Within the automotive industry today, data collection, for legacy manufacturing equipment, largely relies on the data being pushed from the machine’s PLCs to an upper system. Not only does this require programmers’ efforts to collect and provide the data, but it is also prone to errors or even intentional manipulation. External monitoring, is available through Open Platform Communication (OPC), but it is time consuming to set up and requires expert knowledge of the system as well. A nomenclature based methodology has been devised for the external monitoring of unknown controls systems, adhering to a minimum set of rules regarding the naming and typing of the data points of interest, which can be deployed within minutes without human intervention. The validity of the concept will be demonstrated through implementation within an automotive body shop and the quality of the created log will be evaluated. The impact of such a fine grained monitoring effort on the communication infrastructure will also be measured within the manufacturing facility. It is concluded that, based on the methodology provided in this paper, it is possible to derive OPC groups and items from a PLC program without human intervention in order to obtain a detailed event log.
|Title of host publication||Intelligent Data Engineering and Automated Learning – IDEAL 2018|
|Subtitle of host publication||19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part II|
|Editors||Hujun Yin, David Camacho, Paulo Novais, Antonio J. Tallón-Ballesteros|
|Number of pages||10|
|Publication status||Published - 1 Nov 2018|
Koehler, W., & Jing, Y. (2018). Automated, Nomenclature Based Data Point Selection for Industrial Event Log Generation. In H. Yin, D. Camacho, P. Novais, & A. J. Tallón-Ballesteros (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2018: 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part II (Vol. 2, pp. 31-40) https://doi.org/10.1007/978-3-030-03496-2_5