An effective maintenance policy for a multi-component dynamic system using factored POMDPs
MetadataShow full item record
CitationÖzgür Ünlüakın, D. & Kıvanç, İ., (2019). An effective maintenance policy for a multi-component dynamic system using factored POMDPs. Paper presented at the , 290-300. doi:10.1007/978-3-030-29765-7_24
With the latest advances in technology, almost all systems are getting substantially more uncertain and complex. Since increased complexity costs more, it is challenging to cope with this situation. Maintenance optimization plays a critical role in ensuring effective decision-making on the correct maintenance actions in multi-component systems. A Partially Observable Markov Decision Process (POMDP) is an appropriate framework for such problems. Nevertheless, POMDPs are rarely used for tackling maintenance problems. This study aims to formulate and solve a factored POMDP model to tackle the problems that arise with maintenance planning of multi-component systems. An empirical model consisting of four partially observable components deteriorating in time is constructed. We resort to Symbolic Perseus solver, which includes an adapted variant of the point-based value iteration algorithm, to solve the empirical model. The obtained maintenance policy is simulated on the empirical model in a finite horizon for many replications and the results are compared to the other predefined maintenance policies. Drawing upon the policy results of the factored representation, we present how factored POMDPs offer an effective maintenance policy for the multi-component systems.
SourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
The following license files are associated with this item:
Except where otherwise noted, this item's license is described as info:eu-repo/semantics/closedAccess
Showing items related by title, author, creator and subject.
Özgür Ünlüakın, Demet; Kıvanç, İpek (The Society for Modeling and Simulation International, 2019-07)Taking maintenance decisions is one of the well-known stochastic sequential decision problems under uncertainty. Partially Observable Markov Decision Processes (POMDPs) are powerful tools for such problems. Nevertheless, ...
Şavkay, Osman Levent; Cesur, Evren; Yıldız, Nerhun; Yalçın, Mustak Erhan; Tavşanoğlu, Ahmet Vedat (IEEE, 2014)In this paper, hardware optimization of the preprocessing and software implementation of the processing blocks of a computer aided semen analysis (CASA) system are proposed, which is also implemented on an FPGA and ARM ...
Özgür Ünlüakın, Demet; Türkali, Busenur; Karacaörenli, Ayşe; Aksezer, Sezgin Çağlar (Elsevier Sci Ltd, 2019-10)Thermal power plants consist of several complex systems having many interacting hidden components. Any unexpected failure may lead to prolonged downtime and serious lost profits. Therefore, implementing an effective ...