The MANTIS Book

Abstract

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Methodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledge

“…Industrial companies mainly use two data collection methods, classified into real machine and simulation. On the one hand, real machine data is retrieved either manually by an operator or automatically from a CPS-based platform; like the one proposed in the Mantis project by Albano et al (2018). On the other hand, simulations create data aimed to simulate real machine behaviour under different conditions; which are based on physical testbeds such as the ones presented by Borodulin et al (2017), or Digital Twins (Aivaliotis et al 2019;Alexopoulos et al 2020), which are software simulations based on techniques like theoretical domain knowledge, finite element method, data mining or statistics.…”

An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance

“…Nevertheless, there is another type of maintenance called Proactive Maintenance, which aims to analyse the state of the machine and perform actions to ensure its useful life. The difference with Predictive Maintenance is that it not only tries to analyse when the next failure will occur, but also suggests how to manage that situation to obtain optimal machine health [19,32,3].…”