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Dynamic Predictive Maintenance in industry 4.0 based on real time information: Case study in automotive industries by Einabadi, B.; Baboli, A.; Ebrahimi, M. is a Engineering article available to read on EtoBox.

What is Dynamic Predictive Maintenance in industry 4.0 based on real time information: Case study in automotive industries about?

In order to respond to today's dynamic needs of customers, customized mass production systems have been more and more developed that, are facing with different challenges. Maintenance planning and scheduling is one of the most important manufacturing components in such systems, due to importance of availability and high investment for this kind of system. In order to consider real machine operation state, recently, predictive maintenance method is proposed. However, in traditional methods, historical failure data is the main source for this planning. In this paper, we propose a methodology for dynamic predictive maintenance for a real case in automotive industries with considering multicomponent structural and positive economic dependencies between them. In our methodology, we propose to gather data science with mathematical optimization method. Prediction of Remaining Useful Life (RUL) of machine parts has been made by Artificial Neural Network method with considering sensors data. With this RUL values and other cost values and optimization model parameters, and by solving proposed mathematical model, an optimal schedule is achieved with minimization of maintenance costs. Through

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Author

Einabadi, B.; Baboli, A.; Ebrahimi, M.

Publisher

Elsevier ; IFAC Secretariat; Kidlington: Elsevier Ltd; Elsevier BV (ISSN 2405-8963)

Published

2019

Language

EN

Field

Engineering (Physical Sciences)