Open Access

ARTICLE

Intelligent Choice of Machine Learning Methods for Predictive Maintenance of Intelligent Machines

Marius Becherer^∗, Michael Zipperle^†, Achim Karduck^‡

Furtwangen University

* Corresponding Authors:
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‡

Computer Systems Science and Engineering 2020, 35(2), 81-89. https://doi.org/10.32604/csse.2020.35.081

Abstract

Machines are serviced too often or only when they fail. This can result in high costs for maintenance and machine failure. The trend of Industry 4.0 and the networking of machines opens up new possibilities for maintenance. Intelligent machines provide data that can be used to predict the ideal time of maintenance. There are different approaches to create a forecast. Depending on the method used, appropriate conditions must be created to improve the forecast. In this paper, results are compiled to give a state of the art of predictive maintenance. First, the different types of maintenance and economic relationships are explained. Then factors for the forecast are explained. Requirements for the data are collected and algorithms for machine learning are presented. Based on the relationships found, a process model is presented that shows a fast implementation of the predictive maintenance for machines.

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Keywords

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Citations