiding the measured physical property model into an inference machine by taking the metal category as a label, guiding the metal corrosion data into a metal corrosion data rapid retrieval library for retrieval after fuzzy labeling treatment by the inference machine, and obtaining the most similar corrosion grade;
s5, measuring the metal corrosion texture, the metal corrosion color, the metal corrosion shape, the metal corrosion total amount and the metal surface damage condition of the metal piece to be detected, simultaneously, exporting a plurality of sample parameters corresponding to the corrosion grade in S4 in the metal corrosion data rapid search library, comparing the sample parameters with the metal corrosion texture, the metal corrosion color, the metal corrosion shape, the metal corrosion total amount and the metal surface damage condition of the metal piece to be detected, simultaneously comparing the digital photo of the sample with the photo of the metal piece to be detected, and selecting the most similar group, thereby determining the detailed corrosion grade.
Preferably, when the metal corrosion data fast search library is searched in S4, the difference between the parameter item data of at least 3/5 in the physical property model is within 5%, the difference between the remaining parameter items is within 20%, the similar items can be identified, the searched similar items are sorted according to the total difference ratio from small to large, and three samples with the minimum total difference are selected as the output items of the search operation.
Preferably, S5 inherits the output items in S4 and performs a second comparison, and selects the output item with the smallest data difference as the final search result to output, after comparing five parameter items of the metal corrosion texture, the metal corrosion color, the metal corrosion shape, the total metal corrosion amount, and the metal surface damage condition.
Preferably, in S2, the data of five parameters, i.e., texture of metal corrosion, color of metal corrosion, shape of metal corrosion, total amount of metal corrosion, and damage of metal surface, are digitized and marked on the digitized picture and output in S5 as a whole.
Preferably, in the fuzzification inference in S3, five adjacent groups of data are preliminarily grouped and secondarily compared, and if the difference between the five groups is less than 5%, the data can be classified into the same corrosion level, and if the difference between the five groups is greater than 5%, the data is divided into nodes and secondarily grouped.
Compared with the prior art, the invention has the following beneficial effects: before the database is constructed, universal data is obtained through a large number of scattered tests, and a metal corrosion data rapid search library is constructed on the basis of the universal data, so that rapid sampling detection and data output of a single metal piece to be detected can be realized, and the analysis efficiency is improved.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "axial," "radial," "circumferential," and the like are used in the indicated orientations and positional relationships based on the illustrated orientation or positional relationship, merely to facilitate description of the invention and to simplify description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are not to be construed as limiting the present invention.
In the present invention, unless otherwise specifically stated or limited, the terms "mounted," "connected," "fixed," and the like are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally formed; the connection can be mechanical connection, electrical connection or communication connection; either directly or indirectly through intervening media, either internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood according to specific situations by those of ordinary skill in the art.
The technical solution of the present invention will be described in detail below with specific examples. These particular embodiments may be combined with each other below, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Example (b): a method for rapidly identifying metal corrosion failure comprises the following steps:
s1, establishing a material library of industrial metals, establishing a metal corrosion state and parameter comparison model according to categories contained in the material library, setting a plurality of corrosion states according to general experience aiming at each metal category in the material library, retaining a photo for each corrosion state, testing an ultrasonic imaging graph, a resistance value, a tensile property, fatigue resistance and hardness of the photo, and establishing a physical property model corresponding to each corrosion state;
s2, digitally storing each photo retained in the corrosion state, calculating the metal corrosion texture, the metal corrosion color, the metal corrosion shape, the metal corrosion total amount and the metal surface damage condition of the photo, recording the data into a database, corresponding the data to the digital photo one by one, constructing a metal corrosion data rapid search library, introducing the physical performance model in S1 into the metal corrosion data rapid search library and matching and corresponding, digitalizing the data of the five parameters of the metal corrosion texture, the metal corrosion color, the metal corrosion shape, the metal corrosion total amount and the metal surface damage condition, marking the data in the digital photo and integrally outputting the digital photo in S5 after the correspondence is completed;
s3, introducing an inference machine, importing the metal corrosion data rapid search library into the inference machine, carrying out fuzzification inference, establishing corrosion levels in the metal corrosion data rapid search library after the inference is finished, carrying out fuzzification inference, primarily grouping and carrying out secondary comparison on five adjacent groups of data, if the difference value between the five groups of data is less than 5%, attributing to the same corrosion level, and if the difference value between the five groups of data is more than 5%, segmenting the data by taking the data as a node and carrying out secondary grouping;
