On-line parameter estimation for a failure-prone system subject to condition monitoring

DOI10.1239/jap/1077134679zbMath1045.62105OpenAlexW2030692843MaRDI QIDQ4819449

Viliam Makiš, Daming Lin

Publication date: 24 September 2004

Published in: Journal of Applied Probability (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1239/jap/1077134679

zbMATH Keywords

condition-based maintenancerecursive maximum likelihoodpartially observable systemcontinuous-discrete modelon-line parameter estimation

Mathematics Subject Classification ID

Related Items (1)

Availability maximization under partial observations

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• A state space condition monitoring model for furnace erosion prediction and replacement
• Maximal wearing-out of a deteriorating system: An optimal stopping approach
• Analysis of an adaptive control scheme for a partially observed controlled Markov chain
• Optimal Stopping by Means of Point Process Observations with Applications in Reliability
• Recursive estimation in mixture models with Markov regime
• Optimal Replacement In The Proportional Hazards Model
• The Optimal Control of Partially Observable Markov Processes over the Infinite Horizon: Discounted Costs
• Optimal control-limit strategies for a partially observed replacement problem†
• On-line estimation of hidden Markov model parameters based on the Kullback-Leibler information measure
• Recursive filters for a partially observable system subject to random failure
• Recursive nonlinear filter for a continuous discrete-time model: separation of parameters and observations
• Filters and parameter estimation for a partially observable system subject to random failure with continuous-range observations
• General necessary conditions for partially observed optimal stochastic controls

This page was built for publication: On-line parameter estimation for a failure-prone system subject to condition monitoring