Product failure detection for production lines using a data-driven model

作者：

Highlights：

• A data-driven product failure detection framework was developed.

• Sampling strategies improved the performance of the models.

• ANOVA selection technique improved the performance.

• The RUSBoosted Tree algorithm provided satisfactory results.

• The proposed methodology is flexible to be applied in other sectors.

摘要

•A data-driven product failure detection framework was developed.•Sampling strategies improved the performance of the models.•ANOVA selection technique improved the performance.•The RUSBoosted Tree algorithm provided satisfactory results.•The proposed methodology is flexible to be applied in other sectors.

论文关键词：Machine learning,Production lines,Data analytics,Data mining,Product failure detection

论文评审过程：Received 30 October 2021, Revised 17 March 2022, Accepted 25 April 2022, Available online 27 April 2022, Version of Record 30 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117398