Use this url to cite publication: https://hdl.handle.net/20.500.12259/57511
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A review of predictive maintenance systems in industry 4.0
Type of publication
Tezės kituose recenzuojamuose leidiniuose / Theses in other peer-reviewed publications (T1e)
Title
A review of predictive maintenance systems in industry 4.0
Publisher
Hong Kong : Solari Co
Date Issued
2017
Extent
p. 68-68
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
Description
eISSN 2071-2987
Field of Science
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.
Is Referenced by
Type of document
type::text::conference object::conference proceedings::conference paper
ISSN (of the container)
2223-523X
Other Identifier(s)
VDU02-000022158
Coverage Spatial
Taivanas / Taiwan Province of China (TW)
Language
Anglų / English (en)
Bibliographic Details
0