Semi-Automated Bearing Diagnostics
- Three Case Studies
N.
Sawalhi and R.B. Randall
School of Mechanical and
Manufacturing Engineering,
The University of New South Wales,
Sydney
2052, Australia
Email:
b.randall@unsw.edu.au
Abstract
Most commercial vibration data
loggers and analyzers are using diagnostic technology developed
twenty years ago. Many new developments in applied signal processing
have occurred since that time, and could easily be incorporated into
such analyzers. A number of techniques found invaluable for rolling
element bearing diagnostics are illustrated through three case
histories on very different machines, a very high speed bearing test
rig, a planet bearing fault in a helicopter gearbox, and the main
bearing of a very low speed radar tower. A primary technique
(discrete-random separation) separates bearing from gear signals
(and any other discrete frequencies), since the latter quite often
dominate, even when in good condition.
Another technique (spectral
kurtosis) gives an indication of which frequency bands are dominated
by bearing fault signals, and thus what frequency band to demodulate
for optimum envelope analysis. This technique only works when the
impulses from the bearing faults are well separated, and this is not
always the case for very high speed machines. A further technique
(minimum entropy de-convolution) sharpens up the impulses so that
they become separated and give much earlier fault indications. The
separation of the impulses from entry into and exit from faults such
as spalls, made possible by this technique, gives information about
the size of the fault, whose evolution with time can thus be used
prognostically. The three case histories illustrate the
semi-automatic application of these techniques in very different
situations. Only very minor user interaction is required, to define
frequency ranges of interest, basic diagnostic parameters etc. The
results show clear diagnosis, and valuable trending of parameters,
in situations with a great deal of background noise and masking.
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Introduction
Rolling element bearings are a component of most
machines, from the very fast, eg gas turbines, to the very slow, eg
slew bearings in mining equipment. Vibration analysis can be applied
very effectively to their condition monitoring in all three aspects
(detection, diagnosis, prognosis), but current monitoring equipment,
for either permanent or intermittent monitoring, does not make use
of many techniques which have been developed in recent years, and
which now allow a semi-automated approach, even for bearings
covering a wide range of machine speeds, loads and applications.
This paper demonstrates this by applying a standardized approach to
the signals from three widely different machines. The first is a
helicopter gearbox, characterized by having a wide range of shaft
speeds internally, many different bearings on different shafts, as
well as strong vibration signals from the large number of different
gears, including a planetary gear set. The signals from the planet
bearings, for example, have to travel via a tortuous, time-varying
path to external acceleration transducers. The second is a high
speed bearing used in a gas turbine, but in this case mounted in a
bearing test rig. A characteristic of the signals from high speed
bearings is that the individual impulse responses, from impacts with
the faults, tend to overlap and not give a clear series of pulses as
normally required for the envelope analysis method used to diagnose
them. The third case is from the support bearing of a rotating radar
tower, and thus with extremely low speed. The tower makes one
revolution in 12 s.
VIBRATION SIGNALS FROM FAULTY BEARINGS
Vibration signals from localized faults in
rolling element bearings consist of a series of impulse responses
from the impacts on the faults by the mating elements. These
typically excite a wide range of resonance frequencies up to tens of
kHz (lower for larger machines). The repetition frequencies (as a
multiple of shaft speed) are characteristic of the fault location (eg
outer race, inner race, rolling element), but the formulas for the
characteristic frequencies are the kinematic frequencies assuming no
slip. However, they contain a factor cosφ , which is the angle of
the load from the radial, and this means that there is always some
slip, because φ can only be defined on the average, and the ratio of
radial to axial load varies for each rolling element as it rotates.
Some rolling elements are trying to rotate faster than others, but
the cage keeps the mean frequency constant (though different from
the kinematic value), while permitting a small random variation in
the spacing of the rolling elements (and thus in their impact
frequencies).
The excited impulse responses, in particular in
terms of acceleration, which is the most commonly measured
parameter, are dominated by the resonance frequencies, and a simple
frequency analysis of the raw signal contains almost no additive
component at the low harmonics of the bearing frequencies. As shown
in Ho and Randall (2000), they usually contain little diagnostic
information at the higher resonance frequencies either, because of
the random period variation which causes smearing of the higher
harmonics. It has recently been postulated that the bearing signals
can be modeled as cyclo-stationary, although the original
cyclo-stationary model of Randall et al (2001) was later modified in
[Antoni and Randall (2002), Antoni and Randall (2003)] to be
“pseudo-cyclostationary” because true cyclo-stationary signals
require the statistics to be (quasi-) periodic, not just
approximately so, as in the case of bearings.
Fig 1: Early spall in the inner race
Fig 2: The development of kurtosis
with and without MED, compared against fault size
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