Hidden Markov model applications
Thanos Kontogiannis (TU Delft - Aerospace Engineering)
Mariana Salinas-Camus (TU Delft - Aerospace Engineering)
Nick Eleftheroglou (TU Delft - Aerospace Engineering)
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Abstract
Prognostics and health management (PHM) in aviation systems aim to predict remaining useful life (RUL), enhancing reliability, while considering operational uncertainties. Hidden Markov Models (HMMs) model degradation processes when damage states are unobservable, using representative features from condition monitoring (CM) data. Traditional HMMs struggle with geometric decay in hidden state durations, leading to the introduction of hidden semi-Markov models (HSMMs), albeit with increased computational complexity. This study compares HMMs and HSMMs, while introducing a dynamic prognostic expression. Using NASA's C-MAPSS dataset, encompassing diverse flight conditions and simulated engine failures, we validate the superiority of HSMMs over HMMs. Moreover, our novel time-dependent prognostic expression outperforms standard ones, highlighting its effectiveness in RUL prognosis.