A distributed topology for identifying anomalies in an industrial environment

Author:

Zayas-Gato Francisco,Michelena Álvaro,Jove EstebanORCID,Casteleiro-Roca José-Luis,Quintián Héctor,Novais Paulo,Méndez-Pérez Juan Albino,Calvo-Rolle José Luis

Abstract

AbstractThe devastating consequences of climate change have resulted in the promotion of clean energies, being the wind energy the one with greater potential. This technology has been developed in recent years following different strategic plans, playing special attention to wind generation. In this sense, the use of bicomponent materials in wind generator blades and housings is a widely spread procedure. However, the great complexity of the process followed to obtain this kind of materials hinders the problem of detecting anomalous situations in the plant, due to sensors or actuators malfunctions. This has a direct impact on the features of the final product, with the corresponding influence in the durability and wind generator performance. In this context, the present work proposes the use of a distributed anomaly detection system to identify the source of the wrong operation. With this aim, five different one-class techniques are considered to detect deviations in three plant components located in a bicomponent mixing machine installation: the flow meter, the pressure sensor and the pump speed.

Funder

Universidade da Coruña

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference32 articles.

1. European Commission (2021). A European Green deal. https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en. Accessed 18 February 2021

2. Eurostat (2021). Renewable energy statistics. https://ec.europa.eu/eurostat/statistics-explained/index.php/Renewable_energy_statistics#Wind_and_water_provide_most_renewable_electricity.3B_solar_is_the_fastest-growing_energy_source. Accessed 18 February 2021

3. Repsol’s Economic Research Department (2020) Annual energy-statistics 2020. https://www.repsol.com/content/dam/repsol-corporate/en_gb/energia-e-innovacion/annual-energy-statistics-2020_tcm14-168076.pdf. Accessed 4 Nov 2021

4. Owusu PA, Asumadu-Sarkodie S (2016) A review of renewable energy sources, sustainability issues and climate change mitigation. Cogent Eng 30(1):1167990

5. Lund H (2007) Renewable energy strategies for sustainable development. Energy 32(6):912–919

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Comparative Analysis of Intelligent Techniques for Categorization of the Operational Status of LiFePo4 Batteries;Lecture Notes in Computer Science;2023

2. Machine Learning Based System for Detecting Battery State-of-Health;18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023);2023

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3