Towards data-driven reliability modeling for cyber-physical production systems

Jonas Friederich*, Sanja Lazarova-Molnar


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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.


Konference12th International Conference on Ambient Systems, Networks and Technologies, ANT 2021 / 4th International Conference on Emerging Data and Industry 4.0, EDI40 2021 / Affiliated Workshops

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Publisher Copyright:
© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (


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