s4, measuring an ultrasonic imaging graph, a resistance value, a tensile property, fatigue resistance and hardness of a metal piece to be detected, guiding the measured physical property model into a reasoning machine by taking a metal category as a label, guiding into a metal corrosion data quick retrieval library for retrieval after fuzzy labeling processing is carried out by the reasoning machine, when the metal corrosion data quick retrieval library is retrieved, sorting the retrieved similar items according to a total difference ratio from small to large, selecting three samples with the minimum total difference as output items of the retrieval action at this time, and obtaining the most similar corrosion grade, wherein the parameter item data difference of at least 3/5 in the physical property model is within 5 percent, and the residual parameter item difference is within 20 percent;
s5, measuring the metal corrosion texture, the metal corrosion color, the metal corrosion shape, the metal corrosion total amount and the metal surface damage condition of the metal piece to be detected, simultaneously inheriting the output item in S4 and carrying out secondary comparison, comparing the five parameter items of the metal corrosion texture, the metal corrosion color, the metal corrosion shape, the metal corrosion total amount and the metal surface damage condition, then selecting the output item with the minimum data difference value as a final retrieval result to be output, comparing the output item with the metal corrosion texture, the metal corrosion color, the metal corrosion shape, the metal corrosion total amount and the metal surface damage condition of the metal piece to be detected, simultaneously comparing the digital photo of the sample with the photo of the metal piece to be detected, and selecting the most similar group, thereby determining the detailed corrosion grade.
In the present invention, unless otherwise explicitly specified or limited, the first feature "on" or "under" the second feature may be directly contacting the first feature and the second feature or indirectly contacting the first feature and the second feature through an intermediate. Also, a first feature "on," "above," and "above" a second feature may be directly or obliquely above the second feature, or may simply mean that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lower level than the second feature. In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example" or "some examples," or the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example.
Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, those skilled in the art will appreciate that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. A method for rapidly identifying metal corrosion failure is characterized in that: the method comprises the following steps:
s1, establishing a material library of industrial metals, establishing a metal corrosion state and parameter comparison model according to categories contained in the material library, setting a plurality of corrosion states according to general experience aiming at each metal category in the material library, retaining a photo for each corrosion state, testing an ultrasonic imaging graph, a resistance value, a tensile property, fatigue resistance and hardness of the photo, and establishing a physical property model corresponding to each corrosion state;
s2, digitally storing the retained photos of each corrosion state, measuring and calculating the metal corrosion texture, the metal corrosion color, the metal corrosion shape, the metal corrosion total amount and the metal surface damage condition of the retained photos, recording the data into a database, corresponding the data to the digitized photos one by one, constructing a metal corrosion data rapid retrieval library, and introducing the physical performance model in S1 into the metal corrosion data rapid retrieval library and matching and corresponding the data;
s3, introducing an inference machine, introducing the metal corrosion data rapid search library into the inference machine, carrying out fuzzification inference, and establishing a corrosion grade in the metal corrosion data rapid search library after the inference is finished;
s4, measuring the ultrasonic imaging graph, the resistance value, the tensile property, the fatigue resistance and the hardness of the metal piece to be measured, guiding the measured physical property model into an inference machine by taking the metal category as a label, guiding the metal corrosion data into a metal corrosion data rapid search library for searching after fuzzy labeling treatment by the inference machine, and obtaining the most similar corrosion grade;
s5, measuring the metal corrosion texture, the metal corrosion color, the metal corrosion shape, the metal corrosion total amount and the metal surface damage condition of the metal piece to be detected, simultaneously, exporting a plurality of sample parameters corresponding to the corrosion grade in S4 in the metal corrosion data rapid search library, comparing the sample parameters with the metal corrosion texture, the metal corrosion color, the metal corrosion shape, the metal corrosion total amount and the metal surface damage condition of the metal piece to be detected, simultaneously comparing the digital photo of the sample with the photo of the metal piece to be detected, and selecting the most similar group, thereby determining the detailed corrosion grade.
2. The method for rapidly identifying metal corrosion failure according to claim 1, wherein: when the metal corrosion data rapid search library is searched in the S4, the difference value of at least 3/5 parameter item data in the physical performance model is within 5%, the difference value of the residual parameter items is within 20%, the residual parameter items can be regarded as similar items, the similar items are sorted from small to large according to the total difference ratio, and three samples with the minimum total difference value are selected as output items of the search action.
