OPTIMIZATION OF FAULT DIAGNOSIS IN HELICOPTER HEALTH AND USA(46)

曝光台 注意防骗 网曝天猫店富美金盛家居专营店坑蒙拐骗欺诈消费者

6.2.1 Basic Progression Types
From a HUMS analyst’s point of view, the progression of an indicator belongs to one of three classes; normal, step or trend. Indicators in the normal class have a constant expected value, although some indicators have considerable scatter on around of this mean. A step is a sharp transition between two levels, and is usually caused by maintenance actions. This corresponds to a reset of the indicator / condition model, as the set of indicator values corre-sponding to a given condition changes. Consequently, when working on fault detection methods using the absolute indicator values directly, any model must be re-estimated to compensate for this. The trend class represents a gradual increase or decrease in the expected value, and is usually associated with mechanical degradation, i.e. a traversal through component conditions.
6.2. PROGRESSION ANALYSIS

 

Mechanical degradation is also frequently associated with an increase in indicator
scatter.
Figures
6.1,
6.2
and
6.3
contain
examples
and
characteris-
tics of normal, step and trend behavior.
Trying
to
model
the
progression
of
an
indicator
(Eq.
6.1),
the
time
se-
ries can be split in a deterministic component d and a random process r. Although the contextual normalization introduced in the previous chapter removes some of the outliers and gaussian noise on the indicators, the data still remains noisy. This due to in.uence by processes which cannot be prop-erly modeled and predicted. This scatter is thus labeled as the random process r. As established in the previous section, any change in component condition will cause changes in the value of d and / or in the gain of r, for one or more indicator associated with the component.
i(t)= d(t)+ r(t) (6.1)
Looking
at
.gure
6.3,
it
is
clear
that
the
indicator
series
produced
by
this
fault cases consist of several transitions, showing that the associated asset is traversing several conditions, or fault propagation stages. In an operational environment it is however impossible to observe the changes taking place in a mechanical system hour by hour. It is only the .nal result observed when a gearbox is removed and stripped which can be matched to its indicator progression pattern. As a given end result, or observable failure mode, can have several propagation patterns, it is di.cult to match a known failure mode to an observed propagation pattern. Further, it is from an operational point of view su.cient to know if a component is in a healthy condition or not. If a component is suspected faulty, it will in any case be removed and inspected manually. Assuming that the initial condition of a component is healthy, it is for fault detection su.cient to detect any change in component condition, corresponding to changes in d and / or changes in the gain of r.
　
中国航空网 www.aero.cn
航空翻译 www.aviation.cn
本文链接地址：OPTIMIZATION OF FAULT DIAGNOSIS IN HELICOPTER HEALTH AND USA(46)