CN118688204A - A remote real-time nondestructive flaw detection monitoring method for cables - Google Patents
A remote real-time nondestructive flaw detection monitoring method for cables Download PDFInfo
- Publication number
- CN118688204A CN118688204A CN202410723373.5A CN202410723373A CN118688204A CN 118688204 A CN118688204 A CN 118688204A CN 202410723373 A CN202410723373 A CN 202410723373A CN 118688204 A CN118688204 A CN 118688204A
- Authority
- CN
- China
- Prior art keywords
- cable
- damage
- time
- monitoring
- real
- 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
- 238000012544 monitoring process Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 19
- 238000001514 detection method Methods 0.000 title claims abstract description 17
- 230000005540 biological transmission Effects 0.000 claims abstract description 4
- 238000011156 evaluation Methods 0.000 claims description 7
- 238000009659 non-destructive testing Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 3
- 230000000877 morphologic effect Effects 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000013135 deep learning Methods 0.000 claims description 2
- 230000000007 visual effect Effects 0.000 abstract 1
- 238000010276 construction Methods 0.000 description 3
- 101100012902 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) FIG2 gene Proteins 0.000 description 2
- 101100233916 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) KAR5 gene Proteins 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 241001391944 Commicarpus scandens Species 0.000 description 1
- 101001121408 Homo sapiens L-amino-acid oxidase Proteins 0.000 description 1
- 101000827703 Homo sapiens Polyphosphoinositide phosphatase Proteins 0.000 description 1
- 102100026388 L-amino-acid oxidase Human genes 0.000 description 1
- 102100023591 Polyphosphoinositide phosphatase Human genes 0.000 description 1
- 229910000831 Steel Inorganic materials 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
- G06T5/30—Erosion or dilatation, e.g. thinning
-
- 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
- G06T7/10—Segmentation; Edge detection
-
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
-
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
-
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Geometry (AREA)
- Quality & Reliability (AREA)
- Signal Processing (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
Description
技术领域Technical Field
本发明提供一种针对缆索的远程实时无损探伤监测方法，属于工程监测领域。The invention provides a remote real-time nondestructive flaw detection monitoring method for cables, belonging to the field of engineering monitoring.
背景技术Background Art
缆索作为水电工程中施工中运输材料的关键零部件，其由高强度的钢丝编绕制成，具有抗拉强度高，自重轻，柔韧性能好等特点。虽然缆索不易骤然整根折断，但是在西南山区水电工程开展的恶劣工况下长期工作，会出现各种损伤，如断丝、磨损、锈蚀、变形等(附图1)。并且，钢丝绳损伤还会随着工作时间的增加而日益严重，引发工程事故造成无法挽回的经济损失，甚至威胁施工人员生命安全。目前缆索无损探伤主要采用电磁学的方法以及人工监测方法，其存在以下问题：Cables are key components for transporting materials during construction of hydropower projects. They are made of high-strength steel wires and have the characteristics of high tensile strength, light weight, and good flexibility. Although cables are not easy to break suddenly, they will suffer various damages such as broken wires, wear, rust, and deformation after working for a long time under the harsh working conditions of hydropower projects in the southwestern mountainous areas (Figure 1). In addition, the damage to the wire rope will become increasingly serious as the working time increases, causing engineering accidents that cause irreparable economic losses and even threatening the lives of construction workers. At present, non-destructive testing of cables mainly uses electromagnetic methods and manual monitoring methods, which have the following problems:
(1)人工检测方法依靠索检查人员工作态度和技术素质的，受人为因素影响较大，可靠性差，并且这种主观性都会降低检测的准确性和可靠性。(1) Manual detection methods rely on the work attitude and technical quality of inspectors, which are greatly affected by human factors and have poor reliability. Moreover, this subjectivity will reduce the accuracy and reliability of detection.
