Abstract
Process mining is a field of research that provides mining of more and more useful hidden information to the industry. The core of effective information retrieval lies in the application of process mining tools that best fits the task and the data. The current problem is that there is no universal solution available to track the formation of faulty products in time and space to make it possible to be reduced. To solve this problem, methods have been developed that can be used to analyze a production process from multiple perspectives. The methods were also implemented in software and tested on real production data. The methods created are based on time and space distribution and grouping of faulty products. The methods were applied to the processing and measurement data of an automated coil production and assembly line. The data is originally stored in different files, so before they were used, they had to be transformed and sorted into database. Using the software which use the methods, a comprehensive view of the production process can be obtained, and conclusions can be drawn from the generated statements about the state of the production tools and the possible source of the errors. The results make it possible to design more efficient maintenance, reduce outage time, and increase production time, thus reducing the number of faulty products.
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We acknowledge the financial support of Széchenyi 2020 under the EFOP-3.6.1-16-2016-00015.
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Nagy, Z., Werner-Stark, Á., Dulai, T. (2018). An Industrial Application Using Process Mining to Reduce the Number of Faulty Products. In: Benczúr, A., et al. New Trends in Databases and Information Systems. ADBIS 2018. Communications in Computer and Information Science, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-030-00063-9_33
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DOI: https://doi.org/10.1007/978-3-030-00063-9_33
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