Data, Models, and Performance: A Comprehensive Guide to Predictive Maintenance in Industrial Settings

Industrial engineering and management.

Abstract

With the ever-growing complexity of different assets in a factory, the main focus of predictive maintenance solutions has shifted from model-based approaches to data-driven and hybrid approaches. This shift as a result highlights the importance and the inevitable impact of data, data quality, model maintenance, and model interpretability on the performance and acceptability of these predictive maintenance approaches in industry. In this chapter, the hurdles for developing effective predictive maintenance solutions for original equipment manufacturers (OEMs) and small and medium-sized enterprises (SMEs) with different levels of digitalization are introduced. Furthermore, it is discussed how to choose a suitable strategy for developing a predictive maintenance model, given the different constraints in the availability of data and the requirements of the customer.

Authors 2

1. Kiavash Fathi Aachen

   RWTH Aachen University · ZHAW Zurich University of Applied Sciences

   Affiliation as printed

   Information and Automation Systems for Process and Material Technology, RWTH Aachen University, Aachen, Germany

   Zurich University of Applied Sciences, Winterthur, Switzerland

2. Hans Wernher van de Venn

   ZHAW Zurich University of Applied Sciences

   Affiliation as printed

   Zurich University of Applied Sciences, Winterthur, Switzerland