A Graphical Model to Diagnose Product Defects with Partially Shuffled Equipment Data
- Title
- A Graphical Model to Diagnose Product Defects with Partially Shuffled Equipment Data
- Author
- 허선
- Keywords
- 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, Page. 1-13
- 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.
- URI
- https://www.proquest.com/docview/2550237217?accountid=11283https://repository.hanyang.ac.kr/handle/20.500.11754/182883
- ISSN
- 2227-9717;2227-9717
- DOI
- 10.3390/pr7120934
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
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