Unsupervised and supervised learning for the reliability analysis of complex systems Gámiz Pérez, María Luz Navas-Gómez, Fernando Raya Miranda, Rocío Birnbaum importance measure Dependent components Factor analysis Isotonic smoothing Logistic regression Spanish Ministry of Science and Innovation, Grant/Award Numbers: RTI2018-099723-B-I00, PID2020-120217RB-I00; Junta de Andalucía, Grant/Award Number: B-FQM-284-UGR20; IMAG-Maria de Maeztu, Grant/Award Number: CEX2020- 001105-M/AEI/10.13039/501100011033 The authors are grateful for constructive comments from two anonymous Reviewers and the Associate Editor. This work was supported in part by the Spanish Ministry of Science and Innovation through Grant Numbers RTI2018-099723-B-I00 and PID2020-120217RB-I00, the Spanish Junta de Andalucía through Grant Number B-FQM-284-UGR20, and the IMAG-Maria de Maeztu Grant CEX2020-001105-M/AEI/10.13039/501100011033. Open Access Funding provided by Universidad de Granada / CBUA. In this paper, a strategy to deal with high-dimensional reliability systems with multiple correlated components is proposed. The goal is to construct a state func- tion that enables the classification of the states of components in one of two categories, that is, failure and operative, in case of dealing with a large number of units in the system. To this end, it is proposed a new algorithm that combines a factor analysis algorithm (unsupervised learning) with local-logistic and iso- tonic regression (supervised learning). The reliability function is estimated and system failures are predicted in terms of the variables in the original state space. The dimensions in the latent state space are defined by blocks of units with a cer- tain dependence structure. The flexibility of the model allows quantifying locally the effect that a particular unit has on the system performance and a ranking of components can be obtained under the philosophy of the Birnbaum impor- tance measure. The good performance of the proposal is assessed by means of a simulation study. Also a real data case is considered to illustrate the method. 2023-05-11T10:01:47Z 2023-05-11T10:01:47Z 2023-03 journal article Gámiz ML, Navas-Gómez F, Nozal-Cañadas R, Raya-Miranda R. Unsupervised and supervised learning for the reliability analysis of complex systems. Qual Reliab Eng Int. 2023;1-22. [https://doi.org/10.1002/qre.3311] https://hdl.handle.net/10481/81463 10.1002/qre.3311 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Wiley