A Big Data implementation of the MANTIS reference architecture for predictive maintenance-Reference-Cited by-同舟云学术

A Big Data implementation of the MANTIS reference architecture for predictive maintenance

Published:2019-03-21 Issue:10 Volume:233 Page:1361-1375
ISSN:0959-6518
Container-title:Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
language:en
Short-container-title:Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering

Author:

Larrinaga Felix¹,Fernandez-Anakabe Javier¹^ORCID,Zugasti Ekhi¹,Garitano Iñaki¹,Zurutuza Urko¹,Olaizola Jon¹,Anasagasti Mikel²,Mondragon Mikel²

Affiliation:

1. Electronics and Computer Engineering Department, Mondragon University, Mondragon, Spain

2. Goizper S. Coop, Antzuola, Spain

Abstract

This article presents the implementation of a reference architecture for cyber-physical systems to support condition-based maintenance of industrial assets. It also focuses on describing the data analysis approach to manage predictive maintenance of clutch-brake assets fleet over the previously defined MANTIS reference architecture. Proposals for both the architecture and data analysis implementation support working on Big Data scenarios, due to the usage of related technologies, such as Hadoop Distributed File System, Kafka or Apache Spark. The techniques are (1) root cause analysis powered by attribute-oriented induction clustering and (2) remaining useful life powered by time series forecasting. The work has been conducted in a real use case within the H2020 European project MANTIS.

Funder

ministerio de industria, energía y turismo

electronic components and systems for european leadership

eusko jaurlaritza

MANTIS

European Union’s Horizon 2020

Publisher

SAGE Publications

Subject

Mechanical Engineering,Control and Systems Engineering

Link

http://journals.sagepub.com/doi/pdf/10.1177/0959651819835362

Reference18 articles.

Cited by 4 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. Remaining useful life prediction for motor systems by iteratively updated wiener process based on closed-loop performance degradation monitoring;Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering;2023-11-08

2. Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability;Sensors;2022-11-25

3. The fundamentals and strategies of maintenance, repair, and overhaul (MRO) in Industry 4.0;2022 International Conference on Electrical, Computer and Energy Technologies (ICECET);2022-07-20

4. System of Systems Lifecycle Management—A New Concept Based on Process Engineering Methodologies;Applied Sciences;2021-04-09