r
In order to correct the signals, a reference environment pe is chosen. The correction .lter G(pe)(ω) represents the ratio between the reference power spectrum and the power spectrum corresponding to any contextual environ-ment.
Due
to
the
division
of
two
AR
.lters,
the
correction
.lter
(Eq.
5.11)
becomes an autoregressive moving average (ARMA).
H(ias)(ω).
H.(pr)(ω)G(pe)(ω)= e(5.11)
H.(pe)(ω)
r
.bpe (pe) .jω ..(pe) .jω)
(1 + z.ez e
0 k∈Kk k
G(pe)(ω)= (5.12)
.(pe)(pre) .jω ..(pre) .jω)
b0 (1 + z.ez e
kk k
The
surface
of
the
correction
model
(Eq.
5.12)
is
so
that
if
multiplied
with
the
airspeed
model
(Eq.
5.10),
it
returns
the
reference
power
spectrum
for all values of pe. The time-domain .lter coe.cients for a given pe, bk and ak, is the coe.cients corresponding to the numerator and denominator polynomials of G(pe)(ω). Deriving a correction .lter from the airspeed of each signal, and applying them before further processing is performed, will largely remove
the
signal’s
contextual
sensitivity
(Fig.
5.8).
5.5. CONCLUSION
5.5 Conclusion
The methods introduced here are an attempt to correct the environmental in.uence on HUMS vibration data, so that data acquired at di.erent con-ditions are more comparable. This is favorable for all rotorcraft, especially those spending much time outside cruise, as it permits reducing data scatter and increase the overall reliability of the system. Also aircraft operating in near optimal condition can bene.t from having its data corrected for envi-ronmental changes.
The underlying physical explanations for these correlations have not been addressed during this study. A subject for further research could be to es-tablish a theoretical framework explaining why these correlations occur, and perhaps correlate indicators’ environmental dependency to factors like incor-rect equipment installation.
Chapter 6
Feature Extraction
6.1 Introduction
Most HUMS in use today evaluate each vibration measurement indepen-dently, with minimal correlation of these measurements along the time line. Progression analysis taking place in commercial HUMS is mainly limited to requiring N out of M indicator values to breach their threshold, this to avoid spikes setting o. false alarms. This is in sharp contrast to the methods used by human HUMS analysts, which for most indicator types pay more attention to the progression of the indicator than its absolute value.
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