Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations
Journal article, 2020

Published by Informa UK Limited, trading as Taylor & Francis Group. The technological advancements of recent years have increased the complexity of manufacturing systems, and the ongoing transformation to Industry 4.0 will further aggravate the situation. This is leading to a point where existing systems on the factory floor get outdated, increasing the gap between existing technologies and state-of-the-art systems, making them incompatible. This paper presents an event-based data pipeline architecture, that can be applied to legacy systems as well as new state-of-the-art systems, to collect data from the factory floor. In the presented architecture, actions executed by the resources are converted to event streams, which are then transformed into an abstraction called operations. These operations correspond to the tasks performed in the manufacturing station. A sequence of these operations recount the task performed by the station. We demonstrate the usability of the collected data by using conformance analysis to detect when the manufacturing system has deviated from its defined model. The described architecture is developed in Sequence Planner–a tool for modelling and analysing production systems–and is currently implemented at an automotive company as a pilot project.

smart factories

Industry 4.0

data acquisition and aggregation

manufacturing systems

data streams

sequence anomaly detection

big data

conformance checking

Author

Ashfaq Hussain Farooqui

Chalmers, Electrical Engineering, Systems and control, Automation

Kristofer Bengtsson

Chalmers, Electrical Engineering, Systems and control, Automation

Petter Falkman

Chalmers, Electrical Engineering, Systems and control, Automation

Martin Fabian

Chalmers, Electrical Engineering, Systems and control, Automation

International Journal of Production Research

0020-7543 (ISSN) 1366-588X (eISSN)

Vol. 58 16 4947-4963

Subject Categories

Embedded Systems

Computer Science

Computer Systems

DOI

10.1080/00207543.2020.1735660

More information

Latest update

9/11/2020