A Graphical Model to Diagnose Product Defects with Partially Shuffled Equipment Data
- Authors
- Ahn, Gilseung; Hur, Sun; Shin, Dongmin; Park, You-Jin
- Issue Date
- Dec-2019
- Publisher
- MDPI
- Keywords
- partially shuffled time series; graphical model; equipment data analysis; defect diagnosis; multi-source data fusion
- Citation
- PROCESSES, v.7, no.12, pp.1 - 13
- Indexed
- SCIE
SCOPUS
- Journal Title
- PROCESSES
- Volume
- 7
- Number
- 12
- Start Page
- 1
- End Page
- 13
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/2013
- DOI
- 10.3390/pr7120934
- ISSN
- 2227-9717
- 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.
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Collections - COLLEGE OF ENGINEERING SCIENCES > DEPARTMENT OF INDUSTRIAL & MANAGEMENT ENGINEERING > 1. Journal Articles
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