(2)电磁学方法不适用复杂环境，由于钢丝绳本身结构和使用环境的不同检测时钢丝绳相对于传感器的晃动和不规则扭转钢丝绳的拉伸程度损伤的深度、宽度和形态环境温度的变化和外界的电磁干扰等都会对检测信号产生较大干扰，并且钢丝绳自身结构的复杂性绳中单丝以及单股严重阻碍了缺陷信号的输出。(2) The electromagnetic method is not suitable for complex environments. Due to the different structures of the wire rope itself and the different usage environments, the shaking and irregular torsion of the wire rope relative to the sensor, the stretching degree of the wire rope, the depth, width and shape of the damage, the changes in ambient temperature and external electromagnetic interference will all have a significant interference on the detection signal. In addition, the complexity of the wire rope's own structure, the single wire and single strand in the rope seriously hinder the output of the defect signal.
目前，在西南山区中广泛开展的水电工程中，都需要大范围布设缆索进行材料运输。但是在具体使用过程中，缺少对缆索损伤实时可靠探测的方法，目前大多采用主观性强的人工检测以及稳定性差、不直观、缺少统一标准的电磁学探测方法，导致无法实时客观直观的监测缆索状态，为了保证缆索的安全使用，只能采用凭借主观经验在短期内定时更换的方法。从而造成经济损失以及加剧缆索使用安全风险。At present, in the hydropower projects widely carried out in the mountainous areas of the southwest, cables need to be laid on a large scale for material transportation. However, in the specific use process, there is a lack of real-time and reliable detection methods for cable damage. At present, most of them use subjective manual detection and electromagnetic detection methods with poor stability, non-intuitiveness, and lack of unified standards, which makes it impossible to monitor the cable status in real time, objectively and intuitively. In order to ensure the safe use of the cable, the only method is to use regular replacement in a short period of time based on subjective experience. This causes economic losses and exacerbates the safety risks of cable use.
发明内容Summary of the invention
本发明主要针对山区工程施工过程中使用缆索时，由于缺少实时直观获取缆索损伤情况，导致不能实时了解缆索损伤情况以及工作状态，导致缆索更换时间主观性强，造成了经济成本过高以及造成安全风险加剧的情况等问题。利用本发明提出的方法，可以在西南山区复杂的工程环境中，24小时对缆索进行无损探伤监测，确定缆索工作状态以及损伤程度，提高缆索使用的经济效益和安全性。The present invention is mainly aimed at the problems that when cables are used in mountain engineering construction, the cable damage and working status cannot be understood in real time due to the lack of real-time intuitive acquisition of the cable damage, resulting in strong subjectivity in the cable replacement time, resulting in excessive economic costs and increased safety risks. Using the method proposed by the present invention, non-destructive testing and monitoring of cables can be performed 24 hours a day in the complex engineering environment of the southwestern mountainous areas to determine the working status and damage degree of the cables, thereby improving the economic benefits and safety of cable use.
本发明创造的具体技术方案：The specific technical solution created by the present invention is:
一种针对缆索的远程实时无损探伤监测方法，包括以下步骤：A remote real-time nondestructive flaw detection monitoring method for cables comprises the following steps:
(1)缆索图像采集与预处理：(1) Cable image acquisition and preprocessing:
通过集成高清摄像头的一体化采集设备，获取缆索工作中的高清分辨率图像；Through the integrated acquisition device with integrated high-definition camera, high-definition resolution images of the cable work can be obtained;
对缆索进行滤波处理、形态学运算从而增强画质；Perform filtering and morphological operations on the cables to enhance the image quality;
对图像进行分割，提取存在缆索的部分，得到缆索的线性长度L。The image is segmented, the part where the cable exists is extracted, and the linear length L of the cable is obtained.
(2)缆索图像传输：(2) Cable image transmission:
一体化采集设备通过4G网络，基于TCP协议传预处理后的输图像文件；The integrated acquisition device transmits the pre-processed image files based on TCP protocol through 4G network;
远程服务器24小时接受图像文件，按日期时间存储；The remote server accepts image files 24 hours a day and stores them by date and time;
(3)缆索损伤监测：(3) Cable damage monitoring:
基于Yolo深度学习方法与缆索在不同情况下损伤示例数据，预训练缆索损伤监测模型；Pre-train the cable damage monitoring model based on the Yolo deep learning method and cable damage example data under different conditions;
对实时传输回的缆索图片进行缆索损伤监测，得到损伤范围长度Ls；Perform cable damage monitoring on the cable images transmitted back in real time to obtain the damage range length L s ;
基于图像缆索线性长度L以及损伤范围长度Ls得到缆索实时损伤评价S：Based on the image cable linear length L and damage range length Ls, the real-time cable damage assessment S is obtained:
S＝Ls/L*100％。S= Ls /L*100%.
