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Information retrieval in industrial production environments

: Windmann, Stefan; Niggemann, Oliver


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
IEEE 23rd International Conference on Emerging Technologies and Factory Automation, ETFA 2018. Proceedings : Politecnico di Torino, Torino, Italy, 04-07 September 2018
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-7108-5
ISBN: 978-1-5386-7107-8
ISBN: 978-1-5386-7109-2
International Conference on Emerging Technologies and Factory Automation (ETFA) <23, 2018, Torino>
Conference Paper
Fraunhofer IOSB ()

The complexity of industrial production systems is steadily growing. Hence, the plant stuff has to search in an increasing number of documents within the daily work routine, e.g. in manuals, commissioning instructions, service notes, shift books, process data, repair instructions, data sheets, R/I flow charts, CAD drawings etc. To support the plant stuff, an intelligent search engine for industrial production environments is proposed in this paper. Characteristics of the developed search engine with respect to the domain of industrial production environments, e.g. tailored synonym replacements and document classifications, are outlined. Particularly, two methods for document classifications, a k-nearest-neighbor classifier and a Naive Bayes classifier, are evaluated with documents from industrial production environments.