3. The method for rapidly identifying metal corrosion failure according to claim 2, wherein: and S5 inherits the output item in S4 and carries out secondary comparison, and after comparing five parameter items including metal corrosion texture, metal corrosion color, metal corrosion shape, metal corrosion total amount and metal surface damage condition, the output item with the minimum data difference is selected as a final retrieval result to be output.
4. The method for rapidly identifying metal corrosion failure according to claim 1, wherein: in S2, the data of five parameters, i.e., the texture of the metal corrosion, the color of the metal corrosion, the shape of the metal corrosion, the total amount of the metal corrosion, and the damage on the metal surface, are digitized and marked on the digitized picture, and the digitized picture is output as a whole in S5.
5. The method for rapidly identifying metal corrosion failure according to claim 1, wherein: when fuzzification reasoning is performed in S3, five groups of data that are adjacent to each other are preliminarily grouped and secondarily compared, and if the difference between the five groups of data is less than 5%, the data can be classified as the same corrosion level, and if the difference between the five groups of data is greater than 5%, the data is used as a node to segment the data and secondarily grouped.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110485476.9A CN113160909A (en) | 2021-04-30 | 2021-04-30 | Method for rapidly identifying metal corrosion failure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110485476.9A CN113160909A (en) | 2021-04-30 | 2021-04-30 | Method for rapidly identifying metal corrosion failure |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113160909A true CN113160909A (en) | 2021-07-23 |
Family
ID=76873113
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110485476.9A Pending CN113160909A (en) | 2021-04-30 | 2021-04-30 | Method for rapidly identifying metal corrosion failure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113160909A (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2917519A1 (en) * | 2006-09-27 | 2008-12-19 | Peugeot Citroen Automobiles Sa | Metallic surface corrosion estimating method for motor vehicle, involves offering thickness value of thin film electrolyte as input parameter for modeling corrosion according to time and place on metallic surface |
| JP2009293993A (en) * | 2008-06-03 | 2009-12-17 | Central Res Inst Of Electric Power Ind | Method and apparatus for evaluating degree of corrosion and program |
| CN103674968A (en) * | 2013-12-20 | 2014-03-26 | 纪钢 | Method and device for evaluating machine vision original-value detection of exterior corrosion appearance characteristics of material |
| CN107067078A (en) * | 2017-05-31 | 2017-08-18 | 江西制造职业技术学院 | A kind of metal corrosion test sample and test specimen automatic measure grading equipment and its ranking method |
| US20180365555A1 (en) * | 2016-12-22 | 2018-12-20 | Naveed Aslam | Artificial intelligence based algorithm for predicting pipeline leak and corrosion detection |
| FR3074904A1 (en) * | 2017-12-13 | 2019-06-14 | Psa Automobiles Sa | DETERMINING THE EXTENT OF CORROSION AREAS OF A METAL SAMPLE |
| CN110009632A (en) * | 2019-04-17 | 2019-07-12 | 国网重庆市电力公司电力科学研究院 | A kind of appraisal procedure, device and the equipment of metal erosion state |
| US20190320875A1 (en) * | 2016-06-17 | 2019-10-24 | Ucl Business Plc | Method And Apparatus For Estimating The Value Of A Physical Parameter In A Biological Tissue |
| CN111413348A (en) * | 2020-05-09 | 2020-07-14 | 苏州华碧微科检测技术有限公司 | Method for rapidly identifying failure of metal component |
| CN111815576A (en) * | 2020-06-23 | 2020-10-23 | 深圳供电局有限公司 | Method, device, equipment and storage medium for detecting corrosion condition of metal part |
| CN111949815A (en) * | 2020-08-17 | 2020-11-17 | 西南石油大学 | Artificial intelligent corrosion material selection system based on image recognition technology and working method |
| CN112413413A (en) * | 2020-11-20 | 2021-02-26 | 成都艾斯皮埃尔科技有限公司 | Pipeline leakage monitoring and positioning method combining deep learning and multiple measurement technology |
-
2021
- 2021-04-30 CN CN202110485476.9A patent/CN113160909A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2917519A1 (en) * | 2006-09-27 | 2008-12-19 | Peugeot Citroen Automobiles Sa | Metallic surface corrosion estimating method for motor vehicle, involves offering thickness value of thin film electrolyte as input parameter for modeling corrosion according to time and place on metallic surface |
