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Type of publication: conference paper
Type of publication (PDB): 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
Affiliation(s): Baltijos pažangių technologijų institutas, Vilnius
Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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