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Predictive Maintenance in Distributed Environments

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Link to refer to this document:
10.3311/BMEZalaZONE2022-018
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  • Conference on BME ZalaZONE 2022 [22]
Abstract
In this paper we present a concept to implement predictive maintenance in distributed environments. A distributed environment can involve several problems to be solved such as the different speed and frequency of data, the different priorities of data sources, the different dimensions and data structures and also the volume of the data to be processed. There are software, frameworks, algorithms to work out these problems, however the interoperability is harder to maintain between these. Recently it become possible to use historical and real-time datasets of parameters and key performance indicators of environments in order to prevent failures and monitor the system state. The aim is to outline a solution, using open-source tools, to support the lifecycle from the data extraction, through the transformation and algorithmic steps, until the usage and visualization of the gained information and feedback of the data to the environment.
Title
Predictive Maintenance in Distributed Environments
Author
Sik, Dávid
Ekler, Péter
Levendovszky, János
Date of issue
2022
Access level
Open access
Copyright owner
Budapest University of Technology and Economics
Conference title
The First Conference on ZalaZONE Related R&I Activities of Budapest University of Technology and Economics 2022
Conference place
Budapest University of Technology and Economics
Conference date
2022.03.31
Language
en
Page
85 - 88
Subject
data lake, distributed environment, predictive maintenance, time series
Version
Kiadói változat
Identifiers
DOI: 10.3311/BMEZalaZONE2022-018
Title of the container document
Proceedings of The First Conference on ZalaZONE Related R&I Activities of Budapest University of Technology and Economics 2022
ISBN, e-ISBN
ISBN 978-963-421-873-9
University
Budapest University of Technology and Economics
Faculty
Faculty of Transportation Engineering and Vehicle Engineering
Department
Department of Automotive Technologies

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