统计缆索运动一个来回时间T内的总损伤长度Lsum，此为单根缆索总损伤长度，得到缆索运行情况评价；其中La为已知的工作缆索总长度：The total damaged length L sum of the cable within a round trip time T is calculated, which is the total damaged length of a single cable, and the cable operation status is evaluated; where La is the known total length of the working cable:
S总＝Lsum/La*100％。 Stotal = Lsum /La*100%.
(4)缆索损伤监测情况反馈：(4) Cable damage monitoring feedback:
实时绘制缆索实时损伤评价S曲线，得到监测点位缆索是否出现极端损伤，为是否紧急更换缆索提供决策基准；Draw the cable real-time damage evaluation S curve in real time to determine whether the cable at the monitoring point is extremely damaged, providing a decision-making basis for emergency cable replacement;
绘制在时间T内的缆索运行状况评价，全局评价缆索损伤程度，判断缆索是否到达使用寿命，为是否更换缆索提供直观决策基准。The cable operation status evaluation within time T is plotted to globally evaluate the cable damage degree and determine whether the cable has reached its service life, providing an intuitive decision-making benchmark for whether to replace the cable.
本发明提供的一种针对缆索的远程实时无损探伤监测方法，能有效监测西南山区处于工作状态的缆索的表面损伤，为更换缆索提供决策依据，并为监测缆索工作状态是否安全提供直观数据，提高了工程中使用缆索的安全性和经济性。The present invention provides a remote real-time nondestructive flaw detection monitoring method for cables, which can effectively monitor the surface damage of cables in working condition in the southwestern mountainous area, provide a decision-making basis for replacing cables, and provide intuitive data for monitoring whether the working condition of cables is safe, thereby improving the safety and economy of using cables in engineering projects.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为实施例不同情况下缆索损伤示例数据-深度学习模型训练数据；FIG1 is a diagram of cable damage example data under different conditions in the embodiment - deep learning model training data;
图2为实施例集成高清摄像头的一体化采集设备；FIG2 is an integrated acquisition device with an integrated high-definition camera according to an embodiment;
图3为实施例缆索损伤监测设备工作状态示意图；FIG3 is a schematic diagram of the working state of the cable damage monitoring device according to the embodiment;
图4为实施例经过图像处理的实时监测画面；FIG4 is a real-time monitoring screen after image processing in the embodiment;
图5为实施例切割缆索画面，并获取实际长度L；FIG5 is a screen showing the cable being cut in the embodiment and obtaining the actual length L;
图6为实施例获取的缆索监测数据-局部展示；FIG6 is a partial display of cable monitoring data obtained in the embodiment;
图7为实施例缆索损伤检测结果示例：局部大图；FIG7 is an example of cable damage detection results according to an embodiment: a partial large image;
图8为实施例缆索实时损伤检测结果示例：局部大图；FIG8 is an example of the real-time damage detection result of the cable in the embodiment: a partial large picture;
图9为实施例缆索综合损伤检测结果示例：局部大图；FIG9 is an example of comprehensive cable damage detection results in the embodiment: a partial large picture;
具体实施方式DETAILED DESCRIPTION
下面结合具体实例，说明本发明的具体使用方式。The specific usage of the present invention is described below with reference to specific examples.
(1)基于缆索在不同情况下损伤示例数据，预训练缆索损伤监测模型，如图1。(1) Based on the cable damage example data under different conditions, a cable damage monitoring model is pre-trained, as shown in Figure 1.
