MONITORING OF OIL ANALYSIS THROUGH SENSORS

Author:

Magalhães Viegas Junior Daniel

Abstract

Inserting the Industry 4.0 universe into companies is necessary to guarantee their competitiveness and continuity in the market. And, one of the areas in which industry 4.0 and its technologies are most prominent is maintenance, as the use of intelligent mechanisms are capable of promoting the reliability of systems functioning, predicting failures and anticipating problems and breakdowns in equipment. , thus contributing to increased performance and reduced aggregate costs. The present study then starts from the idea of using Lab-on-chip technology for the hydraulic fluid and lubricant monitoring system and aims to verify the application of Microelectromechanical Systems (MEMS) in maintenance. From the literary review, it was possible to verify that studies relating the use of microsensors for monitoring lubricants are still scarce and from this, applied research was suggested for this purpose, developing a lab-on-chip that be capable of replacing complex and high-cost laboratory analyses.

Publisher

Periodicojs

Reference47 articles.

1. Baglee, D., & Marttonen, S. (2015). The need for big data collection and analyses to support the development of an advanced maintenance strategy. In Proceedings of the International Conference on Data Science (ICDATA) (p. 3). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).

2. Baheti, R., & Gill, H. (2011). Cyber-physical systems. The impact of control technology, 12(1), 161-166.

3. Borlido, D. J. A. (2017). Indústria 4.0: Aplicação a Sistemas de Manutenção. f. Dissertação (Mestrado) - Curso de Engenharia Mecânica, Universidade do Porto, Porto.

4. Campos, J., Sharma, P., Jantunen, E., Baglee, D., & Fumagalli, L. (2016). The challenges of cybersecurity frameworks to protect data required for the development of advanced maintenance. Procedia Cirp, 47, 222-227.

5. Civerchia, F., Bocchino, S., Salvadori, C., Rossi, E., Maggiani, L., & Petracca, M. (2017). Industrial Internet of Things monitoring solution for advanced predictive maintenance applications. Journal of Industrial Information Integration, 7, 4-12.

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