| JP2009293993A (en) * | 2008-06-03 | 2009-12-17 | Central Res Inst Of Electric Power Ind | Method and apparatus for evaluating degree of corrosion and program |
| CN103674968A (en) * | 2013-12-20 | 2014-03-26 | 纪钢 | Method and device for evaluating machine vision original-value detection of exterior corrosion appearance characteristics of material |
| US20190320875A1 (en) * | 2016-06-17 | 2019-10-24 | Ucl Business Plc | Method And Apparatus For Estimating The Value Of A Physical Parameter In A Biological Tissue |
| US20180365555A1 (en) * | 2016-12-22 | 2018-12-20 | Naveed Aslam | Artificial intelligence based algorithm for predicting pipeline leak and corrosion detection |
| CN107067078A (en) * | 2017-05-31 | 2017-08-18 | 江西制造职业技术学院 | A kind of metal corrosion test sample and test specimen automatic measure grading equipment and its ranking method |
| FR3074904A1 (en) * | 2017-12-13 | 2019-06-14 | Psa Automobiles Sa | DETERMINING THE EXTENT OF CORROSION AREAS OF A METAL SAMPLE |
| CN110009632A (en) * | 2019-04-17 | 2019-07-12 | 国网重庆市电力公司电力科学研究院 | A kind of appraisal procedure, device and the equipment of metal erosion state |
| CN111413348A (en) * | 2020-05-09 | 2020-07-14 | 苏州华碧微科检测技术有限公司 | Method for rapidly identifying failure of metal component |
| CN111815576A (en) * | 2020-06-23 | 2020-10-23 | 深圳供电局有限公司 | Method, device, equipment and storage medium for detecting corrosion condition of metal part |
| CN111949815A (en) * | 2020-08-17 | 2020-11-17 | 西南石油大学 | Artificial intelligent corrosion material selection system based on image recognition technology and working method |
| CN112413413A (en) * | 2020-11-20 | 2021-02-26 | 成都艾斯皮埃尔科技有限公司 | Pipeline leakage monitoring and positioning method combining deep learning and multiple measurement technology |
Non-Patent Citations (2)
| Title |
|---|
| YEONG HO HO ET AL.: "Development of an Equipment Failure Identification Expert System with Multiple Reasoning Approaches", 《APPLIED MECHANICS AND MATERIALS》, vol. 479, pages 1001 - 1005 * |
| 费逸伟 等: "金属腐蚀图像数字处理技术发展综述", 《数字技术与应用》, no. 11, pages 45 - 46 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| TWI691914B (en) | Method for automated classification, and apparatus, system and computer-readable medium thereof | |
| CN109816031A (en) | A kind of Transformer State Assessment clustering method based on the unbalanced measurement of data | |
| CN109409425B (en) | Fault type identification method based on neighbor component analysis | |
| CN112257963A (en) | Defect prediction method and device based on aerospace software defect data distribution outlier | |
| CN116975771B (en) | Automatic abnormality identification method and system for motor production | |
| CN107168995A (en) | A kind of data processing method and server | |
| CN110503288A (en) | Consider the System and method for of the interactive identification yield loss reason of board | |
| CN108470194B (en) | Feature screening method and device | |
| Tello et al. | Automatic flower number evaluation in grapevine inflorescences using RGB images | |
| CN117274864A (en) | Industrial Internet of things based on anomaly identification and control method | |
| CN111126865B (en) | Technology maturity judging method and system based on technology big data | |
| CN113406502B (en) | Defective battery screening method, apparatus and medium based on machine learning | |
| CN108380527B (en) | Battery batch sorting system and method | |
| CN108537249B (en) | Industrial process data clustering method for density peak clustering | |
| CN113160909A (en) | Method for rapidly identifying metal corrosion failure | |
| CN116881530B (en) | Device surface defect detection system based on deep learning | |
| CN113011256A (en) | Cross-category fault diagnosis method and system based on small sample learning and storage medium | |
| CN115937195A (en) | Surface defect detection method and device and computer equipment | |
| CN113298148B (en) | Ecological environment evaluation-oriented unbalanced data resampling method | |
| US20230325413A1 (en) | Error Factor Estimation Device and Error Factor Estimation Method | |
| CN114819768A (en) | Condenser lead-out wire welding control system | |
| CN113448840A (en) | Software quality evaluation method based on predicted defect rate and fuzzy comprehensive evaluation model | |
| CN117523324B (en) | Image processing method and image sample classification method, device and storage medium | |
| CN110490417A (en) | A kind of sampling observation method, storage medium and terminal device controlling food quality | |
| CN118229155A (en) | Mobile phone motherboard function test management method and system |
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 |