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Dynamic joint sensor selection and maintenance optimization in partially observable
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AI
DOI：10.1016/j.cie.2023.109853.png)
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Abstract
En 中文
We consider a degrading system with costly but silent failures. The system can be partially observed using a set of heterogeneous noisy sensors at a given cost, where each sensor noise level is a function of the system state. The goal is to devise a dynamic joint sensor selection and maintenance optimization model to minimize the total expected discounted cost of the system. We develop an infinite-horizon discrete-time partially observable Markov decision process model to dynamically prescribe the sensor subsets for system monitoring and the timing of maintenance activities. We perform numerical experiments for a system with up to ten sensors and compare the proposed optimal policy with four heuristic policies. We observe that the proposed approach consistently outperforms all heuristic policies considered. Through a comprehensive analysis of numerical experiments, our study shows the efficacy of the proposed model's optimal policy in strategically selecting sensors to achieve precise system state estimation while managing costs. Through these experiments, we find that it is optimal to use lower-cost, higher-variance sensors and often fuse them for more accurate estimations. However, the choice depends on the system state and sensor quality distribution. Importantly, our model excels in determining the best action by considering these factors, underscoring its practical applicability.
Keywords:
Maintenance
Markov decision processes
Partial information
Bayesian modeling
Dynamic sensor selection
Journal
IF:
6.5
Papers:
1.0W
Citations:
3.8W
