partially shuffled time series; graphical model; equipment data analysis; defect diagnosis; multi-source data fusion
Issue Date
2019-12
Publisher
MDPI
Citation
PROCESSES, v. 7, No. 12, Article no. 934
Abstract
The diagnosis of product defects is an important task in manufacturing, and machine learning-based approaches have attracted interest from both the industry and academia. A high-quality dataset is necessary to develop a machine learning model, but the manufacturing industry faces several data-collection issues including partially shuffled data, which arises when a product ID is not perfectly inferred and yields an unstable machine learning model. This paper introduces latent variables to formulate a supervised learning model that addresses the problem of partially shuffled data. The experimental results show that our graphical model deals with the shuffling of product order and can detect a defective product far more effectively than a model that ignores shuffling.