On-Line Fault Diagnosis and Failure Prognosis Using Particle Filters
Theoretical Framework and Case Studies
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This work introduces an on-lineparticle-filtering-based framework for faultdiagnosis and failure prognosis in nonlinear,non-Gaussian systems. This framework considers hybridstate-space models of the system under analysis (withunknown time-varying parameters) andparticle-filtering (PF) algorithms to estimate thecurrent probability density function (pdf) of thestate, enabling on-line computation of theconditional fault probability (fault diagnosismodule) and the pdf of the remaining useful life(RUL) in the case of a declared fault condition(failure prognosis module). The proposed methodallows to...
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 method
allows to use the state pdf estimate of the diagnosis
module as initial condition for the prognosis module,
improving the accuracy of RUL estimates at the early
stages of the fault condition. This framework
provides information about precision and accuracy of
long-term predictions, RUL expectations, and 95%
confidence intervals for the condition under study.
Ground truth data from a seeded fault test are used
to validate the proposed approach.
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 method
allows to use the state pdf estimate of the diagnosis
module as initial condition for the prognosis module,
improving the accuracy of RUL estimates at the early
stages of the fault condition. This framework
provides information about precision and accuracy of
long-term predictions, RUL expectations, and 95%
confidence intervals for the condition under study.
Ground truth data from a seeded fault test are used
to validate the proposed approach.
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