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.
|Titel||Towards 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|
|Redaktører||Ann-Louise Andersen, Rasmus Andersen, Thomas Ditlev Brunoe, Maria Stoettrup Schioenning Larsen, Kjeld Nielsen, Alessia Napoleone, Stefan Kjeldgaard|
|Status||Udgivet - 2022|
|Navn||Proceedings of the 8th International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV 2021)|