HMMs for diagnostics and prognostics in machining processes

DOI10.1080/00207540412331327727zbMath1068.90554OpenAlexW2086021758MaRDI QIDQ5460564

Pundarikaksha Baruah, Ratna Babu Chinnam

Publication date: 18 July 2005

Published in: International Journal of Production Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207540412331327727

zbMATH Keywords

diagnosticsremaining useful lifeprocess monitoringprognosticshidden-Markov-models

Mathematics Subject Classification ID

Production models (90B30)

Related Items (9)

Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis ⋮ Health assessment and prognostics based on higher‐order hidden semi‐Markov models ⋮ Fault diagnosis and prognosis of bearing based on hidden Markov model with multi-features ⋮ A hidden semi-Markov model with duration-dependent state transition probabilities for prognostics ⋮ A novel approach to equipment health management based on auto-regressive hidden semi-Markov model (AR-HSMM) ⋮ An asset residual life prediction model based on expert judgments ⋮ A tutorial on nonlinear time-series data mining in engineering asset health and reliability prediction: concepts, models, and algorithms ⋮ A model for real-time failure prognosis based on hidden Markov model and belief rule base ⋮ Shear wave imaging and classification using extended Kalman filter and decision tree algorithm

Cites Work

• The hierarchical hidden Markov model: Analysis and applications
• Factorial hidden Markov models
• Maximum likelihood estimation for multivariate observations of Markov sources
• Statistical Inference for Probabilistic Functions of Finite State Markov Chains

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