Xrepo 2.0: A Big Data Information System For Education In Prognostics And Health Management

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 development and utilization of the algorithms commonly adopted for PHM analyses. Despite the widespread of PHM datasets, education in PHM is complicated by the unavailability of a platform that standardizes the data format into a unified metamodel. To cope with this, XRepo 2.0 is proposed: a big data information system that allows professors to share PHM sensor data in a standard format within an experimental and educational context. In this work, a metamodel is introduced to represent PHM datasets, and the Hadoop framework is integrated with a document database to enable the management of the large amount of data available today. MapReduce processing engine is utilized to enable teachers to 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 is 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 will contribute to the unification and sharing of selected sensor data with the academic community for the development of competences in PHM.