Machine condition recognition via hidden semi-Markov model

In intelligent manufacturing systems, machines are subject to condition deterioration.Identifying machine condition is crucial for making practical decisions in production management. This paper studies the machine condition recognition problem in wafer fabrication. A sequence of processing times co...

Full description

Saved in:
Bibliographic Details
Published inComputers & industrial engineering Vol. 158; p. 107430
Main Authors Yang, Wenhui, Chen, Lu
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.08.2021
Subjects
Hidden semi-Markov model
Job processing time
Machine condition recognition
Hidden semi-Markov model
Job processing time
Machine condition recognition
Online AccessGet full text
ISSN0360-8352
1879-0550
DOI10.1016/j.cie.2021.107430

Cover

More Information
Summary:In intelligent manufacturing systems, machines are subject to condition deterioration.Identifying machine condition is crucial for making practical decisions in production management. This paper studies the machine condition recognition problem in wafer fabrication. A sequence of processing times collected from past production is used to train a hidden semi-Markov model (HSMM). To improve the precision of the HSMM in the application of wafer fabrication, state duration dependency is considered. Experimental analyses based on real data demonstrate the effectiveness of the HSMM and reveal some managerial insights.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2021.107430