This work introduces an on-line
particle-filtering-based framework for fault
diagnosis and failure prognosis in nonlinear,
non-Gaussian systems. This framework considers hybrid
state-space models of the system under analysis (with
unknown time-varying parameters) and
particle-filtering (PF) algorithms to estimate the
current probability density function (pdf) of the
state, enabling on-line computation of the
conditional fault probability (fault diagnosis
module) and the pdf of the remaining useful life
(RUL) in the case of a declared fault condition
(failure prognosis module). The proposed...