(2)安装集成高清摄像头的一体化采集设备，如图2，包括一体化监测箱1，一体化监测箱1外设有保护箱2，还包括高清摄像头3，高清摄像头3安装在支撑平台4上，高清摄像头3连接一体化监测箱1，还包括电源5为设备提供电源。在工作区域内安装仪器，如图3。(2) Install an integrated acquisition device with an integrated high-definition camera, as shown in FIG2 , including an integrated monitoring box 1, a protective box 2 is provided outside the integrated monitoring box 1, and a high-definition camera 3 is installed on a supporting platform 4, the high-definition camera 3 is connected to the integrated monitoring box 1, and a power supply 5 is provided to provide power to the device. Install the instrument in the working area, as shown in FIG3 .
(3)获取缆索工作中的高清分辨率图像，进行滤波处理、形态学运算从而增强画质，如图4；(3) Obtain high-resolution images of the cable in action, perform filtering and morphological operations to enhance the image quality, as shown in Figure 4;
(4)对图像进行分割，提取存在缆索的部分如图5，得到缆索的线性长度L：37.8cm。(4) The image is segmented and the portion where the cable exists is extracted as shown in FIG5 . The linear length L of the cable is obtained as 37.8 cm.
(5)远程服务器24小时接受图像文件，按日期时间存储，如图6。(5) The remote server receives image files 24 hours a day and stores them by date and time, as shown in Figure 6.
(6)对实时传输回的缆索图片进行缆索损伤监测，得到损伤范围长度Ls：0.6cm，如图7。(6) The cable damage monitoring is performed on the cable images transmitted back in real time, and the damage range length L s is obtained: 0.6 cm, as shown in FIG7 .
(7)基于图像缆索线性长度L以及损伤范围长度Ls得到缆索实时损伤评价S：(7) Based on the image cable linear length L and damage range length Ls, the real-time cable damage assessment S is obtained:
S＝Ls/L*100％＝0.6cm/37.8cm*100％＝2.15％。S=Ls /L *100%=0.6cm/37.8cm*100%=2.15%.
(8)统计缆索运动一个来回时间(T)内的总损伤长度Lsum＝49.2cm，此为单根缆索总损伤长度。La为已知的工作缆索总长度：500m。得到缆索运行情况评价S总＝0.098％。(8) The total damaged length L sum of the cable in one round trip time (T) is 49.2 cm, which is the total damaged length of a single cable. La is the known total length of the working cable: 500 m. The cable operation evaluation S total is obtained as 0.098%.
(9)实时绘制缆索实时损伤评价S曲线，得到监测点位缆索是否出现极端损伤，如图8。(9) Draw the real-time cable damage evaluation S curve in real time to determine whether the cable at the monitoring point has extreme damage, as shown in Figure 8.
(10)绘制在时间T内的缆索运行状况评价，全局评价缆索损伤程度，断缆索是否到达使用寿命，为是否更换缆索提供直观决策基准。如图9。(10) Draw the evaluation of the cable operation status within time T, globally evaluate the cable damage degree, and whether the broken cable has reached the end of its service life, providing an intuitive decision-making benchmark for whether to replace the cable. As shown in Figure 9.
