TESConf 2021 - 10th International Conference on Through-Life Engineering Services最新文献

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Physical Interacting Aerial Robots for ‘In-Situ’ Inspection and Maintenance of Wind Turbine Blade 用于风力发电机叶片“原位”检测和维护的物理交互空中机器人

TESConf 2021 - 10th International Conference on Through-Life Engineering Services Pub Date : 2021-10-18 DOI: 10.2139/ssrn.3945115

Hanieh Esmaeeli, Ayham Alharbat, Abeje Mershaa

{"title":"Physical Interacting Aerial Robots for ‘In-Situ’ Inspection and Maintenance of Wind Turbine Blade","authors":"Hanieh Esmaeeli, Ayham Alharbat, Abeje Mershaa","doi":"10.2139/ssrn.3945115","DOIUrl":"https://doi.org/10.2139/ssrn.3945115","url":null,"abstract":"Wind turbines are green energy sources that have a great potential in playing a crucial role in mitigating climate change. Regular ‘in-situ’ inspection and maintenance of wind turbines, especially the leading edge, is needed to ensure system efficiency and durability. Typical inspection and maintenance activities consist of a set of physical tasks, such as sanding, brushing, or painting at high altitudes, which are dangerous for human operators, time-consuming, and can only be carried out under certain conditions. If such activities are not done timely and steadily, it may result in significant downtime to the system due to the maintenance and even replacement which is also very expensive for the owner. The use of aerial robots with the ability of physical interaction that perform a variety of maintenance tasks proposes an advanced and consistent inspection and maintenance technique that mitigates limitations of the current approach. Although currently aerial robot applications to maintenance beyond monitoring and inspection tasks are not common, this research focuses on the applicability of aerial robots to carry out inspection and maintenance tasks that require physical interaction with the environment. The main contribution of this paper is a novel control system for the physical interaction of aerial robots that enables the maintenance of a 3D wind turbine blade. This work first aims at investigating and classifying the properties of wind turbine blade maintenance in order to identify the main requirements for control design. Then, we design and implement a controller based on the identified physical requirements. The applicability of the proposed control system of the aerial robot is demonstrated in a mock-up environment.","PeriodicalId":162865,"journal":{"name":"TESConf 2021 - 10th International Conference on Through-Life Engineering Services","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117100695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning Approaches to Identify Environmental Damage on Superalloys 识别高温合金环境损伤的机器学习方法

TESConf 2021 - 10th International Conference on Through-Life Engineering Services Pub Date : 2021-10-18 DOI: 10.2139/ssrn.3945090

O. Oluwafemi, Pavan Ganti, Vijaya Babu, Shiyi Wang, Lichao Yang, S. Gray, Yifan Zhao, G. Castelluccio

{"title":"Machine Learning Approaches to Identify Environmental Damage on Superalloys","authors":"O. Oluwafemi, Pavan Ganti, Vijaya Babu, Shiyi Wang, Lichao Yang, S. Gray, Yifan Zhao, G. Castelluccio","doi":"10.2139/ssrn.3945090","DOIUrl":"https://doi.org/10.2139/ssrn.3945090","url":null,"abstract":"Corrosive environments have a significant detrimental impact on aircraft engine turbine blades resulting in early degradation and a higher risk of failure. Currently, human visual inspection evaluates the condition of blades and identify premature degradation such as cracking or corrosion. While this approach works, it is time-consuming to carry out manual examinations and susceptible to human error. More so, it lacks a robust and objective strategy to identify the conditions of the turbine in terms of thermal cycle and exposure. Instead, machine learning approaches have ample potential to identify and quantify degradation from images and classify damage conditions in a robust and economical manner. Hence, this study explores the use of deep neural networks to determine the environment to which a nickel-base superalloy was exposed in laboratory testing. A machine learning approach was implemented to predict temperature, salt flux, material type and exposure times using a database with 3000 images of sample cross sections. We compared two machine learning environments (MATLAB, and Python) and we enriched the database by cropping images. The results demonstrate that machine learning approaches have impressive predictive power for laboratory samples that can sometimes be superior to that of human experts. We further identify the environmental attributes that are more difficult to predict and which predictions can be achieved confidently.","PeriodicalId":162865,"journal":{"name":"TESConf 2021 - 10th International Conference on Through-Life Engineering Services","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122893643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

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