Autoregressive Hidden Markov Models with partial knowledge on latent space applied to aero-engines prognostics
Pablo Juesas,
Emmanuel Ramasso,
Sébastien Drujont
et al.
Abstract:This paper describes an Autoregressive Partially-hidden Markov model (ARPHMM) for fault detection and prognostics of equipments based on sensors' data. It is a particular dynamic Bayesian network that allows to represent the dynamics of a system by means of a Hidden Markov Model (HMM) and an autoregressive (AR) process. The Markov chain assumes that the system is switching back and forth between internal states while the AR process ensures a temporal coherence on sensor measurements. A sound learning procedure… Show more
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