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A dynamic auto-adaptive predictive maintenance policy for degradation with unknown parameters
- Source :
- European Journal of Operational Research, European Journal of Operational Research, 2020, 282 (1), ⟨10.1016/j.ejor.2019.08.050⟩, European Journal of Operational Research, Elsevier, 2020, 282 (1), ⟨10.1016/j.ejor.2019.08.050⟩
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- International audience; With the development of monitoring equipment, research on condition-based maintenance (CBM) is rapidly growing. CBM optimization aims to find an optimal CBM policy which minimizes the average cost of the system over a specified duration of time. This paper proposes a dynamic auto-adaptive predictive maintenance policy for single-unit systems whose gradual deterioration is governed by an increasing stochastic process. The parameters of the degradation process are assumed to be unknown and Bayes' theorem is used to update the prior information. The time interval between two successive inspections is scheduled based on the remaining useful life (RUL) of the system and is updated along with the degradation parameters. A procedure is proposed to dynamically adapt the maintenance decision variables accordingly. Finally, different possible maintenance policies are considered and compared to illustrate their performance.
- Subjects :
- Information Systems and Management
General Computer Science
Maintenance
Computer science
Remaining useful life
0211 other engineering and technologies
02 engineering and technology
Interval (mathematics)
Management Science and Operations Research
Industrial and Manufacturing Engineering
Predictive maintenance
[SPI.AUTO]Engineering Sciences [physics]/Automatic
0502 economics and business
Duration (project management)
Average cost
[STAT.AP]Statistics [stat]/Applications [stat.AP]
050210 logistics & transportation
021103 operations research
Stochastic process
05 social sciences
Increasing stochastic process
Reliability engineering
Bayesian update
Condition-based
Modeling and Simulation
[STAT.ME]Statistics [stat]/Methodology [stat.ME]
Degradation (telecommunications)
Subjects
Details
- ISSN :
- 03772217 and 18726860
- Volume :
- 282
- Database :
- OpenAIRE
- Journal :
- European Journal of Operational Research
- Accession number :
- edsair.doi.dedup.....75bda615d0cdc371ff037615aa87b0c4