Bayesian enhanced strategic decision making for reliability

European Journal of Operational Research

Volumn 139, Issue 1, 2002, Pages 133-145

  Percy, David F ^a  

^a UNIVERSITY OF SALFORD   (United Kingdom)

Author keywords

Bayesian analysis; Predictive elicitation; Reliability; Simulation

Indexed keywords

COMPUTER SIMULATION; CONDITION MONITORING; DECISION MAKING; PREVENTIVE MAINTENANCE; RELIABILITY;

BAYESIAN ANALYSIS;

OPERATIONS RESEARCH;

EID: 0037118175     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0377-2217(01)00177-1     Document Type: Article

References (27)

1. Modelling preventive maintenance for deteriorating repairable systems
2. Techniques for analysing multivariate failure data
3. Bayesian decision analysis of the hazard rate for a two-parameter Weibull process
4. Bayesian estimation of life parameters in the Weibull distribution
5. Developments in delay time analysis for modelling plant maintenance
6. Condition monitoring: A new perspective
7. How to determine maintenance frequencies for multi-component systems? A general approach
8. Modelling in the absence of data: A case study of fleet maintenance in a developing country
9. Assessment and propagation of model uncertainty (with discussion)
10. Experiences in elicitation
11. Sensitivity analyses for preventive-maintenance models
12. Towards an intelligent maintenance optimization system
13. The philosophy of statistics (with discussion)
14. Optimal replacement in the proportional hazards model
15. Eliciting expert beliefs in substantial practical applications
16. Bayesian analysis of fixed-interval preventive-maintenance models
17. Setting preventive-maintenance schedules when data are sparse
18. On the application of mathematical models in maintenance
19. An interactive PC-based procedure for reliability assessment incorporating expert opinion and survival data
20. Bayesian analysis of the Weibull process with unknown scale and shape parameters
21. Maintenance policies for multicomponent systems: An overview
22. On the application of a model of condition-based maintenance