Within Industry 4.0, Prognostics and Health Management (PHM) holds great potential due to its ability to bring deep insights into the current state of manufacturing equipment. When developing PHM competences in higher education, it is desirable to train students in the design and utilization of the algorithms commonly adopted for PHM analyses. However, the unavailability of a widespread big data platform to standardize the data format and easily access sensor data complicates this purpose. To cope with this, XRepo 2.0 is introduced in this work: a big data information system that allows professors to share PHM sensor data in a standard format within an experimental and educational context. To enable the management of the large amount of data available today, the presented information system is designed and implemented by integrating the Hadoop framework with a document database. Moreover, teachers can pre-process the data on the cloud infrastructure, which is a crucial aspect for the assessment of the algorithms developed by the students. Finally, a prototype of XRepo 2.0 has been deployed on the Azure Cloud and validated with respect to functionality and performance criteria. Given the importance of PHM within Industry 4.0, we expect that XRepo 2.0 contributes to the unification and sharing of selected sensor data with the academic community for the development of competences in PHM.
Education, Prognostics and Health Management, Information System, Big Data, Hadoop, MapReduce
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