Detection of machine failure: Hidden Markov Model approach

Allen H. Tai; Wai Ki Ching; L. Y. Chan

doi:10.1016/j.cie.2008.09.028

Detection of machine failure: Hidden Markov Model approach

Allen H. Tai, Wai Ki Ching, L. Y. Chan

The Hong Kong Polytechnic University

Research output: Journal article publication › Journal article › Academic research › peer-review

54 Citations (Scopus)

Abstract

Hidden Markov Models (HMMs) are widely used in applied sciences and engineering. The potential applications in manufacturing industries have not yet been fully explored. In this paper, we propose to apply HMM to detect machine failure in process control. We propose models for both cases of indistinguishable production units and distinguishable production units. Numerical examples are given to illustrate the effectiveness of the proposed models.

Original language	English
Pages (from-to)	608-619
Number of pages	12
Journal	Computers and Industrial Engineering
Volume	57
Issue number	2
DOIs	https://doi.org/10.1016/j.cie.2008.09.028
Publication status	Published - Sept 2009

Keywords

Hidden Markov Model
Machine failure
Statistical control process
Transition probability

ASJC Scopus subject areas

General Computer Science
General Engineering

More information

10.1016/j.cie.2008.09.028

Cite this

@article{a33bc04f8cec4e678e46ecbb4210c52f,

title = "Detection of machine failure: Hidden Markov Model approach",

abstract = "Hidden Markov Models (HMMs) are widely used in applied sciences and engineering. The potential applications in manufacturing industries have not yet been fully explored. In this paper, we propose to apply HMM to detect machine failure in process control. We propose models for both cases of indistinguishable production units and distinguishable production units. Numerical examples are given to illustrate the effectiveness of the proposed models.",

keywords = "Hidden Markov Model, Machine failure, Statistical control process, Transition probability",

author = "Tai, {Allen H.} and Ching, {Wai Ki} and Chan, {L. Y.}",

year = "2009",

month = sep,

doi = "10.1016/j.cie.2008.09.028",

language = "English",

volume = "57",

pages = "608--619",

journal = "Computers and Industrial Engineering",

issn = "0360-8352",

publisher = "Elsevier Ltd",

number = "2",

}

TY - JOUR

T1 - Detection of machine failure

T2 - Hidden Markov Model approach

AU - Tai, Allen H.

AU - Ching, Wai Ki

AU - Chan, L. Y.

PY - 2009/9

Y1 - 2009/9

N2 - Hidden Markov Models (HMMs) are widely used in applied sciences and engineering. The potential applications in manufacturing industries have not yet been fully explored. In this paper, we propose to apply HMM to detect machine failure in process control. We propose models for both cases of indistinguishable production units and distinguishable production units. Numerical examples are given to illustrate the effectiveness of the proposed models.

AB - Hidden Markov Models (HMMs) are widely used in applied sciences and engineering. The potential applications in manufacturing industries have not yet been fully explored. In this paper, we propose to apply HMM to detect machine failure in process control. We propose models for both cases of indistinguishable production units and distinguishable production units. Numerical examples are given to illustrate the effectiveness of the proposed models.

KW - Hidden Markov Model

KW - Machine failure

KW - Statistical control process

KW - Transition probability

UR - http://www.scopus.com/inward/record.url?scp=68649097825&partnerID=8YFLogxK

U2 - 10.1016/j.cie.2008.09.028

DO - 10.1016/j.cie.2008.09.028

M3 - Journal article

AN - SCOPUS:68649097825

SN - 0360-8352

VL - 57

SP - 608

EP - 619

JO - Computers and Industrial Engineering

JF - Computers and Industrial Engineering

IS - 2

ER -

Detection of machine failure: Hidden Markov Model approach

Abstract

Keywords

ASJC Scopus subject areas

More information

Other files and links

Fingerprint

Cite this