Hidden markov models in reliability and maintenance Gámiz Pérez, María Luz Limnios, Nikolaos Segovia García, María del Carmen Reliability Hidden Markov chain Maintenance Asymptotic properties EM algorithm Acknowledgements The authors are grateful to two anonymous reviewers and the Editor for many valuable comments and suggestions, which have helped to improve the quality of the article. The authors gratefully acknowledge support from the Spanish Ministry of Science and In- novation - State Research Agency through grant number PID2020- 120217RB-I00. This work is supported in part by the IMAG Maria de Maeztu grant CEX2020-001105-M/AEI/10.13039/50110 0 011033. Funding for open access charge: Universidad de Granada / CBUA. Although the hidden Markov models (HMM) are very popular in many applied areas their use in reliabil- ity engineering is limited. Problems such as the selection of the HMM model by choosing the appropriate number of states, or problems of prediction of failures have not been widely covered in the literature. This paper is concerned with the use of HMMs where the state of the system is not directly observable and instead certain indicators of the true situation are provided via a control system. A hidden model can provide key information about the system dependability such as the failed component of the sys- tem, the reliability of the system and related measures. A maximum-likelihood estimator of the system reliability is obtained and its asymptotic properties are studied. Finally, the maintenance of the system is considered in this context and new preventive maintenance strategies are defined and their efficiency is measured in terms of expected cost. To prove the finite sample performance of the methodology, an extensive simulation study is developed. 2022-07-05T08:19:45Z 2022-07-05T08:19:45Z 2022 journal article M.L. Gámiz, N. Limnios and M.d.C. Segovia-García, Hidden markov models in reliability and maintenance, European Journal of Operational Research, [https://doi.org/10.1016/j.ejor.2022.05.006] http://hdl.handle.net/10481/75826 10.1016/j.ejor.2022.05.006 spa http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier