Machine condition recognition via hidden semi-Markov model

被引：14

作者：

Yang, Wenhui ^{[1
]}

Chen, Lu ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai, Peoples R China

来源：

COMPUTERS & INDUSTRIAL ENGINEERING | 2021年 / 158卷 / 158期

基金：

中国国家自然科学基金;

关键词：

Machine condition recognition; Job processing time; Hidden semi-Markov model; SINGLE-MACHINE; SCHEDULING PROBLEM; PERIODIC MAINTENANCE; DETERIORATING JOBS; TIME; ALGORITHM; MAKESPAN;

D O I：

10.1016/j.cie.2021.107430

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页数：9

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