Fuzzy Inference Based Monitoring, Diagnostics, and Prognostics
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To date the majority of the research related to thedevelopment and application of monitoring, diagnostic, and prognosticsystems has been exclusive in the sense that only one of the threeareas is the focus of the work. While previous research progresses each ofthe respective fields, the end result is a variable grab bag oftechniques that address each problem independently. Also, the new field ofprognostics is lacking in the sense that few methods have beenproposed that produce estimates of the remaining useful life (RUL)of a device or can be realistically applied to real-world systems. Thiswork...
To date the majority of the research related to the
development and
application of monitoring, diagnostic, and prognostic
systems has been
exclusive in the sense that only one of the three
areas is the focus of
the work. While previous research progresses each of
the respective
fields, the end result is a variable grab bag of
techniques that address
each problem independently. Also, the new field of
prognostics is
lacking in the sense that few methods have been
proposed that
produce estimates of the remaining useful life (RUL)
of a device or can
be realistically applied to real-world systems. This
work addresses
both problems by developing the nonparametric fuzzy
inference
system (NFIS) which is adapted for monitoring,
diagnosis, and
prognosis and then proposing the path classification
and estimation
(PACE) model that can be used to predict the RUL of a
device that does
or does not have a well defined failure threshold.
development and
application of monitoring, diagnostic, and prognostic
systems has been
exclusive in the sense that only one of the three
areas is the focus of
the work. While previous research progresses each of
the respective
fields, the end result is a variable grab bag of
techniques that address
each problem independently. Also, the new field of
prognostics is
lacking in the sense that few methods have been
proposed that
produce estimates of the remaining useful life (RUL)
of a device or can
be realistically applied to real-world systems. This
work addresses
both problems by developing the nonparametric fuzzy
inference
system (NFIS) which is adapted for monitoring,
diagnosis, and
prognosis and then proposing the path classification
and estimation
(PACE) model that can be used to predict the RUL of a
device that does
or does not have a well defined failure threshold.
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