A New Authentic Cloud Dataset from a Production Facility for Anomaly Detection

Emil Blixt Hansen, Emil Robenhagen van der Bijl, Mette Busk Nielsen, Morten Svangren Bodilsen, Simon Vestergaard Berg, Jan Kristensen, Nadeem Iftikhar, Simon Bøgh

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Abstrakt

As technology advances and modern Industry 4.0 solutions are becoming more widespread, the need for better-suited datasets is rising. The commonly used datasets for training machine learning focus on simple data of often publicly available information. Within the industry, there is only a handful of datasets publicly available to use. In this paper, we present a new authentic industrial cloud data (AICD) dataset collected from an actual operating pick-and-place machine handling items with variations in shape, size, and weight. The AICD dataset contains various analogue sensor values and states of the machine, collected from an existing cloud solution. Within the data, an error is present when the machine fails. Therefore, this dataset is suited for testing and developing predictive maintenance and anomaly detection algorithms to be used in the industry. Moreover, the paper also presents a baseline implementation as a performance indicator for future models.
OriginalsprogEngelsk
TitelTowards Sustainable Customization : Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021
RedaktørerAnn-Louise Andersen, Rasmus Andersen, Thomas Ditlev Brunoe, Maria Stoettrup Schioenning Larsen, Kjeld Nielsen, Alessia Napoleone, Stefan Kjeldgaard
Antal sider8
ForlagSpringer
Publikationsdato2022
Sider415-422
ISBN (Trykt)978-3-030-90699-3
ISBN (Elektronisk)978-3-030-90700-6
DOI
StatusUdgivet - 2022
NavnProceedings of the 8th International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV 2021)

Fingeraftryk

Dyk ned i forskningsemnerne om 'A New Authentic Cloud Dataset from a Production Facility for Anomaly Detection'. Sammen danner de et unikt fingeraftryk.

Citationsformater