A Literature Review Framework and Open Research Challenges for Predictive Maintenance in industry 4.0
Lukas Meitz, Julia Senge, Tim Wagenhals, Thorsten Schöler, Jörg Hähner, Janick Edinger, Christian Krupitzer
Abstract
Production issues at Volkswagen in 2016 led to dramatic losses in sales of up to 400 million Euros per week. This example shows the huge financial impact of a working production facility for companies. Especially in the data-driven domains of Industry 4.0 and Industrial IoT with intelligent, connected machines, a conventional, static maintenance schedule seems to be old-fashioned. In this paper, we present an overview of the current state of the art in predictive maintenance for Industry 4.0. Based on a structured literature survey, we present a classification of predictive maintenance in the context of Industry 4.0 based on 249 publications. Additionally, we discuss identified challenges, i.e., complexity issues, as well as missing benchmark datasets that are relevant for production and the integration of machine learning.