Abstract
Enterprise Asset Management (EAM) software is commonly used in large industrial enterprises. The main task of EAM is to optimize performance, extend the life cycles of equipment, minimize downtime as well as operational costs. In a simple approach, EAM can only be used to support service and repair actions and their registration without using the form to enter data. In this case, maintenance logs contain useful pieces of information, however access to them is difficult due to the method of storage. There is a lack of standardization of records, several activities in one record that relate to different machine components, the presence of spelling errors, jargon, mental shortcuts, etc. In such situations, further analysis of maintenance data is very onerous and comes down to many time consuming manual operations, especially if the analyses are related to combining these records with other data sources. Their automation requires the use of advanced analytical tools that must be customized to individual business needs and database capabilities. In this paper we propose use of TextMining tool to automize the analytical process in failure analysis area based on data from many various sources.
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Stachowiak, M., Skoczylas, A., Stefaniak, P., Śliwiński, P. (2021). Multidimensional Failure Analysis Based on Data Fusion from Various Sources Using TextMining Techniques. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_66
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DOI: https://doi.org/10.1007/978-3-030-68154-8_66
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