Towards Data-Driven Reliability Modeling for Cyber-Physical Production Systems

Reliability is one of the most important performance indicators in contemporary production facilities. Increasing reliability of manufacturing systems results in their prolonged lifetimes, and reduced maintenance and repair costs. Reliability modeling is a common technique for deriving reliability measurements and illustrating relevant fault-dependencies. There is a significant body of research focusing on hardware- and software reliability models, such as Fault Trees, Petri Nets and Markov Chains. Up until now, development of reliability models has been a labor-intensive and expert-knowledge-driven process. To remedy that, through the prevalence of data stemming from the new and technologically advanced manufacturing systems, we propose that data generated in modern manufacturing lines could be used to either automate or at least to support development of reliability models. In this paper, we elaborate on the details of our proposed framework for data-driven reliability assessment of cyber-physical production systems. We, furthermore, introduce a case study that will aid the development and testing of the proposed novel data-driven approach. (C) 2021 The Authors. Published by Elsevier B.V.

Automated summary

Reliability is crucial in contemporary production facilities, and modeling techniques like Fault Trees and Markov Chains are commonly used to derive reliability measurements. This paper suggests utilizing data from modern manufacturing systems to automate or support the development of reliability models, and introduces a case study for testing a new data-driven approach.