Enhancing manufacturing intelligence through an unsupervised data-driven methodology for cyclic industrial processes

作者：

Highlights：

• Semi-supervised labelling of production cycles for predictive maintenance.

• Wide applicability to any cycle-based production process without ground truth labels.

• Exploitable by domain experts thanks to transparent self-tuning techniques.

摘要

•Semi-supervised labelling of production cycles for predictive maintenance.•Wide applicability to any cycle-based production process without ground truth labels.•Exploitable by domain experts thanks to transparent self-tuning techniques.

论文关键词：Cluster analysis,Self-tuning machine learning,Industry 4.0,Predictive maintenance,Data analytics

论文评审过程：Received 13 December 2019, Revised 19 March 2021, Accepted 20 May 2021, Available online 25 May 2021, Version of Record 27 May 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115269