Claims (5)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410723373.5A CN118688204A (en) | 2024-06-05 | 2024-06-05 | A remote real-time nondestructive flaw detection monitoring method for cables |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410723373.5A CN118688204A (en) | 2024-06-05 | 2024-06-05 | A remote real-time nondestructive flaw detection monitoring method for cables |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118688204A true CN118688204A (en) | 2024-09-24 |
Family
ID=92773729
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410723373.5A Pending CN118688204A (en) | 2024-06-05 | 2024-06-05 | A remote real-time nondestructive flaw detection monitoring method for cables |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118688204A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106053591A (en) * | 2015-04-13 | 2016-10-26 | 法策股份公司钢丝绳厂 | Inspection and monitoring system for a cable railway, and method for operating the same |
| CN109859170A (en) * | 2019-01-04 | 2019-06-07 | 中国矿业大学 | A kind of steel wire rope surface damage intelligent monitoring method and system based on LBP feature |
| CN114764792A (en) * | 2022-04-14 | 2022-07-19 | 安徽理工大学 | Cable flaw detection system and method based on neural network and embedded fusion |
| CN116805302A (en) * | 2023-04-28 | 2023-09-26 | 兰州交通大学 | A cable surface defect detection device and method |
| CN117368224A (en) * | 2023-10-13 | 2024-01-09 | 深圳市创环环保科技有限公司 | Pipe network detection system and method based on intelligent interaction of software and hardware |
| US20240070836A1 (en) * | 2022-08-24 | 2024-02-29 | Schlumberger Technology Corporation | Method and apparatus to perform a wireline cable inspection |
-
2024
- 2024-06-05 CN CN202410723373.5A patent/CN118688204A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106053591A (en) * | 2015-04-13 | 2016-10-26 | 法策股份公司钢丝绳厂 | Inspection and monitoring system for a cable railway, and method for operating the same |
| CN109859170A (en) * | 2019-01-04 | 2019-06-07 | 中国矿业大学 | A kind of steel wire rope surface damage intelligent monitoring method and system based on LBP feature |
| CN114764792A (en) * | 2022-04-14 | 2022-07-19 | 安徽理工大学 | Cable flaw detection system and method based on neural network and embedded fusion |
| US20240070836A1 (en) * | 2022-08-24 | 2024-02-29 | Schlumberger Technology Corporation | Method and apparatus to perform a wireline cable inspection |
| CN116805302A (en) * | 2023-04-28 | 2023-09-26 | 兰州交通大学 | A cable surface defect detection device and method |
| CN117368224A (en) * | 2023-10-13 | 2024-01-09 | 深圳市创环环保科技有限公司 | Pipe network detection system and method based on intelligent interaction of software and hardware |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111862083B (en) | A comprehensive monitoring system and method for wire rope status based on visual-electromagnetic detection | |
| Błażej et al. | The use of magnetic sensors in monitoring the condition of the core in steel cord conveyor belts–Tests of the measuring probe and the design of the DiagBelt system | |
| CN106018544B (en) | A kind of steel wire rope Holographic test system | |
| Sukhorukov et al. | Electromagnetic inspection and diagnostics of steel ropes: technology, effectiveness and problems | |
| Peterka et al. | Failure analysis of hoisting steel wire rope | |
| CN106470930B (en) | Rope real-time monitoring | |
| CN109823941B (en) | Be applied to wire rope monitoring devices on initiative safety elevator | |
| CN108225906A (en) | The identification of drag-line corrosion monitoring and estimating method for fatigue life based on computer vision | |
| EP3356991B1 (en) | Non-destructive evaluation of cordage products | |
| CN104965055A (en) | On-line detection and safety assessment method and system for mining steel wire rope | |
| CN112268547A (en) | Mining cable running monitoring system and monitoring method based on video image change analysis | |
| Bajda et al. | A new tool in belts resistance to puncture research | |
| Xu et al. | Fatigue life and fatigue reliability assessment for long-span spatial structure based on long-term health monitoring data | |
| CN118688204A (en) | A remote real-time nondestructive flaw detection monitoring method for cables | |
| Błażej | Review of the newest NDT equipment for conveyor belt diagnostics | |
| CN207866359U (en) | A kind of overhead transmission line Vibration Condition Monitoring device | |
| CN105911074B (en) | Adaptive threshold scaling method in wire-core belt lacings X-ray on-line checking | |
| CN114359779A (en) | Belt tearing detection method based on deep learning | |
| CN111912463A (en) | Real-time online health state monitoring system for fixed winch hoist | |
| CN116086677B (en) | Multi-wire rope tension balance monitoring method and system and electronic equipment | |
| CN108007935B (en) | On-line monitoring system for buried cracks based on distributed optical fiber sensing technology structure | |
| Davies et al. | Bend over sheave durability of fibre ropes for deep sea handling operations | |
| Mei et al. | On-line intelligent evaluation of the fatigue state of a composite conveyor belt | |
| CN211496642U (en) | Round sling with built-in monitoring device | |
| CN206556798U (en) | A kind of crossover sub with pulling force display function |
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 |