October - Library - Central Queensland University

1. Pattern A is the well known bath tub curve. It begins with a high incidence of failure, known as infant mortality, followed by a
constant or gradually increasing failure rate, then a wear-out zone.
2. Pattern B shows constant or slowly increasing failure probability, ending in a wear-out zone.
3. Pattern C shows slowly increasing probability of failure, but there is no identifiable wear-out age.
4. Pattern D shows low failure probability when the item is new then a rapid increase to a constant level.
5. Pattern E shows a constant probability of failure at all ages - random failure.
6. Pattern F starts with high infant mortality, which drops to a constant or very slowly increasing failure probability. (Moubray
1991, pp. 12 to 13).
Put another way, the patterns of failure as depicted in Figure 5 indicate that all failures are either age related or non-age related.
AGE RELATED FAILURES
Referring to Figure 5, patterns A, B and C depicts the age related failure patterns - i.e. the probability of failure increases, as the
item gets older. For patterns A and B in part i c u l a r, they will reach a point called the wear out zone where the conditional pro b a b i l i t y
of failure will rapidly increase. Pattern C however is more difficult to predict given the steady increase in probability of failure.
In theory, all age related failure patterns display a point in which there is an increase in the conditional probability of failure. It was
in the past assumed there f o re that just before this point that maintenance should intervene and apply the appropriate fixed time
action to either overhaul the equipment or replace components so as to prevent or minimise the consequences of the failure.
The key to selecting fixed time maintenance lay in reliable historical maintenance re c o rds. To this end one can appreciate that
selecting fixed time maintenance for new equipment and/or in organisations that are reactive would (almost) be impossible.
If we take a minute to reflect back on the effectiveness of changing a light bulb based on fixed time. The same learning can be
applied to the paradigm of thinking that suggests performing some kind of fixed time preventive maintenance such as scheduled
overhauls, scheduled replacement of items, intrusive inspections of equipment, etc will ensure cost effective plant and equipment
reliability. Put simply this is folly if there is no dominant age-related failure mode. In reality, performing fixed time maintenance can
(and will) actually increase the overall failure rates by introducing infant mortality (this will be discussed further at a later time in
the paper) into an item that basically may have had nothing wrong with it. To coin an old phrase, “if it ain’t broke, don’t fix it”.
NON-AGE RELATED FAILURES
Referring (again) to Figure 5, patterns D, E and F depicts that the majority of equipment conforms to these failure modes and more
importantly have no correlation as a function of age (excluding an initial period for D and F). In actual fact the data suggests that
the majority of equipment either:
1. Fails prematurely - i.e. infant mortality or
2. Fails randomly - i.e. failures occur randomly throughout the life of the equipment.
Generally speaking, these failure patterns are representative of complex items. For example, electronic, pneumatic and hydraulic
equipment, which would more likely conform to the failure patterns of E and F, and rolling element bearings which would conform
to pattern E. Based on the above data, the best way to combat these type of failures is:
1. In the case of failure patterns E and F: improved design, better storage practices, more rigour and discipline around shut
down and start up practices, less intrusive maintenance, improved operations, improved workmanship (alignment and
balancing practices, training, information, motivation, etc).
2. In the case of equipment that fails randomly: perform predictive maintenance / condition monitor.
B e f o re discussing the diff e rent condition monitoring techniques at our disposal, we need to further explore the fact that a lot of
items do not fail instantaneously. In actual fact the onset of failure, depending on the equipment, operating conditions, etc, can
actually develop and take years, months, weeks, days, etc until the item actually fails. Based on this, the objective is to select the
most cost effective monitoring frequency (and technique) which will enable a reasonable lead time to take action.
Let us use the example of a rolling ball bearing: The initial point at which the bearing starts to fail may not be detectable. It is at
some stage after this that the impending failure of the bearing will become detectable. It is the period between this point of potential
failure and the actual failure of the bearing, identified as point F, that enable action be taken. This period, in which the impending
failure usually deteriorates at an accelerating rate, is known as the P-F interval.
As discussed earlier, the P-F interval can vary quite considerably for diff e rent equipment, which is a key consideration when
determining the monitoring frequency. Generally speaking though, Moubray (1991, p. 119) suggests that the monitoring frequency
should be half of the P-F interval. In most cases this will be sufficient to enable enough time to take action to avoid the impending
failure, once the potential failure has been detected.
One other factor to consider is the lead time requirement to take action. This time is influenced by factors that include; lead time to
procure parts, organise labour, access time to equipment, to name just a few.
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