Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/57511
Type of publication: Tezės kituose recenzuojamuose leidiniuose / Theses in other peer-reviewed publications (T1e)
Field of Science: Informatika / Informatics (N009)
Author(s): Varoneckas, Audrius;Mackutė-Varoneckienė, Aušra;Krilavičius, Tomas
Title: A review of predictive maintenance systems in industry 4.0
Is part of: International journal of design, analysis and tools for integrated circuits and systems (IJDATICS). Hong Kong : Solari Co, 2017, vol. 6, no. 1
Extent: p. 68-68
Date: 2017
Note: eISSN 2071-2987
Keywords: Predictive maintenance;Internet of things;Machine learning
Abstract: Today we live in fourth industrial revolution, called Industry 4.0 where cyber physical systems (CPS), Internet of Things (IoT), Cloud Computing (CC), and Artificial Intelligence (AI) are integrating for advanced manufacturing. Many production systems, manufacturing processes and their state, equipment, and tools need to be monitored all the time. As equipment begins to fail, it causes stops in manufacturing process which is not efficient. Monitoring of manufacturing systems for maintenance helps to identify equipment condition and failures before equipment brakes-down. Intelligent data analysis of historical data and knowledge of the specific domain can improve decisions on maintenance. In this paper overview of Predictive Maintenance (PdM) in Industry 4.0 is analysed. Maintenance strategies can be corrective maintenance (occurs after a fault detection), improvement maintenance (occurs on demand) and preventive maintenance (occurs before a fault detection). Preventive maintenance (PM) is divided into Condition Based Maintenance (CBM) which covers Equipment-driven and Time-driven maintenance, and can be scheduled, continuous, or on request; and Predetermined Maintenance which defines the goals of Predictive-maintenance. Preventive Maintenance and spare parts of equipment replacement schedule can be defined using multiobjective evolutionary algorithms. To create real-time monitoring system or predictive maintenance system of manufacturing equipment it is important to have appropriate sensors for data capturing, effective intelligent data analysis methods, Key Performance Index (KPI) for evaluation and perform decisions under supervision plan
Internet: http://ijdatics.datics.net/current_issues/IJDATICS_06_01/IJDATICS_06_01_19.pdf
Affiliation(s): Baltijos pažangių technologijų institutas, Vilnius
Informatikos fakultetas
Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

Files in This Item:
marc.xml7.44 kBXMLView/Open

MARC21 XML metadata

Show full item record
Export via OAI-PMH Interface in XML Formats
Export to Other Non-XML Formats

Page view(s)

180
checked on Mar 5, 2020

Download(s)

16
checked on Mar 5, 2020

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.