A hidden semi-Markov model with duration-dependent state transition probabilities for prognostics

DOI10.1155/2014/632702zbMath1407.90336OpenAlexW2016416178WikidataQ59066733 ScholiaQ59066733MaRDI QIDQ1718805

Can Saygin, Ning Wang, Zhi-qiang Cai, Shuai Zhang, ShuDong Sun

Publication date: 8 February 2019

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2014/632702

Mathematics Subject Classification ID

Markov processes: estimation; hidden Markov models (62M05) Reliability, availability, maintenance, inspection in operations research (90B25) Markov renewal processes, semi-Markov processes (60K15) Markov and semi-Markov decision processes (90C40)

Related Items (2)

Hidden semi-Markov models for predictive maintenance ⋮ Health assessment and prognostics based on higher‐order hidden semi‐Markov models

Cites Work

• Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis
• Hidden semi-Markov models
• A tutorial on nonlinear time-series data mining in engineering asset health and reliability prediction: concepts, models, and algorithms
• Joint Integrated Importance Measure for Multi-State Transition Systems
• HMMs for diagnostics and prognostics in machining processes
• Unnamed Item

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