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A Hidden Semi-Markov Model with Duration-Dependent State Transition Probabilities for Prognostics
Joint Authors
Saygin, Can
Sun, Shu-dong
Wang, Ning
Cai, Zhi-qiang
Zhang, Shuai
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-14
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Realistic prognostic tools are essential for effective condition-based maintenance systems.
In this paper, a Duration-Dependent Hidden Semi-Markov Model (DD-HSMM) is proposed, which overcomes the shortcomings of traditional Hidden Markov Models (HMM), including the Hidden Semi-Markov Model (HSMM): (1) it allows explicit modeling of state transition probabilities between the states; (2) it relaxes observations’ independence assumption by accommodating a connection between consecutive observations; and (3) it does not follow the unrealistic Markov chain’s memoryless assumption and therefore it provides a more powerful modeling and analysis capability for real world problems.
To facilitate the computation of the proposed DD-HSMM methodology, new forward-backward algorithm is developed.
The demonstration and evaluation of the proposed methodology is carried out through a case study.
The experimental results show that the DD-HSMM methodology is effective for equipment health monitoring and management.
American Psychological Association (APA)
Wang, Ning& Sun, Shu-dong& Cai, Zhi-qiang& Zhang, Shuai& Saygin, Can. 2014. A Hidden Semi-Markov Model with Duration-Dependent State Transition Probabilities for Prognostics. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-486776
Modern Language Association (MLA)
Wang, Ning…[et al.]. A Hidden Semi-Markov Model with Duration-Dependent State Transition Probabilities for Prognostics. Mathematical Problems in Engineering No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-486776
American Medical Association (AMA)
Wang, Ning& Sun, Shu-dong& Cai, Zhi-qiang& Zhang, Shuai& Saygin, Can. A Hidden Semi-Markov Model with Duration-Dependent State Transition Probabilities for Prognostics. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-486776
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-486776