Multi-attribute Bayesian fault prediction for hidden-state systems under condition monitoring
DOI10.1016/j.apm.2021.10.015zbMath1525.94073OpenAlexW3208536194MaRDI QIDQ6135592
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Publication date: 25 August 2023
Published in: Applied Mathematical Modelling (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.apm.2021.10.015
semi-Markov decision processprognostics and health managementfault detection schememulti-attribute optimizationhidden-state systems
Applications of statistics in engineering and industry; control charts (62P30) Reliability, availability, maintenance, inspection in operations research (90B25) Fault detection; testing in circuits and networks (94C12) Reliability and life testing (62N05)
Cites Work
- Unnamed Item
- Unnamed Item
- Production-driven opportunistic maintenance for batch production based on MAM-APB scheduling
- Comparative analysis of the machine repair problem with imperfect coverage and service pressure condition
- Field degradation modeling and prognostics under time-varying operating conditions: a Bayesian based filtering algorithm
- GSA for machine learning problems: a comprehensive overview
- Evaluating attack helicopters by AHP based on linguistic variable weight
- Genetic algorithm to the machine repair problem with two removable servers operating under the triadic \((0, {Q}, {N}, {M})\) policy
- A hybrid deep computation model for feature learning on aero-engine data: applications to fault detection
- Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure
- Multi-objective open shop scheduling by considering human error and preventive maintenance
- Joint Optimization of Sampling and Control of Partially Observable Failing Systems
- Multivariate Bayesian Control Chart
- Parameter estimation for partially observable systems subject to random failure
- Computing the distribution of quadratic forms in normal variables
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