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A reverse engineering algorithm for mining a causal system model from system data

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  • Nong Ye

Abstract

Although having structural system models which determine system behaviours is critical to plan, control and manage many complex systems (e.g. manufacturing and production systems), we often do not have pre-defined structural system models. We need to perform reverse engineering which is to collect and mine observable system data in order to discover structural system models. This paper presents a reverse engineering algorithm that can be used to discover a causal system model which is one kind of structural system model and represents causal relations of system factors. In a causal relation, the presence of one system factor causes the presence of another system factor. The paper also shows the computational complexity of the algorithm. The paper presents the application and performance of the reverse engineering algorithms to data in two application fields.

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  • Nong Ye, 2017. "A reverse engineering algorithm for mining a causal system model from system data," International Journal of Production Research, Taylor & Francis Journals, vol. 55(3), pages 828-844, February.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:3:p:828-844
    DOI: 10.1080/00207543.2016.1213913
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    Cited by:

    1. Christopher Hagedorn & Johannes Huegle & Rainer Schlosser, 2022. "Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2027-2043, October.

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