Abstract
The sensorization of machines used in industries (Industry 4.0) and the ability to connect them to a data network, have changed the way companies maintain and optimize the performance of their machines. Each one is capable of generating large volumes of data daily, big data methodologies can now be applied to these data in order to extract knowledge, this was an impossible task not so long ago. However, in many cases sensorization and data analysis are not enough to detect faults or alarms and once they occur, an operator must fix them manually. The purpose of this paper is to use a semantic analyzer, based primarily on a case-based reasoning system which extracts information from the reports written by operators about the faults they resolved in machines. Thus, when a fault or alarm occurs and there are previous reports about this machine, the developed system independently proposes a solution and there is no need for an operator to identify the problem. To do this, a text analysis platform has been created, it applies case-based reasoning to report the causes of the problem. In the majority of cases, the proposed system can successfully resolve the problem and it is not necessary to revise the machine in order to detect a malfunction and also simplifies the repair process by providing the operator with a glossary of key terms based on the history of repair reports.
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Acknowledgments
This research has been partially supported by the European Regional Development Fund (FEDER) under the IOTEC project grant 0123_IOTEC_3_E and by the Spanish Ministry of Economy, Industry and Competitiveness and the European Social Fund under the ECOCASA project grant RTC-2016-5250-6. The research of Alfonso González-Briones has been co-financed by the European Social Fund (Operational Programme 2014–2020 for Castilla y León, EDU/128/2015 BOCYL).
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Rivas, A. et al. (2018). Semantic Analysis System for Industry 4.0. In: Uden, L., Hadzima, B., Ting, IH. (eds) Knowledge Management in Organizations. KMO 2018. Communications in Computer and Information Science, vol 877. Springer, Cham. https://doi.org/10.1007/978-3-319-95204-8_45
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DOI: https://doi.org/10.1007/978-3-319-95204-8_45
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